1. health and fitness

JOURNAL OF PUBLIC BUDGETING,
ACCOUNTING & FINANCIAL MANAGEMENT
Editor: Khi V. Thai, Florida Atlantic University
Governmental Accounting Editor: Donald R. Deis, Texas A&M UniversityCorpus Christi
Healthcare & Nonprofit Organizations Section Editor, Dana A. Forgione.
University of Texas at San Antonio
Copy Editors: Paula Altizer and Vivian Mydlarz
Artistic Designer: Loy Nguy
Editorial Board
Roy Bahl, Georgia State University
John Bartle, University of Nebraska at Omaha
Jane Beckett-Camarata, Rutgers University
Melvin V. Borland, Western Kentucky University
Cynthia Jones Bowling, Auburn University
Patricia Byrnes, University of Illinois at Springfield
Cyril F. Chang, Memphis State University
Carol Ebdon, University of Nebraska at Omaha
Jerry Gianakis, Suffolk University, Sawyer Business School
Merl M. Hackbart, University of Kentucky
Jean Harris, Pennsylvania State University-Harrisburg
Rebecca Hendrick, University of Illinois-Chicago
W. Bartley Hildreth, Georgia State University
Alfred Ho, Indiana University-Purdue University Indianapolis
Beth Walter Honadle, University of Cincinnati
Marc Holzer, Rutgers University-Newark
Dennis S. Ippolito, Southern Methodist University
Craig L. Johnson, Indiana University at Bloomington
Janet Kelly, University of Louisville
Aman Khan, Texas Tech University
William Earle Klay, Florida State University
Robert Kravchuk, Indiana University at Bloomington
Josephine M. Laplante, University of Southern Maine
Thomas P. Lauth, University of Georgia
Shuanglin Lin, Peking University, China and University of Nebraska at Omaha
David Matkin, Florida State University
Justin Marlowe, University of Washington
Cliff McCue, Florida Atlantic University
Charles E. Menifield, University of Missouri
Hiroshi Mifune, Chuo University
John L. Mikesell, Indiana University
Gerald J. Miller, Arizona State University
Patrick R. Mullen, University of Illinois-Springfield
Jun Ping, University of Arizona
Yuhua Qiao, Missouri State University
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William C. Rivenbark, University of North Carolina at Chapel Hill
ii
J. OF PUBLIC BUDGETING, ACCOUNTING & FINANCIAL MANAGEMENT
Mark Robbins, University of Connecticut
James D. Savage, University of Virginia
Walt Schubert, La Salle University
Joe Shepard, Florida Gulf Coast University
Daniel Smith, New York University
Kenneth A. Smith, University of Washington
Douglas R. Snow, Suffolk University
Charlie B. Tyer, University of South Carolina
M. Peter Van Der Hoek, Erasmus University-Rotterdam
Xiaohu Wang, University of Central Florida
Douglas J. Watson, University of Texas at Dallas
Jeffrey A. Weber, East Stroudsburg University
Samuel J. Yeager, Wichita State University
Jerry Zhao, University of Minnesota
Governmental Accounting Section Editorial Board
William R. Baber, Georgetown University
Rich Brooks, West Virginia University
Rita Cheng, University of Wisconsin-Milwaukee
Paul Copley, James Madison University
Mary Fischer, University of Texas at Tyler
Dana Forgione, University of Texas at San Antonio
Robert J. Freeman, Texas Tech University
Gary A. Giroux, Texas A&M University
Rhoda C. Icerman, Florida State University
Larry Johnson, Colorado State University
Saleha B. Khumawala, University of Houston
Suzanne Lowensohn, Colorado State University
Santanu Mitra, Wayne State University
Terry Patton, Midwestern State University
K. K. Raman, University of Texas at San Antonio
Jackie Reck, University of South Florida
Robin W. Roberts, University of Central Florida
Walter Robbins, University of Alabama
Marc A. Rubin, Miami University (Ohio)
Pamela C. Smith, University of Texas at San Antonio
Alan K. Styles, California State University San Marcos
Thomas Vermeer, University of Delaware
Jayaraman Vijayakumar, Virginia Commonwealth University
Rahib Zeidan, Texas A&M University – Corpus Christi
Healthcare & Nonprofit Organizations Section Editorial Board
Luca G. Brusati, Bocconi University (Italy)
Donald R. Deis, Jr., Texas A&M–Corpus Christi
Mary L. Fischer, University of Texas at Tyler
James E. Guthrie, University of Sydney (Australia)
Nathalie Halgand, University of Nantes (France)
J. OF PUBLIC BUDGETING, ACCOUNTING & FINANCIAL MANAGEMENT
Regina Herzlinger, Harvard University
Noel Hyndman, University of Ulster at Jordanstown (UK)
Kathryn Jervis, University of Rhode Island
John E. Karayan, California State Polytechnic University–Pomona
Mehmet C. Kocakulah, University of Southern Indiana
Irvine Lapsley, The University of Edinburgh (UK)
Raef A. Lawson, State University of New York–Albany
Suzanne Lowensohn, Colorado State University
Miroslav Mastilica, University of Zagreb (Croatia)
Wolfgang Mayrhofer, University of Economics & Business Administration
(Austria)
Jeffrey E. Michelman, University of North Florida
Leslie S. Oakes, University of New Mexico
Terry K. Patton, Midwestern State University
Catherine Plante, University of New Hampshire
Vaughan Radcliffe, University of Western Ontario (Canada)
Kevin T. Rich, Marquette University
Karen A. Shastri, University of Pittsburgh
Pamela C. Smith, University of Texas at San Antonio
Emidia Vagnoni, University of Ferrara (Italy)
Thomas E. Vermeer, University of Delaware
Mustafa Z. Younis, Jackson State University
iii
JOURNAL OF
PUBLIC BUDGETING, ACCOUNTING & FINANCIAL MANAGEMENT
Volume 25, Number 1, Spring 2013
CONTENTS
HEALTHCARE AND NONPROFIT ORGANIZATIONS
New “Healthcare and Nonprofit Organizations” Section:
Foreword ………………………………………….…………………….....................
D. A. Forgione
From Providers to Primary Health Organisations: An Institutional
Analysis of Nonprofit Primary Health Care Governance in New
Zealand .......................................................…………………………………..
B. Howell and C. Cordery
1
4
Auditor Size and Internal Control Reporting Differences in
Nonprofit Healthcare Organizations .........................................……… 41
D. M. López, K. T. Rich and P. C. Smith
Charitable Ratings and Financial Reporting Quality: Evidence
from the Human Service Sector ..................................................…… 69
Q. Ling and D. G. Neely
Public University OPEB Burden: Recognition, Funding and
Future Obligations .........................................………………………………. 91
M. Fischer, T. Marsh, G. L. Hunt, B. A. Hora and L. Montondon
Going-Concern Modified Audit Opinions for Non-Profit
Organizations ...........................................…………………………………… 113
T. E. Vermeer, K. Raghunandan and D. A. Forgione
REGULAR ARTICLES
Audited Financial Statements in the US Federal Government:
The Question of Policy and Management Utility ………………………… 135
D. A. Brook
SYMPOSIUM
The Impact of the Great Recession on the Financial
Management Practices of State and Local Governments: Part I
M. J. Luby
158
J. OF PUBLIC BUDGETING, ACCOUNTING & FINANCIAL MANAGEMENT
v
Symposium Introduction .…….……….………….…….….…...……….…….
M. J. Luby
159
Reporting on the Financial Condition of the States: 2002-2010
S. N. Kioko
165
The Collapse of the Municipal Bond Insurance Market: How
Did We Get Here and Is There Life for the Monoline Industry
Beyond the Great Recession? ...……………………………………………… 199
T. T. Moldogaziev
INVITATION TO AUTHORS
Journal of Public Budgeting & Accounting Financial Management (JPBAFM)
encourages practitioners and scholars to submit manuscripts dealing
with the practice and study of public procurement at all levels of
government in every country.
Manuscript Submissions. All manuscripts should be submitted to
Please see “Information for Contributors” at the end of this issue for
manuscript style and submissions.
Suggestions. JPBAFM invites readers to submit comments,
communications and suggestions for the reprinting of informative
government reports to the editor (Khi V. Thai at [email protected]). For further
information, please visit www.pracademics.com.
J. OF PUBLIC BUDGETING, ACCOUNTING & FINANCIAL MANAGEMENT, 25 (1), 1-3
SPRING 2013
NEW HEALTHCARE AND NONPROFIT ORGANIZATIONS SECTION:
FOREWORD
We are delighted to present the inaugural set of papers in our
new Healthcare & Nonprofit Organizations Section of the Journal of
Public Budgeting, Accounting & Financial Management.
Healthcare is the single largest industry in the American economy.
It is typically one of the largest program cost areas in federal, state
and local government budgets, and is at the center of public policy
debate. In most countries, healthcare is entirely run by the
government.
As the US population ages, the demand for healthcare services
will grow exponentially until nearly one-third of the population is
retired and dependent on the federal Medicare program by 2030. The
need for cost effective, quality healthcare that provides access to the
greatest number of people is ever increasing. Yet there is no journal
that focuses on research in healthcare financial management—either
on a domestic or international level.
Furthermore, nonprofit organizations relieve governments of the
burden of providing social services that meet the needs of vast
numbers of people. Many nonprofit organizations provide services in
the same labor, supply and client markets as for-profit organizations.
Yet the financial and managerial issues associated with these factors
are largely under-researched and under-published.
This journal is pleased to fill the need for a premier journal outlet
in the areas of research in the financial management of healthcare
and nonprofit organizations through the addition of this new section.
It builds upon the legacy of pioneering work done by the section
editor and distinguished members of the editorial board.
The aims and scope of this section are to actively promote the
collaboration of clinicians, managers, practitioners and academics in
Copyright © 2013 by PrAcademics Press
2
FORGIONE
developing an interdisciplinary approach to financial management
and policy throughout the world. This section serves as a forum for
dissemination and interchange of ideas and experience in a premier
quality international, double-blind peer-reviewed format. Case
studies, demonstration projects, simulations and other types of
theoretical, experimental, analytical or applied policy and practice
analyses, from both quantitative and qualitative research
approaches, are welcome. Replications, extensions, integrative
literature reviews and historical analyses are also encouraged.
In this inaugural issue, we have an outstanding group of authors
who address important issues, ranging from analysis of healthcare
providers in New Zealand, to colleges and universities in the US.
Specifically, Bronwyn Howell and Carolyn Cordery examine the effects
of government policy reforms for the delivery of primary healthcare in
New Zealand. Their paper applies theories of competition for firm
ownership & control and finds that provider financial incentives
dominate the formation and governance of the new entities. Dennis
M. López, Kevin T. Rich, and Pamela C. Smith then investigate
internal financial controls of nonprofit healthcare organizations in the
US. Using a sample of Circular A-133 audit reports, they find that
audits performed by Big 4 firms are less likely to disclose reportable
conditions and material weaknesses in internal control than those
performed by the smaller CPA firms. They then discuss the
implications of this finding for audit quality, market dominance, and
healthcare client size.
Addressing nonprofit entities, Qianhua (Q.) Ling and Daniel Neely
examine the relation of financial reporting quality and charity ratings
in the human services sector. They consider whether the financial
reporting quality of charities varies with charitable ratings, and find
that highly rated organizations are more likely to under-report
fundraising expenses and overstate program service ratios. They
conclude that stakeholders should be cautious when using rating
information and that boards of directors should exercise due
diligence in assessing the reasonableness of reported expense
allocations.
Mary Fischer, Treba Marsh, George L. Hunt, Bambi Hora and
Lucille M. Montondon follow with a timely study of other postemployment benefits (OPEB) of US colleges and universities. They
find that land grant institutions cover their OPEB costs on a pay-as-
NEW HEALTHCARE AND NONPROFIT ORGANIZATIONS SECTION
3
you-go basis and the liabilities are significantly underfunded. They
conclude that revenue shortfalls and current fiscal pressures raise
serious concerns about how they will be able to meet their OPEB
obligations for retired personnel.
Finally, Thomas E. Vermeer, K. Raghunandan and Dana A.
Forgione, examine the fiscal viability of nonprofit organizations. They
study going-concern modified audit opinions for 3,567 nonprofits
exhibiting signs of financial stress, and find that the organizations are
smaller, in worse financial condition, expend less on program-related
activities, and have more internal control related problems. However,
only 27 percent of the non-profits receiving an initial going-concern
modified audit opinion filed for dissolution in the subsequent four
fiscal years. Their findings address an important area that has
received little research attention, and also provide a useful financial
benchmark for non-profits and their auditors.
These authors provide each of us with more knowledge, not only
of the financial management issues that continue to challenge
healthcare and nonprofit organizations, but also valuable insights on
how to address the financial aspects of proposed public policy
reforms. These efforts, coupled with others focused on the
organization and delivery of healthcare and valued social services,
continue to build a foundation of empirical evidence that is useful for
(a) the development and tests of theory, (b) evidence-based policy
making, and (d) advancement of management practice. We trust that
you will enjoy reading these excellent papers and that they will
promote further research and interchange of ideas in the future.
Dana A. Forgione, Section Editor
J. OF PUBLIC BUDGETING, ACCOUNTING & FINANCIAL MANAGEMENT, 25 (1), 4-40
SPRING 2013
FROM PROVIDERS TO PRIMARY HEALTH ORGANISATIONS:
AN INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE
GOVERNANCE IN NEW ZEALAND
Bronwyn Howell and Carolyn Cordery*
ABSTRACT. Policy reforms to primary health care delivery in New Zealand
required government-funded firms overseeing care delivery to be constituted
as nonprofit entities with governance shared between consumers and
producers. This paper examines the consumer and producer interests in
these firms’ allocation of ownership and control utilising theories of
competition. Consistent with pre-reform patterns of ownership and control,
provider interests appear to have exerted effective control over these
entities’ formation and governance in all but a few cases where community
(consumer) control pre-existed. Their ability to do so is implied from the
absence of a defined ownership stake and the changes to incentives facing
the different stakeholding groups. It appears that the pre-existing patterns
will prevail and further intervention will be required if policy-makers are to
achieve their underlying aims.
INTRODUCTION
The cost of health care is escalating and, while, since the end of
World War II, governments have assumed increased responsibility for
funding their citizens’ health care (van Kemenade, 1997), the ability
of nation states to fund increasing demands for health care is
--------------------------* Bronwyn Howell, Ph.D. Student, School of Government, Victoria University
of Wellington, is General Manager, New Zealand Institute for the Study of
Competition and Regulation Inc. Research interests include Information
Economics and Industrial Organisation with a focus on the health and
technology sectors. Carolyn Cordery, Ph.D., is a Senior Lecturer, School of
Accounting and Commercial Law, Victoria University of Wellington. Her
research interests are focused in nonprofit accounting and accountability.
Copyright © 2013 by PrAcademics Press
INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE GOVERNANCE IN NEW ZEALAND
5
declining. However, the questions of who should receive the
increased funds, and the optimal institutional structures to manage
them, are vexed.
Health care delivery markets1 worldwide are characterised by the
presence of many firms constituted in forms other than the classic
shareholder-owned firm, so as to reduce the inherent moral hazard
that would restrict access to health care, and information asymmetry
that would increase health care costs. Yet, absent legislative or
contractual prohibitions, these firms typically co-exist and compete
with classic shareholder-owned firms (Brown, 2010). This suggests
that whilst alternatively-constituted firms may offer advantages in
certain circumstances, it is not axiomatic that such structures and
their attendant advantages are generalisable across an entire market
or industry sector.
Nonetheless, some government funders have signalled strict
preferences for nonprofit firms to provide health care as a means of
ensuring value for money from increased spending. For example, in
the New Zealand Primary Health Care Strategy (NZPHCS), government
funding is restricted to newly-created geographically-defined Primary
Health Organisations (PHOs) which are required to demonstrate a
nonprofit objective (Minister of Health, 2001).2 The justification for
nonprofit firms “to guard against public funds being diverted from
health gain and health services to shareholder dividends” (Minister of
Health, 2001, p. 14) derives from the “trust” that theoretically can be
placed in these firms because they have no owners whose incentives
might lead them to act contrarily to the interests of the funder or
patient.
Nevertheless, governments should be wary of specifying firmtypes as it is not axiomatic that these benefits will accrue. In practice,
all firms operate as a nexus of relationships between a variety of
ownership and control interests (Coase, 1937; Williamson, 1985)
which generates ultimately the firm’s residual asset value and future
income streams (Milgrom & Roberts, 1992). A nonprofit objective,
operationalised in the “nondistribution constraint” preventing defined
owners appropriating excessive profits at the expense of other
stakeholders (James & Rose-Ackerman, 1986), speaks only to the
matter of distributing income streams (profits).3 The nondistribution
constraint is silent on the matter of who controls decisionmaking in
respect of the assets used to generate those income streams, or how
6
HOWELL & CORDERY
any income acquired will be applied in the absence of shareholders to
whom it would otherwise be distributed as either dividends or
increased equity. The constraint cannot, for example, preclude the
distribution of surpluses via higher salaries paid to employees who,
absent the restrictions, would have appropriated the same surpluses
as owners (Robinson, Jakubowski & Figueras, 2005; Howell, 2006).
The allocation of decisionmaking control rights is thus likely to be at
least equally, if not more important to the efficient operation of the
firm and the achievement of the government’s distributional
objectives than the presence or absence of defined shareholding
interests (Jensen & Meckling, 1976; Fama & Jensen 1983a, 1983b).
This paper utilises an industrial organisation framework derived
from Hansmann’s (1996) theories of competition in the markets for
ownership and control of firms, to evaluate the allocation of
ownership and control (governance) of primary4 health care delivery
firms. In addition to addressing the economic case for these firms
having owners or not, it addresses the circumstances in which control
(either in the form of shareholdings, or the exercise of governance
control in a nonowned firm) is optimally exercised by service provider
or consumer interests. By way of case studies, the primary health
care sectors in New Zealand both prior to and after the
implementation of the NZPHCS are analyzed to explain the
consequences of the allocation of PHO control rights between
consumer and service provider interests, and how these allocations
have influenced the formation and governance of the newly-created
nonprofit PHOs.
We contend that, despite the New Zealand government’s
intentions to establish nonprofit PHOs with governance shared
between provider and community (consumer) interests, control of
new nonprofit PHOs has largely followed the patterns established
prior to the NZPHCS implementation when the ownership form of
firms receiving government funding was not limited. This case study
largely confirms the hypotheses developed from Hansmann’s
framework that the economics of ownership and market contracting
drive towards practitioner interests being the most likely controllers of
primary health care firms, except in limited circumstances where
there are either strong consumer preferences for differentiated care
or the economic risks of practitioner ownership are too high.
Mandating shared governance of nonprofit primary health care firms
INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE GOVERNANCE IN NEW ZEALAND
7
is unlikely to alter the exertion of effective control in those firms that,
but for government policy, would have been governed by either
practitioner or consumer interests alone. Rather, it likely increases
costs of ownership as the otherwise more efficient owner-interest
seeks to reassert the control that it would otherwise have exerted
absent the restrictions.
This paper thus challenges the notions that mandating a specific
organisational form alone will resolve funders' concerns about
diversion of scarce public and private resources. Instead, it reinforces
the need to understand the ways in which economic incentives lead
to markets for control by different stakeholding groups, even in firms
with no identified owners. Funders and purchasers must understand
the identity of the controlling interests in order to ensure that their
contractual and other relationships with the firm are structured
optimally. These findings have implications not only for the future of
primary health care funding and delivery in New Zealand, but also in
other jurisdictions where funders and purchasers may have
preferences for dealing with nonprofit primary health care firms. For
example, the UK National Health System contracts out to nonprofit
and for-profit providers (including GPs).
The paper proceeds as follows; section one develops the
theoretical framework, with section two applying the framework to the
general case of primary health care ownership and governance. In
sections three and four the New Zealand primary health care market
is examined respectively prior to and following the implementation of
the NZPHCS.
Section five concludes with discussion and
recommendations for further research.
RELEVANT THEORY
An Industrial Organisation View of Nonprofit Health Care Delivery
Firms
Ever since Kenneth Arrow (1963) articulated the advantages of a
nonprofit objective in reducing inefficiencies arising in the markets for
medical care, there has been great interest in the contribution of
medical firms adopting forms other than the standard shareholderowned model. The form most analysed is the classic “non-profit” with
no owners and where the non-distribution constraint is held to
militate against the risks associated with defined owners
8
HOWELL & CORDERY
appropriating excessive profits at the expense of other stakeholders
(James & Rose-Ackerman, 1986; Silverbo, 2004). However, attention
has also been given to firms which, although having defined owners,
might be constrained by either an explicit objective eschewing the
pursuit of profits as the primary motivation (Glaeser & Shleifer, 2001)
or reliance upon the owners’ personal altruistic motivations to
counter the pursuit of profits in excess of reasonable costs of service
provision (Besley & Ghatak, 2005; Lakdawallah & Philipson, 2006).5
Rationale
Two rationales are commonly offered for the observed prevalence
of nonprofit firms6 in the health sector. The first is to ensure the
provision of “third sector” or “public good” services, which would not
otherwise be offered by (or would be under-supplied by) for-profit or
government entities (e.g. Weisbrod, 1975; 1988). Arguably, this
justification accounts for the nonprofit provision of goods for which
there is a “missing market” – for example ensuring that in
monopolistically competitive markets7 product variants are offered
that cater to the differentiated demands of special interest groups
(such as ethnic communities, religious groups or other such
associated patient collectives). The second rationale is the presence
of information asymmetries between service purchasers (either
patients or third-party donors and insurers) and health care providers
(Arrow, 1963; Newhouse, 1973; Rose-Ackerman, 1996). Whilst the
asymmetry leads in the first instance to under-provision, it also
predisposes beneficiaries to risks of exploitation by more
knowledgeable parties (usually service providers) that are not always
easily ameliorated by contractual or regulatory constraints
(Williamson, 1985; Hansmann, 1980). By signalling that the firm has
no explicit profit-maximising objective, or that in the event that profits
are made, the surpluses cannot be appropriated by specific
individuals but will instead be distributed via the firm’s operations
(e.g. as services to beneficiaries), the firm has assured stakeholders
that they can “trust” that they will not be exploited financially as a
result of the firm’s information advantages.
Some theorists consider the structure of firms and the markets in
which they operate as a static framework. In the structure-conductperformance view of industrial organisation (Mason, 1939, 1949;
Bain, 1959), it is essentially a matter of strategic choice whether a
INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE GOVERNANCE IN NEW ZEALAND
9
firm will be constituted in a shareholder-owned, for-profit form or as a
nonprofit. This view asserts an optimal structure exists both for the
firms and the sector, that once established will lead to desired
behaviours and consequently the most efficient sector outcomes.
The role of government policy and legislative force is to ensure that
socially optimal structures are imposed and enforced exogenously so
that the desired outcomes will ensue.8
By contrast, however, the price theory view (Coase, 1937; Stigler,
1968; Williamson, 1975) considers the form of firms and markets as
endogenous – that is, they evolve over time. These theorists hold
that the economic incentives facing individuals and firms explain
market phenomena such as the organisational structure of a firm and
the interactions occurring in a market. In this view, a firm’s
institutional structure (design and governance arrangements), the
nature of its objective function(s), and the identity of its shareholders
(including having none – that is, a classic “non-owned” firm) are
determined endogenously. If an opportunity exists for a party to be
better off as a consequence of a change in the prevailing
arrangements, then the ensuing interactions in the markets for both
ownership (where possible) and control will gravitate towards
securing that outcome.
Markets for (Non)Ownership and Control
Using price theory precepts, Hansmann (1996) contends that
absent restrictions, markets for the ownership and control of firms
will result in a firm being owned by the group of stakeholders whose
ownership results in the least combined costs of ownership and
market contracting. The costs of “ownership” include the costs of coordinating (e.g. communicating with shareholders, making decisions)
and motivating (e.g. ensuring management runs the firm efficiently;
using incentives; avoiding losses from imperfect agency
relationships). Market contracting costs include transaction costs,
costs of market power imbalances (including those arising from
information asymmetries), contractual incompleteness, bounded
rationality and contractual hold-up costs (Williamson, 1985).
Hansmann classifies stakeholders as either suppliers to the firm
(including suppliers of raw materials, labour and finance – both
equity (shareholders) and debt) or its customers. He uses his
theories to explain why, for example, dairy farmers (suppliers) have
10
HOWELL & CORDERY
tended to own downstream processing activities (dairy factories),
whereas consumers have tended to own insurance companies
(particularly via the “mutual” ownership instrument), and the firms
supplying consumer goods in small-scale markets. Hansmann’s
theories also serve to explain why firm ownership changes in
response to changes in the environment in which it operates. For
example, better regulation and information availability gradually
enabled third party (capital) suppliers to compete in the markets for
ownership of insurance companies, leading to widespread
demutualisation in the 1980s.
Hansmann further suggests that “non-owned” firms (i.e. with no
defined shareholders) will emerge endogenously when the costs of
maintaining defined ownership stakes outweigh the benefits. In
these circumstances, the costs are least when the controls and
disciplines typically applied by shareholder-owners (whether of
supplier or customer disposition) on the directors and managers of
the firm are substituted with a set of fiduciary obligations. These
fiduciary requirements will specify, in lieu of shareholders, in whose
interests the assets of the firm will be applied and how the revenues
derived will be utilised. Accordingly, if the firm would otherwise have
been owned by suppliers (e.g. doctors, nurses), the fiduciary duties
could be expected to reflect supplier beneficial interests. Alternatively, if the ownership interests would otherwise have been vested in
consumers (e.g. patients) then the fiduciary duties could be expected
to reflect consumer beneficial interests.
The NZPHCS Environment
By invoking the “trust” arguments in support of the ex ante
specification that PHOs must be nonprofit entities, the NZPHCS
design appears to be underpinned by the “structure-conductperformance” view of institutional design. That is, government has
mandated PHOs to be nonprofit firms with mixed governance as this
is the arrangement that has been exogenously determined as
delivering the desired behaviours leading to optimal sector
performance – that is, it will constrain the self-interested actions of
those who would otherwise own the firms by limiting the scope for
welfare-reducing profit distributions to take place (market
performance). The policy was implemented in an environment where
primary health care firms already interacted, and were observed to
INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE GOVERNANCE IN NEW ZEALAND
11
have adopted forms across the entire spectrum from shareholderowned for profit to unowned nonprofit (Crampton, 1999; Coster &
Gribben, 1999). This view holds that, as nonprofit firms are ex ante
optimal, firms choosing to adopt alternative forms are militating
against the pursuit of a social welfare-maximising outcome. Unless
they are restructured, the desired outcome will not be achieved.
The price theory view, alternatively, would suggest that the
plurality of ownership forms pre-dating the NZPHCS was a function of
the heterogeneity of patient and service provision preferences and
economic circumstances in specific sub-markets. Firm ownership
and fiduciary interests would reflect not just the trading off of the
relevant costs and benefits, but also the identity of the stakeholding
group(s) (i.e. suppliers or consumers) whose control of the firm
rendered the least costly institutional arrangements. As the firms do
not face the same economic circumstances, it is quite plausible that
the least-cost arrangements for one firm might differ from those of
another firm, and that both can co-exist without necessarily having a
detrimental effect upon overall welfare. By the Coase Theorem, if any
of the stakeholding groups was disadvantaged by the prevailing
arrangements, then in order to reduce their own disadvantage, unless
they were otherwise prevented from doing so, they would take actions
that ameliorated their own position and ultimately improve societal
welfare. In this view, the role of government is restricted to
intervening only in respect of those actions that will be detrimental to
long-run total welfare (e.g. intervening to prevent the exertion of
market power). By mandating both that PHOs form as nonprofit firms
and have decisionmaking processes incorporating both supplier and
consumer interests, the NZPHCS appears to be prescribing a single
set of artificial ownership and governance obligations on PHOs that by
the price theory view would be unlikely to emerge endogenously. If
this is true and these arrangements are not consistent with the
minimisation of the joint costs of ownership and market contracting,
then the price theory view would expect that the governance
arrangements of PHOs will evolve in such a manner as to minimise
ownership and market contracting costs within the new policy
constraints. If there are compelling cost-based reasons why one
stakeholder group should be the beneficial owners of a PHO, then
regardless of the government-mandated nonprofit objective or the
imposition of a nondistribution constraint, it would be expected that
the arrangements actually employed will ultimately reflect those
12
HOWELL & CORDERY
interests. Although the firm may masquerade externally as an
NZPHCS-compliant PHO (albeit at some additional cost of ownership
and governance), its underlying activities will likely reflect the
otherwise-lower cost ownership interests.
Primary Health Care: Who Will Own and Govern?
This section uses a Price Theory-based analysis of the costs of
ownership and market contracting drawn from Hansmann (1996) to
examine the case for either consumers or service providers to own
the firms delivering primary health care. It also examines the
circumstances where it might be economically most efficient for
ownership interests to be foregone, and in that case how decisionmaking rights might be allocated across the consumer and service
provider interests.
Why Enter into Patient (Consumer) Ownership?
Costs of Market Contracting
The predominant market contracting reason for consumers to
own the firms providing primary health care pertains to the moral
hazard that ownership by other interests present. Service providers
can utilise their superior information (or other factors conferring
market power, such as limited competition) for personal pecuniary
gain – for example by overcharging patients for services or
recommending unnecessary procedures (the moral hazard of
“supplier-induced demand” – Pauly, 1968). To the extent that these
risks cannot be adequately controlled by other mechanisms (e.g.
regulation, registration requirements, etc.), patient ownership, where
service providers are hired as employees by consumer-owners,
potentially overcomes the overcharging problem. Patient-owners can
set the prices they will charge to themselves as consumers to reflect
the actual costs of the services provided. The incentive to overcharge
is mitigated, as any proceeds raised in fees in excess of costs will
simply be paid to the patient-owners as dividends. By granting the
patients the power of employer in a contractual relationship with
service providers, patient ownership also potentially addresses the
risks of “supplier-induced demand.” Providing sufficient information
is available to detect its occurrence, patient-owners can discipline
employee-practitioners who engage in such behaviour.9 Furthermore,
patient-employers can design employee remuneration contracts in
INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE GOVERNANCE IN NEW ZEALAND
13
order to reduce the likelihood of such behaviour occurring (e.g.
utilising performance incentives that share the risk of opportunism
with employee-providers [Robinson, 2001]).
Costs of Ownership
With regard to the costs of ownership, patients may be the
optimal (indeed, default) owners of a primary health care firm when
the risks of ownership are too high for the alternative owners (i.e.
service providers) to be willing to commit the capital (both physical
and human) required to the firm (i.e. there is a “missing market” for
practitioner ownership). Such risks may arise because demand for
primary health care in a given locality is either too small or too
uncertain to induce practitioners to invest in firm ownership (given
that they likely face less risky ownership options offering more certain
or higher returns on their human and physical capital in other
localities).
In order to ensure that any primary health care is provided in that
locality, consumers (or their agents) must undertake the risks of firm
ownership. Under these arrangements, practitioners are usually hired
on a salaried basis (thereby reducing supplier uncertainty), but to
offset higher risks, either fees charged to patients must be higher
than in other practices, or revenues to cover operating shortfalls must
be procured from other sources – for example, philanthropic
donations, fundraising or subsidies from other consumer-owned and
governed activities (e.g. taxation). Whilst ownership might be
assumed in these circumstances by individuals who are not strictly
themselves patients of the firm, it is important to note that any thirdparty engagement arises because those parties are giving effect to
what would otherwise be consumer ownership of the firm, if only the
consumers themselves could afford to assume the risks. Thus, the
balance of governance interests in this case should lie with patients –
because they are ultimately bearing the financial risks associated
with the firm – rather than practitioners – who, having assumed the
ownership risk-free status of salaried employees, have effectively
signalled their aversion to bearing the financial risks of ownership.
Which Patient-Owners?
When individual patients have defined shareholdings, with a
specified claim on all of residual assets, income streams and control,
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HOWELL & CORDERY
such an arrangement constitutes a classic shareholder-owned firm. It
now matters which patients will own the firm – a subset or all
patients jointly. Assuming only a subset of patients own the firm,
then a risk exists that the patient-owners will overcharge the other
patients. If all patients jointly and equally own the firm, then the risk
of exploitation disappears as the benefits can be shared equally.
However, it also matters how that ownership stake is formalised. If
each consumer owns a defined share of the assets and income
streams (profits distributed as dividends) independent of trading
activity with the firm (as in a standard for-profit firm), then owners
consuming fewer services will have an incentive to charge those
consuming more services higher fees in order to enjoy a higher
dividend.
The equity required by primary health care firms is likely to be
very small (premises can be leased and the typical practice
equipment is, unlike specialist clinics and hospitals, not high-cost). It
is therefore unlikely that individual shareholders have contributed
either large capital sums on which they will be expecting
compensating dividends, or that different shareholders will have
contributed capital disproportionately, thereby necessitating different
share allocations.
When the patients’ interest in the firm is as
consumers, the most appropriate metric via which to define their
ownership interests is from their custom rather than an equity stake.
That is, the firm may have its least cost of ownership when
established as a classic consumer-controlled co-operative. In a cooperative, all patients share equally in the governance of the firm, but
any surpluses are distributed back to patients in proportion to their
custom. The incentive to manipulate fees for personal gain is
obviated – if fees above the cost of production are charged, the
resulting profits are simply returned to precisely the same individuals
who paid them in the first place (Hardesty & Salgia, 2004; Evans &
Meade, 2006). Those who contribute most to the surpluses receive
the greatest benefit (or if the benefit is distributed in the form of
discounted fees, those consuming most services enjoy the greatest
savings) whereas if surpluses were shared equally, healthier patients
might be construed as profiting from the misfortunes of the sick.
Consumer-controlled co-operatives often emerge endogenously
when customers face high costs of market contracting with a
powerful supplier (e.g. groceries and farm supplies in rural locations).
INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE GOVERNANCE IN NEW ZEALAND
15
Such arrangements are especially likely to appeal in health care
provision (relative to equal shareholding) as they also satisfy many
social equity concerns. The merits of the co-operative form are
evident from their endogenous emergence in the health care sector.
Early examples are the Friendly Societies of the 18th and 19th century
and elementary insurance funds such as Blue Cross, and Kaiser
Permanente’s elementary managed care plan (Birchall, 1997, 1998)
(albeit that these organisations were engaged in both the funding of
care and its delivery). Modern examples include trades and student
union-owned clinics.
The literature on co-operatives notes that they are most likely to
offer lower costs of ownership when the consumer-owners are
relatively homogeneous in their demands for the product or are
already closely linked for other purposes (Hendrikse, 2004). Demand
homogeneity means lower likelihood of costly disputes over how the
co-operative should be governed and managed. If the consumerowners are already linked via other interests, then the costs of
governing the new firm are likely to benefit from economies of scale
and scope This provides a powerful explanation for the endogenous
emergence of such co-operatives amongst worker unions and other
extant organisations, such as rural and indigenous communities
(such as Marae10 and Iwi11 Authorities in New Zealand), who are likely
also engaged in co-operative ownership and management of a range
of community facilities.
The greater the heterogeneity of the patient base, and/or in the
absence of any existing entities under which to operate a patientcontrolled co-operative, the more likely it is that the costs of coordinating governance and other decision-making will be high,
thereby reducing the relative advantages of patient ownership over
supplier-owned forms. In these instances, if patient ownership and
control are still desirable, it may be most cost-effective for the
individual patients to forgo a direct control interest in the firm and
allow it to be constituted as one of Hansmann’s non-owned firms (for
example, as a Charitable Trust) (albeit that the lower co-ordination
costs are achieved at the expense of higher costs of countering
managerial opportunism – Milgrom & Roberts, 1992).
The non-owned firm is the form most likely to be observed where
the costs and risks of ownership are so high that neither providerowned firms nor consumer-controlled co-operatives emerge. It is
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HOWELL & CORDERY
noted that there are also strategic advantages in the non-owned form
in the case where there is a “missing market” for other ownership
forms as it enables the trustees to access philanthropic donations
and tax concessions in order to meet the (necessarily) higher costs of
service provision in such circumstances (Rose-Ackerman, 1986).
However, the fiduciary duties of the governors of such firms would be
expected to be aligned directly with those of patients as, but for the
high costs of ownership, patients would have been the logical owners
of the firm. It is noted that in these circumstances, it is usually
patients and patient-agents who assume the responsibility for (and
personal costs of) raising funds to meet shortfalls.
Summary: Patient Ownership
In summary, therefore, under Price Theory, if the combined costs
of ownership and market contracting lead to the conclusion that
patients are the most efficient owners of primary health care firms,
then this will most likely be manifested as either a patient-controlled
co-operative where all patients share equally in the governance of the
firm. Alternatively these will form as a classic non-owned nonprofit
firm where the balance of governance responsibilities, reflected in the
fiduciary duties binding those exerting control of the firm, favours
patients interests.
Why enter into Service Provider (Supplier) Ownership?
If service providers own a primary health care firm, it would be in
their capacity as either the suppliers of physical capital in the first
instance or health professionals as suppliers of human capital in the
second instance.
Service Providers Own and Supply Costly-to-Contract Human Capital
Firms tend to be owned by the suppliers of physical capital
(financiers or professional investors such as insurance and
superannuation funds) when the physical capital requirements are
very much larger than can be supplied by either customers or
suppliers of other production inputs (e.g. labour). Examples include
electricity generators and telecommunications firms. By contrast, in
primary health care, as noted, the demands for physical capital are
very small - premises are typically not highly customised so are
generally leased, and the “tools of trade” (e.g. autoclaves,
INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE GOVERNANCE IN NEW ZEALAND
17
stethoscopes, scales, computers) are comparatively low-cost and
non-specific compared to those required for hospital care delivery
(e.g. radiotherapy treatment machines, MRI scanners, complex
operating theatre equipment). Hence investor ownership of primary
health care firms based upon physical capital needs is uncommon.12
Rather, if suppliers are to own the firm, it is most likely to be in
their capacity as suppliers of human capital essential to the firm’s
activities. Practitioners must invest large amounts in developing their
stock of human capital (education, training and experience). This
investment is a prerequisite for entering into the business, and is
sunk (i.e. cannot be recovered once made). In addition, the requisite
human capital is both highly specific to the business of delivering
primary health care (i.e. cannot easily be deployed into the provision
of other services or at least not for a comparable return on the
investment made), and therefore subject to hold-up. Hold-up occurs
when, under a contractual arrangement (e.g. hiring an employee), the
supplying party has some market power and may use threat of breach
(e.g. withholding services) to extract from owners rents above a fair
price for its acquisition. Furthermore, the one-to-one practitioner-topatient nature of care delivery and the consumption of the good as
part of its provision makes third-party (employer) monitoring of the
level of effort exerted/quality delivered either very difficult or
extremely costly for a third-party owner (Newhouse, 1973).
Together, the hold-up risks and monitoring costs render
practitioner human capital essentially non-contractible (or at least
very costly to contract for), so by Hansmann’s theories, the joint costs
of ownership and market contracting will likely be least when the
owner of the requisite human capital (the practitioner – doctor,
midwife, nurse-practitioner etc) owns the firm. This appears to be
confirmed by market evidence. Privately-owned primary health care
firms are almost exclusively practitioner-owned, typically by a sole
practitioner (Dranove & Satterthwaite, 2000; Scott, 2000).
Partnership: Militating Risks of Market Contracting, Increasing
Ownership Costs
As the need for physical capital is small, there is no obvious
equity-related reason why primary health care firms would comprise
more than one practitioner. Quite often a “group practice” is simply a
convenient arrangement whereby sole practitioners jointly share
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HOWELL & CORDERY
common practice overheads such as premises, administration, and
other services, but still trade under their own legally separate
business identities (e.g. individual patient lists, separate tax
registration).13 Indeed, legal partnership may increase risks arising
from factors such as malpractice suits (Danzon, 1997). Equity-sharing
(jointly-owned) primary health care practices could thus be expected
to arise only when the consequences of market contracting increase
financial risks to sole practitioners, necessitating merging of
individual businesses as a means of pooling the risks facing each
individual practitioner. Such mergers will occur when the costs
associated with managing the risks individually exceed the losses
arising from imperfect contracts between the various owners as
partners.
Financial risks to the practice are greater when the practitioners
are remunerated under capitation contracts than under fee-forservice contracts14 (Dranove, Simon & White, 2002). Income streams
(net of labour costs) are more variable under capitation than fee-forservice as demand and the hence intensity of effort required to
service that varies in both timing and care intensity but income does
not (Robinson, 2001). Practice mergers enable losses incurred by
practitioners whose patients demand more care or more intensive
care than they are remunerated for to be offset by the surpluses
earned by practitioners whose patients demand less, or less
intensive, care than the firm is remunerated for.
However, as each practitioner’s activities are imperfectly
monitored by others in the practice, the practitioner in an equitysharing practice may exert less effort in that organisation than when
working on his own account (due to moral hazard – Newhouse, 1973;
Pauly, 1970; Zeckhauser, 1979). All else held equal, equity-sharing
partnerships therefore tend to be less productively efficient than sole
practices, so the gains from better risk-bearing arrangements in the
merged firm must outweigh the lost productivity for the change in
ownership form to be economically justified.
Is Supplier-Controlled “Non-Ownership” Ever Optimal?
As primary health care firms having defined supplier ownership
stakes appears to be the norm, it begs the question when it might be
conceivable that the costs of supplier ownership are so great as to
warrant foregoing it in favour of a nonprofit firm with fiduciary duties
INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE GOVERNANCE IN NEW ZEALAND
19
constructed so as to reflect the interests of the otherwise optimal
supplier-owners.
Whilst it is not uncommon for practitioners
delivering hospital care to merge and form nonprofit firms in order to
induce philanthropic donations of costly physical capital,16 the same
does not appear to apply to primary health care, principally because
the physical capital needs are so small. Furthermore, the argument
that having many owners makes it prohibitively costly to maintain
individual ownership interests likewise does not seem to apply to
primary health care, given the economic characteristics trend strongly
in favour of sole practitioner ownership. Whilst capitation funding
might increase practice risk, leading to increased merger activity,
even at full capitation (i.e. all market trading risks borne by the
practitioners) in the United States context at least, it appears feasible
for practices of around 25 primary health care practitioners to
adequately manage the variations in patient demand (Hagen, 1999).
It does not seem plausible that the costs of co-ordinating and
motivating such a small number of practitioners would substantially
outweigh the large counterfactual benefits of maintaining an
ownership stake.15
The one exception borne out in the evidence appears to be the
historic delivery of community-based care by religious charities and
missions, where the care deliverers were themselves members of the
religious or charitable order. To all intents and purposes, the
philanthropic donations made to their supporting bodies were to
finance the care deliverers in the first instance (such posts were often
termed “livings,” reinforcing the conclusion that funding the
deliverer’s existence was the highest priority). The fiduciary duties of
these organisations were focused strongly upon the interests of the
care deliverers (indeed, “missionaries” were often appointed as
board members as well as managers of the firm, and many worked in
isolation from colleagues with near total control over their time and
resources made available).
Moreover, unlike the consumerbeneficiaries of nonprofit firms and consumer co-operatives
described above, the patients of the supplier-controlled firms had no
genuine role as consumers in care delivery transactions – rather they
were seen as subservient beneficiaries of charity care controlled and
dispensed by the suppliers of that care in a manner that primarily
satisfied the deliverers’ objectives (e.g. religious conversion). Few
examples of this form of care delivery entity survive in the modern
world due to a move away from philanthropic paternalism (Salamon,
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HOWELL & CORDERY
1995) (although it has been argued that some charitable care
delivered in third-world countries may still be motivated more by the
need to satisfy the deliverer’s sensitivities than the recipients’ needs
and preferences).
Provider Ownership Summary
In summary, therefore, it would appear that holding all other
factors constant, if suppliers are to own primary health care firms, it
will most likely be as sole practitioners.
Whilst contractual
remuneration forms and increasing capital requirements as more
complex services are devolved from hospital and specialist care to
community-based providers may encourage mergers to occur, it is
most likely that the economic considerations encompassing the
combined costs of ownership and market contracting mean that the
firms will likely remain small and predominantly practitioner-owned.
“Mixed” Ownership and Governance
As discussed in section four, the government NZPHCS reforms
(under the structure-conduct-performance theory) proscribe a
governance model whereby control of PHOs is shared between
practitioners and patients. Yet, the price theory suggests it will not be
economically optimal for consumer and practitioner interests to own
the firm jointly, and to replace all defined ownership interests with
fiduciary duties.
Joint ownership typically tends to emerge endogenously as a
solution to either substantial future uncertainty leading to risks that
are not possible to anticipate and assign contractually (e.g. joint
ventures for exploration and research and development), or mutual
holdup of essential resources that, again, is not amenable to
resolution by contract (this occurs in, for example, coal mines,
railways and coal-fired electricity plants). Whilst there may be risk of
hold-up of human capital by suppliers (market contracting costs),
which could be ameliorated by customer integration into ownership, it
is not clear what health care resource could be mutually held up,
except for the case of monopsony purchase by (for example)
government funders. Therefore, while such a situation might be
resolved by mutual ownership, this outcome is not necessarily
superior to contractual resolution. Further, as the human capital
which is the subject of supplier hold-up poses contractual difficulties
INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE GOVERNANCE IN NEW ZEALAND
21
in any case, the issue of remuneration for services provided must be
addressed contractually regardless.
Indeed, internalising the
contractual negotiations within a single firm (rather than separating
them between a distinct purchaser acting on behalf of consumers
(e.g. an insurance company) and a provider firm) may lead to even
greater governance tensions due to the increased heterogeneity of
interests posed by having both consumer and supplier interests
involved in the relevant decisionmaking. The outcome is likely to be
increased costs of ownership, without necessarily reducing the costs
of contracting human capital. Indeed, internalising the transaction
may reduce the ability to use competitive market mechanisms to
differentiate suppliers of human capital, thereby exposing the firm to
even greater risks of holdup and board capture occurring.
For these reasons, it would be highly unusual to see such a
“mixed” ownership arrangement emerging endogenously. Whilst the
joint ownership of facilities is often observed (e.g. joint governance of
hospital facilities or health clinics), the contracts for service provision
tend to remain external to the operation of the facilities – either
under employment or other arms-length contracts.
A potential caveat arises, however, when the entity engages in the
subcontracted purchase of services for patients rather than, or in
addition to, delivering them directly. If the only role for the firm is
purchasing, with no supply involved, then the firm is not a service
provider but more properly a purchaser. Shared control of a
purchasing entity between the patients for whom the services are
purchased and the very providers from whom they will be purchased
(who already possess information advantages over customers) would
appear to invoke such a severe conflict of interest that the
arrangement appears untenable. Consumer interests would likely
seek to obtain full control of the purchasing entity and engage in
arms-length purchase agreements with the relevant providers. If,
however, the firm's role is predominantly as provider, it begs the
question of what additional value mixed control might offer. If there
was no tangible benefit from engaging consumer interests, then
provider interests would prevail - as indicated in the preceding
subsections where free of restrictions primary health care delivery
firms are almost always owned by provider interests.
This price theory logic tends towards the conclusion that “mixed”
governance will be observed only in the presence of restrictions in the
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HOWELL & CORDERY
markets for ownership and control that prevent one set of interests or
the other gaining legitimate superiority (for example, provisions such
as those in the NZPHCS that limit the payment of government funding
to firms with “mixed” control). Furthermore, it cannot be discounted
that the economically more efficient outcomes would not be achieved
by the optimal ownership interests exerting effective control internally
whilst masquerading externally as having “mixed control.”
CASE STUDY
Pre-NZPHCS Primary Health Care Ownership and Governance
The paper now considers the case study of New Zealand and the
observed patterns of ownership of primary health care delivery firms
in New Zealand prior to and following the implementation of the
NZPHCS. Through the NZPHCS, a new firm structure was introduced
to a mature primary health care sector comprising a variety of
providers across the entire spectrum of owned, for-profit, non-owned
and nonprofit forms.
Extant ownership interests and market
transactions would be likely to have a significant effect upon the ways
in which PHO ownership forms and governance arrangements have
“emerged” following this government mandate. In particular, prior
structures would probably determine both how PHOs formed, and
whose interests – patients or providers - the balance of control of
their governance functions of those PHOs would favour.
Pre-NZPHCS: Supplier Co-operation
Prior to the implementation of the NZPHCS in 2002 consistent
with the theories of the combined costs of ownership and market
contracting, General Practitioners (GPs) (the principal providers of
primary health care in New Zealand) were mostly self-employed sole
practitioners operating for-profit businesses. Fee-for-service
government funding meant there were few financial risk-based
reasons to merge practices. Rigorous registration processes and
disciplinary procedures overseen by the Government and Medical
Association acted as a check on the quality of service providers
entering and practising in the profession.17 Robust competition law,
competition between practitioners for patients, and some
government oversight of fees in respect to the co-payments made by
those individuals qualifying for treatment subsidies, acted as a check
INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE GOVERNANCE IN NEW ZEALAND
23
on the ability of practitioners to charge prices substantially in excess
of cost.18
Whilst most GPs operated independent businesses, for the
purposes of delivering health care, they were linked collectively via
their membership of the New Zealand Medical Association (NZMA).
Furthermore, some collaborated via geographically distributed
Independent Practitioner Associations (IPAs). The IPAs in particular,
had emerged in response to market-based reforms in the early 1990s
for the purchasing and supply of all health care services, including
primary health care.
IPAs as Practitioner-Controlled Co-Operatives
IPAs formed initially as geographically-based collectives of GPs
working in supplier-owned private practices, therefore GPs employed
by government or non-GP owned entities such as consumer-owned
co-operatives and nonprofit entities were not eligible for IPA
membership. Each IPA is a legally distinct entity. The legal forms of
their incorporation vary with some having defined shareholdings, but
all are characterised by having nonprofit objectives and control
exercised ultimately by their controller-members.
As their
predominant purpose was initially to provide services to their (GP)
members, in essence they began as one of Hansmann’s consumerowned co-operatives – that is, GP-members consumed IPA services
and governed their operation.
IPAs were initially formed for the purpose of supplying to GPs
those services which were costly to co-ordinate and self-provide at the
level of an independent practice or small group practice. This
included education, training, locum management and other services
benefiting from scale economies. GP members held the balance of
power in governance arrangements in nonprofit IPAs. Membership
was voluntary, and by no means did all practice-owning GPs join IPAs
when they were initially formed in the 1980s. Following health care
reforms in the 1990s, many IPAs levered off their existing
relationships with GPs in private practice to become vocal advocates
for the interests of their members, and membership increased
commensurate with an increase of the benefits to members from IPA
activities. By the implementation of the NZPHCS in 2002, 67% of
GPs had joined IPAs (Controller and Auditor-General, 2002).
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HOWELL & CORDERY
Government policy changes in 1995 enabled government funds
to be spent on a much wider range of services than the historic
subsidies paid to GPs for classic primary health care consultations. As
entities linking service providers, IPAs were ideally placed to devise
new care delivery models and tender for the funding to operate these
new services. Classic economies of scope and scale meant that the
ownership and contracting costs for IPAs to provide these services
were lower than the counterfactual of establishing new entities for
delivering equivalent services.
However, as the GP-members
continued to be paid to deliver traditional services in their surgeries,
any new services tendered for by IPAs tended to be complementary to
the classic GP consultation. These included control of laboratory and
pharmaceutical budgets accessed by their members; new
programmes targeting sufferers of specific diseases (e.g. asthma,
diabetes), immunisation programmes and school health services.
New IPA-controlled services were typically provided by staff hired
as employees specifically to operate the new programmes, rather
than by IPA members as part of their membership of the co-operative.
Although some members did become IPA employees for specific
programmes, they were generally contracted and remunerated
separately for these activities, which they delivered in addition to their
GP practice activities. Profits generated from IPA-delivered services
were applied to improve the services provided to GP members (e.g.
increased education and training, development of computer systems
for GP practices and linking practices to each other and the IPA
electronically). Whilst IPAs' original advocacy and practice support
activities continued to be provided to GP members as consumers, in
respect of the new services IPAs morphed from being simple
consumer-owned co-operatives serving member GPs, to complex
organisations embracing historically-provided membership services
and supplier-controlled co-operative arms contracting to sell services
(via government contracts) to end consumers (patients). Thus they
were conjointly operating as GP consumer-owned co-operatives and
provider-owned co-operatives.
The fiduciary duties specified for the governance of IPA-provided
activities quite appropriately reflected the interests of the GP
members as the nominal owners and controllers of the organisation,
in respect of both the GP-as-consumer and GP/IPA-as-supplier
activities. The IPA nonprofit objective had no demonstrable effect
INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE GOVERNANCE IN NEW ZEALAND
25
upon the identity of the controlling interest. The interests of the final
consumers of the services delivered by IPAs would not have been
expected normally to have entered into the governance arrangements
of the firm, as they could quite reasonably have been addressed in
the contract for sale and purchase of the services without any
recourse to any additional governance obligations on the firm
(Jensen, 1991). The corollary is the governance arrangements of the
GP-owned firms supplying services to patients. Patients have no
legitimate expectation of a governance role in those firms, so likewise
were not expected to have a governance role in a firm which was in
effect the collective manifestation of many such GPs “merged”
notionally into a larger co-operative. If there were some additional
costs of market contracting that indicated consumers would be more
efficient owners of the firm, then resolution would have been
achieved by consumers purchasing the IPA-firm, installing governors
and fiduciary duties that reflect their interests and hiring the GPs as
employees (or setting up a consumer-controlled firm to compete with
the IPA-owned firm). However, this did not occur.
That provider-controlled General Practices and IPAs dominated
the supply of primary health care prior to the implementation of the
NZPHCS (and no legal or regulatory prohibitions other than the
preferences of government purchasers prevented competition from
firms of alternative ownership) suggests that, all else held equal,
there was no compelling economic justification for any other
stakeholding interests to seek to acquire them.
Consumer-Governed Models
Despite the dominance of provider-controlled firms, consumer
ownership of primary health care services also arose endogenously in
New Zealand. Prior to the 1995 reforms that opened up new health
care delivery options, consumer controlled firms emerged
endogenously only in a limited number of circumstances. These firms
mainly served comparatively homogeneous consumers of health care
services whose service preferences differed from those of the
customers of supplier-controlled services. The consumer-controlled
firms were most usually already organised co-operatively for the
provision of other services. Examples include primary health care
services owned by worker or student unions, where patients have
similar life- and health-state conditions, and iwi health clinics where
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HOWELL & CORDERY
patients may favour care delivered according to specific cultural
criteria that is not normally provided in the wider community of health
care providers.
Staffed by salaried GPs, and initially formed under a government
funding arrangement that remunerated only GP-provided services
following the 1995 reforms, the consumer-controlled firms began to
expand both in number and in the range of services provided. Iwibased services, leveraging off the economies of scale and scope
present from the range of social and welfare activities already
undertaken by these communities experienced the greatest growth.
Similar ventures arose around Pacific Island communities (often
church-based) and youth activity centres (Crampton, Davis & Lay-Yee,
2005). The firms were either explicitly owned by the entities coordinating the consumer interests (albeit that some were themselves
non-owned entities) or were strictly non-owned. In either case, the
governance arrangements and fiduciary duties reflected the interests
of their consumers as the economically logical owner-beneficiaries.
An important subset of consumer-controlled firms also emerged
as a consequence of the “missing market” for ownership of primary
health care firms. This is commonly seen in rural areas of New
Zealand, where low population density and isolation discourage
primary health care providers from wanting to practise in that
community. Typically, the organisation emerges when the existing
practitioner (often of long standing) wishes to sell and cannot find a
buyer for the business (e.g. upon retirement). Rather than lose
medical services altogether, community representatives assume
responsibility for the co-ordination and provision of services. Whilst on
the one hand, this may lead to “innovation,” because often necessity
opens up opportunities to create new ways of providing services (e.g.
nurse clinics rather than GP clinics; use of new technologies to
provide advice to patients in the absence of a physical practitioner;
transport co-ordination to take patients to practitioners in other
locations), on the other hand it is usually a second-best to having
dedicated general practitioner services. It is also a high-cost option,
and the firm’s future may also be uncertain due to the vagaries of
charity revenue and historic reliance upon short-term funding
contracts that militate against practitioners making a long-term
commitment to the community.19
INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE GOVERNANCE IN NEW ZEALAND
27
Strategic Positioning under the NZPHCS
The NZPHCS was introduced in 2002. A key feature was a
substantial increase in the amount of government funding. To access
the increased resources, primary health care practitioners were
required to affiliate with new nonprofit PHOs. Government funding
was channelled to the PHOs through District Health Boards (DHBs);
the latter were prohibited from contracting with for-profit
organisations (for primary health care) so that public monies "would
not be diverted into dividends" (Minister of Health, 2001, p.14).
By 2007, 80 PHOs had been established. PHOs were charged
with either providing, or contracting for the supply of, primary health
care services for enrolled populations. Each acts as an intermediary
between the government funder (one of the 21 DHBs) on the one
hand, and General Practitioners (GPs) and other primary health care
providers on the other. As a result of factors such as their historical
origins, the rapidity of the establishment of PHOs during the first 18
months of the NZPHCS, subtle differences in the demands the 21
funding DHBs placed on PHOs in their districts, and variations in the
demographics and health needs of the patients enrolled in PHOs, a
range of PHO legal forms emerged and are “tolerated” by the DHB
funders.
The system’s structure is shown in Figure 1. The Ministry of
Health is responsible for vision and policy and funds the 21 DHBs.
These DHBs run hospital services from a provider arm and contract
with nongovernment organisations (including PHOs) for other health
care services. PHO funding is based on the number of members
(patients) enrolled either directly with the PHO or with the GPs who
are contracted to the PHO. Although GPs may themselves maintain
independent for-profit practices with their own “patient lists,” they
must contract exclusively with only one PHO. The institutional
structure thus predisposes PHOs to operate in effect as provider
entities supplying services or ensuring the supply of services to
registered patients.
28
HOWELL & CORDERY
FIGURE 1
Structure of New Zealand’s Primary Health Care System
As the NZPHCS was introduced into an environment where three
distinct types of nonprofit entities – two consumer-controlled and one
provider-controlled - had emerged endogenously, it is perhaps
unsurprising that many different legal forms for PHOs operate. It
might be expected that the extant nonprofit entities would have a
significant advantage in being able to quickly satisfy the non-
INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE GOVERNANCE IN NEW ZEALAND
29
ownership requirements mandated by government in order to receive
increased primary health care monies. However, the NZPHCS
required PHOs to demonstrate both service provider and constituent
community representation on their governing bodies in the apparent
belief that structure would change conduct and produce improved
performance (Mason, 1939, 1949; Bain, 1959). As there was no
evidence of “mixed governance” entities pre NZPHCS, meeting this
obligation required PHOs emerging from provider-governed origins to
include consumers in their governing bodies, and consumer-governed
PHOs to include providers.
From the theories of ownership costs, this mandating of a
“mixed governance” model most likely imposed substantial additional
costs of ownership as a consequence of having to co-ordinate the
interests of an extremely heterogeneous set of stakeholders. For
example, as consumer-governors needed to be apprised of provider
issues, and vice versa, it would take much longer to make decisions
than under the counterfactual of respectively provider-controlled and
consumer-controlled firms.20 Furthermore, the dual governance
model invokes the risk of capture of the firm’s governance agenda by
one set of interests at the expense of the other. In that case, rather
than the PHO being governed in the mutual interests of both its
stakeholding groups, the PHO would likely operate as either a de
facto supplier-controlled or consumer-controlled entity depending
upon which interests were captured or engaged in the capturing.
In practice, the competitive interactions emerging under the
NZPHCS made speed of PHO formation the main imperative (Howell,
2005). As community-controlled groups were almost always nonowned nonprofits, often with some (minority) provider representation
already in place on their boards, migration to PHO status occurred
quickly. Indeed, it would appear that the government's requirements
for PHO governance had been modelled upon examples of
community-governed organisations (e.g., Crampton & Davis, 2005).
Case study research recounts an example of a union-based
health care organisation joining with other community health care
providers to form a Charitable Trust-based PHO (Cordery, 2008). They
had a ten-member board elected by community representatives
annually. This comprised iwi and Pacific Island representation, a
nurse, a GP, and the balance were community providers and
representatives. This operated as a non-owned nonprofit firm with the
30
HOWELL & CORDERY
balance of governance favouring patient interests. Of the consumerorigin PHOs, this was the most popular form. However, in areas where
a structure had not previously existed, developing this PHO form was
costly in terms of resources (e.g. Lockett-Kay, 2005).
In another example, Abel et al. (2005) describe how a welldeveloped iwi network which operated health care contracts for some
years prior to the NZPHCS, formed as a consumer-owned cooperative.
Its prior structure (including a broad-based democratically elected
consumer board) fitted well with the NZPHCS, but Abel et al. (2005)
noted that the formation of the PHO still required considerable
energy, especially mediating the complaints (mainly from GPs)
against the lack of provider representation on the board.
On the contrary, in order to avoid losing patients to newly-formed
PHOs receiving higher levels of funding (a cost of market contracting),
existing providers (namely independent GPs) faced very strong
incentives to join or form their own PHO as soon as possible. IPAs
offered the logical vehicle via which to achieve this. Consequently,
most IPAs responded by forming “subsidiary” operations where they
continued to exercise supplier control by appointing board members
(typically their members and staff such as nurses working for GP
clinics) and determining the processes via which other
representatives were appointed. More than half of these formed as a
Charitable Trust - a non-owned firm – rather than as a co-operative
with defined member-owners but a nonprofit objective as might have
been expected in the industrial organisation viewpoint. However,
these organisational forms are unregulated and we observed that in
some cases this structure was adopted as a strategic response to
pressure from the government funder. Of the 77 PHOs in existence in
2004 covering 95% of the population, 33 had their origins in
community-led organisations and 44 emerged from practitioner-led
initiatives. The PHOs with community origins delivered services to
12% of the registered population. Provider-origin PHOs covered the
remaining 88%.
While there is a mix of owned and non-owned provider firms, the
case study in Howell (2005) of a large urban PHO provides an
example of a non-owned provider PHO where the governance
arrangements “on paper” met the NZPHCS requirements for
governance by mutual interests, but in practice these were biased
towards the interests of their originating IPA stakeholders. The IPA
INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE GOVERNANCE IN NEW ZEALAND
31
controlled the process by which community representatives were
appointed. Moreover, a management company owned by this IPA was
contracted to provide management services to five PHOs in adjoining
geographic locations. In the 2003-04 fiscal year, the PHO had a board
of eleven trustees. Six were general practitioners and directors of the
IPA. Two more were non-GPs whose appointment to the PHO was
controlled by the IPA.21 The chair of the PHO was the chair of the IPA.
Clearly, the balance of governance control of this PHO lay squarely
with the IPA. While it was a non-owned nonprofit firm, the balance of
governance favoured provider interests. This therefore reduced the
otherwise expected higher costs associated with governance shared
between provider and consumer interests, and reflects the
prevalence of provider over consumer interests despite the
government policy preferences. However, the costs of ownership are
likely to be higher than under the counterfactual of full provider
control, as the presence of even a minority consumer representation
is likely to pose some limitations on the extent to which decisions can
be made in provider interests.
The other option that emerged is also shown in Cordery (2008)
where, in another region, the PHO was operated as a wholly-owned
subsidiary of the IPA. The one PHO staff member was employed by
the IPA and GPs contracted with the IPA which operated the PHO
contract through a nonprofit limited liability company. That is, the
PHO, while a nonprofit firm, was wholly owned by provider interests.
The five member board included the Chair (a GP who was also on the
IPA Board), two other GPs, a nurse (who worked for one of the GPs on
the Board) and a community representative. Fiduciary duties were
undertaken by these people who were selected and appointed by the
IPA-member GPs. There was very little community input.
CONCLUSION
This paper has taken industrial organisational theories and
applied them to health care delivery. In order to assess the
theoretical application, the NZPHCS has been used as a case study.
By utilising this strategy, the government appeared to believe that the
structure-conduct-performance theory would operate, that is, by
mandating a nonprofit form, public funds would not be diverted to
private gain. The case studies would appear to confirm that, despite
the intentions articulated in the NZPHCS for governance of PHOs to
32
HOWELL & CORDERY
be shared between provider and consumer interests, effective control
of the majority of new entities has followed the service provider
ownership interests prevailing prior to the implementation of the
policy. When analysed against the different forms, provider interests
have dominated in the formation of PHOs which cover over 88% of
the population (3.27 million out of 3.69 million). This would be
expected given the extent to which provider interests controlled firms
prior to the implementation of the strategy.
Consumer interests appear to have prevailed in PHOs that serve
only a minority of the population and generally have emerged from
entities which, for a variety of endogenous economic reasons, were
already consumer-controlled prior to the NZPHCS. Therefore, whilst
the government instituted a policy that required evidence of mixed
governance, it is quite likely that the underlying economic realities of
the costs of ownership and market contracting biased the actual
governance of these entities towards their original controlling
interests. Further exogenous controls are likely to be needed to
redress this failing of the NZPHCS.
We note however, that the case study analysis undertaken for this
paper has provided only a snapshot of the new entities based upon
secondary data and relates to the formation of PHOs and not current
practices. Consequently, further research is indicated in two
dimensions. The first is to investigate whether the balance of control
by either consumer or provider interests indicated by the secondary
data at the time of PHO formation is reflected in the nature of
decision-making processes actually undertaken by the governing
bodies. The second is to investigate whether the effect of recent PHO
amalgamation, partly in response to policy directives as a
consequence of the burgeoning costs of PHO operation, has altered
the balance of effective control of PHOs between provider and
consumer interests.
The New Zealand situation provides a unique opportunity to
observe the endogenous exercise of price theory precepts, despite
exogenous encouragement to the contrary. The NZPHCS “experiment”
provides a salient lesson to other policy-makers who seek to reduce
the diversion of public funds to private gain. Absent contractual
limiting and regulation, it is apparent that the mandating of specific
organisational structures alone will not result in remediation of
unwelcome conduct within health care delivery.
INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE GOVERNANCE IN NEW ZEALAND
33
ACKNOWLEDGEMENTS
The authors wish to acknowledge the helpful comments of Lewis
Evans, Dave Heatley and Richard Meade, the research assistance of
Laura Hubbard and Mina Moayyed, and the two anonymous
reviewers. The views in this paper solely reflect those of the authors,
and do not necessarily represent those of the institutions with which
they are affiliated or their constituent members. Any errors or
omissions remain the responsibility of the authors.
NOTES
1. The authors acknowledge that the wider health care sector
embodies the nexus of markets for health care delivery and
markets for the provision of risk management (insurance – either
privately or socially provided – see Howell, 2005). However, for
the purposes of this paper, we have chosen to address only those
aspects relating to the exchange of products and services in the
health care delivery markets – that is, care transactions between
patients and service providers.
2. In turn PHOs have entered into contracts for service provision
with a vast range of service providers, including community and
charitable trusts, indigenous (Maori) incorporations and trades
unions, and independent practitioners associations (IPAs), as well
as traditional privately-owned for-profit firms (usually General
Practitioner clinics – GPs) (Howell, 2005a).
3. That is, the “ownership” component of Berle and Means’ (1932)
classic separation of “ownership” and “control.”
4. The focus of this paper is on primary health care delivery
because: (a) it was the subject of the New Zealand policy which
we examine; and (b) most of the literature on ownership
differences in health care examines the delivery of hospital care.
This focus enables us to make an unique contribution by
examining both the economic rationale for observations of the
nonprofit form emerging in primary care markets where private
practitioner ownership has typically been the norm (section 2)
and the likely economic consequences of mandating alternative
ownership and governance arrangements in the sector (section
4).
34
HOWELL & CORDERY
5. Nonprofit firms may have defined shareholders or be “unowned”
– that is, where there are no defined owners with a claim on
residual assets or income streams, and where all control rights
regarding the application of the assets and income streams are
vested in a governing body bound principally by adherence to
fiduciary duties (Fama & Jensen, 1986; 1986a).
6. For a fuller discussion of the modelling of nonprofit institutional
structures and behaviours, see Hughes & Luksetich (2010).
7. The market for medical care – and in particular, primary health
care services – is widely presumed to exhibit monopolistically
competitive characteristics as a consequence of both the
repeated interaction that occurs between a patient and a primary
health care practitioner and the high search costs incurred by
patients in finding a practitioner whose differentiated service
provision characteristics offer the best match for that specific
patient (Dranove & Satterthwaite, 2000).
8. Third party insurers could act as governments do where they are
powerful purchasers with the power to dictate firm-type. These
findings would be most applicable where there was a monopsony
insurer.
9. It is acknowledged that it may be difficult to detect such
behaviour.
10. Marae is Mäori for the communal meeting area and also has
great spiritual significance.
11. Iwi is Mäori for tribe/tribal.
12. Arguably costs may be increasing as more complex diagnostic
and treatment services are provided in community locations.
However, it is not clear that even in these circumstances it is GP
clinics that invest in such equipment – rather it tends to be
specialist firms providing services to GPs and their clients who
invest in these activities.
13. It is noted that other professionals with similar physical and
human capital and sunk cost profiles and hard-to-monitor-andverify single client service delivery also use this same trading
model – for example independent barristers operating in
“chambers.”
INSTITUTIONAL ANALYSIS OF NONPROFIT PRIMARY HEALTH CARE GOVERNANCE IN NEW ZEALAND
35
14. Under capitation, firms are paid a fixed amount on an annualised
basis to supply primary health care services to the patients under
their care. This does not stop them from also charging patients a
“top-up” fee. Whereas under a fee-for-service arrangement,
patient visits to a health professional are subsidised by the
purchaser making a fixed payment to the provider for each visit.
15. It is noted that philanthropic donations to practitioner-controlled
nonprofit hospitals are almost always solicited and provided with
the intention of benefiting practitioners in the first instance (e.g.
the ability to perform a high-technology procedure (and in the
case of a fee-paying system, charge fees accordingly) with patient
benefits typically accruing as a secondary consideration. If there
was a primary benefit to patients, either they or their insurers
would be willing to pay fees reflecting the opportunity cost of the
providers purchasing the equipment and providing services under
commercial terms satisfactory to the providers of debt finance.
Not surprisingly, such donations are typically made to teaching
and research hospitals (Sloan, 2000).
16. This would appear to be the case only if the effort exerted under
joint ownership is very substantially smaller than the effort
exerted under the ownership counterfactual, and that nonownership of itself will overcome almost all of the reduction in
effort exerted – an unlikely scenario in practice.
17. Similar processes also existed for other health professionals (e.g.
nurses via the New Zealand Nurses’ Organisation and midwives
via the New Zealand College of Midwives).
18. Primary health care delivery, with repeat custom and sunk human
capital costs, exhibits a pattern of monopolistic competition
where individual preferences and high search costs mean that
prices tend towards average rather than marginal cost, and are
typically higher the more practitioners there are competing in a
given geographic location, all else equal (Carlton & Perloff, 2005).
19. These clinics could be staffed by employees, by GPs in a profitsharing arrangement with their community or be leased as a rentmaking function for the community.
20. Howell (2005) reports one PHO representative (service provider)
noting that it took eighteen months for the consumer
36
HOWELL & CORDERY
representatives on the board of his organisation to “get up to
speed” on the issues facing providers in the sector.
21. The two directors concerned were practice nurses employed by
IPA members.
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J. OF PUBLIC BUDGETING, ACCOUNTING & FINANCIAL MANAGEMENT, 25 (1), 41-68
SPRING 2013
AUDITOR SIZE AND INTERNAL CONTROL REPORTING DIFFERENCES
IN NONPROFIT HEALTHCARE ORGANIZATIONS
Dennis M. López, Kevin T. Rich and Pamela C. Smith*
ABSTRACT. We investigate whether auditor size is associated with the
disclosure of internal control exceptions among Circular A-133 audits of
nonprofit healthcare organizations. Our analysis is motivated by recent
growth and transparency concerns within the sector. Using a sample of
1,180 audit reports from 2004 to 2008, we find evidence that audits
performed by Big 4 firms are less likely to disclose internal control
weaknesses than those performed by smaller firms. Additional analyses
indicate this relation only remains statistically significant for a subsample of
small organizations, possibly due to greater selectivity or lower efforts by the
Big 4 auditors. We discuss the implications of these findings from an audit
quality, market dominance, and client size perspective. The results are
relevant to hospital financial managers seeking high quality audits at low
cost.
INTRODUCTION
In the wake of recent corporate scandals, there are increased
demands for accountability within the domestic nonprofit sector.
Federal and state regulators have questioned whether nonprofit
organizations continue to meet the needs of the community, and
have encouraged the formation of the Panel on the Nonprofit Sector
--------------------------* Dennis M. López, Ph.D., CPA, is an Assistant Professor in the Department
of Accounting at the University of Texas at San Antonio. His research
interests are in auditing, forensic accounting issues, and governmental and
nonprofit organizations. Kevin T. Rich, Ph.D., is an Assistant Professor in the
Department of Accounting at Marquette University. His research interests
are in corporate governance, financial reporting quality, and governmental
organizations. Pamela C. Smith, Ph.D., is an Associate Professor in the
Department of Accounting at the University of Texas at San Antonio. Her
research interests are in taxation and nonprofit organizations.
Copyright © 2013 by PrAcademics Press
42
LÓPEZ, RICH & SMITH
to investigate strategies to improve the oversight of charitable
organizations (Smith, McTier, & Pope, 2009). One critical aspect of
governmental oversight of nonprofit organizations involves the
administration of federal awards. Nonprofit organizations that spend
more than $500,000 in federal funds are required to meet the audit
and internal control requirements of Circular A-133 of the Single Audit
Act of 1984 (U.S. Congress, 1984; OMB, 2003). The purpose of this
study is to investigate whether auditor size is associated with the
disclosure of internal control exceptions among the Circular A-133
audits of nonprofit healthcare organizations.
An understanding of the factors influencing audit report outcomes
within the context of the nonprofit healthcare sector is important for
several reasons. First, healthcare-related expenditures accounted for
approximately 16.2 percent (i.e., $2.2 trillion) of the U.S. gross
domestic product in 2007 (AHA, 2009). Moreover, public charity
health organizations accounted for over 14 percent of all public
charities, generated over $673 billion in revenues, and held over
$826 billion in assets in 2005 (Blackwood, Wing, & Pollack, 2008).
Second, according to the Fraud Report of the Association of Certified
Fraud Examiners (ACFE, 2008), the healthcare industry is one of the
top three U.S. industries facing incidents of fraud. One of the most
prominent allegations of fraud in the sector involves former
executives of HealthSouth, where the SEC alleged a $1.4 billion
scheme to systematically overstate earnings (SEC, 2003). The ACFE
also reports that corruption within the healthcare industry accounted
for over 26 percent of fraud cases during the two-year period under
investigation (ACFE, 2008). Third, healthcare organizations are
subject to unique regulatory and reporting provisions, particularly
those pertaining to Medicare and Medicaid, that differentiate them
from most other types of nonprofit institutions (Vermeer,
Raghunandan, & Forgione, 2009). These regulatory provisions
promote a renewed focus on compliance and require nonprofit
organizations to concentrate on methods designed to improve
reporting transparency (Troyer, Jose, & Brashear, 2004).
Despite efforts to improve the accountability of federal funds
recipients, the U.S. Government Accountability Office (GAO) has often
questioned the quality of Circular A-133 audits (GAO, 1986). Recently,
a GAO report documented a lack of satisfactory internal control
testing and documentation in federal audits, as well as deficiencies
AUDITOR SIZE AND INTERNAL CONTROL REPORTING DIFFERENCES IN NONPROFIT HEALTHCARE
43
among audits performed by non-governmental auditors, particularly
among those performed by smaller CPA firms (GAO, 2007). The report
suggests a three-pronged approach to reduce these deficiencies,
including the establishment of continuing professional education
requirements for auditors to be eligible to perform Circular A-133
audits. This training would focus on compliance and internal control
testing, which are among the most significant deficiencies noted in
the report.
Our empirical procedures focus on the relation between audit firm
size and the disclosure of internal control exceptions in Circular A133 audits of nonprofit healthcare organizations. Using a sample of
1,180 Circular A-133 audit reports from 2004 to 2008, our
multivariate analyses provide evidence that audits performed by the
Big 4 firms are less likely to involve the disclosure of reportable
conditions and material weaknesses than those performed by small
CPA firms.1 This result is consistent with work in the general nonprofit
sector (Petrovits, Shakespeare, & Shih, 2011); yet it contrasts with
prior research in both for-profit and governmental settings, which
generally finds a positive association between the disclosure of
internal control exceptions and audits performed by the Big 4 firms
(Ashbaugh-Skaife, Collins, & Kinney, 2007; Bedard, Hoitash, &
Hoitash, 2009; López & Peters, 2010). Based on the DeAngelo
(1981) argument that audit quality is the probability that an auditor
will disclose and report a breach in their client’s accounting system,
our findings pose the question of whether the GAO’s concerns of
suspect lower audit quality among audits performed by small CPA
firms have merit within the context of the nonprofit healthcare sector
(GAO, 1986, 2007).
Additional analyses indicate that the negative association
between the presence of a Big 4 auditor and disclosed internal
control weaknesses only remains statistically significant for a
subsample of small healthcare organizations. This finding may reflect
increased scrutiny by Big 4 auditors over the small healthcare
organizations they choose to take as clients (Petrovits, Shakespeare,
& Shih, 2011), or from cost considerations in terms of audit fees
faced by smaller organizations (Keating, Fischer, Gordon, & Greenlee,
2005). An alternative explanation is that Big 4 auditors devote
relatively less effort in the audits of small nonprofit healthcare
organizations, possibly because of reduced concerns of reputational
44
LÓPEZ, RICH & SMITH
risk in the audits of smaller entities (López & Peters, 2010), or
superior performance by local CPA firms trying to establish a
reputation in the nonprofit healthcare sector. Moreover, our
descriptive statistics indicate that Big 4 firms perform fewer first-time
Circular A-133 audits and have a decreasing market share in the
nonprofit healthcare sector. Together with our multivariate analysis,
these findings could be interpreted as early evidence that small CPA
firms are gaining ground in the market for audit services in the
nonprofit healthcare sector. Alternatively, the Big 4 firms may be
decreasing their collective presence in this market.
We contribute to the existing auditing literature on nonprofit
organizations in two primary ways. First, although prior studies
address the impact of auditor characteristics on audit report
outcomes in the nonprofit sector (Krishnan & Schauer, 2000; Keating
et al., 2005; Brennan & Solomon, 2008; Petrovits, Shakespeare, &
Shih, 2011), there is little existing research focused on healthcare
entities. This sector has seen tremendous growth over the past
decade (AHA, 2009), and is one where specialized industry
knowledge is likely to be important for auditors, particularly after
considering the distinctive operating structure and regulatory
environment of nonprofit healthcare organizations (Tate, 2007;
Vermeer, Raghunandan, & Forgione, 2009). Our finding of a lower
likelihood of disclosed internal control exceptions among audits
performed by Big 4 audit firms questions whether these firms are
maintaining the necessary level of expertise required for top level
work in the sector.
Second, although the Big 4 firms performed the majority of the
audits in our sample (i.e., approximately 58.6 percent), they hold a
significantly smaller share of the nonprofit healthcare market than in
other sectors. For instance, Hoitash Hoitash, and Bedard (2009)
document that approximately 85 percent of publicly traded
companies engage a Big 4 firm to perform their financial statement
audits. Given concerns among practitioners that the availability of
well-suited auditors may be limited to or dependent on the size and
complexity of a nonprofit organization (PNS, 2005), our findings
contribute to an understanding of the impact of auditor size on audit
report outcomes in a market that is not entirely dominated by the Big
4 firms.
AUDITOR SIZE AND INTERNAL CONTROL REPORTING DIFFERENCES IN NONPROFIT HEALTHCARE
45
The remainder of this study is organized as follows. We first
present a literature review that includes a discussion of the Single
Audit Act and Circular A-133 audits, as well as a summary of prior
research on auditor size and audit report outcomes in both the forprofit and the nonprofit sector. Our sample selection procedures and
methodology are presented next, followed by a discussion of the
results. The last section provides concluding remarks.
LITERATURE REVIEW
The Single Audit Act and Circular A-133
The Single Audit Act of 1984 (SAA) requires that either a single or
program-specific audit be conducted for governmental and nonprofit
entities that spend more than $500,000 in federal awards during a
fiscal year (U.S. Congress, 1984; OMB, 2003). Prior to the enactment
of the SAA, federal audits were conducted on a grant-by-grant basis
(U.S. CFOC, 2001). The SAA was designed to improve the consistency
of the federal audit process by requiring disclosures of compliance
with applicable regulations and internal control deficiencies (U.S.
Congress, 1996). Circular A-133 of the Office of Management and
Budget details the specific reporting requirements and
responsibilities of nonprofit organizations subject to the provisions of
the SAA. Entities subject to examination must maintain internal
control over federal programs, manage federal awards to ensure
compliance with regulations and contractual agreements, and
prepare appropriate financial statements (OMB, 2003).
Auditors performing Circular A-133 audits are required to
determine whether the expenditures of federal awards are presented
fairly in all material respects in relation to the financial statements. In
addition, the audit report must disclose any reportable conditions and
material weaknesses in internal controls noted during the audit
(OMB, 2003). According to the American Institute of Certified Public
Accountants (AICPA), this requires auditors to perform tests that
demonstrate an understanding of the recipient’s internal control
systems in order to support their risk assessments (AICPA, 2005).
Despite constant efforts to improve the effectiveness of the single
audit process, several criticisms concerning the quality of Circular A133 audits still remain. A report released by the GAO in 2006 states
that “while the Single Audit Act has provided oversight of more than
46
LÓPEZ, RICH & SMITH
$300 billion in annual federal grants, questions have been raised
about the usefulness and effectiveness of oversight for federal funds”
(GAO, 2006: p. 38). In a separate report the GAO documented a lack
of satisfactory internal control testing, documentation, and other
deficiencies among audits performed by non-governmental auditors,
especially among those performed by smaller accounting firms (GAO,
2007). These concerns may be especially pronounced among the
audits of smaller federal funds recipients (i.e., those receiving less
than $50 million in federal awards), where the audit reports have
been shown to be significantly more likely to be labeled as “limited in
reliability” or “unacceptable” (PCIE, 2007).
Auditor Size and Audit Report Outcomes
Prior research involving publicly traded companies generally
assert that the Big 4 firms are associated with indicators of higher
audit quality (Francis, 2004). Among others, Lennox (1999) finds that
Big 6 auditors are more likely to successfully forecast financial
distress than are smaller auditors. Specific to internal controls,
Ashbaugh-Skaife, Collins, and Kinney (2007) find that an audit is
more likely to disclose internal control deficiencies under Section 302
of SOX when the company engages the services of a “dominant”
auditor (i.e., a Big 4 firm, BDO Seidman, or Grant Thornton). The
authors interpret this finding as an indication that larger CPA firms
possess a greater ability to detect existing internal control exceptions,
and that higher litigation concerns prompt auditors to pressure their
clients to disclose those exceptions.
However, not all studies based on publicly traded companies
document a positive association between the presence of a Big 4
auditor and the disclosure of internal control exceptions (Zhang,
Zhou, & Zhou, 2007; Hoitash, Hoitash, & Bedard, 2009). Possible
explanations for the lack of significance in those studies involve the
avoidance of risky clients by the Big 4 firms or the inability of smaller
clients to pay the premiums demanded by the Big 4 firms (Zhang,
Zhou, & Zhou, 2007). Interestingly, the evidence from AshbaughSkaife, Collins, and Kinney (2007) highlights that when the Big 4
firms are analyzed separately from the rest of the “dominant”
auditors group, there is a negative association between the
disclosure of internal control weaknesses and audits performed by
them. One possible interpretation of this finding is that medium-sized
AUDITOR SIZE AND INTERNAL CONTROL REPORTING DIFFERENCES IN NONPROFIT HEALTHCARE
47
firms exercise greater diligence in identifying internal control
problems to develop a reputation with their public company clients.
Similar to the literature on publicly traded companies, some
studies in the governmental and nonprofit sectors have raised
questions against the assumption of superior performance by the Big
4 firms. For example, Samelson, Lowensohn, and Johnson (2006)
present survey evidence that local government finance directors do
not associate the Big 4 firms with perceptions of higher audit quality,
while Baber, Gore, Rich, and Zhang (2011) provide evidence that the
presence of a Big 4 auditor is negatively associated with the
probability of accounting restatements. Brown and Raghunandan
(1995) find that federal program audits conducted by state and local
auditors are less likely to have significant audit inadequacies
compared to those performed by CPA firms. Similarly, Petrovits,
Shakespeare, and Shih (2011) find evidence suggesting fewer
internal control weaknesses among nonprofit organizations that
employ a Big 4 auditor when compared to those that employ smaller
auditors, although the authors attribute this condition to a more
careful selection of clients by the Big 4 firms.
In contrast, other studies in the governmental and nonprofit
sector provide support for the general assumption of superior
performance by Big 4 auditors. For instance, Krishnan and Schauer
(2000) find evidence that the nonprofit clients of the Big 6 firms are
more likely to comply with GAAP than are the clients of other CPA
firms, implying greater levels of audit oversight among the clients of
the Big 6 firms. Furthermore, a recent study on U.S. cities and
counties by López and Peters (2010) finds that larger CPA firms
appear more likely to issue audit reports that identify internal control
concerns than are smaller CPA firms or governmental auditors. In
light of prior empirical and anecdotal evidence, López and Peters
(2010) suggest that SOX may have had cascading effects that helped
improve the performance of larger CPA firms in their governmental
audits.
Hammersley (2006) proposes an alternative explanation for the
mixed results on the association between auditor type and the
disclosure of audit findings. Experimental evidence from her study
suggests that industry specialists are more likely to identify
misstatements requiring industry-specific knowledge than are
auditors working outside their primary area of industry specialization.
48
LÓPEZ, RICH & SMITH
Unlike publicly traded companies, whose focus is on the profit
maximization goals of shareholders, nonprofit organizations are
subject to a non-distribution constraint and the need to preserve the
operating mission of the organization (Hansmann 1980). The unique
emphasis on the program service activities of nonprofit organizations
may lead to differences in the risks and audit efforts associated with
nonprofit audit engagements (Hardiman, Squires, & Smith, 1987a,
1987b; Vermeer, 2008). Tate (2007) also suggests that auditing in
the nonprofit sector can be very different from auditing in the forprofit sector, given differences in culture, organizational structure,
and accounting rules.
One specific distinction of healthcare entities from other
nonprofits involves the requirements to comply with Medicare and
Medicaid regulations that may involve additional checks and
balances on the financial reporting system Vermeer, Raghunandan, &
Forgione, 2009). As a result, there is the possibility for differences in
auditor effort and diligence, which may affect the outcome of audits
in the sector. Keating et al. (2005) find that audits of healthcare
organizations disclose more reportable conditions and going concern
opinions than those of any other type of nonprofit entity, although
they do not consider auditor size in their analysis.
In sum, prior GAO studies have repeatedly questioned the
reliability of internal control testing as part of Circular A-133 audits
(GAO, 1986, 2006), especially across different types of auditors
(GAO, 2007). Furthermore, the academic literature provides mixed
evidence regarding the general association between auditor type and
audit report outcomes both inside and outside the for-profit sector
(Krishnan & Schauer, 2000; Ashbaugh-Skaife, Collins, & Kinney,
2007; López & Peters, 2010; Petrovits, Shakespeare, & Shih, 2011).
Given that audits in the nonprofit healthcare sector require significant
levels of industry specific knowledge for optimal auditor performance
Vermeer, Raghunandan, & Forgione, 2009), we conduct a series of
empirical tests to explore the association between auditor size and
the audit report outcomes of Circular A-133 audits of nonprofit
healthcare organizations.
AUDITOR SIZE AND INTERNAL CONTROL REPORTING DIFFERENCES IN NONPROFIT HEALTHCARE
49
RESEARCH DESIGN
Sample Selection
Our analysis is based on data obtained from the Single Audit
Clearinghouse of the U.S. Census Bureau. The Clearinghouse
maintains a comprehensive database of single audit results that
include details about recipient entities, federal award amounts, and
audit report outcomes. Our sample is composed of Circular A-133
reports of nonprofit healthcare organizations for fiscal years 2004 to
2008, and includes entities from the 48 contiguous U.S. states,
Alaska, and Hawaii. We focus on the post-SOX time period to control
for the potential spillover effects of this regulation to the nonprofit
and governmental sectors (Frank & Fink, 2008; López & Peters,
2010). Our search procedures identify a total of 1,198 records, from
which we remove 18 observations from audits performed by
governmental oversight bodies.2 The final sample consists of 1,180
cross-sectional entity-year observations representing 361 different
nonprofit healthcare organizations.
Regression Model
We build on the existing audit literature to develop our regression
model as follows:
Prob ( INDEX )   0  1 BIG 4 _ CPAi ,t   2 MED _ CPAi ,t 
 3 FUNDS _ AUDi ,t   4 LRISKi ,t 
 5 MAJOR i, t   6 COG_AGENCYi, t 
 7 CLIENTSi, t   8 FIRST_A133i, t 
 9 AUD _ SWITCH i, t  10 PRIV_ORGi, t 
  k FUND_SRCk    jYEAR j   i ,t
Where i indicates nonprofit healthcare organization and t
indicates fiscal year.
The dependent variable is INDEX, which proxies for the severity of
internal control weaknesses disclosed within a Circular A-133 audit
report. Following López and Peters (2010), we operationalize INDEX
as a categorical variable where audit reports disclosing no internal
control weaknesses are coded as 0, those disclosing at least one
50
LÓPEZ, RICH & SMITH
significant deficiency but no material weaknesses are coded as 1,
and those disclosing at least one material weakness are coded as 2.3
We use ordered logistic regression to estimate the regression
equation, given that the dependent variable conditions on one of
three different values with an intrinsic logical order.
We consider three different categories of auditor size in our
analysis. First, we define BIG4_CPA as an indicator that equals 1 if an
audit is performed by a Big 4 firm (0 otherwise). We also distinguish
medium-sized firms by defining MED_CPA as an indicator that equals
1 if an audit is performed by an auditor with regional or national
presence that is not part of the Big 4 auditor group (0 otherwise).4
SMALL_CPA is an indicator that equals 1 if an audit is performed by a
CPA firm not included in the BIG4_CPA or the MED_CPA group (0
otherwise). By construction, the reference group in the estimation of
the regression model involves audits conducted by smaller CPA firms
(SMALL_CPA). Given the limited amount of empirical evidence linking
auditor size and audit report outcomes in the nonprofit healthcare
sector, we express no expectations in terms of the direction of the
estimated regression coefficients for these auditor size variables.
The regression model includes controls for other client and
auditor factors shown in prior studies to impact the incidence and
disclosure of internal control exceptions, as well indicator variables as
for the fixed effects of time. FUNDS_AUD is the log of total federal
funds spent by a nonprofit healthcare entity. This variable controls for
the size of the Circular A-133 audit engagement and also proxies for
potentially omitted variables (Davidson and Neu 1993; Becker,
Defond, Jiambalvo, & Subramanyam, 1998). Given evidence from
Ashbaugh-Skaife, Collins, and Kinney (2007) of an inverse
association between firm size and internal control disclosures, we
predict a negative coefficient for FUNDS_AUD. We define LRISK as an
indicator that equals 1 if the auditor classifies an auditee as low risk
(0 otherwise) (Keating et al., 2005). Circular A-133 requirements
allow auditors to classify certain audits as low risk, thereby
decreasing the required percentage of program expenditures that
need to be audited. As a result, we predict a negative association
between LRISK and the likelihood of internal control weaknesses. We
also include a control for the proportion of an organization’s total
federal awards originating from major programs (MAJOR). Circular A133 guidelines define major programs as those that are larger or
AUDITOR SIZE AND INTERNAL CONTROL REPORTING DIFFERENCES IN NONPROFIT HEALTHCARE
51
carry higher levels of risk. Given that entities with large proportions of
funds from major programs are likely to require additional auditing
efforts, we predict a positive association between MAJOR and the
likelihood of internal control weaknesses (López & Peters, 2010).
The Office of Management and Budget assigns a cognizant
agency to entities that spend more than $50 million a year in federal
awards. COG_AGENCY is an indicator that takes a value of 1 if an
auditee is assigned a cognizant agency (0 otherwise) (López and
Peters 2010). Cognizant agencies are expected to conduct quality
reviews and provide technical advice to fund recipients and their
auditors, which should reduce the likelihood of internal control
exceptions. Therefore, we predict a negative coefficient for
COG_AGENCY. We define CLIENTS as the number of Circular A-133
audits of nonprofit healthcare organizations performed by an entity’s
auditor during a given fiscal year. This variable controls for the effects
of industry expertise on auditors’ ability to detect and disclose
existing internal control exceptions (Deis & Giroux, 1992). Given that
industry experts have been shown to be more likely to identify
industry specific misstatements (Hammersley, 2006), we predict a
positive association between the variable CLIENTS and the likelihood
of internal control exceptions.
FIRST_A133 is an indicator that equals to 1 for nonprofit
healthcare organizations receiving their first Circular A-133 audit
within the last four years (0 otherwise). This variable controls for the
unique characteristics and challenges of first time audits. Similarly,
AUD_SWITCH is an indicator that equals to 1 for audits that involved
a different auditor than in the prior fiscal year (0 otherwise), and
controls for the effects of auditor turnover. Prior studies indicate that
longer auditor tenures are associated with higher quality accruals
(Johnson, Khurana, & Reynolds, 2002; Myers, Myers, & Omer, 2003),
a greater likelihood of going concern opinions prior to bankruptcy
(Geiger & Raghunandan, 2002), and a lower likelihood of accounting
restatements (Stanley & DeZoort, 2007). Given that prior research
finds that material weaknesses are more likely when there is an
auditor switch (Bedard, Hoitash, & Hoitash, 2009), we predict a
positive association between AUD_SWITCH and the likelihood of
internal control exceptions. PRIV_ORG is an indicator that equals 1 if
the auditee is a private healthcare organization (0 otherwise), and
controls for potential differences in the internal control environment
52
LÓPEZ, RICH & SMITH
of healthcare organizations run by private versus public entities
(Fama & Jensen, 1983; Eldenburg et al., 2004).
The regression model also includes a set of indicator variables
that identify the different U.S. government agencies providing federal
funds to the entities in the sample (FUND_SRC). These variables
control for potential differences in the oversight and monitoring
efforts of these agencies. The agencies represented are the
Department of Agriculture (USDA), Department of Defense (DOD),
Department of Housing and Urban Development (HUD), Department
of Education (EDUCATION), the Department of Health and Human
Services (HEALTH), and other federal agencies providing less than 5%
of all federal funds received by the entities in the sample (OTHER).
Finally, YEAR is a set of indicator variables that control for temporal
differences that may affect the general operating environment of
auditors.
RESULTS AND DISCUSSION
Univariate Results
Table 1 provides the mean, median, and standard deviation
values for all variables in this study. As shown by the mean values for
the different INDEX categories, 84 percent of the observations involve
no internal control weakness (INDEX = 1), 12 percent involve the
disclosure of at least one reportable condition but no material
weaknesses (INDEX = 2), and 4 percent involve the disclosure of at
least one material weakness (INDEX = 2). Big 4 auditors (BIG4_CPA =
1) performed approximately 58 percent of all audits in the sample,
medium sized CPA firms (MED_CPA = 1) performed 12 percent, and
small CPA firms (SMALL_CPA = 1) performed the remaining 30
percent. This suggests a more even distribution of market shares in
the nonprofit healthcare sector, when compared to the 85 percent
market share controlled by Big 4 auditors in public company audits
(Hoitash, Hoitash, & Bedard, 2009). Auditors assess 58 percent of
audits in the sample as low risk (LRISK = 1), and 7 percent of the
audits were assigned a cognizant agency (COG_AGENCY = 1). A total
of 86 percent of the observations in the sample come from private
nonprofit healthcare organizations (PRIV_ORG = 1), as opposed to
those run by public or governmental entities. Lastly, 87 percent of all
observations are associated with funds from the Department of
AUDITOR SIZE AND INTERNAL CONTROL REPORTING DIFFERENCES IN NONPROFIT HEALTHCARE
53
TABLE 1
Univariate Statistics (Number of observations = 1,180)
Variable
INDEX = 0
INDEX = 1
INDEX = 2
BIG4_CPA
MED_CPA
SMALL_CPA
FUNDS_AUD
LRISK
MAJOR
COG_AGENCY
CLIENTS
FIRST_A133
AUD_SWITCH
PRIV_ORG
USDA
DOD
HUD
EDUCATION
HEALTH
OTHER
Mean
0.84
0.12
0.04
0.58
0.12
0.30
15.10
0.58
0.80
0.07
24.66
0.07
0.10
0.86
0.31
0.14
0.24
0.29
0.87
0.40
Median
1.00
0.00
0.00
1.00
0.00
0.00
14.70
1.00
0.89
0.00
26.00
0.00
0.00
1.00
0.00
0.00
0.00
0.00
1.00
0.00
STD
0.37
0.33
0.20
0.49
0.32
0.46
1.56
0.49
0.22
0.26
21.22
0.26
0.30
0.35
0.46
0.35
0.43
0.45
0.33
0.49
Health (HEALTH = 1), making this agency the most common provider
of federal funds to the entities in the sample.
Table 2, Panel A summarizes the frequency of internal control
exceptions for the 361 entities in our sample. During the five-year
period covered by the sample, the audits of 70.6 percent of the
entities do not disclose internal control exceptions (n = 255), while
the audits of the remaining 29.4 percent of the entities (n = 106)
disclose internal control exceptions. Among the 106 entities with
internal control exceptions, 46.2 percent disclose reportable
conditions during multiple years, while 53.8 percent do so on only
one occasion. In addition, among the 27 entities that report material
weaknesses, 40.7 percent of them report this type of condition on
multiple occasions.5
54
LÓPEZ, RICH & SMITH
Panels B and C of Table 2 present the percentage of reportable
conditions and material weaknesses by auditor size and year,
respectively. Panel B shows that there is a marked increase in the
percentage of Big 4 audit reports disclosing reportable conditions
during the sample window, although this trend is interrupted in 2008.
In contrast, Panel C reveals that there is no discernable trend for the
frequency of material weaknesses among Big 4 auditors. Panel C also
TABLE 2
Details on Internal Control Exceptions
Panel A: Entities with Internal Control Disclosures
Reportable
Material
Conditions
Weaknesses
Count (in years)
Entities
%
Entities
%
0
255 70.6%
334 92.5%
1
57 15.8%
16
4.4%
2
26
7.2%
6
1.7%
3
13
3.6%
2
0.6%
4
3
0.8%
1
0.3%
5
7
1.9%
2
0.6%
Total
361 100.0%
361 100.0%
Only one year with exceptions
57 53.8%
16 59.3%
Multiple years with exceptions
49 46.2%
11 40.7%
Entities with exceptions
106 100.0%
27 100.0%
Panel B: Frequency of Reportable Conditions
Year
Auditor Size
2004 2005 2006 2007 2008 All Obs.
Big 4 auditors
0.10 0.12 0.10 0.18 0.10
0.12
Medium auditors
0.17 0.22 0.15 0.09 0.04
0.14
Small auditors
0.14 0.12 0.13 0.11 0.13
0.13
All observations
0.12 0.13 0.12 0.15 0.10
0.12
Panel C: Frequency of Material Weaknesses
Year
Auditor Size
2004 2005 2006 2007 2008 All Obs.
Big 4 auditors
0.03 0.05 0.07 0.04 0.03
0.04
Medium auditors
0.00 0.00 0.03 0.00 0.00
0.01
Small auditors
0.04 0.03 0.05 0.05 0.07
0.05
All observations
0.03 0.04 0.06 0.04 0.04
0.04
AUDITOR SIZE AND INTERNAL CONTROL REPORTING DIFFERENCES IN NONPROFIT HEALTHCARE
55
presents evidence of a slight increase in the incidence of material
weaknesses in audits performed by small auditors.
In Table 3 we investigate the possible sources of the low client
acceptance rates among the Big 4 firms. This table highlights the
collective market share of the different auditor size categories by
fiscal year in terms of the number of nonprofit healthcare audits
performed. The percentage of audits performed by the Big 4 firms
steadily decreases from 64.9 to 53.0 percent, while the percentage
of audits conducted by small CPA firms increases from 24.7 to 32.8
percent. This could be interpreted as evidence of possible structural
changes in the market for Circular A-133 audits in the nonprofit
healthcare sector during the years covered by the sample.6
TABLE 3
Market Shares by Year and Number of Clients
Auditor Size
Big 4 auditors
%
Medium auditors
%
Small auditors
%
All observations
%
2004
150
64.9%
24
10.4%
57
24.7%
231
100%
2005
164
60.1%
32
11.7%
77
28.2%
273
100%
Year
2006
144
56.5%
33
12.9%
78
30.6%
255
100%
2007
136
57.1%
22
9.2%
80
33.6%
238
100%
2008 All Obs.
97
691
53.0%
58.6%
26
137
14.2%
11.6%
60
352
32.8%
29.8%
183
1,180
100%
100%
Multivariate Regression Results
Table 4 presents the results of our ordered logistic regression. In
the estimation of this model we “winsorized” all continuous variables
at the 1st and 99th percentiles, and used robust standard errors
(White, 1980). The model is significant when taken as a whole (chisquare = 56.01; p-value < 0.0001), and has a pseudo r-square of
4.64 percent. The variance inflation factors (VIFs) for an OLS
specification of the same model (untabulated) are below 5.0 for all
variables, which helps mitigate concerns of multicollinearity issues in
the data (Kennedy, 2003).
56
LÓPEZ, RICH & SMITH
A negative coefficient for BIG4_CPA indicates that audits
conducted by the Big 4 firms are less likely to disclose reportable
conditions or material weaknesses than are audits performed by
small CPA firms (z-statistic = −2.22). This finding contrasts with prior
evidence for publicly traded companies and governmental entities,
which generally shows a positive association between the presence of
Big 4 auditors and the disclosure of internal control weaknesses
(Ashbaugh-Skaife, Collins, & Kinney, 2007; Bedard, Hoitash, &
Hoitash, 2009; López & Peters, 2010). However, this finding is
consistent with recent work on the general nonprofit sector (Petrovits,
Shakespeare, & Shih, 2011). In conjunction with the univariate
results highlighting that the Big 4 firms have a decreasing market
share of the nonprofit healthcare sector (see Table 3), this result
suggest that smaller firms may be gaining ground in the market for
audit services to healthcare clients. Nevertheless, the impact of
careful client screening procedures by the Big 4 firms remains a
viable alternative interpretation for this finding. An insignificant
coefficient on MED_CPA suggests that there is no statistical evidence
of a difference in the likelihood of disclosing internal control
weaknesses when an audit is performed by a medium sized CPA firm
versus a small CPA firm.
The results for the control variables suggest that audits assessed
as low risk are significantly less likely to involve the disclosure of
internal control exceptions, as evidenced by the negative coefficient
for LRISK (z-statistic = −4.26). A positive coefficient for MAJOR
indicates an increasing association between the proportion of funds
with a major program classification and the likelihood that an audit
will disclose internal control exceptions (z-statistic = 1.70). We find a
positive coefficient for CLIENTS, which suggests that audits
conducted by CPA firms with larger numbers of nonprofit healthcare
organizations in their client portfolios are associated with a higher
likelihood of internal control exceptions (z-statistic = 3.59).7 This
finding is consistent with experimental evidence from Hammersley
(2006) that suggests that industry experts are more likely to discover
misstatements that require industry specific knowledge. Lastly, the
coefficient for PRIV_ORG is negative and significant (z-statistic = –
3.09), with one possible interpretation being that private healthcare
organizations possess more robust internal control structures than
their public counterparts.
AUDITOR SIZE AND INTERNAL CONTROL REPORTING DIFFERENCES IN NONPROFIT HEALTHCARE
57
TABLE 4
Ordered Logistic Regression
Variable
BIG4_CPA
MED_CPA
Control Variables
FUNDS_AUD
LRISK
MAJOR
COG_AGENCY
CLIENTS
FIRST_A133
AUD_SWITCH
PRIV_ORG
Funding sources
USDA
DOD
HUD
EDUCATION
HEALTH
Intercept (INDEX = 2)
Intercept (INDEX = 1)
Model Chi-square
Pr > Chi-square
Pseudo R-squared
# of observations
Pred. Sign
±
±
Coefficient t-statistic
-0.81** (-2.22)
-0.37
(-1.22)
−
−
+
−
+
±
+
±
-0.05
-0.75***
0.76*
0.50
0.03***
-0.49
0.29
-0.70***
(-0.64)
(-4.26)
(1.70)
(1.35)
(3.59)
(-1.40)
(0.98)
(-3.09)
±
±
±
±
±
±
±
0.08
(0.43)
-0.08
(-0.28)
0.07
(0.31)
0.09
(0.44)
0.40
(1.37)
1.30
(1.05)
2.90**
(2.34)
56.01
< 0.0001
4.64%
1,180
Notes: * p < 0.10, ** < 0.05, *** < 0.01. Two-tailed tests, unless signs are
predicted. Robust z-statistics are reported in parentheses with White
(1980) standard errors. Year fixed effects included but not tabulated for
brevity.
Leone (2007) argues that relations between auditor size and the
presence of internal control weaknesses among publicly traded
companies are likely to be different for small versus large audit
clients. In particular, he finds that the associations documented by
Ashbaugh-Skaife, Collins, and Kinney (2007) are concentrated among
the smaller clients. Although the estimated regression coefficient for
FUNDS_AUD in Table 4 is not significant, we investigate the possibility
of similar effects in our study. To accomplish this task, we split our
58
LÓPEZ, RICH & SMITH
sample into two subsamples based on the median amount of federal
funds audited, and estimate equation (1) for each of these two
subsamples. Note that we do not include the variable COG_AGENCY
in these tests because there are no entities with an assigned
cognizant agency in the small client subsample. Table 5 depicts the
regression results, which suggest that the negative association
between the presence of Big 4 auditors and the disclosure of internal
control exceptions documented in Table 4 is only statistically
significant the small client subsample.8
We offer two possible explanations for the negative coefficient on
BIG4_CPA in the small client subsample. One possibility is that the
Big 4 auditors perform a lower fraction of the audits of small
healthcare organizations with deficient internal control systems,
ceteris paribus. This could stem from either increased selectivity by
the Big 4 auditors among smaller nonprofit entities (Petrovits,
Shakespeare, & Shih, 2011), or from the audit fee barriers faced by
many of these entities (Keating et al., 2005). Another possibility is
that the Big 4 auditors devote relatively less effort in the audits of
small nonprofit healthcare organizations, ceteris paribus. This could
reflect reduced concerns of reputational risk in the audits of smaller
nonprofit entities (López & Peters, 2010), or superior performance by
local CPA firms trying to establish a reputation in the nonprofit
healthcare sector.
TABLE 5
Ordered Logistic Regression Based on Small versus Large
Organization Subsamples
Variable
BIG4_CPA
MED_CPA
Control variables
FUNDS_AUD
LRISK
MAJOR
CLIENTS
FIRST_A133
AUD_SWITCH
PRIV_ORG
Small organizations Large organizations
-1.02*
(-1.80) -0.73
(-1.33)
-0.79*
(-1.85) 0.22
(0.46)
-0.24
-0.54**
1.67***
0.03***
-0.65
0.24
-0.80**
(-0.84)
(-2.27)
(2.64)
(2.58)
(-1.49)
(0.53)
(-2.41)
0.19*
-1.00***
-0.31
0.03***
-0.21
0.16
-0.75**
(1.90)
(-3.67)
(-0.50)
(2.65)
(-0.35)
(0.41)
(-2.10)
AUDITOR SIZE AND INTERNAL CONTROL REPORTING DIFFERENCES IN NONPROFIT HEALTHCARE
59
TABLE 5 (Continued)
Funding sources
USDA
DOD
HUD
EDUCATION
HEALTH
Intercept (INDEX = 2)
Intercept (INDEX = 1)
Model Chi-square
Pr > Chi-square
Pseudo R-squared
# of observations
-0.30
-0.42
-0.11
-0.28
0.27
-0.51
1.09
(-0.84)
(-0.59)
(-0.23)
(-0.95)
(0.61)
(-0.13)
(0.27)
36.24
0.006
6.28%
590
0.20
(0.85)
-0.13
(-0.40)
0.10
(0.37)
0.36
(1.28)
0.40
(0.93)
4.20*** (2.82)
5.85*** (3.77)
37.17
0.005
6.6%
590
Notes: * p < 0.10, ** p < 0.05, *** p < 0.01. Two-tailed tests, unless signs
are predicted. Robust z-statistics are reported in parentheses with White
(1980) standard errors. Year fixed effects included but not tabulated for
brevity.
In contrast, larger healthcare organizations are likely to have
more resources and better internal control systems; this portion of
the market is also likely to be more homogeneous and competitive
across auditors. The counterbalancing effects of these factors help
explain why the estimated coefficient on BIG4_CPA in the large client
subsample is not significant. In light of these findings, we recommend
that future studies in the sector either incorporate additional client
size variables into their models, or split their analyses into
subsamples according to client size.
Robustness Tests and Other Analyses
One concern in studies examining auditor characteristics involves
the potential for selection bias (Ireland & Lennox, 2002; Chaney,
Jeter, & Shivakumar, 2004). We perform a Heckman (1979) twostage procedure to test for bias stemming from the choice by sample
entities to select a Big 4 auditor. The first stage in this procedure
involves regressing a series of client characteristics against the
presence of a Big 4 firm, which we use to calculate the inverse Mills
ratio for inclusion in a regression of equation (1) in the second stage.
Untabulated results from the second stage regression suggest that
60
LÓPEZ, RICH & SMITH
selection bias is an issue, given a significant coefficient for the
inverse Mills ratio. However, the estimated coefficients for BIG4_CPA
and MED_CPA are quantitatively similar to those presented in Table 4
and the overall interpretation of our results remains the same after
the correction.
Due to the potential influence from different types of auditor
turnover during our time period, we also perform a supplemental
analysis where we expand the AUD_SWITCH variable into a set of
three different indicator variables. This alternative operationalization
of AUD_SWITCH allows us to distinguish between switches within the
same auditor size category (SWITCH_SAME), switches to larger audit
firms (SWITCH_UP), and switches to smaller audit firms
(SWITCH_DOWN). Entities that remained with the same auditor serve
as the reference group in the estimation of this alternative regression
model. We estimate this expanded model using the main sample (n =
1,180) and subsamples of large and small Circular A-133 audits (i.e.,
below and above median). Untabulated results indicate that this
alternative operationalization of the variable AUD_SWITCH does not
impact the regression results presented in tables 4 and 5.
CONCLUDING REMARKS
The primary purpose of this study is to investigate whether
auditor size is associated with the disclosure of internal control
exceptions in Circular A-133 audits in the nonprofit healthcare sector.
This is of interest because of the importance of public healthcare
organizations in the U.S. economy, and the unique regulatory
environment that differentiates these entities from other nonprofit
organizations (Blackwood, Wing, & Pollack, 2008; AHA, 2009;
Vermeer, Raghunandan, & Forgione, 2009). In contrast with prior
research involving publicly traded companies and governmental
entities (Ashbaugh-Skaife, Collins, & Kinney, 2007; Bedard, Hoitash,
& Hoitash, 2009; López & Peters, 2010), our empirical results
suggest that audits performed by the Big 4 firms are less likely to
involve the disclosure of reportable conditions and material
weaknesses than those performed by small CPA firms. This finding is
consistent with empirical work in the general nonprofit sector and
could relate to changes in the market for audit services among
nonprofit healthcare organizations.
AUDITOR SIZE AND INTERNAL CONTROL REPORTING DIFFERENCES IN NONPROFIT HEALTHCARE
61
There are some limitations in the analyses in this study. First, the
sample consists of nonprofit healthcare organizations subject to the
single audit requirements of Circular A-133. Given the complexities of
these organizations, our findings may not generalize to nonprofits
that do not receive federal program funding, or to those outside the
healthcare sector. Another limitation is that a particular auditor could
provide its clients with better audit feedback over the course of
several fiscal periods, which might result in stronger internal control
systems and a lower likelihood of reported exceptions. Last, while our
regression model is designed to control for the fixed effects of time
and auditor size, unaccounted variation could still arise from auditor
differences at the local office level and ex ante differences in the
internal control systems of the entities in the sample.
Overall, our study contributes to the audit literature in two primary
ways. First, we extend recent audit research on nonprofits by focusing
exclusively on nonprofit healthcare organizations. Our study
underscores the association between auditor size and the reporting
of internal control exceptions in audits of nonprofit organizations, a
topic surrounded by conflicting empirical findings. Second, our study
contributes to an understanding of the associations between auditor
size and audit report outcomes in a market that is not dominated by
the Big 4 firms and that requires significant amounts of industry
expertise. Future studies could investigate whether similar auditor
size effects exist in audits of other nonprofit entities requiring high
levels of auditor industry specialization. Although data is limited, the
literature would likely benefit from a more comprehensive
understanding of the audit quality determinants in audits of smaller
nonprofit entities. Our suggestions also apply to studies in the forprofit sector, where similar industry or size-related issues may exist.
NOTES
1. The Statement on Auditing Standards No. 112 revised the
language surrounding disclosure of internal control weaknesses
for audits conducted after December 15, 2006, with one specific
change involving replacement of the term “reportable condition”
with “significant deficiency” (AICPA, 2006). For continuity, we use
the term “reportable condition” when referring to either a
“reportable condition” in years 2004 to 2006, or a “significant
deficiency” in years 2007 and 2008.
62
LÓPEZ, RICH & SMITH
2. Nonprofit healthcare entities are those with a value of “2” in the
third digit of the “organization type” code of the Circular A-133
database. This includes county, city, regional, and state level
entities, along with other nonprofit healthcare entities and those
run by tribal and Indian governments.
3. According to Generally Accepted Auditing Standards (GAAS), a
reportable condition is a significant deficiency in the design or
operation of internal controls that could adversely affect the
financial statements, while a material weakness is a reportable
condition so severe that the internal control components cannot
reduce the risk of material noncompliance to an acceptable level
(Wilson, Kattellus, & Reck, 2007).
4. MED_CPA identifies non-Big 4 firms in the list of the top 25
largest domestic auditors based on total revenue (GAO, 2003, p.
17).
5. Auditors are required to report the type of compliance issues
discovered, choosing from a pre-determined list of 15 different,
non-mutually exclusive compliance issues (OMB, 2003). Further
analysis of these data indicates that the most common type of
compliance issues in our sample appears to be “allowable
costs/cost principles” and “reporting issues”.
6. We also perform similar market share analyses based on the total
amount of federal awards audited and the number of first-time
audits (untabulated). We find results similar to those presented in
Table 3.
7. We also perform our analysis with an alternative measure of
auditor industry specialization based on market share by year in
terms of federal funds audited (Lowensohn, Johnson, Elder, &
Davies, 2007), with the results substantially similar to those
presented in Table 4.
8. Our tests of difference in regression coefficients indicate that the
estimated coefficients on BIG4_CPA are not significantly different
from one another. The only significant difference in regression
coefficients occurs with the major programs indicator (MAJOR).
AUDITOR SIZE AND INTERNAL CONTROL REPORTING DIFFERENCES IN NONPROFIT HEALTHCARE
63
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J. OF PUBLIC BUDGETING, ACCOUNTING & FINANCIAL MANAGEMENT, 25 (1), 69-90
SPRING 2013
CHARITABLE RATINGS AND FINANCIAL REPORTING QUALITY:
EVIDENCE FROM THE HUMAN SERVICE SECTOR
Qianhua Ling and Daniel Gordon Neely*
ABSTRACT. Prior research has shown that many donors utilize charity
ratings for decisions and they give more to higher rated charities. Because
ratings are partly or completely based on financial information, the financial
reporting quality of highly rated charities is more critical to donors than that
of the poorly rated ones. In this study, we examine whether the financial
reporting quality of charities systematically varies with charitable ratings.
Examining a sample of human service charities, we find that highly rated
organizations are more likely to underreport fundraising expenses and
overstate program ratios. Highly rated organizations appear to be exercising
accounting discretion to achieve this desirable outcome. Collectively, our
findings suggest that stakeholders should be cautious when they use the
rating information.
INTRODUCTION
The number of nonprofit charitable organizations exempt from
income tax has increased substantially in recent times. The
information returns filed by these organizations rose from 207,272 in
1998 to 315,184 in 2008.1 In this environment, nonprofits face an
increasingly competitive market for charitable contributions. Similarly,
donors have an increasingly difficult task in selecting appropriate
nonprofit recipients. As the number of choices goes up, donors
increase their demand for financial information (Gordon &
--------------------------------* Qianhua (Q.) Ling, Ph.D., is an Assistant Professor of Accounting at
Marquette University. Her research interests are in financial reporting,
governance, information intermediary, and especially in the governmental
and nonprofit accounting area. Daniel Gordon Neely, Ph.D., CPA, is an
Assistant Professor of Accounting at the University of Wisconsin-Milwaukee.
His research interests are in governmental and nonprofit accounting.
Copyright © 2013 by PrAcademics Press
70
LING & NEELY
Khumawala, 1999). They are also more likely to rely on charity rating
information for their decisions. Prior studies find that charitable
ratings affect public contributions. Donors give more to highly rated
organizations (Tinkelman, 1998; Sloan, 2009; Gordon et al., 2009).
Because ratings are partly or completely based on financial
information, the financial reporting quality of highly rated charities is
more critical to donors than that of the poorly rated charities. In this
study, we examine whether the financial reporting quality of
nonprofits systematically varies with charity ratings.
The Taxpayer Bill of Rights 2 requires nonprofits in the U.S. to
provide their financial information to the public via Internal Revenue
Service (IRS) Form 990, Return of Organization Exempt from Income
Tax. Nonprofits are required to file Form 990 annually and they
provide a large amount of information on the Form. Rating agencies
facilitate the processing of the information which intends to help
stakeholders make informed decisions. Ratings provided by these
information intermediaries generate at least three advantages. First,
ratings greatly reduce the information processing cost for
stakeholders by providing concise ratings. For example, The American
Institute of Philanthropy (AIP) gives top performers a letter grade “A,”
followed by “B,” “C,” and “D.” It assigns an “F” to organizations with
the least satisfactory performance. The Better Business Bureau’s
Wise Giving Alliance (BBB) has only two categories for the nonprofits.
A nonprofit organization either “meets” or “does not meet” the BBB
standards. The Charity Navigator summarizes its evaluation using a
number of stars, zero, one, two, three, or four stars. The zero-star
nonprofits are the least favorable and the four-star nonprofits are the
most desirable.
Second, these rating systems promote sustainable practice.
Judging charities based solely on the percentage of expenditures on
charitable purposes and fundraising is not theoretically sound
(Bhattacharya & Tinkelman, 2009). Rating systems go beyond these
two measures and try to neutralize this issue. For example, the BBB
standards cover not only financial performance but also governance,
oversight and measuring effectiveness. Charity Navigator analyzes
charity performance in seven areas.2
Finally, the ratings incorporate the institutional knowledge of the
rating agencies. For evaluation purpose, the BBB requires
organizations to submit their budgets which allow the agency to
CHARITABLE RATINGS AND FINANCIAL REPORTING QUALITY
71 analyze this privately available information (bbb.org). Charity
Navigator varies its scoring criteria by sector (charitynavigator.org).
For example, food banks operate with fewer administration activities
than other charities. The cut-off point of the administrative expense
ratio is much lower for these organizations to score a high rating.
Prior studies find a strong association between ratings and
donations (Tinkelman, 1998; Sloan, 2009; Gordon et al., 2009).
Tinkelman (1998) finds that non-individual donors respond negatively
to unfavorable ratings, while Sloan (2009) reports that donors react
to positive ratings only. Gordon et al. (2009) document a positive
association between rating change and the change in contribution.
These ratings rely heavily on financial information for assessment. For
example, Charity Navigator, BBB, and AIP all evaluate an
organization’s program ratio. Because financial information can be
misreported (e.g., Krishnan et al., 2006; Jones & Roberts, 2006), it is
important to examine whether highly rated charities are more likely to
misreport their financial information.
In this study, we examine a hand-collected sample of human
service organizations rated by Charity Navigator. Charity Navigator
covers more charities than any other rating agencies. Its ratings range
from zero to four stars with four-stars indicating top performance. A
list of four-star charities is prominently displayed on the rating
agency’s website. The link to the list is posted on the top part of every
webpage. Our analyses show that highly rated organizations are more
likely to underreport fundraising expenses and overstate program
ratios. We also find that these highly rated organizations tend not to
engage in obvious misreporting practices such as zero fundraising
expenses, zero executive compensation, or using joint costs to inflate
program expenses. It appears that it is the generous allocation of
expenses to the program category that contributes to the low
fundraising expenses and high program ratios.
Our study contributes to the rating literature in two distinct ways.
First, while prior studies examine the impact of ratings on donor
decisions (Tinkelman, 1998; Sloan, 2009; Gordon et al., 2009), our
study is the first to show rated nonprofits likely consider the rating
implication in utilizing their financial reporting discretion. Our findings
indicate that highly rated organizations present a higher degree of
underreported fundraising expense and overstated program ratios.
Second, we add to the literature by demonstrating that inflating
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LING & NEELY
program ratios may not be done in an extreme way such as reporting
zero fundraising expense; rather, it may be done in a subtle way. For
example, Keating et al. (2008) find that nonprofits may report smaller
amounts of telemarketing expenses as fundraising expense than the
amounts actually incurred. We provide some evidence that inflated
program ratios may be because of the generous allocations of
employee wages and benefits, and joint costs to the program expense
category.
Collectively, our findings suggest that stakeholders should
exercise judgment when they use the rating information.
Stakeholders should not base their decisions solely on the ratings
because nonprofits may use discretion in assigning expenses to
different categories to show better performance. The findings from
this study should stir debate about whether rating agencies
contribute to creating an efficient philanthropic market or are
unintentionally driving improper use of discretion in financial
reporting practice.
The remainder of this paper is organized as follows. We discuss
related prior literature first and then present our hypothesis which
links ratings and reporting quality. Following that we report the
measures of financial quality, sample selection, and findings. We
provide a conclusion section at the end of the paper.
RELATED PRIOR RESEARCH
Prior research suggests that donors care about how effectively
and efficiently nonprofits use donations. It is a challenge to measure
effectiveness because effectiveness is difficult to quantify and
nonprofits provide only limited disclosures in this regard (Parsons,
2003). However, financial information can be used to calculate
efficiency measures. Financial information is widely available since
charities were required to provide their Form 990s to the public in
1996 (Gordon et al., 1999). Research suggests that donors respond
to summary financial measures. One widely used summary financial
measure is the program ratio, the percentage of total expenses
devoted to the mission of the charity. From the donors’ perspective,
the program ratio is the inverse of their “price” to “purchase” a dollar
of service for the beneficiaries of charitable efforts. The higher the
program ratio, the lower the donor purchase price. An early study by
Weisbrod and Dominguez (1986) find a negative relation between
CHARITABLE RATINGS AND FINANCIAL REPORTING QUALITY
73 total donations and donor purchase price. Later studies such as
Tinkelman (1999) also find a significantly negative relation between
total direct contributions and donor purchase price. Research
studying the phenomena in the United Kingdom (Posnett & Sandler,
1989), Canada (Callen, 1994), and Singapore (Wong et al., 1998)
reports similar findings. These studies suggest that donors reward
efficient charities.
Another summary financial measure is the portion of expense
spent on the administrative function. Low spending on administration
leaves more funds for communicating and fulfilling the mission of the
nonprofit. Greenlee and Brown (1999) define the administrative ratio
as the portion of the total expense spent on administrative and
program activities. They find that the ratio is negatively associates
with total contributions. Tinkelman and Mankaney (2007) point out
that some prior studies may contain observations with unreliable
administrative ratios. They therefore restrict their samples to
nonprofits that spend more than $1,000 on fundraising and
administrative functions, have been established for four or more
years, have received more than $100,000 in donations in the prior
year, and whose donation is at least ten percent of prior year total
revenue. Their findings generally support a significant negative
association between the accounting measures of administrative
efficiency and donations.
Watchdog groups publish ratings, aimed at helping donors choose
nonprofit organizations. Tinkelman (1998) examines whether
donations are lower if a nonprofit violates the standards set by the
National Charities Information Bureau (NCIB) or the Council of Better
Business Bureaus (CBBB). He finds that non-individual donors
respond negatively to a violation of standards established by these
agencies. Sloan (2009) studies the effect of the rating from the
Better Business Bureau’s Wise Giving Alliance on the amount of
public support, grants, and dues received by nonprofits. She finds
that a pass rating by the Alliance has a significantly positive effect.
Using more recent data, Gordon et al. (2009) investigate the relation
between the change in the Charity Navigator rating and the change in
contributions. They report that contributions change with the change
in the rating. The ratings in Tinkelman (1998) and Sloan (2009)
evaluate a nonprofit’s financial performance and governance practice,
whereas the ratings in Gordon et al. (2009) evaluate financial
74
LING & NEELY
performance based on information reported on Form 990s. Overall,
donors prefer nonprofits with higher ratings and support these
organizations with their scarce resources.
The above studies provide evidence that stakeholders rely on
financial information for decision making. This may provide nonprofits
with incentives to manage financial reporting. In addition, Form 990s
are generally not required to be audited (Yetman & Yetman, 2012)
and the probability that the Form will be audited by the IRS is low
(GAO, 2002). Misreporting is therefore possible. Indeed, some studies
indicate that misreporting exists and the primary purpose is to inflate
the program expense ratio (e.g., Trussel, 2003; Wing et al., 2006;
Krishnan et al., 2006; Jones & Roberts, 2006; Tinkelman, 2009). For
example, Krishnan et al. (2006) report that many nonprofits engage
in fundraising activities but fail to report fundraising expense or report
smaller than actual amounts. The possibility of this misreporting
increases with the sensitivity of executive compensation and
donation to program ratios. Unique to nonprofits, if an activity serves
a public education purpose and a fundraising appeal simultaneously,
nonprofits can allocate the cost of this activity (i.e., joint costs) among
program expense, fundraising expense, and administrative expense.
Jones and Roberts (2006) find that nonprofits use both the level of
joint costs and the share of joint costs allocated to programs to avoid
reporting changes in program ratios. Keating et al. (2008) document
that some nonprofits inappropriately net telemarketing expenses
against revenue or understate the amount that should be reported as
fundraising expense; in doing so, nonprofits report lower fundraising
ratios and thus higher program ratios. In a recent case study,
Tinkelman (2009) details how the Avon Foundation managed their
reporting to meet rating agency guidelines. Specifically, prior to 2003
the Avon Foundation reported all special event expenses associated
with a major fundraising event as fundraising expenses. However,
from 2003 to 2006, large amounts of event expenses were
unreasonably assigned to programs. This change in reporting enabled
the Avon Foundation to meet the 2003 BBB guidelines.
RATING AND FINANCIAL REPORTING QUALITY
We examine the human service charities rated by Charity
Navigator. Charity Navigator was founded in 2001. It is the largest
nonprofit rating agency in the U.S. Charity Navigator evaluates over
CHARITABLE RATINGS AND FINANCIAL REPORTING QUALITY
75 5,000 charities. Its rating information is free to the public and the
website enables easy comparison of ratings across charities and over
time. Stakeholders appear to increasingly rely on Charity Navigator
ratings as over five million visits to the website occurred in 2011 and
four million in 2007 (charitynavigator.org; Gordon et al., 2009).
Analyzing Charity Navigator ratings, Gordon et al. (2009) show that
changes in ratings are positively associated with changes and levels
of contributions. Together with other studies (Tinkelman, 1998; Sloan,
2009), Gordon et al. (2009) provide evidence that higher ratings
mean more donations.
The primary purpose of human service charities is to provide
direct support to people in need such as the disadvantaged, the
elderly, and the disabled. Gordon and Khumawala (1999) suggest
that donors who are not direct beneficiaries of the charitable work are
more likely to seek financial information than those who are.
Following this theory, since donors of human service organizations
are generally not direct beneficiaries of the charitable services, they
are more likely to do comparison shopping. As a consequence,
human service organizations may have strong incentives to report
high financial performance and thus higher ratings. If misreporting
prevails, one would expect that highly rated charities are likely to
have poor reporting quality.
Alternatively, highly rated charities may be truly high quality
organizations. The non-distribution nature of nonprofits likely limits
the extent of self-serving management activity (Hansmann, 1980;
Fama & Jensen, 1983). Further, managers in nonprofits self-select
into the sector. They are more driven by altruism and less by financial
gains. To these managers, providing charitable services is a top
priority and they try to minimize perquisites and fundraising activities
(Rose-Ackerman, 1987). Managers of nonprofits may also have high
ethical standards. Yetman (2001) reports that charities do not
allocate expenses from tax-exempt categories to taxable categories to
reduce tax liabilities. All these factors contribute to high operational
performance and the performance is faithfully represented by the
financial measures used to rate the charities.
In summary, the performance hypothesis argues for a positive
association whereas the misreporting hypothesis argues for a
negative association between ratings and reporting quality. Absent
theories regarding the relative strength of these arguments, we do
76
LING & NEELY
not take an a priori stand on which is more likely to dominate. Instead,
we consider the association between ratings and reporting quality an
empirical issue. Accordingly, we state the hypothesis in null form:
there is no systematic reporting quality difference across ratings.
MEASURES OF FINANCIAL REPORTING QUALITY
Prior research suggests that in the nonprofit world the objective of
an organization is not to maximize profit but to maximize charitable
output. Partly because the quality of the output is difficult to measure,
the focus is placed on the reported charitable spending (Parsons,
2003; Trussel, 2003). As donors and watchdog groups all show
special interest in this spending, nonprofits have incentives to inflate
reported program expenses. This may be done by underreporting
fundraising and/or administrative expenses and thus overstating
program spending (e.g. Trussel, 2003; Krishnan et al., 2006; Jones &
Roberts, 2006; Keating et al., 2008; Tinkelman, 2009).
We adopt three proxies for reporting quality. Our first measure
relates to fundraising expenses. Nonprofits engage in various kinds of
activities to raise funds. Higher fundraising expenses may not
necessarily lead to higher contributions, but higher contributions
(public support, indirect public support, and government grants) are
very likely to be the result of higher fundraising expenses. Based on
the discussion of the theoretical link between donations and
fundraising expenses by Steinberg (1986), Yetman and Yetman
(2012) develop a fundraising expense expectation model. To test
whether charities understate their fundraising expenses, we estimate
this expectation model. The residuals from the model are used as an
indicator of reporting quality. A negative residual indicates that an
organization reports less fundraising expenses than the model
predicts. The lower the value, the higher the amount underreported.
The fundraising expectation model is specified as:3
Fundraising Expensesit-1 = β0 + β1 Direct Public Supportit
+ β2 Indirect Public Supportit + β3 Government Grantsit
+ γkYear Indicatorsit + δj Group Indicatorsit + εi
(1)
Similarly, to test whether administrative expenses are
underreported, we estimate an administrative expense model. In
addition to the contributions variables, this model includes Total
Expenses, Total Assets, and the square of Total Assets. This reflects
CHARITABLE RATINGS AND FINANCIAL REPORTING QUALITY
77 the fact that large organizations incur higher administrative expenses
than smaller organizations and that administrative expenses increase
at a diminishing rate. Following Yetman and Yetman (2012), the
model is specified as:
Administrative Expensesit-1 = β0 + β1 Direct Public Supportit
+ β2 Indirect Public Supportit + β3 Government Grantsit
+ β4 Total Expensesit+ β5 Total Assetsit+ β6 Total
Assets2it + γkYear Indicatorsit + δj Group Indicatorsit + εi
(2)
The residuals of this model are another indicator of reporting quality.
The lower the value, the higher the amount underreported. We use
the administrative expense residuals as our second measure of
reporting quality.
Unlike the two measures above that indirectly suggest whether an
organization inflates its program expenses or not, our last measure
directly examines the program ratio. Building on Baber et al. (2001),
the expected program ratio is a function of the organization’s size, its
strategy, and fundraising spending. The program ratio will be higher
for larger organizations, lower for organizations approaching small
donations, and lower for organizations spending more on fundraising.
Trussel (2003) posits that organizations with standardized residuals
significantly greater than zero at the 10% level (one-tailed) are very
likely to overstate the program ratio, thus, they are potential
accounting manipulators. The program ratio expectation model is:
Program Expenseit / Total Expenseit = β0 + β1 Ln (Total Revenueit)
+ β2 (Professional Fundraising Expenseit / Total
Expenseit) + β3 (Total Fundraising Expenseit / Total
Contributionsit) + δj Group Indicatorsit + εi
(3)
The performance hypothesis predicts that the fundraising
expense residuals and the administrative expense residuals would be
higher (more positive) for the highly rated organizations, and that
there would be fewer potential accounting manipulators in the highly
rated groups. The opposite is expected by the misreporting
hypothesis. The highly rated organizations would show lower (more
negative) fundraising expense residuals and/or administrative
expense residuals, and there would be more potential accounting
manipulators in the highly rated groups.
78
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SAMPLE
Our rating information for the human service organizations was
collected from Charity Navigator in 2008. We start with 2,680 charityyear observations. We lose 38 observations during the matching
process with the Guidestar financial information. We exclude 10 more
observations because of a change in the fiscal year. Our final sample
includes 2,632 charity-year observations. This sample selection
process is presented in Table 1.
TABLE 1
Sample Selection
Charity Navigator ratings
Less: no financial information in Guidestar
Less: fiscal year change
Final sample
N
2,680
38
10
2,632
FINDINGS
We first establish whether organizations are likely to report their
numbers inaccurately by comparing the fundraising residual,
administrative expense residual, and program ratio residual from the
previously discussed models (1), (2), and (3). All three models have a
high explanatory power, 80%, 97%, and 65% respectively, with a
mean residual of zero. Table 2 reports the descriptive statistics of
these residuals and the percentage of potential accounting
manipulators by rating group. The four-star group has negative means
and medians for fundraising residuals and administrative residuals.
The three-star group also has a negative mean and median for
fundraising residuals. Its administrative residuals have a negative
median, but a positive mean. The means of fundraising and
administrative residuals are positive for lower-rated groups. The
program ratio residuals are greater for the highly-rated groups than
the lower-rated groups. The percentage of possible accounting
manipulators decreases with the ratings.
CHARITABLE RATINGS AND FINANCIAL REPORTING QUALITY
79 TABLE 2
Descriptive Statistics for Residuals of Expectation Models and
Potential Accounting Manipulators
Variable
FundResid
AdminResid
ProgRatioResid
ManageProgRatio
Sample Size
4-Star
Mean
-287
Median -108
Mean
-51
Median -95
Mean
0.02
Median 0.02
Mean
0.51
748
3-Star
-23
-55
7
-80
0.00
0.01
0.42
945
2-Star
110
30
13
-85
-0.01
0.00
0.38
632
1-Star
317
105
138
-36
-0.03
-0.03
0.23
252
0-Star
1,643
1,080
-196
-233
-0.08
-0.04
0.13
55
Note: This table represents descriptive statistics for the sample based
on Charity Navigator Ratings. FundResid = The residual (in
$000) from the fundraising expense expectation model (1);
AdminResid = The residual (in $000) from the administrative
expense expectation model (2); ProgRatioResid = The
standardized residual from the program expense expectation
model (3); ManageProgRatio = 1 if the standardized
ProgRatioResid is positive and statistically greater than zero at
the 99% confidence level.
Table 3 presents statistical tests of differences between the
mean residuals across rating groups. The statistical tests support the
pattern we observe from Table 2 that highly rated organizations are
more likely to understate fundraising expenses and overstate
program expenses, resulting in higher program expense ratio
residuals for highly rated nonprofits. Specifically, four- and three-star
organizations understate their fundraising expenses relative to zero-,
one-, or two-star organizations. The differences in administrative
expenses are not statistically significant in many cases, and do not
provide conclusive evidence of over- or under-stating. Because of high
program ratio residuals, there are more potential accounting
manipulators in the highly rated groups than in the lower rated groups.
Overall, the evidence suggests that highly rated organizations
overstate their program expenses and understate their fundraising
expenses.
80
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TABLE 3
Test of Differences between the Star Ratings
4-3 4-2 4-1
-264 -397 -604
FResid
*** *** ***
-189
AResid -58
-65
**
.02
.03
.05
PResid
*** *** ***
.09
.13
.28
ManPR
*** *** ***
4-0
-1,930
***
144
***
.10
***
.38
***
3-2 3-1 3-0
-133 -340 -1,666
*** *** ***
202
-7
-131
***
.01 .03 .08
*** *** ***
.19 .29
.04
*** ***
2-1 2-0
-207 -1,533
*** ***
209
-124
***
.02 .07
*** ***
.15 .25
*** ***
1-0
-1,326
***
333
***
.05
**
.10
*
Note: The table represents the average difference in the variables for
different star comparisons. ***, **, * statistically significant at
the 1%, 5%, and 10% respectively. The z-statistic is the reported
test statistic for test of differences. FResid = The residual (in
$000) from the fundraising expense expectation model; AResid
= The residual (in $000) from the administrative expense
expectation model; PResid = The standardized residual from
the program expense expectation model; ManPR = 1 if the
standardized PResid is positive and statistically greater than
zero at the 99% confidence level.
While the preceding analyses support the notion that higher rated
nonprofits overstate program expenses and understate fundraising
expenses, it is not clear that these results hold after controlling for
factors known to be associated with misreporting. Table 4 provides
descriptive statistics by rating of measures found by Yetman and
Yetman (2012) to be associated with misreporting of overhead
expenses. These control variables are available for 1,151
observations. Four-star organizations tend to be younger than lower
rated organizations, more likely to receive an A-133 audit, have more
assets, and more likely to have outstanding tax exempt bonds.
Table 5 presents the multivariate results with the residuals from
the three expectation models representing the dependent variable
respectively. Along with control variables, an independent variable is
included for the overall rating of an organization. In addition, separate
models are run with the independent variable of ratings replaced by a
dichotomous variable for whether an organization is four-star rated or
CHARITABLE RATINGS AND FINANCIAL REPORTING QUALITY
81 TABLE 4
Descriptive Statistics of Control Variables by Rating
Overall Rating = 0
Variable
Donor Restriction
Outside Accountant
Age
Audit A-133
Current Ratio
Total Assets
Municipal Bonds
Voting Directors
Overall Rating = 1
Variable
Donor Restriction
Outside Accountant
Age
Audit A-133
Current Ratio
Total Assets
Municipal Bonds
Voting Directors
Overall Rating = 2
Variable
Donor Restriction
Outside Accountant
Age
Audit A-133
Current Ratio
Total Assets
Municipal Bonds
Voting Directors
Overall Rating = 3
Variable
Donor Restriction
Outside Accountant
Age
Audit A-133
Current Ratio
Total Assets
Municipal Bonds
Voting Directors
Obs
15
15
15
15
15
15
15
15
Mean
0.04
0.47
16.93
0.00
22.76
1,466
0.00
6.67
Std. Dev.
0.16
0.52
11.62
0.00
49.03
2,061
0.00
4.45
Min
-0.11
0.00
5.00
0.00
0.00
6
0.00
0.00
Max
0.61
1.00
47.00
0.00
186.38
7,711
0.00
14.00
Obs
102
102
102
102
102
102
102
102
Mean
0.25
0.58
37.16
0.16
27.10
10,600
0.03
16.32
Std. Dev.
0.31
0.50
17.63
0.37
126.81
22,600
0.17
13.12
Min
0.00
0.00
6.00
0.00
0.00
64
0.00
0.00
Max
1.24
1.00
73.00
1.00
1,236.34
146,000
1.00
108.00
Obs
262
262
262
262
262
262
262
262
Mean
0.26
0.55
37.57
0.33
68.95
24,000
0.04
20.51
Std. Dev.
0.27
0.50
40.13
0.47
847.07
91,200
0.19
16.84
Min
0.00
0.00
4.00
0.00
0.00
355
0.00
0.00
Max
1.87
1.00
611.00
1.00
13,710.44
870,000
1.00
132.00
Obs
420
420
420
420
420
420
420
420
Mean
0.31
0.60
34.05
0.37
9.36
23,400
0.06
24.78
Std. Dev.
1.28
0.49
18.82
0.48
67.48
50,300
0.25
31.02
Min
-0.28
0.00
5.00
0.00
-1,231.21
191
0.00
0.00
Max
26.06
1.00
84.00
1.00
448.63
352,000
1.00
474.00
82
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TABLE 4 (Continued)
Overall Rating = 3
Variable
Donor Restriction
Outside Accountant
Age
Audit A-133
Current Ratio
Total Assets
Municipal Bonds
Voting Directors
Overall Rating = 4
Variable
Donor Restriction
Outside Accountant
Age
Audit A-133
Current Ratio
Total Assets
Municipal Bonds
Voting Directors
Obs
420
420
420
420
420
420
420
420
Mean
0.31
0.60
34.05
0.37
9.36
23,400
0.06
24.78
Std. Dev.
1.28
0.49
18.82
0.48
67.48
50,300
0.25
31.02
Min
-0.28
0.00
5.00
0.00
-1,231.21
191
0.00
0.00
Max
26.06
1.00
84.00
1.00
448.63
352,000
1.00
474.00
Obs
352
352
352
352
352
352
352
352
Mean
0.26
0.58
31.22
0.47
21.80
35,300
0.07
23.62
Std. Dev.
0.26
0.49
18.86
0.50
76.40
247,000
0.26
18.29
Min
-0.09
0.00
4.00
0.00
0.06
96
0.00
0.00
Max
1.14
1.00
87.00
1.00
1,300.26
4,510,000
1.00
159.00
Note: The total number of observations drops from 2,632 to 1,151 due to
missing observations for the control variables. The variables are defined
as follows: Donor Restriction = Restricted Net Assets / Total Net Assets;
Outside Accountant = 1 if reported accounting fees is greater than zero;
Age = the age of the organization measured by subtracting the current
year from the ruling year; Audit A-133 = 1 if the organization reports
greater than $500,000 in government grants; Current Ratio = (cash +
savings + securities investments) / (total liabilities - mortgage payable tax exempt bonds); Total Assets = total end of year assets (in $000);
Municipal Bonds = 1 if the organization reports tax exempt bonds;
Voting Directors = the number of voting board members.
not. The regression results are consistent with the previously
discussed univariate results. Specifically, the coefficients on both the
overall rating and the four-star rating variables are positive and
statistically significant for the residual program ratio model, negative
for the residual fundraising expense model, and not significant for the
residual administrative expense model. These results suggest that
after controlling for other known factors associated with misreporting
CHARITABLE RATINGS AND FINANCIAL REPORTING QUALITY
83 overhead costs, higher rated organizations are more apt to overstate
their program expenses and understate their fundraising expenses.
TABLE 5
Regression Results for Program Ratio, Fundraising Expense, and
Administrative Expense Residuals
Residual Residual
Program Program
Ratio
Ratio
Residual
Fundraising
Expense
Overall
Rating
Four Star
.02***
(5.34)
-255.98***
(-4.10)
Donor
Restriction
.00
(-1.43)
Outside
Accountant
Age
-.01
(-1.17)
.00
(-0.9)
Audit A-133 -.02***
(-3.49)
Current
.00
Ratio
(-0.7)
Total Assets .00
(-1.16)
Municipal
-.02**
Bonds
(-2.16)
Voting
.00**
Directors
(2.38)
Voting
.00***
Directors2
(-3.37)
State
Yes
Control
N
1,151
R-Square
9%
Residual
Fundraising
Expense
Residual
Admin
Expense
Residual
Admin
Expense
-54.15
(-0.90)
.02***
(4.92)
.00
(-1.26)
-16.97
(-0.73)
-403.72***
-85.81
(-1.07)
(-2.84)
-23.31
43.64** 42.29**
(-0.97)
(2.28)
(2.15)
-.01
(-0.97)
.00
(-1.07)
-.01***
(-2.81)
.00
(-1.05)
.00
(-1.22)
-.01*
(-1.92)
.00***
(2.82)
.00***
(-3.65)
Yes
127.72
(1.17)
2.12
(1.14)
-380.10***
(-2.88)
-.05***
(-4.38)
.00
(-1.56)
-516.71*
(-1.68)
-.64
(-0.2)
.00
(0.64)
Yes
111.93
(1.03)
2.38
(1.31)
-415.7***
(-3.08)
-.04***
(-3.36)
.00
(-1.57)
-543.47*
(-1.75)
-1.97
(-0.61)
.01
(0.91)
Yes
144.31
(1.47)
2.88
(1.62)
-5.00
(-0.06)
-.03
(-1.4)
.00***
(-2.81)
944.38**
(2.28)
-2.97
(-0.8)
.01
(0.96)
Yes
140.97
(1.47)
2.93
(1.58)
-12.48
(-0.16)
-.02
(-1.39)
.00***
(-2.82)
938.75**
(2.26)
-3.25
(-0.88)
.01
(1.01)
Yes
1,151
7%
1,151
16%
1,151
15%
1,151
10%
1,151
10%
Note: Coefficient estimates and t-statistics (in parentheses) are presented.
Control variables are previously defined in Table 4. All models include
controls for state of location and standard errors clustered by EIN.
Overall Rating = the star rating received by an organization (i.e. 0-4);
Four Star = 1 if an organization is rated four stars. Significance tests are
two-tailed. *, **, *** represent significance levels of 10 percent, 5
percent and 1 percent respectively.
84
LING & NEELY
To understand how highly rated nonprofits misstate their
expenses, we further examine three obvious misreporting practices.
First, consistent with prior literature that finds organizations
understate fundraising expenses, we measure whether organizations
receive direct public support but report zero fundraising expenses
(Krishnan et al., 2006; Yetman & Yetman, 2012). Our second
measure tests whether organizations report zero officer
compensation. Nonprofits have been scrutinized in recent years for
paying their officers excessive compensation. Failure to report officer
compensation on Form 990 is indicative of an organization not being
transparent in compensation reporting and has been utilized in prior
literature as a measure of reporting quality (Neely, 2011). Finally, we
measure whether organizations report joint costs. Prior literature
finds that organizations utilize joint cost allocations to smooth their
program expense ratio (Jones & Roberts, 2006).
As Table 6 shows, the program expense ratio increases with the
ratings. This is not surprising because one of the variables utilized in
Charity Navigator's rating scheme is the program expense ratio.
However, the descriptive statistics show that four-star organizations,
the highest rated group, report fewer incidences of zero executive
compensation, joint cost allocations, or zero fundraising. The threestar group also reports fewer incidences of joint cost allocations. Its
reporting of zero compensation and zero fundraising are comparable
to those of the lower rated groups. The tests of differences in Table 7
suggest that statistically the likelihood of reporting zero
compensation or zero fundraising is not different between highly
rated groups and lower rated groups. The reporting of joint costs is
less likely in the highly rated groups.
While the three measures of obvious misreporting do not support
the finding that highly rated nonprofits are more likely to misstate
their financials, a closer look at their expense allocations suggests
how they are able to accomplish a higher program expense ratio. We
checked the labor related expenses of the sample. On average,
nonprofits spend 38% of total expenses on officer compensation and
other employee wages and benefits. Also reported in Table 6, the
percentages of wages, benefits, and joint costs allocated to program
expense (compared to fundraising or administrative expense) are all
higher for highly rated nonprofits. Given the discretion involved in
CHARITABLE RATINGS AND FINANCIAL REPORTING QUALITY
85 TABLE 6
Descriptive Statistics for Variables of Interest
Variable
ProgExpRatio
ZeroComp
JointCost
ZeroFundR
CompRate
WageRate
BenefitsRate
JointRate
Sample Size
Mean
Median
Mean
Mean
Mean
Mean
Median
Mean
Median
Mean
Median
Mean
Median
4-Star
0.87
0.87
0.09
0.09
0.00
0.53
0.64
0.81
0.84
0.80
0.82
0.73
0.82
748
3-Star
0.83
0.83
0.12
0.10
0.01
0.54
0.64
0.80
0.83
0.79
0.81
0.65
0.72
945
2-Star
0.79
0.80
0.12
0.14
0.01
0.53
0.56
0.77
0.79
0.75
0.79
0.63
0.69
632
1-Star
0.68
0.70
0.11
0.25
0.01
0.55
0.60
0.73
0.76
0.70
0.74
0.51
0.50
252
0-Star
0.22
0.15
0.15
0.35
0.02
0.51
0.51
0.42
0.33
0.42
0.38
0.22
0.17
55
Note: This table represents descriptive statistics for the sample based on
their Charity Navigator Rating.
ProgExpRatio = Program Expenses / Total Expenses; ZeroComp = 1 if
officer compensation is zero or missing; JointCost = 1 if the organization
reports having joint costs; ZeroFundR = 1 if the organization reports
direct public support and zero fundraising expenses; CompRate = The
percentage of officer compensation allocated to programs; WageRate =
The percentage employee wages (other than officers) allocated to
programs; BenefitsRate = the percentage of employee benefits
allocated to programs; JointRate = the percentage of total joint costs
allocated to programs.
making these expense allocations, it is probable that generous
expense allocations to programs are responsible for some of the
reported performance differential between highly rated nonprofits
and lower rated nonprofits. Statistical tests in Table 7 confirm that
the expense allocation to programs is more generous in highly rated
nonprofits compared to their lower rated peers. Specifically, four- and
three-star organizations allocate a greater percentage of wages,
benefits, and joint costs to programs relative to zero- to two-star
organizations.
86
LING & NEELY
TABLE 7
Test of Differences between the Star Ratings
Variable
ProgExp
Ratio
Zero
Comp
Joint
Cost
Zero
FundR
Comp
Rate
Wage
Rate
Benefits
Rate
Joint
Rate
4 -3
.04
***
-.03
4 -2
.08
***
-.03
4 -1
.19
***
-.02
4 -0
.65
***
-.06
3 -2
.04
***
.00
3 -1
.15
***
.01
3 -0
.61
***
-.03
2 -1
.11
***
.01
2 -0
.57
***
-.03
1 -0
.46
***
-.04
-.01
-.16
***
-.01
-.26
***
-.02
-.04
**
.00
-.15
***
.00
-.25
***
-.01
-.09
***
.00
-.21
***
-.01
-.10
-.01
-.01
-.05
**
-.01
*
.00
-.02
.02
.01
-.01
.03
-.02
.02
.04
.01
*
.01
*
.08
**
.04
***
.05
***
.10
***
.08
***
.10
***
.22
***
.39
***
.38
***
.51
***
.03
***
.04
***
.02
.07
***
.09
***
.14
***
.38
***
.37
***
.43
***
.04
***
.05
***
.12
***
.35
***
.33
***
.41
***
.31
***
.28
***
.29
***
-.01
Note: This table represents the average difference in the variables of interest
for different star comparisons. ***, **, and * statistically significant
at the 1%, 5%, and 10% respectively. The z-statistic is the reported
test statistic for test of differences.
ProgExpRatio = Program
Expenses / Total Expenses; ZeroComp = 1 if officer compensation is
zero or missing; JointCost = 1 if the organization reports having joint
costs; ZeroFundR = 1 if the organization reports direct public support
and zero fundraising expenses; CompRate = The percentage of officer
compensation allocated to programs; WageRate = The percentage
employee wages (other than officers) allocated to programs;
BenefitsRate = the percentage of employee benefits allocated to
programs; JointRate = the percentage of total joint costs allocated to
programs.
CONCLUSION
Prior research has shown that many donors utilize ratings for
decisions (Tinkelman, 1998; Sloan, 2009; Gordon et al., 2009). What
is not clear is whether charitable organizations manage their
numbers to achieve a higher rating. The findings from this study
indicate that highly rated organizations likely overstate their
performance compared to their lower rated peers. Highly rated
organizations appear to be exercising accounting discretion to
CHARITABLE RATINGS AND FINANCIAL REPORTING QUALITY
87 increase their program expenses and decrease their fundraising
expenses. Stakeholders relying on the rating systems should consider
this possibility and consider additional data (such as nonfinancial
information) before making definitive conclusions about the
performance of a highly rated nonprofit organization. Prior research
shows that after the passage of the AICPA’s SOP 98-2, some
nonprofits stopped allocating joint costs and those that continued to
report joint costs allocated less to programs (Roberts, 2005).
Because reported high performance can be achieved by using
discretion in allocating expenses to the program expense category, it
would be beneficial if accounting regulators and/or the IRS provided
more guidance on the allocation of expenses shared by program,
fundraising, and administrative activities. Given nonprofit boards’
financial oversight duty, the IRS suggests that boards review Form
990s before they are filed (Kehrer & Matthews, 2009). Consistent
with the IRS’ point of view, our findings suggest boards exercise due
diligence in assessing the reasonableness of the expense allocation
and whether the allocations are consistent over time.
ACKNOWLEDGMENTS
We thank Guidestar for providing some of the data used in this
study.
NOTES
1. This information is obtained from the Internal Revenue Service
(IRS) website. See the IRS Statistics of Income (SOI) Bulletin: Fall
2002 (http://www.irs.gov/pub/irs-soi/99eochar.pdf) and the SOI
Bulletin: Fall 2011 (www.irs.gov/pub/irs-soi/11eofallbulteorg.pdf)
for more details.
2. On September 20, 2011, Charity Navigator launched a new rating
system, which adds accountability and transparency indicators to
the original seven financial performance measures.
3. Yetman and Yetman (2012) include industry indicator variables in
their fundraising expense and administrative expense models.
The model by Trussel (2003) also has charity type indicators.
Even though our observations are all human service
organizations, Charity Navigator further classifies them into six
groups based on 1) the activity code each organization selects in
88
LING & NEELY
its fillings with the IRS, 2) the description of the organization’s
programs and services, and 3) how the organization functions
financially (www.charitynavigator.org/index.cfm?bay= content.
view&cpid= 34). The group classification reflects the rating
agency’s industry knowledge.
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J. OF PUBLIC BUDGETING, ACCOUNTING & FINANCIAL MANAGEMENT, 25 (1), 91-112
SPRING 2013
PUBLIC UNIVERSITY OPEB BURDEN:
RECOGNITION, FUNDING AND FUTURE OBLIGATIONS
Mary Fischer, Treba Marsh, George L. Hunt,
Bambi A. Hora and Lucille Montondon*
ABSTRACT. Public universities began reporting the costs for nonpension
retiree benefit obligations known as other postemployment benefits (OPEB)
in their fiscal 2008 financial statements. The reported OPEB obligation is the
projected benefits to be paid after an employee retires. This descriptive
study examines the status of OPEB funding at land grant universities,
composition of the benefits provided, and whether modifications are under
consideration. Results indicate land grant institutions cover their costs on a
pay-as-you-go basis, OPEB liabilities are significantly underfunded, and
universities provide comparable types of benefits in their OPEB plan.
Revenue shortfalls and current fiscal pressures raise concerns about how
they can support the OPEB liabilities. Thus many institutions are evaluating
the OPEB cost and the benefits currently provided.
INTRODUCTION
The recent recession brought about revenue shortfalls, smaller
investment returns and institutional financial pressures increasing
--------------------* Mary Fischer, Ph.D., CGFM, is Professor of Accounting, College of Business
and Technology, the University of Texas at Tyler. Her research interests
include financial accounting, nonprofit and government organizations,
auditing and accounting education. Treba Marsh, DBS, CPA, is Professor of
Accounting, Gerald W. Schlief School of Accountancy, Stephen F. Austin
State University. Her research interests include state and local public
finance, nonprofit organizations, ethics and internal control. George L. Hunt,
Ph.D., CPA, CIA, CMA, CFE, is Associate Professor of Accounting, Gerald W.
Schlief School of Accountancy, Stephen F. Austin State University. His
research interest includes public budgeting, taxation and accounting
education. Bambi A. Hora, JD, CPA, is a Professor of Accounting, University
of Central Oklahoma. Her research interests include taxation, governmental
and costing tools. Lucille Montondon, Ph.D. is Professor of Accounting,
Texas State University. Her teaching and research interests include
governmental accounting and pedagogy.
Copyright © 2013 by PrAcademics Press
92
FISCHER, MARSH, HUNT, HORA & MONTONDON
concerns that universities and other governmental entities will be
forced to manage and/or reduce other postemployment benefits
(OPEB) cost. The purpose of this descriptive study is to determine
what OPEB are being provided by land grant universities and to
determine the extent to which these organizations recognize, record,
and fund current as well as future OPEB obligations. We explore
whether the institution’s location within the United State impacts the
types of benefits included in the OPEB package or the funding of the
benefits. Additional information is gathered from the universities’
chief financial officer to identify what, if any, changes to the OPEB or
cost containment measures are being considered.
The findings indicate that universities across the United States
provide OPEB and recognize OPEB costs and its funding at a
comparable rate to state and local governments. Land grant
institutions fund OPEB liabilities on a pay-as-you-go basis and few
institutions fully fund the accrued OPEB liability. Cost containment
and benefit modifications are under consideration due to state
appropriation and other revenue reductions.
BACKGROUND
In 2004 the Government Accounting Standards Board (GASB)
issued Statement No. 45, Accounting and Reporting for Other
Postemployment Benefits, which requires the accrual of
postemployment benefits other than pensions (OPEB) during the
years that active employees earn the benefits
Previously OPEB liabilities were generally accounted for on a cash
or pay-as-you-go basis resulting in many governments accumulating
huge unrecorded obligations for previous years’ service. Government
Accounting Standards Board (GASBS) No. 45 requires recording the
liability on an accrual basis with the entity estimating and reporting
the value of benefits earned for both past and current employees as a
liability in the financial statements. Valuations of OPEB liabilities are
complex because they require assumptions about health care cost,
worker and retiree mortality, employee turnover rate, inflation rate,
and discount rates.
State governments and their not-for-profit business-type activities
began reporting the costs of paying for nonpension retiree benefits on
their fiscal 2008 financial statements in accordance with GASBS No.
PUBLIC UNIVERSITY OPEB BURDEN: RECOGNITION, FUNDING AND FUTURE OBLIGATIONS
93
45. These costs are referred to as OPEB and typically include an array
of health care benefits such as medical, hospitalization, dental,
vision, and life insurance as well as other related benefits including
Medicare B reimbursement.
The OPEB Challenge
GASBS No. 45 is a continuation of the GASB’s attempt to improve
financial reporting transparency by including previously off-balance
sheet obligations as part of the financial statements. OPEB
obligations are employee benefits earned today that will be paid after
the employee retires. As such, OPEB costs are recognized as part of
today’s operating costs and not in the future when resources are
disbursed (GASB, 2004). Although GASBS No. 45 provides OPEB
reporting guidance it does not require that OPEB costs be prefunded.
Thus governments and governmental business-type activities may
use a pay-as-you-go approach to fund the benefits. However by using
the pay-as-you-go funding, governments constrain other operating
support as the cost of the OPEB liabilities can accelerate relative to
other operating needs. The level of concern is underscored by a Pew
Charitable Trust study (PEW, 2010) that estimates state governments
have OPEB liabilities totaling $402 billion with $385 billion (over 95
percent) unfunded. The Pew study concludes that the historical
method of using a pay-as-you-go system where current revenues are
used to pay the OPEB costs will crowd out spending on other key or
mandated government functions. This funding method is a particular
challenge to governments that are obligated to pay both OPEB and
pensions liabilities estimated to exceed $2.35 trillion.
Part of the OPEB challenge is that retiree health care benefits
were established when the benefit costs were more affordable
(Devitt, 2008). The rapid increase in insurance premium costs and
general health care inflation was not known and therefore could not
be considered when establishing benefits to be paid decades later.
Using the information available, governments offered OPEB believing
that they could pay them easily out of current revenue.
The Controversy
A major problem is the manner in which liabilities are portrayed
on the organization’s financial statements and the resulting impact
on the entity’s ability to borrow and raise capital. Under the pay-as-
94
FISCHER, MARSH, HUNT, HORA & MONTONDON
you-go method of accounting for postemployment benefits prior to the
issuance of GASBS No. 45 (GASB, 2004), OPEB liabilities were not
reflected on the financial statements of most governmental
employers. Any OPEB payments disbursed were combined with other
benefit costs and not disclosed as a discrete expenditure or expense.
The comingling of disbursements and the lack of OPEB liability
disclosure were the source of concern on the part of the bond rating
agencies. Raters found it was difficult, if not impossible, to properly
evaluate the financial health of a governmental employer (Fitch,
2007).
GASBS No. 45 requires governments to report in the
Comprehensive Annual Financial Report (CAFR) notes to the financial
statements the actuarially based annual required contribution (ARC)
related to OPEB for the current year. Related unfunded liabilities for
past years must be disclosed but can be amortized over a period of
up to 30 years. This recognition is required even if there is no written
plan and funding is approved on a yearly basis. It is also required
even if retirees must pay their full healthcare premium as part of a
group plan. If retirees are treated as a separate group, the premiums
are assumed to be higher because of the likelihood of chronic
illnesses and a greater need for health insurance later in life. Thus,
even if retirees are paying their own premiums, the standard
assumes they are receiving a benefit of lower premiums by being
included in the group plan.
A specific difference of OPEB liabilities compared to pension
benefit programs is OPEB are not necessarily legally guaranteed. This
allows governments to modify the benefit structure over time to
reduce costs (Mattoon, 2008). In spite of this, the GASBS No. 45
requirement to recognize the OPEB obligation as part of the entity’s
liabilities forces governments to calculate the amount of the
obligation.
In 2007, Fitch reported based on early estimates
governments were reporting ARCs of anywhere from two to ten times
the current pay-as-you-go amount (p. 3).
Katz (2008) reports
governments were shocked when they calculated the amount of the
OPEB liabilities. He alleges governments promised generous retiree
health benefits without knowing the true cost of the promises. Katz
claims most politicians are in the state of denial, compounding the
problem.
For example, the Texas Legislature enacted a law
PUBLIC UNIVERSITY OPEB BURDEN: RECOGNITION, FUNDING AND FUTURE OBLIGATIONS
95
authorizing State employers to ignore the GASBS No. 45 OPEB
recognition requirements (Texas Government Code, 2007).
Available evidence (Dickens & Bovbjerg, 2009, p. 9; Ives, 2010,
p. 46) suggests that state and local current OPEB liabilities are
significantly underfunded. The GAO (2009, p. 23) reports that healthrelated spending represents the fastest growing common budget
component at all levels of government (federal, state, city and
county). Recession-induced revenue shortfalls and current fiscal
pressures facing governments and governmental not-for-profit
business-type activities raise concerns about the action governments
can take to address their OPEB liabilities.
Montondon et al. (2010) surveyed Texas counties and
municipalities to discover whether the local governments
implemented the OPEB standard and made changes to the promised
benefits. Their study also investigated what OPEB funding, if any, was
currently in place. They found over 70 percent of the Texas counties
and municipalities provide OPEB benefits including health insurance,
dental care and life insurance. Only 45 percent of the counties and
municipalities report they implemented the reporting standard using
full accrual accounting for reporting purposes. Counties and
municipalities fund the benefits on a pay-as-you-go basis and report
that OPEB expenditures consumes up to three percent of their annual
cash flow.
Keating and Berman (2007) and Patton and Bauer (2008) focus
on the national funding of employee healthcare. They each discuss
the overall OPEB liability with a full range of options for funding and
restructuring the benefit plans. However, neither presents an
optimum solution to the funding problem.
THIS STUDY
This study focuses on OPEB reporting and the related level of
funding by public land grant universities in the United States. Given
the OPEB liability issues reported by earlier studies, this investigation
looks at governmental not-for-profit business-type activities to
determine whether similar OPEB recognition, funding and reporting
occurs. This descriptive study contributes to the literature in multiple
ways. First, it provides a comprehensive study of the extent of
financial reporting of OPEB obligations and funding contributions by
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FISCHER, MARSH, HUNT, HORA & MONTONDON
public institutions of higher education. Second, most of the prior
research concerns state and local governments rather than
governmental not-for-profit business-type activities such as public
colleges and universities.
The 50 institutions (See Appendix A) used in this study are large,
comprehensive universities with diverse programs. A majority of the
institutions (n=43) report that they are a component unit of the state
(n=27), a state agency (n=11), or a part of the primary government
(n=5). The remaining seven institutions are a combination of
independent not-for-profit organizations (n=3) and other types of
public institutions (n=4). Furthermore, the universities are located
throughout the US allowing for the investigation of any regional
influence on the composition of the OPEB plan and/or its funding.
Because of the amount of public support devoted to public
institutions, they are subject to increased state monitoring (Gordon et
al., 2002).
Plus, land grant institutions are unique in their
relationship to state governments thanks to their funding
relationship. For example, Baber (1983, p. 226) found that due to
public support, there is increased state monitoring and review by the
state auditor. Gordon et al. (2002) found more disclosures for highly
visible universities i.e., larger institutions audited by state auditors.
The institutions included in this study fit the visible, large, and state
audited category of institutions and therefore report sufficient
disclosures with the data required for this study.
The remainder of the paper is organized as follows: first, the
population reviewed is discussed; second, analyses used in the study
are outlined; and finally, results and additional support information
are reported together with a conclusion with suggested future
research.
The Population
There were 1690 public higher education institutions in the US in
2008 (HEP, 2009, p. 1). A majority (59.6 percent) of the public
institutions are two-year colleges. The remaining 683 are four-year
baccalaureate and post-baccalaureate institutions. In accordance
with the Morrill Act provisions (Brubacher & Rudy, 1997), the 50
public institutions in this study represent only 7.3 percent of all US
four-year public institutions but report over 22 percent of the total
PUBLIC UNIVERSITY OPEB BURDEN: RECOGNITION, FUNDING AND FUTURE OBLIGATIONS
97
2008 four-year public institutions’ revenue (NCES, 2009, Tables 234
and 352).
Land grant universities have their origin with the Morrill Act of
1862 that was amended in 1890 to provide federal lands to endow
state universities dedicated to agriculture and mechanical arts
(Brubacher & Rudy, 1997, p. 63). The Morrill Act stipulates federal
appropriations be provided to the states solely to support public
higher education (Brubacher & Rudy, 1997, p. 163). Before the
enactment of the Morrill Act, the federal government, however, had
already donated 4 million acres of public land to be sold to endow
universities in 15 states (Brubacher & Rudy, 1997, p. 154). Cornell
postponed selling their endowed land and realized the largest
amount ($5 million). A few other schools realized almost a million
dollars but 17 states realized $150,000 or less (Brubacher & Rudy,
1997, p. 379).
In selecting a sample population, we chose land grant schools
because they had the advantage of the land endowments and as
public institutions their annual financial reports are publicly available.
Each of the 50 institutions in this study holds an engineering
accreditation and is most commonly classified as a doctoral and/or
research university (HEP, 2009). With respect to student body size,
the 1.3 million students enrolled in the schools in 2008 (HEP, 2009)
represents 18.35 percent of all students enrolled in public four-year
institutions in the US (NCES, 2009). The average number of students
enrolled at each institution is 26.8 thousand who pay an average
$8,528 in tuition and fees per term (HEP, 2009).
Data Collection and Content Analysis
The annual financial reports for fiscal years 2008 and 2009 were
downloaded from the 50 universities’ web pages and analyzed.
Content analysis procedure was used to extract selected financial
information and information pertaining to specific benefits included in
the OPEB plan, participation, liability recognition, and funding
process. The amounts recorded for the OPEB expense, amount
funded and outstanding obligation liability also were retrieved. To
guide the content analysis procedure and to ensure consistency, a
coding instrument was developed to include all the characteristics
and monetary amounts gathered in the data collection. Each annual
report was scored by two of the authors. The interscorer agreement
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FISCHER, MARSH, HUNT, HORA & MONTONDON
rating was 95.3 percent due to rounding error discrepancies that
were easily resolved.
ANALYSIS AND RESULTS
This study focuses on the extent of OPEB and their funding by
public US land grant universities. Financial disclosure may be viewed
as only including the information provided in audited notes and
financial statements or broadened to encompass information
gleaned from web pages or other public documents. In this study, the
financial information and OPEB disclosures examined are those
found in the annual financial reports. Publicly available information in
other documents was not considered.
TABLE 1
FY 2007-2008 (In $ Millions)
Min
Operating
expenses
Operating
revenues
State
appropriations
Balance sheet
assets
Balance sheet
liabilities
Balance sheet net
assets
OPEB expenses*
OPEB obligation
payments*
Outstanding OPEB
liability*
Max
sd
Mean
FY 2008-2009 (In $ Millions)
Min
Max
sd
Mean
205.9
4,038.4 1,059.6 1,360.7 223.1 4,209.1 1,119.1 1,430.5
5.1
141.1
3,473.6
856.7
963.4 168.0 3,723.8
906.2 1,024.2
6.3
3.2
979.6
233.3
339.5
234.7
4.7 1,012.1
330.9 -2.5
228.9 10,607.3 2,135.6 2,461.8 297.4 9,093.2 2,040.1 2,475.4
78.9
150.0
0.0
0.0
3,067.9
755.7
844.9 115.5 3,313.8
820.1
908.6
0.6
7.5
8,038.6 1,482.3 1,616.9 181.7 6,072.5 1,301.4 1,566.8 -3.1
176.6
53.5
34.1
14.2
22.3
11.8
n/a
0.1
0.0
116.9
96.6
27.8
20.6
0.0 1,044.2
191.4
23.5 5.5
15.1 28.3
78.9
Note: *OPEB data includes only the 44 institutions that report OPEB information.
Source: Summarized Annual Financial Report date
Mean
Change as %
Selected Financial Information: Fifty US Land Grant Institutions
PUBLIC UNIVERSITY OPEB BURDEN: RECOGNITION, FUNDING AND FUTURE OBLIGATIONS
99
Financial information for fiscal years 2007-2008 and 2008-2009
was analyzed to determine institutional financial size using various
data. As displayed in Table 1, the institutions’ assets remained
stable over the two fiscal years studied. OPEB expenses and overall
expenses increased less than six percent from fiscal year 2008 to
2009. Although general revenues increased over 6.3 percent, fewer
resources were available to the institution because state
appropriations decreased 2.5 percent. This decrease in resources
was exacerbated by the almost 8 percent increase in total
obligations.
The amount institutions funded the OPEB liability
increased 28.3 percent from fiscal year 2008 to 2009 for the 29
institutions (66 percent of the 44 institutions) that report OPEB
funding (see Table 2 Panel A). Of the 16 institutions reporting no
OPEB obligations, six reported nothing on the face of the financial
report or in the note disclosures because the state assumes the
entire OPEB obligation. Another three have fully funded the accrued
OPEB obligation and seven institutions did not include an OPEB
obligation disclosure but did include a disclosure of the provided
benefits. All 50 institutions in the study report an unqualified audit
opinion for fiscal years 2008 and 2009.
A majority (88 percent) of the institutions provide for the retirees’
post-employment nonpension benefits. Six of the institutions did not
disclose any information pertaining to OPEB, obligations, or costs.
Even though institutional OPEB costs and obligations are funded and
reported by the state for a third of the institutions (34.1 percent), the
institution’s annual financial report discloses the composition of the
OPEB retiree benefits.
Although 69.0 percent of the 29 institutions that provide OPEB
funding accrue liabilities for the OPEB obligations, a greater percent
(79.3) of those providing OPEB funding pay the costs on a pay-as-yougo basis. Regardless of whether the institution or the state pays the
OPEB obligation, retirees at 20 institutions (45.5 percent) pay a
portion of the benefits’ cost. Medical and hospitalization insurance
are the primary benefits afforded retirees. Over half of the retirees
(54.5 percent) are provided life insurance and 43.2 percent receive
prescription drug coverage (see Table 2 Panel B). It is interesting that
only Ohio State University, University of Connecticut, and University of
Tennessee reimburse Medicare B costs as part of their OPEB plans.
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FISCHER, MARSH, HUNT, HORA & MONTONDON
TABLE 2
Fifty US Land Grant Institutions
Selected OPEB Data
N = 50
Panel A
Who provides OPEB funding?
State
Institution
Institutional recognition of OPEB obligations
Accrue obligation
Recognizes only current obligation
Does not report any obligation
OPEB obligation funding
State pays all obligations
State pays obligations but institution include
data in annual financial report
Institution pays cost as they go (cost basis)
Institution pays accrued obligation
Institution contributes nothing
Panel B
OPEB plan coverage*
Medical insurance
Hospitalization insurance
Prescription drug coverage
Dental insurance
Life insurance
Reimburses Medicare B
Other – vision, child care, etc.
As a
percent
21
29
42.0%
58.0%
20
14
16
40.0%
28.0%
32.0%
6
9
12.0%
18.0%
23
3
9
46.0%
6.0%
18.0%
40
39
19
14
24
3
10
90.9%
88.6%
43.2%
31.8%
54.5%
6.8%
22.7%
Note: *Institutions may include multiple coverage in their program.
Source: Annual Financial Report data.
Eight institutions (16 percent) do not participate in the U.S. Social
Security program. However, all eight institutions provide a full array of
benefits including medical, hospitalization, prescription coverage,
dental and life insurance in their OPEB coverage. Three of the eight
institutions that do not participate in the Social Security program,
Louisiana State University, Colorado State University and University of
California at Davis, contribute to the institution’s OPEB annual
PUBLIC UNIVERSITY OPEB BURDEN: RECOGNITION, FUNDING AND FUTURE OBLIGATIONS
101
obligation on a pay-as-you-go basis. The other five institutions report
the OPEB obligation but make no contribution to the cost.
State Support
The implementation of GASBS No. 45 marks the first time that
states are required to acknowledge and report their retiree health
and other benefit obligations. Since states have only started to
recognize OPEB liabilities, many still have not put aside adequate
resources to pay for these obligations. On average, states have only
put aside 7.1 percent of the resources needed to fund their retiree
OPEB obligations. Nine states have set aside more than 7.1 percent
of the OPEB liabilities while 20 states have not set aside any
resources (Pew, 2010).
Only two states, Arizona and Alaska, have set aside 50 percent or
more of the resources needed to cover their OPEB obligations.
Arizona has funded 65 percent, leading all states, and Alaska has put
aside nearly 56 percent of the assets needed to cover its liabilities.
Another seven states, including Colorado, Kentucky, North Dakota,
Ohio, Oregon, Virginia and Wisconsin, are making progress on OPEB
liability funding ranging from 10.4 percent to 38.2 percent (Pew,
2010). Land grant institutions in five of the nine states that are
making progress in their OPEB funding report the state provided their
OPEB. Three other institutions among the nine states making
progress indicate that they provide the OPEB obligation funding rather
than the state. One institution among the nine states making
progress on the OPEB funding did not disclose any information
pertaining to OPEB obligations or costs.
Irrespective of the size of the liabilities—whether small or large,
implicit (e.g., OPEB subsidies) or explicit (e.g., OPEB plans)—there is a
great deal of variation among states and how they account for the
OPEB obligations. For example, New Jersey’s liability of $68.9 billion
is the largest of any state and wholly unfunded. Virginia’s obligation is
nearly $4 billion and almost 39 percent funded. Kansas’ obligations
totaled $316 million, a fraction of New Jersey’s, but Kansas had not
set aside any resources (PEW, 2010). Eight institutions with OPEB
obligations supported by the state are located in states that have
partially funded the obligation while only two institutions are located
in a state with a wholly unfunded OPEB obligation.
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FISCHER, MARSH, HUNT, HORA & MONTONDON
Of the 29 land grant institutions where the institution provides the
OPEB liability funding, three institutions, Clemson, University of
Kentucky and University of Maryland, have fully funded their
outstanding OPEB obligations. The remaining 26 institutions have an
average $75 million unfunded OPEB liability.
Regional Influence
Given the difference in state OPEB liability funding found by the
Pew (2010) study and that over half of the institutions in this study
have employee collective bargaining (n=26), we investigate whether a
regional influence exists regarding the institutions’ OPEB plan or
funding.
Exhibit 1 displays a US grid that displays the region each
institution was assigned. When a state spans two areas, the
institution was assigned to the area in which the majority of the state
is located. A list of states in each region is found in Appendix B.
All institutions in this study receive state appropriations and 47
are recognized as state affiliated. Appendix A displays the specific
entity type of each institution as reported in the institution’s annual
financial report disclosures. Michigan State University reports that it
was founded in 1855 as the agricultural college of the state but is not
a component unit of the state as defined by GASB. The university,
however, does follow GASBS No. 34 for financial accounting and
reporting. Three institutions, Cornell University, Pennsylvania State
University and University of Delaware, follow Financial Accounting
Standards Board (FASB) generally accepted accounting principles
(GAAP) guidance to prepare their annual financial report. Cornell is a
private non-profit institution established as the New York land grant
institution by New York State statute (Carren, 1958). The University
of Delaware is privately chartered within Delaware. Pennsylvania
State University reports that it is a state-related institution rather than
a state agency or component unit. These three institutions report
OPEB liability obligations in accordance with Statement of Financial
Accounting Standard (SFAS) Nos. 106 and 158 (FASB, 1990; 2006)
rather than GASBS No. 45. Effective with fiscal years ending after
September 2009, the SFAS guidance became Accounting Standard
Codification (ASC) 715 – Compensation Retirement Benefits (FASB,
2009).
PUBLIC UNIVERSITY OPEB BURDEN: RECOGNITION, FUNDING AND FUTURE OBLIGATIONS
103
EXHIBIT 1
Regions within the United States
West
Central
East
Applying FASB guidance in calculating OPEB liabilities results in a
larger amount being recognized than under GASB guidance.
Therefore, OPEB liability data for the three institutions following FASB
guidance are excluded as outliers when comparing OPEB liability
obligations among the six US regions.
On average in fiscal 2009, land grant institutions located in all
regions within the US report OPEB liabilities of $37 million. The
institutions recognized $25 million OPEB expenses and funded $18
million of the accumulated OPEB liabilities. Benefit plan coverage
within the regions was found to be comparable to that of the entire
population. That is, a majority within each of the regions provide
medical and hospitalization coverage for retirees. Life insurance was
the third most frequent benefit provided retirees. In summary,
analysis of the retiree benefit coverage composition found no
significant regional influence in the OPEB provisions. One difference
found among the regions is employees’ ability to bargain collectively
(see Table 3). The northern regions of the US report more collective
bargaining than the southern regions with the largest number of
institutions with collective bargaining units located in the North East.
However, when an ANOVA analyzed OPEB costs across the six regions
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FISCHER, MARSH, HUNT, HORA & MONTONDON
(F 6.139 significant at .001), collective bargaining had a negative
relationship and was not significant in explaining any difference
among the regions based on size (enrollment, revenues,
expenditures) and plan benefits (healthcare, life
insurance,
prescription, etc.) variables. Only two size variables, i.e., enrollment (t
= 2.599 significant @ .020) and institutional expenses (t= 4.359
significant @ .001), were significance in explaining the difference
among the six regions.
The OPEB obligation disclosures indicate that the benefit cost is
increasing. At the same time, the state appropriation decreased 2.5
percent from fiscal year 2008 to 2009 (see Table 1). If the trend
continues, the institutions may be forced to identify means to reduce
their contribution costs.
COST CONTAINMENT OPTIONS
The recent recession resulted in smaller returns on investments
and institutional financial pressures, raising concerns that
universities and other governmental entities will be forced to manage
and/or reduce OPEB costs. Several alternatives are available. The
head-in-the-sand approach or doing nothing is one option. Other
potential options include lowering benefits for new employees,
changing the benefits’ formula for current employees (if legally
possible), re-defining the monthly stipend benefits, redefine benefits
to a prescribed annuity amount, redefining the OPEB retirement
eligibility, and/or establishing a supplemental OPEB defined benefit
contribution plan.
An example of the multi-faceted approach is one taken by
Gwinnett County, Georgia. The County changed its retiree health
insurance benefit from paying a percentage of the premium to a
defined, fixed monthly contribution with a 10-year vesting
requirement for employees hired after July 1, 2007 (Pickens, 2009).
They also reduced employee and/or dependent’s benefit contribution
when Medicare eligibility was reached. These two actions reduced
the unfunded accrued OPEB liabilities of the county by more than 50
percent.
Some states, including West Virginia and New Jersey, propose
scaling back retiree benefits as a means of reducing their OPEB
liability (Williams, 2010; AP, 2010). Following a series of public
PUBLIC UNIVERSITY OPEB BURDEN: RECOGNITION, FUNDING AND FUTURE OBLIGATIONS
105
hearings across West Virginia, the Public Employees Insurance
Agency (PEIA) voted to eliminate retiree health care subsidies for all
employees hired after July 1, 2010 (Williams, 2009). The Minnesota
courts deliberated whether the state could curtail pension and
healthcare benefits for current retirees from state jobs (Merrick,
2010 p. A5). In June 2011, a Minnesota judge dismissed the
challenge because the retirees did not meet their burden to show
unconstitutionality beyond a reasonable doubt (Walsh, 2011). Cases
similar to Minnesota’s were pending in South Dakota and Colorado
but have been resolved in favor of the state rather than the public
workers. The South Dakota ruling, however, is being appealed as
unconstitutional by retirees (Brokaw, 2012). Other states including
Illinois and New Jersey are watching these test cases as they ponder
solutions to their own pension and OPEB funding problems.
Additional Information
Considering the increased OPEB liabilities and decreased net
assets reported in the universities’ annual financial reports, the chief
financial officer (CFO) of the 50 institutions was contacted to gather
information regarding the status of the OPEB plan at their institution
and whether any modification to the plan was under consideration.
Using a web based survey, the CFOs were asked what changes to the
OPEB provisions, if any, were being considered. The CFOs also were
asked if they believe GASBS No. 45 guidance provides transparency
and should the OPEB reporting requirements be changed. Additional
information was requested about funding sources used to fund the
OPEB plan and what actions, if any, the institution was currently
undertaking, or will take in the future, regarding the OPEB obligation.
Twenty eight CFOs responded to the inquiry (56 percent response
rate). Of the 28 respondents, 23 (82 percent) provided additional
comments and information. The CFOs were promised anonymity so
the comments were sorted by the US region in which the institution is
located (see Exhibit 1 and Appendix A for the list of states). CFOs
from the South Central region provided more responses and
comments than the other regions (see Table 3).
Over half of the responding CFOs report that their institution has
modified (n = 7) or is considering a modification (n = 9) to the OPEB
plan (See Table 3). The seven CFOs who indicated that the OPEB
plan had been modified report various changes including reducing
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FISCHER, MARSH, HUNT, HORA & MONTONDON
eligibility, premium participation, contribution rates, increased age
requirement to receive benefits, or employer subsidies. The nine
CFOs who indicated possible OPEB plan changes are considering
eliminating benefits for new employees, creating new tiers of
benefits, or divesting the institution from the program. Other than
changing benefit tiers, a review of the CFO comments finds little, if
any, commonality or regional influences.
Other than general revenues and all available resources, the
survey found no single resource used as the primarily funding of the
OPEB plan obligations. Only three institutions manage their OPEB
TABLE 3
OPEB are state obligations
2
2
1
1
OPEB are reported by the institution
6
6
12 2
8
10
Total institutions by Region
6
8
12 4
9
11
CFOs who reported additional information
4
4
4
3
8
5
CFO’s institution reports OPEB
4
2
4
2
8
5
Reported that the OPEB plan was modified 1
2
2
2
Reported OPEB modifications under
consideration
1
1
2
1
2
2
Believe GASB No. 45 reporting improves
transparency
3
1
1
6
3
Believe GASB No. 45 reporting is deficient
2
1
4
1
Believe GASB No. 45 reporting should be
revised
1
1
4
Institution has employee collective
bargaining
5
5
11 2
0
2
Note: Responses will not add to 28 as CFO reported in multiple categories.
Panel B
N=
Sources of funding for the OPEB obligations reported by 10 CFOs
Bond revenue funds less than 25% of OPEB obligations
1
General revenue funds 75% or more
5
New fees fund less than 25% of OPEB obligations
1
All available resources used to fund OPEB obligations
6
Contributions placed in irrevocable trust
3
Note: Responses will not add to 10 as CFO reported in multiple categories
Total
Reporting
South
Central
South East
South West
North
Central
North East
Panel A
North West
Land Grant Institutions: Analysis by Region
(States in each Region listed in Appendix A)
6
44
50
28
25
7
9
14
8
6
26
PUBLIC UNIVERSITY OPEB BURDEN: RECOGNITION, FUNDING AND FUTURE OBLIGATIONS
107
plan by placing the contributions in an irrevocable trust fund. The
remainder rely on independent third-party financial institutions or the
state to administer the OPEB plan/benefits.
Fourteen of the CFOs expressed a belief that the OPEB reporting
improves financial transparency for their institution. However, eight
CFOs have concerns that OPEB is an unrealistic liability with faulty
methodology that results in misleading information. Although they
report that disclosing the liability and funding improves transparency,
six CFOs believe that GASBS No. 45 should be changed due its
complexity.
CONCLUSION AND FUTURE RESEARCH
This investigation finds that OPEB recognition and reporting by US
land grant institutions are similar to the recognition and reporting
measures utilized by states and local municipalities. The OPEB
obligation recognition is a complex set of assumptions based on a
benefit composition that is not well disclosed in the annual financial
report. Although GASBS No. 45 requires accrual accounting, the
standard does not direct specific funding. Thus, public colleges and
universities, like states and local municipalities, continue to cover
their costs on a pay-as-you-go basis. This behavior causes the OPEB
obligation to escalate annually.
Land grant institutions report that OPEB costs are increasing at a
more rapid rate than their ability to fund the outstanding liabilities.
This deficiency is of particular concern in light of diminishing state
appropriation support coupled with the rapid rise in the cost of
medical insurance. Medical and hospitalization coverage are the
predominant component of OPEB for retirees although over half of
the institutions also provide life insurance coverage.
Land grant institutions, like other governmental entities, must
evaluate the costs and the consequences of increasing liabilities.
These increasing liabilities may jeopardize future bond ratings and
future institutional growth. Given that these institutions collect tuition
dollars in addition to receiving state funding, the cost of providing an
education in the future will not only reflect the current costs but may
require increases in tuition for future students to pay for the
unfunded OPEB obligations.
Anything that reduces state
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FISCHER, MARSH, HUNT, HORA & MONTONDON
appropriation could result in a less educated population with lower
earning power thus creating a vicious economic circle.
Considering this study’s finding, additional research is needed to
ascertain what actions institutions should employ to manage their
OPEB obligation while fulfilling their educational mission. In addition,
research should investigate the ramifications of any change on the
long-term cost of providing an education, student enrollments, and
the effects on faculty and staff employment and retention.
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Retiree Health Benefits?” Labor Law Journal. 59 (1): 40-46.
Keating, E. & Berman, E. (2007). “Unfunded Public Employee Health
Care Benefits and GASB No. 45.” Accounting Horizons, 21 (3):
245-263.
Mattoon, R. H. (2008, May). “Facing the Challenge of Retiree Health
Care: Liabilities and Responses of State and Local Governments
– A Conference Summary.” Chicago Fed Letter: 250a.
Merrick, A. (2010, September 15). “Case Tests Retirees’ Pension
Cuts.” The Wall Street Journal: A5.
Montondon, L., Hunt, G. & Marsh, T. (2010). “Governmental Health
Care Benefits Reporting:
The Current State of Texas
Governmental Entities.” Today’s CPA, 38 (2): 28-31.
110
FISCHER, MARSH, HUNT, HORA & MONTONDON
National Center for Education Statistics (NCES). (2009). Digest of
Educational Statistics. Washington, DC: US Department of
Education.
Patton, T. K. & Bauer, K. (2008). “The GASB OPEB Standards: To
Comply or Not to Comply. Today’s CPA, 38 (7): 28-31.
The Pew Center on the States (PEW). (2010, February). The Trillion
Dollar Gap: Underfunded State Retirement Systems and the
Roads to Reform. Philadelphia: Pew Charitable Trusts.
Pickens, R. (2009, December). Managing the challenge of OPEB.
Government Finance Review. 25 (6): 43-46.
Texas Government Code (2007). Annotated Title 10, Chapter 2264
Sec. 001-050 House Bill 2365. Enacted by the Legislature of the
State of Texas.
United States Government Accountability Office (GAO). (2009,
November). State and Local Government Retiree Health Benefits
(GAO-10-61). Washington, DC: Author.
Walsh, M. W. (2011, June 30). “Two Rulings Find Cuts in Public
Pensions Permissible.” The New York Times. [Online]. Available at
www.nytimes.com/2011/07/business/01pension.html?
Williams, W. (2009, July 30). “Union Plans to Sue State after PEIA
Board Upholds Slashing Benefits.” The State Journal. [Online].
Available at at http://statejournal.com/story.cfm?func=viewstory
&storyid=63837.
Williams. W. (2010, January 29). “Senate Proposed Scaling Back
Benefits to Cut Looming Liability. The State Journal. [Online].
Available at http://statejournal.com/story.cfm?storyid=74313.
APPENDIX A
Land Grant Colleges and Universities
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Institution
Auburn University
University of Alaska
University of Arizona
University of Arkansas
University of California - Davis
Colorado State University
University of Connecticut
Entity Type¹
CU
CU
P
SP
CU
P
A
Reporting Basis²
BTA
BTA
BTA
BTA
BTA
BTA
BTA
PUBLIC UNIVERSITY OPEB BURDEN: RECOGNITION, FUNDING AND FUTURE OBLIGATIONS
111
APPENDIX A (Continued)
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
University of Delaware
Florida State University
University of Georgia
University of Hawaii
University of Idaho
University of Illinois
Purdue University
Iowa State University
Kansas State University
University of Kentucky
Louisiana State University
University of Maine
University of Maryland
University of Massachusetts
Michigan State University
University of Minnesota
Mississippi State University
University of Missouri
Montana State University
University of Nebraska
University of Nevada
University of New Hampshire
Rutgers The State University of
New Jersey
New Mexico State University
Cornell University
North Carolina State University
North Dakota State University
Ohio State University
Oklahoma State University
Oregon State University
Pennsylvania State University
University of Rhode Island
Clemson University
South Dakota State University
University of Tennessee
Texas A & M University
Utah State University
University of Vermont
Virginia Polytechnic Institution
Washington State University
University of West Virginia
University of Wisconsin
University of Wyoming
PC
CU
A
CU
A
CU
CU
I
A
CU
CU
A
CU
CU
PC
A
CU
CU
CU
A
A
P
FASB
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
CU
P
N
CU
A
I
CU
P
N
CU
CU
CU
CU
A
CU
CU
CU
A
CU
CU
CU
BTA
BTA
FASB
BTA
BTA
BTA
BTA
BTA
FASB
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
BTA
112
FISCHER, MARSH, HUNT, HORA & MONTONDON
APPENDIX A (Continued)
Entity Type
Legend:
A = agency of the state
CU = component unit of the state
I = instrumentality of the state
N - independent of state reporting
P = part of the primary government
n = 11
n = 27
n= 2
n= 2
n= 5
PC = private charter or constitution with public support
SP = special purpose unit
Reporting basis: BTA = business -type activity GASB No. 34 guidance
FASB = FASB No. 117 guidance
n= 2
n= 1
Notes: ¹ Institution's Annual Financial Report disclosure No. 1.
² www.NCES.ed.gov/IPEDS.
APPENDIX B
States in the United States Regions
Regions
North West
North Central
States
Alaska, Hawaii, Idaho, Montana, Oregon, Washington
Illinois, Iowa, Minnesota, Nebraska, North Dakota, South Dakota,
Wisconsin, Wyoming
North East
Connecticut, Indiana, Maine, Massachusetts, Michigan, New
Hampshire, New Jersey, New York, Ohio, Pennsylvania, Rhode Island,
Vermont
South West
Arizona, California, Nevada, Utah
South Central Arkansas, Colorado, Kansas, Louisiana, Mississippi, Missouri, New
Mexico, Oklahoma, Texas
South East
Alabama, Delaware, Florida, Georgia, Kentucky, Maryland, North
Carolina, South Carolina, Tennessee, Virginia, West Virginia
J. OF PUBLIC BUDGETING, ACCOUNTING & FINANCIAL MANAGEMENT, 25 (1), 113-134
SPRING 2013
GOING-CONCERN MODIFIED AUDIT OPINIONS FOR
NON-PROFIT ORGANIZATIONS
Thomas E. Vermeer, K. Raghunandan and Dana A. Forgione*
ABSTRACT. Non-profit organizations constitute an important share of the U.S.
economy, and recent audit failures and GAO findings highlight the
importance of auditor reporting decisions in this sector. In this study, we
examine going-concern modified audit opinions for non-profit organizations.
Using audit opinion data for 3,567 non-profits exhibiting some signs of
financial stress, we find that non-profits are more likely to receive a goingconcern modified opinion if they are smaller, are in worse financial condition,
expend less on program-related activities, and have more internal control
related audit findings. Our analysis of the subsequent resolution of the
going-concern uncertainties suggest that only 27 percent of the non-profits
receiving an initial going-concern modified audit opinion filed for dissolution
in the subsequent four fiscal years. Our findings fill a gap in an important
area that has received little research attention, and provide a useful
benchmark for non-profits and their auditors.
INTRODUCTION
Recent developments have highlighted the importance of audit
opinions in the non-profit sector. What has been called the Enron of
…………………………….
* Thomas E. Vermeer, Ph.D., CPA, is an Associate Professor of Accounting in
the Department of Accounting & Management Information Systems at the
University of Delaware. His research interests are auditing and financial
reporting in the government and nonprofit sectors. K. Raghunandan, Ph.D.,
is the Ryder Eminent Scholar Chair in Business Leadership and a Professor
of Accounting in the School of Accounting at Florida International University.
His research interests are in corporate governance and the market for audit
services. Dana A. Forgione, Ph.D., CPA, CMA, CFE, is the Janey S. Briscoe
Endowed Chair in the Business of Health and a Professor of Accounting in
the College of Business at the University of Texas at San Antonio. His
research interests are in external audits and governance in healthcare and
nonprofit entities, comparative international healthcare accounting,
financing systems, and quality of care.
Copyright © 2013 by PrAcademics Press
114
VERMEER, RAGHUNANDAN & FORGIONE
the non-profit sector, the failure of the Baptist Foundation of Arizona
(BFA) in 1999 is the largest bankruptcy of a non-profit in U.S. history.1
At the center of this failure is the standard unqualified audit opinions
issued by Arthur Andersen from 1984 to 1997. Lynn Turner, former
SEC Chief Accountant, notes that church members “had a right to put
all they had in Baptist Foundation—given the audits.” Janet
Napolitano, Arizona Attorney General, notes that BFA convinced
church members that “it was running a clean operation by producing
audits every year. They were audited by a Big Five accounting firm.
The audits were clean, everything seemed hunky dory” (CBS, 2002, p.
1). Because of the inappropriate audit reports, Andersen settled the
church members’ lawsuit for $217 million and a partner and
manager on the audit surrendered their CPA licenses (Mohrweis,
2003).
In October 2007, the Government Accountability Office (GAO)
issued a report on the quality of audits of non-profits and local
governments under the Single Audit guidelines. In this report, the GAO
notes that “many of these audits may provide a false sense of
assurance and could mislead users of single audit reports” (GAO,
2007, p. 1). The GAO believes that changes need to be made to
address the weaknesses in audit reports for non-profits and
governments that receive federal funding. Although the BFA highlights
the importance of audit reports in the non-profit sector and the GAO
report addresses the need for auditing reform in the non-profit sector,
empirical researchers have not focused on auditor reporting
decisions in the non-profit sector. In this paper we examine goingconcern modified audit opinions for non-profit organizations.
Specifically, we examine (a) factors associated with the issuance of a
going-concern modified audit opinion, and (b) the subsequent
resolution of the going-concern uncertainties, for a sample of nonprofits.
There is a long tradition of research that has examined issues
related to going-concern modified audit opinions in the for-profit
sector. Researchers have modeled going-concern opinions, examined
the subsequent status of firms receiving going-concern modified
opinions, compared the accuracy of going-concern opinions and
models in predicting bankruptcy, analyzed the influence of a variety of
non-financial factors in the issuance of going-concern opinions, and
documented the consequences of receiving a going-concern modified
GOING-CONCERN MODIFIED AUDIT OPINIONS FOR NON-PROFIT ORGANIZATIONS
115
opinion (e.g., Altman, 1982; Mutchler, 1985; Hopwood, McKeown, &
Mutchler, 1989, 1994; McKeown, Mutchler, & Hopwood, 1991a;
Carcello & Palmrose, 1994; Chen & Church, 1996; Nogler, 1995;
Geiger & Raghunandan, 2001, 2002).
Non-profit organizations account for a significant proportion of the
U.S. economy. Non-profits control more than $8 trillion of assets
(Wells, 2005) and employ about 7 percent of the workforce
(Independent Sector, 2002). The role of auditing at non-profits has
assumed increased significance in recent years because of several
well-publicized scandals and failures. These and other recent
scandals at some large non-profit organizations have led to laws (and
legislative proposals) in many states that seek to mandate
independent audits for non-profit organizations (Independent Sector,
2006; Spindel, Tesdahl, & Ramey, 2006). For example, the Nonprofit
Integrity Act (NIA 2004) of California requires that beginning January
1, 2005, non-profit organizations with gross revenues of $2 million or
more prepare financial statements that are in accordance with GAAP
and are audited by an independent public accountant. Similar
legislation related to auditing and governance of non-profits has been
either adopted or is being considered by other legislatures (Hempel &
Borrus, 2004; Independent Sector, 2006). The Finance Committee of
the U.S. Senate has also held hearings on accountability and reform
of non-profits as a prelude to possible federal legislation (Spindel,
Tesdahl, & Ramey, 2006). One of the proposals considered by the
Senate Finance Committee calls for mandatory audits of non-profits
with $1 million or more in annual revenues.
Thus, there is increasing interest from legislators and the public
in issues related to audits of non-profit organizations. Yet, in contrast
to the voluminous research related to going-concern modified audit
opinions for for-profit entities, no prior study has examined factors
related to the issuance and resolution of going-concern modified
audit opinions for non-profits. In this paper, we provide empirical
evidence about going-concern modified audit opinions at U.S. nonprofit organizations.
DATA
As with prior studies in the for-profit sector (e.g., Hopwood,
McKeown, & Mutchler, 1989, 1994; Reynolds & Francis, 2000), we
limit our analysis to a sample of financially distressed non-profits
116
VERMEER, RAGHUNANDAN & FORGIONE
because auditors do not normally issue a going-concern modified
opinion for non-stressed firms. We define a non-profit as financially
distressed if it has either a deficit in total net assets (fund balance) or
a deficit for the current year. Our criteria are consistent with goingconcern research in the for-profit sector (e.g., Hopwood, McKeown, &
Mutchler, 1994; DeFond, Raghunandan, & Subramanyam, 2002;
Krishnan, Raghunandan, & Yang, 2007) where financially distressed
companies are defined as firms with either negative net income,
negative retained earnings, negative working capital, or negative cash
flows from operating activities.
We obtained the names and related financial data for non-profits
with $300,000 or more in government grants and either a deficit in
total net assets (fund balance) or a deficit for the current year from
the National Center for Charitable Statistics (NCCS) for 2001. We
chose 2001 because an earlier period allows us to determine
whether the non-profits that received a going-concern modified
opinion in 2001 failed or remained in business in subsequent years;
we wanted to follow the subsequent status of the non-profits for at
least four more years.2 NCCS collects their data from IRS Forms
990/990EZ and the IRS Business Master File.
We obtained our audit related data from the Federal Audit
Clearinghouse (FAC). The FAC includes data on non-federal
organizations that expend $300,000 ($500,000 for fiscal years
ending after December 31, 2003) or more in a year in federal
awards.3 Our initial sample of financially distressed non-profits from
NCCS yielded 18,488 observations. After deleting 14,829
observations that were included in NCCS but were not included in the
FAC (i.e., did not expend $300,000 or more in federal awards in
2001) and 92 observations that received an adverse, disclaimer, or
qualified opinion in 2001, our final sample includes 3,567
observations. Of these observations, 147 non-profits received a
going-concern modified audit opinion in 2001 and the remaining
3,420 observations received a standard unqualified opinion in 2001.
Prior research (e.g., Mutchler, 1984; Carcello, Hermanson, &
Neal, 2003; Geiger & Rama, 2006) suggests that first-year goingconcern modified opinions are different from subsequent year
modified opinions. Hence, for the 147 firms in the going-concern
modified opinion group, we also checked their opinion in the
preceding year; 89 of the 147 non-profits received a first-time going-
GOING-CONCERN MODIFIED AUDIT OPINIONS FOR NON-PROFIT ORGANIZATIONS
117
concern modified opinion in 2001, while the remaining 58
observations received a going-concern modified opinion in 2001 and
2000.4
METHOD
In the first part of our analysis, we model the likelihood of a nonprofit receiving a going-concern modified audit opinion in 2001. In the
second part of our analysis, we examine the subsequent status of
firms that received a going-concern modified audit opinion in 2001.
Going-Concern Modified Audit Opinion Model
We use the following logistic regression model to examine an
auditor’s propensity to issue a going-concern modified opinion to
financially distressed non-profits:
Prob (GC=1) = F(α + β1EXCESS + β2CURRENT + β3DEBT +
β4CASHFLW + β5RESFND + β6EXPENSE + β7SIZE + β8BIG4 +
β9FINDING + β10REPLAG + β11PREVIOUS)
Where:
= cumulative distribution function of the logistic
distribution;
GC
= 1 if audit opinion is a going-concern modified opinion
in 2001, else 0;
EXCESS
= ratio of excess (or deficit) for current year over total
revenue;
CURRENT = ratio of current assets to current liabilities;
DEBT
= ratio of bonds, mortgages and notes payable, and
other liabilities to total assets;
CASHFLW = ratio of net cash flows provided (used) by operating
activities to total assets;
= ratio of temporarily restricted plus permanently
RESFND
restricted funds to total fund balance;
EXPENSE = ratio of program expense to total expense;
SIZE
= natural log of total assets;
BIG4
= 1 if audited by Big Four accounting firm, else 0;
= 1 if at least one of the following is present (reportable
FINDING
condition, material weakness, material noncompliance, or questioned cost), else 0;
F (·)
118
VERMEER, RAGHUNANDAN & FORGIONE
= number of days from fiscal year end to audit report
date; and
PREVIOUS = 1 if audit opinion is a going-concern modified opinion
in 2000, else 0.
REPLAG
Two consistent findings from studies in the for-profit sector are
that (a) larger entities are less likely to receive a going-concern
modified opinion, and (b) going-concern modified opinions are more
likely for firms experiencing financial difficulties (e.g., Hopwood,
McKeown, & Mutchler, 1989, 1994; DeFond, Raghunandan, &
Subramanyam, 2002; Krishnan, Raghunandan, & Yang, 2007). We
include the natural log of total assets as our size measure, and
anticipate that the coefficient on this variable will be negative. We
include EXCESS, CURRENT, DEBT, CASHFLW, and RESFND as
measures for a non-profit’s financial difficulties. We expect the
coefficients of EXCESS, CURRENT, CASHFLW, and RESFND to be
negative suggesting that non-profits with lower excess of revenues
over expenses, lower current ratio, lower cash flows from operating
activities, and lower fund balances are more likely to receive a goingconcern modified opinion. In contrast, we expect a positive sign for
the coefficient DEBT, suggesting that non-profits with more debt are
more likely to receive a going-concern modified opinion.
The Better Business Bureau (2003) suggests that non-profits
should expend at least 65% of its total expenses on program-related
activities. Non-profits that expend more on program expenses should
be less likely to receive a going-concern modified opinion because
they are more efficient and more likely to receive future donations
because contributors generally do not want their donations used for
management, general, and fundraising expenses. Thus, we include
the variable EXPENSE in the regression model and expect the
coefficient to be negative.
Prior research (DeFond, Raghunandan, & Subramanyam, 2002;
Knechel & Vanstraelen, 2007) suggests that Big Four firms are more
likely to issue a going-concern modified opinion because of reputation
concerns. We include BIG4, a dummy variable, and expect that the
coefficient on this variable will be positive.
Indications of poor management can impact auditors’ decisions
related to non-profits (Tate, 2007). The FAC includes information
regarding whether a non-profit has reportable conditions or material
weaknesses in internal control and material non-compliance or
GOING-CONCERN MODIFIED AUDIT OPINIONS FOR NON-PROFIT ORGANIZATIONS
119
questioned costs in major federal programs. To the extent such
findings are signs of poor internal control non-profits with these
findings should be more likely to receive a going-concern modified
opinion.5 We include the variable FINDING and predict that nonprofits with at least one of these findings are more likely to receive a
going-concern modified opinion.
We also include REPLAG because prior research (Raghunandan &
Rama, 1995, DeFond, Raghunandan, & Subramanyam, 2002)
suggests that going-concern modified opinions are more likely for
entities that have longer reporting delays. We expect the coefficient
on this variable to be positive.
As noted earlier, prior research suggests that initial going-concern
modified opinion decisions are different than repeat going-concern
modified opinion decisions. Hence, we also include a dummy variable
equal to 1 if the non-profit received a going-concern modified opinion
in the prior year (2000). In addition, we also use another model that
excludes the 76 firms that received a going-concern modified opinion
in 2000 (58 that received a going-concern modified audit opinion in
both 2000 and 2001, and 18 that received a going-concern modified
audit opinion in 2000 but a clean opinion in 2001); in such analysis
we exclude the PREVIOUS variable.
Subsequent Status Analysis
Our sample includes non-profits that received a going-concern
modified opinion in 2001. We examine GuideStar to determine the
status of the 147 non-profits that received a going-concern modified
opinion in 2001. We inspect the most recent Form 990 in .pdf form in
GuideStar to categorize these non-profits as either dissolved or
continuing in existence based on the following criteria:
a. If the most recent Form 990 on file with GuideStar has a yearend of December 31, 2006 or later, does not indicate a final
return, has similar items and amounts listed on the balance
sheet at the beginning and end of year, and the response to
question 79 (was there a liquidation, dissolution, termination, or
substantial contraction during the year?) on Form 990 is no, the
non-profit is categorized as continuing in existence.
b. If the most recent Form 990 on file with GuideStar indicates that
it’s a final return and/or question 79 on Form 990 is answered
120
VERMEER, RAGHUNANDAN & FORGIONE
yes, we review the return to determine whether the non-profit
dissolved. In this dataset, when the Form 990 indicates it’s a
final return and/or question 79 is answered yes, the non-profit
always dissolves.
c. If the most recent Form 990 on file with GuideStar has a yearend before December 31, 2006, the return does not indicate
that it’s a final return and the response to question 79 is no, we
contact the non-profit to determine its status. If the phone
number is disconnected and the website no longer exists, the
non-profit is coded as dissolved. When investigating these nonprofits, there was a fairly even distribution of non-profits that
dissolved and continued in existence.
RESULTS
Table 1 provides descriptive statistics regarding the sample of
financially distressed non-profits, partitioned on whether the nonprofit received a standard unqualified opinion in 2001, or a goingconcern modified opinion in 2001. Table 1 shows that in terms of the
raw size measure, firms receiving a going-concern modified audit
opinion are smaller than firms receiving a standard audit opinion in
terms of size but that the difference is not significant at conventional
levels. However, the data also show that the standard deviation of
both the going-concern modified audit opinion and clean opinion
samples are high; this justifies our use of the log transformed
measure (SIZE). In terms of the transformed measure, as expected,
the firms receiving a going-concern modified audit opinion are
significantly smaller than the clean opinion firms. In terms of all other
variables (except CASHFLW), the two groups differ as expected. Firms
receiving a going-concern modified audit opinion are generally in
worse financial condition (that is, have lower values of EXCESS,
CURRENT, RESFND, and higher DEBT), expend less on programrelated activities, more likely to have FINDING, have higher reporting
lags, and are less likely to be audited by a Big 4 firm.
Untabulated results indicate that only two of the bivariate
correlations exceed 0.40 (the correlation between BIG4 and SIZE is
0.41, while that between EXCESS and CASHFLW is 0.50), suggesting
that multicollinearity is not a concern in this dataset. This finding is
validated by the variation inflation factors, none that exceed 1.53.
GOING-CONCERN MODIFIED AUDIT OPINIONS FOR NON-PROFIT ORGANIZATIONS
121
TABLE 1
Descriptive Statistics
Total Assets
($000)
SIZE
EXCESS
CURRENT
DEBT
CASHFLW
RESFND
EXPENSE
BIG4
FINDING
REPLAG
PREVIOUS
Standard Unqualified
Opinion (n = 3,420)
Median
Mean
s.d.
25
136
3
6.58
–0.05
2.85
0.75
0.01
0.11
0.87
0.12
0.13
195
0.00
0.66
0.09
3.00
0.48
0.09
0.22
0.10
0.33
0.34
123
0.00
6.50
–0.03
1.76
0.72
0.02
0.00
0.88
0.00
0.00
171
0.00
Going-Concern
Modified Opinion (n = 147)
Mean
s.d.
Median
10
26
2
6.26
–0.09
1.39
1.04
0.00
–0.02
0.85
0.08
0.38
260
0.39
0.74
0.11
0.98
0.49
0.17
0.21
0.10
0.26
0.48
146
0.49
6.27
–0.06
1.03
1.09
0.00
0.00
0.88
0.00
0.00
264
0.00
t / 2
1.40
5.67***
8.73***
3.90***
–7.01***
0.54
5.87***
2.34**
2.96*
62.47***
–6.15***
Note: This table provides descriptive statistics for 3,567 non-profits
partitioned by whether the non-profit received a standard unqualified
opinion or a going-concern modified opinion in 2001. *, **, *** = p 
0.10, 0.05, 0.01 (two-tailed), respectively.
Going-Concern Opinion Model
Table 2 provides the results from a logistic regression model, with
the audit opinion type as the dependent variable. Model 1 provides
the results from using the full sample of 3,567 firms. The overall
model is significant, with a Pseudo-R2 of = 0.35. Except for BIG4, all
other variables are statistically significant at conventional levels and
the coefficients have the expected signs.
Overall, the results indicate that going-concern modified audit
opinions are more likely for non-profits that (a) are in greater financial
stress, (b) are smaller, (c) expend less on program-related activities,
and (d) have more internal control related audit findings. The first two
points above are similar to the results for for-profit entities, while the
latter two relate to factors that are unique to non-profits.
One factor where the results differ between for-profits and nonprofits is auditor type. In the for-profit sector, research indicates a
significant difference between Big Four and other auditors in their
propensity to issue going-concern modified audit opinions (e.g., Behn,
122
VERMEER, RAGHUNANDAN & FORGIONE
TABLE 2
Logistic Regression Results
Prob (GC=1) = F(α + β1EXCESS + β2CURRENT + β3DEBT +
β4CASHFLW + β5RESFND + β6EXPENSE + β7SIZE
+ β8BIG4 + β9FINDING + β10REPLAG +
β11PREVIOUS)
Model 1
(n = 3,567)
Variable
Intercept
EXCESS
CURRENT
DEBT
CASHFLW
RESFND
EXPENSE
SIZE
BIG4
FINDING
REPLAG
PREVIOUS
Expectation
n/a
–
–
+
–
–
–
–
+
+
+
+
Model 2
p
Pseudo-R2

–1.50
–3.95***
–0.05**
0.58***
–1.24**
–0.46*
–1.08*
–0.37**
–0.25
1.08***
0.01**
4.70***
424.32
 0.001
35.0%
Model 2
(n = 3,491)

0.35
–3.56***
–0.06**
0.63***
–0.94*
–1.05**
–1.82**
–0.45***
–0.23
1.22***
0.01**
121.30
 0.001
15.0%
Note: This table presents the results from a logistic regression with the audit
opinion in 2001 as the dependent variable. Model 1 includes all nonprofits that received a going-concern modified opinion in 2001. Model 2
excludes non-profits that received a going-concern modified opinion in
2000 (58 received a going-concern modified opinion in 2001 and 2000
and eighteen received a going-concern modified opinion in 2000 and a
clean opinion in 2001). The variables are defined in Table 1. P-values
are one-tailed if direction is predicted, otherwise two-tailed.
Kaplan, & Krumwiede, 2001; DeFond, Raghunandan, &
Subramanyam, 2002; Weber & Willenborg, 2003; Geiger & Rama,
2006). Unlike the for-profit sector, our results suggest that the
presence of a Big Four audit firm does not impact the going-concern
decision in the non-profit sector. One notable difference between the
two sectors is that only 12 percent of the non-profits in our sample
are audited by the Big Four. We examine other measures of auditor
GOING-CONCERN MODIFIED AUDIT OPINIONS FOR NON-PROFIT ORGANIZATIONS
123
expertise in our additional analysis to determine whether these
measures impact an auditor’s going-concern reporting decisions.
As noted earlier, prior research suggests that initial year goingconcern modified audit opinions are different from repeat goingconcern modified audit opinions. Hence, we exclude firms that had
received a going-concern modified audit opinion in 2000 and re-run
the model but without the PREVIOUS variable. As seen in the last two
columns of Table 2, the sign and significance of the variables in the
two models are quite similar. As expected, the explanatory power of
model 2 is lower but once again all variables except BIG4 are
significant and the coefficients have the expected signs.
Additional Analysis: Expertise
Prior research has adopted different measures to examine
auditor expertise. In our primary results, we examine whether there
are differences between auditor reporting decisions of Big Four/NonBig Four firms. Since Big Four firms audit only 12% of the non-profits
in our sample, we examine whether the reporting decisions of Big
Four and National firms (RSM McGladrey, Grant Thornton, and BDO
Seidman) are different than the other firms.6 This variable is not
significant in the model and does not impact the results reported in
the paper.
In a study of auditor choice in the non-profit sector, Tate (2007)
defines auditor expertise as an auditor that audits 5% or more of the
observations within a non-profit sector or the auditor auditing the
most observations within that sector if no auditor audits at least 5%.
Using the National Taxonomy of Exempt Entities (NTEE-CC) developed
by the NCCS, we determine the expert audit firms for the 10 major
categories of the NTEE-CC.7 For our 2001 dataset, this approach
provided no additional information because only Big Four firms
qualified as expert audit firms. This expert variable was not significant
and did not impact the results reported in the paper.
Subsequent Resolution of Going-Concern Uncertainty
Table 3 provides the details about the subsequent status of firms
that received a going-concern modified audit opinion for 2001. Of the
147 non-profits that received a going-concern modified opinion in
2001, 30 (20%) dissolved and the remaining 117 (80%) continued in
existence. Of the 30 that dissolved, the following explanations were
124
VERMEER, RAGHUNANDAN & FORGIONE
provided in the final Form 990: no explanation (n = 15), transferred
assets to other non-profits (n = 7), merged with another non-profit (n
= 3), debtor in possession/receivership (n = 3), transferred assets to
a state agency (n = 1), and remaining assets distributed to attorney
and accountant to wind down organization’s affairs (n = 1).8
TABLE 3
Resolution of Going-Concern Status
Panel A: Status of non-profits that received a going-concern modified
opinion in 2001
First Time
GC Opinion
GC Opinion
2001
2001
Status
Continued in existence
117 (80%)
65 (73%)
Dissolved:
No explanation provided
15
12
Transferred assets to another NP
7
7
Merged with another NP
3
3
Debtor in possession / receivership
3
0
Transferred assets to State agency
1
1
Remaining assets distrib. to attorney &
1
1
accountant to dissolve
Total dissolved
30 (20%)
24 (27%)
Total
147 (100%) 89 (100%)
Panel B: Comparison of non-profits receiving a going-concern modified
opinion in 2001 that continued in existence versus dissolved
Variable
EXCESS
CURRENT
DEBT
CASHFLW
RESFND
EXPENSE
SIZE
BIG4
FINDING
REPLAG
PREVIOUS
Continued
(n = 117)
–0.08
1.31
1.07
0.01
–0.02
0.85
6.33
0.09
0.38
262
0.44
GC 2001
Dissolved
(n = 30)
–0.13
1.68
0.93
–0.07
–0.03
0.86
5.95
0.00
0.33
251
0.20
t / 2
2.36**
–1.73*
1.38
2.21**
0.26
–0.39
2.61***
1.75*
0.43
0.38
Continued
(n = 65)
–0.09
1.25
1.03
0.00
–0.01
0.85
6.29
0.08
0.46
258
0.00
First Time
GC 2001
Dissolved
(n = 24)
–0.13
1.74
0.86
–0.09
–0.04
0.85
5.84
0.00
0.33
229
0.00
t / 2
1.20
–2.04**
1.49
2.08**
0.54
–0.47
2.76***
1.40
1.08
0.88
Note: The variables are defined in Table 1. *, **, *** = p  0.10, 0.05, 0.01 (twotailed), respectively.
GOING-CONCERN MODIFIED AUDIT OPINIONS FOR NON-PROFIT ORGANIZATIONS
125
A slightly higher proportion (27%) of the 89 non-profits that received
a first-time going-concern modified opinion in 2001 later dissolved;
the only other difference between the full and initial year modified
opinion groups is that three more firms in the former group provided
no explanation, and none of the initial year opinion firms had debtorin-possession.
We examine if the non-profits that subsequently dissolved after
receiving a going-concern modified audit opinion are different from
others that did not. Panel B of table 3 compares the two sets of firms.
Column four presents t-tests and chi-squared tests for difference
between non-profits with a going-concern modified opinion in 2001
that continued in existence and dissolved. Column seven presents ttests and chi-squared tests for difference between non-profits with a
first-time going-concern modified opinion in 2001 that continued in
existence and dissolved. The data indicate that non-profits that are
smaller, have lower excess and lower cash flows from operations are
more likely to dissolve. Also of note, the Big 4 audited none of the 30
non-profits that subsequently dissolved; this may be an indication
that Big Four firms are less likely to be associated with non-profits
that are in extreme stress.
We also ran a model using the sample of initial going-concern
recipients, with subsequent status (dissolved or not) as the
dependent variable. We use the same independent variables as in
our going-concern modified audit opinion model discussed earlier.
The overall model is significant (p  0.01, Pseudo-R2 = 0.21). The
following variables are negative and significant at conventional levels:
DEBT,9 CASHFLW, RESFND, and SIZE. When we run the same model
with all 147 going-concern recipients, the same variables are
significant (and negative) but the model’s Pseudo-R2 goes down to
0.11.
Subsequent Audit Opinions
As noted earlier, our sample includes 89 non-profits that
received a first-time going-concern modified audit opinion in
2001, and 24 of the 89 entities dissolved subsequently. We
investigate the resolution of the going-concern uncertainty
further by examining the subsequent years’ audit opinions for
126
VERMEER, RAGHUNANDAN & FORGIONE
the 65 non-profits that received a first time going-concern
modified opinion in 2001 and continued in existence.
Panel A of Table 4 provides the audit opinions for the entities for
an additional four subsequent years. As seen in Panel A of Table 4,
we were able to obtain data for 53 of the remaining 65 firms for
2002. Twelve of the firms were no longer in the FAC10 of the
remaining 53 non-profits, 29 continued to have a going-concern
modified audit opinion in 2002 while 24 received a standard
unmodified audit opinion in 2002. Thus, a substantial proportion of
the non-profits that did not dissolve were able to take enough
corrective action to satisfy their external auditors that a going-concern
modified audit opinion was no longer necessary.
In panel B of Table 4, we examine differences between the 53
non-profits that (a) received an initial going-concern modified opinion
in 2001, (b) continued in existence for at least the four subsequent
years, and (c) had subsequent audit opinions available in the FAC for
2002. We partition the 53 non-profits based on whether the entity
received a standard unqualified opinion or a going-concern modified
audit opinion in 2002. The data show that entities that had
improvements in four variables (higher values for EXCESS, CURRENT
and CASHFLW, and lower values for DEBT) are associated with the
subsequent receipt of a standard unqualified opinion.
TABLE 4
Subsequent Audit Opinions
Panel A: Audit opinions in four years after initial going-concern modified
opinion*
Type of Opinion
2002 2003 2004 2005
Not in Federal Audit Clearinghouse
12
17
23
29
Subsequent opinion data available
53
48
42
36
Type of Subsequent Opinion:
Standard unqualified opinion
24
36
36
29
(45%) (75%) (86%) (83%)
Going-concern modified opinion
29
12
6
7
(55%) (25%) (14%) (17%)
Note: * presents the subsequent years’ audit opinions for 65 non-profits
that had a first time going-concern modified opinion in 2001 and
continued in existence for at least the next four years.
GOING-CONCERN MODIFIED AUDIT OPINIONS FOR NON-PROFIT ORGANIZATIONS
127
TABLE 4 (Continued)
Panel B: Comparison of surviving non-profits based on subsequent year’s
audit opinion
2001 Data for
Standard
Going-Concern
all 53 non-profits Unqualified
Modified Audit
Opinion in
Opinion in 2002
Variables
with data in
t / 2
2002
(n = 29)
later years
(n = 24)
EXCESS
–0.09
0.01
–0.04
1.93*
CURRENT
1.31
2.32
1.25
2.60**
DEBT
1.01
0.83
1.11
–1.94*
CASHFLW
–0.01
–0.03
–0.20
1.98*
RESFND
–0.02
–0.02
–0.02
–0.03
EXPENSE
0.86
0.84
0.86
–0.57
SIZE
6.31
6.14
6.31
–0.77
BIG4
0.06
0.04
0.04
0.46
FINDING
0.41
0.42
0.43
–0.29
Note: The variables are defined in Table 1. *, **, *** = p  0.10, 0.05, 0.01
(two-tailed), respectively.
Panel B of Table 4 presents the mean values of variables for 53
non-profits that (a) received a first time going-concern modified
opinion in 2001, (b) continued in existence for at least the four
subsequent years, and (c) had subsequent audit opinions available in
the Federal Audit Clearinghouse. We partition the 53 non-profits
based on whether the entity received a standard unqualified opinion
or a going-concern modified audit opinion in 2002. Column two
presents mean values for 2001 for the 53 non-profits. Column three
presents mean values for 2002 for 24 non-profits that received a first
time going-concern modified opinion in 2001 and a standard
unqualified opinion in 2002. Column four presents mean values for
2002 for 29 non-profits that received a first time going-concern
modified opinion in 2001 and a going- concern modified opinion in
2002. Column five presents t-tests and chi-squared tests for
difference between non-profits with a going-concern modified opinion
in 2002 and a standard unqualified opinion in 2002.
128
VERMEER, RAGHUNANDAN & FORGIONE
SUMMARY AND CONCLUSIONS
Non-profit organizations constitute an important segment of the
U.S. economy, and legislators and the public have become concerned
about auditing and governance issues related to non-profits in the
aftermath of some well-publicized failures in recent years. There is a
long tradition of research related to going-concern modified audit
opinions for firms in financial stress in the for-profit sector; in
contrast, there is no published research related to going-concern
modified audit opinions for non-profits. In this paper, we fill this void
in the literature and examine audit opinions and the subsequent
resolutions of non-profits facing uncertainties related to goingconcern.
Our sample includes 3,567 non-profits that were in some
financial stress during 2001. We find that only 147 of such entities
received a going-concern modified audit opinion. Our audit opinion
model indicates that firms are more likely to receive a going-concern
modified audit opinion if they are smaller, are in worse financial
condition, expend less on program-related activities, or have more
internal control related audit findings. The results also indicate that
there is no association between audit firm type (Big Four or not) and
the type of audit opinion. Our findings can serve as a useful
benchmark for both non-profits and their auditors, in the context of
negotiations related to going-concern modified audit opinions.
We also examine the subsequent resolution of the going-concern
uncertainty for entities that received a going-concern modified audit
opinion in 2001. Based on examination of tax documents filed for the
next four years, we find that 30 (20%) of the non-profits that received
a going-concern modified audit opinion in 2001 dissolved at some
time during the next four years. Further, the data also indicate nearly
half of the non-profits that received a going-concern modified audit
opinion—but did not subsequently dissolve—got a clean audit opinion
in 2002. These findings indicate that, as in the for-profit sector, there
is a high likelihood of Type I errors associated with a going-concern
modified audit opinion.
What explains the relatively high proportion of Type I errors (i.e.,
non-profits that received a going-concern modified opinion but do not
subsequently dissolve) in our sample? In the for-profit sector, the
relatively high percent of Type I errors can be explained by the costly
consequences (including, but not limited to, costs associated with
GOING-CONCERN MODIFIED AUDIT OPINIONS FOR NON-PROFIT ORGANIZATIONS
129
litigation from investors) of failing to issue modified audit reports for
firms that subsequently fail.11 But such an explanation is inadequate
in the non-profit sector, given that there are no “investors” in nonprofits. One explanation is that auditors still have to contend with
possible lawsuits from other financial statement users, such as
creditors. A second explanation is that auditors have to contend with
punitive actions from other regulators, such as state attorney
generals, state accountancy boards or the AICPA; however, such
action by state regulators or voluntary organizations such as the
AICPA are infrequent and not very punitive (GAO, 2007). Another
conjecture is that auditors are conservative in their opinion decisions
because of other pressures, such as adverse media stories.
Many prior studies have examined the usefulness of goingconcern modified audit opinions in predicting bankruptcy in the forprofit sector. However, as of now, there is no centralized database of
non-profit dissolutions. Given recent advances in the availability of
data related to non-profits, it is possible that such a database may
become available in the future; one interesting avenue for further
research is to examine the proportion of Type II audit reporting errors
(dissolutions without a prior going-concern modified audit opinion)
and factors associated with such “errors.”
NOTES
1.
BFA is a non-profit agency of the Arizona Southern Baptist
Convention. In 1984, BFA began to concentrate on selling IRAtype retirement investment plans to church members and
invested these funds in the Arizona real estate market. In 1989,
the Arizona real estate market declined substantially. Instead of
reflecting these losses in its financial statements, BFA set up
over 90 insider-controlled corporations and sold properties to
these companies at the properties’ book values rather than the
significantly lower current values. When BFA failed in 1999, BFA
owed investors over $570 million (CBS 2002; Mohrweis 2003).
2.
Non-profits must file a Form 990 by the 15th day of the 5th
month after the accounting period ends. A non-profit can request
an automatic 3-month extension and can apply for an additional
3-month extension. Thus, the possible time to file is 11½
months; in addition, it takes time for the IRS and NCCS to
process the return, scan the return and place it on-line. We
130
VERMEER, RAGHUNANDAN & FORGIONE
checked the status of each non-profit as of February 2008.
Because of delays related to the filing and processing of IRS
Forms 990/990EZ, we could only be certain of the subsequent
status of the non-profits for four years (i.e., up to fiscal year
ending as of December 31, 2005).
3.
We initially requested from NCCS the names and related
financial data for all non-profits with either a deficit in total net
assets or a deficit for the current year. NCCS requested that we
refine our search because of the number of observations. Since
the FAC only includes data on non-profits that expended
$300,000 or more in a year of federal awards, we included the
government grants criteria to refine our search.
4.
Eighteen of the 3,420 clean opinion observations had a goingconcern modified opinion in 2000. Deleting these 18 firms does
not substantively change any of the regression model results, or
the significance of the individual variables.
5.
These reportable conditions, material weaknesses, noncompliance or questioned costs could also impact a non-profit’s
likelihood of receiving future funding which could impact an
auditor’s propensity to issue a going concern modified opinion.
6.
Big Four and National Firms audited 16% of the observations in
our dataset.
7.
The ten major categories of the NTEE-CC are arts, education,
environment and animals, health care, human services,
international, religion-related, mutual benefit, public and societal
benefit (other), and unknown or unclassified.
8.
Examples of explanations provided include: “During fiscal year
2003, Concord Family & Youth Services (CFYS) merged with
Justice Resource Institute (JRI) (FEIN# 04-2526357). JRI is the
surviving corporation. As part of a merger agreement, JRI
received all of the assets of CFYS and assumed all the liabilities
as of 6/30/03.” “Central East Austin Community Organization,
Inc. (CEACO) lost significant funding in 2001. After struggling for
over a year, the CEACO Board of Directors voted to dissolve the
agency on 12/19/02. CEACO ended its programs on 1/15/03,
closed its doors on 1/31/03 and distributed its assets to other
non-profits on February 7, 2003. The date of official dissolution
was March 31, 2003.”
GOING-CONCERN MODIFIED AUDIT OPINIONS FOR NON-PROFIT ORGANIZATIONS
9.
131
The sign on the DEBT variable was negative, indicating that
entities with more debt are less likely to dissolve. Initially, this
seems surprising; however, if a non-profit has debt then there
may be a motivation for the creditors to keep the non-profit in
operation, especially if the collateral on the loan is less than the
loan value.
10. OMB (1997) circular A-133 requires that non-federal entities
that receive $300,000 or more in one year in federal awards
($500,000 for fiscal years after December 31, 2005) have a
single audit or a program specific audit. The results of these
audits are available in the Federal Audit Clearinghouse. The nonprofits that are not included in the Clearinghouse did file a Form
990 with the Internal Revenue Service (Non-profits must file with
the IRS if revenues exceed $25,000). Thus, these non-profits are
not included in the FAC after 2001 because their federal awards
dropped below the minimum amount required for inclusion in
the FAC.
11. As noted by researchers in the for-profit sector, under extant
audit reporting standards, not issuing a going-concern modified
audit opinion for an entity that subsequently enters bankruptcy
(or, liquidation) is not strictly an error. Nevertheless, as noted by
McKeown, Mutchler, & Hopwood (1991b) many financial
statement users consider the absence of such a modified audit
report to be an audit failure; hence, our use of the term
“reporting error.”
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SPRING 2013
AUDITED FINANCIAL STATEMENTS
IN THE US FEDERAL GOVERNMENT: THE QUESTION OF POLICY AND
MANAGEMENT UTILITY
Douglas A. Brook*
ABSTRACT. The Chief Financial Officers Act and subsequent legislation
require federal agencies to produce corporate-style financial statements.
Arguments for financial statements drew on private sector analogies and
suggested policy makers and managers would use the information to make
better public policy and management decisions and improve accountability
for financial management and program performance. Nearly all major
government agencies have unqualified audit opinions and improvements in
financial management are claimed. But benefits for policy making and
management are not yet well understood. This paper examines the question
by comparison with the private sector and by examining what agencies say
about the uses and users of financial statement information. The emerging
challenge in the evolution of federal financial reporting is to develop better
government-specific analytical tools and other financial information for policy
makers and managers.
INTRODUCTION
A major provision of the Chief Financial Officers Act of 1990
created a pilot program for selected agencies to produce corporatestyle financial statements and subject them to independent audit.
The Government Management Reform Act of 1994 expanded this
financial statement requirement to major federal agencies, and
mandated a consolidated financial statement for the U.S.
Government. Later, the Accountability of Tax Dollars Act of 2002
expanded the financial statement requirement to all federal entities
--------------------------

Douglas A. Brook, Ph. D., is Professor of Public Policy and Director of the
Center for Defense Management Research at the Naval Postgraduate
School. His research and teaching interests are in public budgeting and
financial management.
Copyright © 2013 by PrAcademics Press
136
BROOK
with budget authority in excess of $25 million. Arguments made for
corporate-style financial statements for the federal government drew
heavily on private sector analogies and the idea that better
management would result. For instance, the Grace Commission’s
Federal Management System Task Force asserted, “As in the private
sector, it is the responsibility of central government, essentially the
government’s corporate headquarters, to see that more is achieved
from the combined efforts of the departments than they are able to
achieve independently […]. It requires better information” (President’s
Private Sector Survey on Cost Control, 1983, p. 71). The Grace
Commission report served as a precursor for the arguments that led
eventually to adoption of the CFO Act (Haller, 1983).
The 20th anniversary of the CFO Act has recently passed. All
departments and agencies are producing financial statements and
21 of the 24 major government agencies have achieved unqualified
audit opinions, 2 have qualified opinions, and only one, the
Department of Defense, remains disclaimed (Executive Office of the
President, 2011). Previous research has explored the outcomes from
the requirement to produce financial statements and has assessed
the value derived from subjecting the statements to independent
audit. The main results have been improved financial management
as demonstrated by better internal controls and modernized
integrated financial systems. Agencies that have achieved unqualified
audit opinions report reputational benefits on matters of
accountability and stewardship (Brook, 2010). Value is derived from
the audit requirement itself as audits provide evidence of good
financial management, force improvement in internal controls and
financial management systems and processes, and reduce
information risk by providing assurances about underlying financial
information. (Brook 2011). The CFO Council (2011) found that
implementation of the CFO act “has increased transparency, fostered
accountability, established a government-wide financial management
leadership structure, promoted new accounting and reporting
standards, generated auditable financial statements, strengthened
internal controls, improved financial management systems and
enhanced performance information” (p. 1).
While advances have clearly been made inside the financial
management domain the questions of who uses the financial
statements and for what purposes remain to be examined.
AUDITED FINANCIAL STATEMENTS IN THE US FEDERAL GOVERNMENT
137
Arguments for audited financial statements asserted that citizens,
policy makers and managers would use the resulting information to
make better public policy and public management decisions and to
assure better accountability for the use of financial resources and the
performance of government programs. One of the purposes stated in
the CFO Act itself was to “Provide for the production of complete,
reliable, timely and consistent information for use by the executive
branch of the Government and the Congress in the financing,
management and evaluation of Federal programs” (Section 102(b)).
Then-Comptroller General David Walker argued, “Legislators,
government officials, and the public want to know whether
government services are being provided efficiently, effectively,
economically, and in compliance with laws and regulations. They also
want to know whether government programs are achieving their
objectives and desired outcomes, and at what cost” (U.S. Government
Accountability Office, 2003). The goals of the CFO Act and the
Government Management Reform Act were “to create reliable,
relevant financial and performance information for sound
management decisions about programs, budgets and fiscal
stewardship, all of which should lead to higher performance
(Amos, et.al., 1997, p. 28). The GAO (1991) asserted, “Several
of the CFO Act’s requirements aim to improve the financial
information available to agency managers, the Congress and
others.” This includes “accounting and financial systems which
report cost information, [...] integration of accounting and
budget information, [...] and the systematic measurement of
performance” (p. 14).
At the beginning, Jones and McCaffery (1993) asserted, “[...]
notwithstanding the experience of a few agencies with audited
financial statements, their practical utility has not yet been proven [...]
(p.71). This paper re-examines this issue by exploring the question of
benefits for policy making, management and ultimately for
accountability.
We explore the question of policy and management utility of
audited financial statements. To the extent that the public sector
analogy is valid, perhaps value can be extracted from government
financial statements using private sector analytical techniques. To the
extent that individual agencies have found way to use this new
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financial information these best practices need to be identified and
shared. And, to the extent that users and uses within the government
are not found new ways of thinking about reporting financial
information and extracting the value of government financial
reporting may be required.
METHODOLOGY
To determine the extent to which audited financial statements are
supporting the goals of policy making, management and
accountability, we first consider the private sector analogy by
identifying users and users and considering whether the analytical
techniques applied to private sector financial statements are useful
in a governmental context. Next we examined the FY 2011
performance and accountability and financial statement reports of
the twenty-four major federal reporting agencies to see what they selfreported about the use and users of financial statement information.
Then we conducted a small number of in-depth telephone and e-mail
interviews with agency CFOs and deputy CFOs and with experts
outside of the agencies to determine their views on the issue. In
analyzing the current and potential uses and users of financial
information, we found helpful the hierarchy of financial needs
suggested by David (2002):
Budget Information- Perhaps the most important financial
information for any federal official is the amount of money
available for obligation or expenditure for his or her program,
service or organization. Knowing the budget is the foundation
of the hierarchy.
Status of Funds- The next layer of the hierarchy, and the
emphasis of most federal financial efforts today, is knowing
how much of the budgeted funds are available for obligation
(or expenditure) at any time. This is typically known as the
status of funds or budget execution and consumes significant
attention and effort from federal policy, program and
operating officials.
Financial Information- The next layer in the hierarchy is the
financial information needed for day-to-day management,
monitoring and decision-making, such as information on
individual accounts, assets, liabilities, etc. The financial
AUDITED FINANCIAL STATEMENTS IN THE US FEDERAL GOVERNMENT
139
information needs of the policy, program and operating
official must be satisfied at the same time the information is
gathered for financial statement purposes.
Cost Management Information- Federal managers recognize
the value of cost information—details on how federal entities
use resources to manage their organizations, programs,
projects and services—and are demanding such information.
Cost management represents a new way of thinking being
embraced by many federal officials.
Cost/Performance Information- This is the top of the
hierarchy, the intersection of cost with performance
information. The relationship of program results with the cost
of providing those results will be increasingly important in
future policy, program and operating decision-making (David,
2002, p. 12).
FINANCIAL REPORTING
Three types of accounting are generally found in the federal
government; budgetary, managerial, and financial. Budgetary
accounting, perhaps the most common in government, accounts for
the budgeting, receipt, obligation, and expenditure of annual
appropriations. Managerial accounting provides current information—
cost accounting data, for instance—to support managerial and policy
decision making. Financial accounting, sometimes called proprietary
accounting, provides historical information on the financial condition
of the organization in accordance with certain accounting and
auditing principles (Candreva, 2004). Of these three types of
accounting, proprietary accounting and its associated financial
statements represent the newest approach to reporting financial
information in the federal government. The GASB explained that “The
idea was […] [to] bring the benefits of accrual accounting—full cost of
service information and consolidated financial statements to
government” (Government Accounting Standards Board, 2006, p.
30).
FINANCIAL STATEMENTS
Federal financial statements look very much like private sector
models. Federal government financial statements contain four
required reports, somewhat similar to the commercial sector:
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- Balance Sheet provides a snapshot of the department’s financial
position at the end of the fiscal year, depicting assets, liabilities,
and net position. Note that the accounting equation for the
balance sheet differs between commercial and governmental
balance sheets. The standard accounting equation for business
entities is:
Assets = Liabilities + Shareholder Equity
It is changed for the federal government to:
Assets = Liabilities + Net Position
- Statement of Net Cost depicts the gross costs of operations for
the period, minus any exchange revenues earned from its
activities.
- Statement of Changes in Net Position presents the sum of
operations since inception, plus unexpended appropriations at
the end of the period.
- Statement of Budgetary Resources reports the use and
availability of budgetary resources at the end of the period (U.S.
Department of Defense, 2009).
The balance sheet mirrors the balance sheet in the commercial
sector with “net position” replacing “shareholder equity” as the
balancing entry. The balance sheet provides a financial snapshot of
the enterprise. The statement of net cost provides a financial
summary of operations for the reporting period. The Statement of
Changes in Net Position and the Statement of Budgetary Resources
are particularly governmental. Changes in net position reflect how net
costs are financed through retained assets and available
appropriations. The Statement of Budgetary Resources reports the
source, use, and balances in budgetary resources, and is the only
statement in the financial reports that is based on budgetary (cashbasis) accounting.
TESTING THE PRIVATE SECTOR ANALOGY: USES AND USERS OF FINANCIAL
STATEMENTS
How is financial information from annual financial statements
used, and by whom? In the business sector, users of audited financial
AUDITED FINANCIAL STATEMENTS IN THE US FEDERAL GOVERNMENT
141
statements are largely external to the firm. The principal uses and
users in the business sector can be grouped as follows:
- For investment information. Existing and potential investors and
lenders, analysts, and rating agencies may use the information to
assess financial condition and make decisions about financial
participation.
- For business decisions. Existing and potential suppliers,
customers, venture partners, competitors, and labor unions may
use financial reports to assess the risks in doing business with
the firm, to identify opportunities, and to benchmark their own
performance.
- To evaluate management. Boards of directors, investors, and
management itself may use audited financial reports as a tool to
assess management performance and to compare performance
with peer organizations.
- To manage financial issues. Boards of directors and management
may use financial reports to identify issues of risk and
opportunities based upon significant year-to-year changes, long
term liabilities or cash position, or current annual payments on
long-term debt, for instance.
- To identify policy issues. Policy analysts and advocates,
politicians, and media interested in issues associated with the
firm may use financial statements for information related to
executive compensation, in , profit levels, etc. (Wang, 2010).
Use of financial statements by internal management in the
business sector is secondary to use by external entities. Financial
statements are generally not a primary source of information for
operational management. Nevertheless, information in the financial
statements is relevant for management. Assets, for instance, are
resources with the potential for providing future financial benefits by
generating future cash inflows or by reducing future cash outflows.
Liabilities, on the other hand, are constraints on future activity that
represent obligations to make future payments of some type, largely
for benefits received in the past. Shareholders’ equity is a residual
claim that the owners have on assets not required to meet
obligations or claims by creditors. The statement of cash flow
indicates the ability of the firm to function smoothly and effectively by
meeting its required short-term and long-term payment obligations.
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Given that the majority of the users of audited financial
statements in the commercial sector are external to the enterprise,
what list of analogous potential uses and users can be identified for
audited financial statements in federal agencies? For instance:
- Investment Decisions. Is it possible to think that Congress, the
Office of Management and Budget (OMB), or taxpayers at large
could make use of financial statements to make decisions about
putting investments, in the form of additional appropriations, into
a particular department or agency based on its audited financial
report? What kind of budgetary and appropriations decisions
would be influenced by audited financial reports? Can Congress
be considered analogous to a board of directors, the OMB as
investors, agency leaders as managers, and the media, taxpayers,
and advocacy groups as external users?
- Assessment Decisions. Could Congress, the OMB, and agency
leaders assess the performance of agency programs and
management using the information in audited financial reports?
How do audited financial statements reflect agency performance?
- Identifying Financial Issues. Can Congress, the OMB, and agency
leaders use financial statements to identify issues such as longterm liabilities, stewardship over assets, and audit opinions as a
proxy for good financial management systems and overall
financial management? How can changes in net position be
interpreted?
- Policy Issues. Do external users such as the media, interest
groups, policy advocates, and internal policy makers in the
agency and elsewhere in the government use financial reports to
identify policy issues? What kinds of policy issues are reflected in
audited financial statements?
The CFO Council (2011, p. 15) argues “Financial and budgetrelated information needs to service multiple stakeholders, including
program managers; elected, appointed, and career officials in both
the legislative and executive branches; the public; and other entities
such as the media, private companies, and public interest groups.”
The OMB identified the four major users of federal financial
reports to be citizens, Congress, executives, and program managers
(Office of Management and Budget, 1993, para. 75). The GASB
identified citizens and their elected representatives, such as
AUDITED FINANCIAL STATEMENTS IN THE US FEDERAL GOVERNMENT
143
legislatures, other oversight organizations, and creditors, as the
primary beneficiaries of the information in governmental financial
reports (Government Accounting Standards Board, 2006, p. 5). The
GASB conceptual framework “places priority on the informational
needs of citizens and elected representatives, two constituencies not
identified as users of business enterprise financial statements by the
FASB” (Government Accounting Standards Board, 2006, p. 2). The
FASAB, in slightly different words, viewed the audiences for the
consolidated federal financial report as citizens, citizen
intermediaries, Congress, federal executives, and program managers
(Federal Accounting Standards Advisory Board, 1996).
IBM’s
government consultants saw the users of federal
financial
information as “senior leadership, program managers, financial
managers and analysts, and claimed that financial information is
used for stronger budget formulation and justification, improved cost
management, enhanced compliance … and more transparent feesetting” (IBM Business Consulting Services, 2005). The intended
users of federal financial information are both policy makers and
government managers. For both groups “GASB says, ‘…reporting
should provide information to assist users in (a) assessing
accountability and (b) making economic, social, and political
decisions.’ In other words, financial reporting is expected to support
the exercise of public accountability and also economic, social, and
political decision making” (Yamada, 2007, p. 5).
Users of government financial reports are interested in
stewardship of public resources, where resources are spent, the costs
of services and programs, and compliance. The GASB recognized
that “The needs of users of financial reports of government and
business enterprises differ […] Government accounting and financial
reporting standards aim to address the need for public
accountability...” (Government Accounting Standards Board, 2006, p.
1).
Perhaps the theoretical users of government financial information
make no use of the actual reports. An Association of Government
Accountants (AGA) survey reported that some interviewees “pointed
out that customers simply do not value, much less read, the
information in financial statements” (Association of Government
Accountants, 2005, p.18). Shortly after, another AGA survey found
that, “many financial professionals do not think the types of financial
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statements used by the private sector and required by the CFO Act
are right for the federal government” (Association of Government
Accountants, 2007, p. 10). But the GASB also argued that, “Creditors
of both [the public and private sectors] are interested in the ability to
repay debt but government creditors are focused on the ability to
raise taxes and the competing demands for resources rather than on
how earnings are generated” (Government Accounting Standards
Board, 2006, p. 2).
And, finally, financial information is seen as needed by program
managers for operational reasons:
With the emphasis on delivering results, program officials
must assume their role as financial managers and finance
officials must move into analysis, evaluation and value-added
activities. With the advent of new information systems,
transaction processing is becoming less important, and
information analysis more important. Both program and
finance officials must learn new skills and, in so doing,
improve delivery of programs and services to taxpayers and
customers” (David, 1997, p. 59).
Persistent claims are made that information associated with
financial statements are, or should be, used by managers and
policymakers: “…policy, program and operating officials (and their
staffs) require timely, accurate, reliable, consistent and useful
financial information for day-to-day operating, strategic, investment
and policy decisions and actions” (David, 2002, p.11). If such claims
have merit, who are turning out to be the users of financial statement
information and what are their uses?
TESTING THE PRIVATE SECTOR ANALOGY: FINANCIAL ANALYSIS
In the business sector, analysts use data in financial reports to
make certain evaluations of the firm, particularly by using ratio and
time series analysis. Drawing on experience at the state and local
government levels, Gauthier (2007) offers a set of purposes for public
sector financial information:
[…] the approach taken to analyzing their financial statements
must necessarily differ in important respects from the
approach taken in the private sector. Local governments offer
no equivalent to a business’s “bottom line.” Instead, users of
AUDITED FINANCIAL STATEMENTS IN THE US FEDERAL GOVERNMENT
145
local government financial statements typically approach a
government’s
finances
from
three
different
but
complementary perspectives: near-term financing situation,
financial position, and economic condition. These three
perspectives correspond to the questions: Will the
government be able to pay its bills on time? Is the
government’s financial health improving or deteriorating? And
is it likely that today’s financial position will improve or
deteriorate? Assessments of a local government’s near-term
financing situation tend to focus on the fund financial
statements. The government-wide financial statements
provide the most useful information for assessing financial
position […]. (p. 11).
Ratio Analysis
Private sector analysts rely heavily on calculation of various
mathematical ratios derived from information in financial statements.
Return on assets (ROA), for instance, measures the use of assets to
generate earnings independent of financing. Short-term liquidity risk,
the risk associated with whether assets will be available to meet
near-term liabilities, can be measured by the current ratio (current
assets/current liabilities) and by the quick ratio (cash + receivables +
marketable assets/current liabilities). Similarly, long-term solvency
can be assessed through debt-to-equity ratios (total liabilities/total
assets). Working capital, an indicator of the firm’s ability to meet
obligations and expand opportunities, is measured by deducting
current liabilities from current assets (current assets - current
liabilities). The debt-to-equity ratio (total liabilities/total shareholders’
equity) is an indicator of the prudent use of debt. Debt-to-equity ratios
in excess of 1.0, for instance, would mean that the debt exceeds the
total investment level of the owners. Return on equity (ROE) (net
income/average stockholder’s equity), is the return on the
shareholders’ value in the firm. It can be compared with the return
that might be available from investment choices.
For government, the current ratio, debt ratio, and debt-to-equity
ratio merit a closer look.
- Current ratio: (current assets/current liabilities) measures short
term liquidity by assessing the ability of the organization to meet
demands for cash as they arise.
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- Debt ratio: (total liabilities/total assets) indicates the ability of the
organization to meet its liabilities with assets, and measures the
proportion of assets financed by debt.
- Debt to equity: (total liabilities/stockholders equity or total
liabilities/net position) measures liabilities against the
organization’s equity base.
An example of these rations can be calculated for the Department
of Defense. Using data in the FY 2010 DoD financial statements
these ratios are calculated as:
Current ratio: ~$522b/$31b = 16.8
Debt ratio:
~$2.3t/$1.9t = 1.21
Debt to equity: ~$2.3t/-$.392t = -5.87
From this, it might be concluded that the DoD has more than
sufficient resources to meet daily demands, but that its longer term
financial health is less strong. A positive current ratio may be good
news for employees, vendors, or suppliers; there are resources
available to pay them. But a negative debt-to-equity ratio may be less
reassuring to investors (Congress, OMB, taxpayers), who might have
to share the burden of future liabilities. The DoD could not retire its
liabilities even if it devoted the entirety of its assets to that effort.
Instead, it will require additional appropriations in the future to meet
recognized liabilities. The financial report provides a glimpse of the
extent of this problem: only $441 billion or 21% of liabilities are
currently covered by budgetary resources. $1.6 trillion for liabilities
such as military retirement and health benefits and environmental
liabilities remains unfunded (U.S. Department of Defense, 2008, p.
9). A CFO at another agency similarly pointed out the visibility of longterm environmental liabilities on the balance sheet (personal
communication with author April 9, 2012). This is the type of
information that agency leadership, the OMB, and Congress, could
and perhaps should address through a long-term plan to fund these
liabilities.
Equity
In a commercial financial statement, one might look at
shareholders’ equity for useful information. Is there an analogous
analysis that can be applied to net position in government financial
statements?
Net position represents cumulative results from
AUDITED FINANCIAL STATEMENTS IN THE US FEDERAL GOVERNMENT
147
operations plus unexpended appropriations. The DoD financial
statements include a statement of changes in net position. The DoD
reports negative net positions: -$476,881.1 million in 2008 and $546,635.4 million in 2007. What accounts for the negative net
position? The DoD’s negative net position derives from the unfunded
liabilities in the Medicare-Eligible Retiree Health Fund and the Military
Retirement Fund. This is useful information for investors (Congress),
as investments (appropriations) into these funds would be required to
reduce the DoD’s unfunded liabilities and negative net position.
Should elimination of negative net position be a financial goal for the
DoD?
Considering both the question of uses and users and the question
of analytical techniques it is apparent that the private sector analogy
is of limited utility in the federal government. If that is the case, then
attention must shift within the government to determine more
government-appropriate uses and users.
GOVERNMENTAL USES AND USERS
The FY 2011 annual financial reports (AFR) of the twenty-four
major CFO Act agencies were examined to see what the agencies selfreported about the uses and users of their financial statements.
Many of the AFRs were silent on the issue. However, some did
address the issue, largely in general, non-specific terms. For instance,
the Agency for International Development reports “Preparing the
Agency’s financial statements creates the opportunity to improve
financial management and provide accurate, reliable information that
is useful for assessing performance and allocating resources” (AID
2011, p. 23). The Department of Education states “The Department
consistently produces accurate and timely financial information that
is used by management to inform decision-making and drive results
in key areas of operation” (Department of Education, 2011, p. 27).
The statements in the agency AFRs can be categorized by some
common terminology. The major terminological themes include:
informing management decision making, linkage to budgeting and
resource allocation, assessing performance, promoting management
accountability, and supporting other financial reports.
The
Department of Health and Human Services (2011) says, “The
production of accurate and reliable financial information is necessary
for making sound decisions, assessing performance, and allocating
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resources” (p. I-18). Some agencies cite the processes involved
in audited financial reporting as a key element. The Department
of Labor (2011), for instance reports, “With [its] emphasis on
internal controls accurate financial information delivery to key
decision makers, and transparent and accountable reporting, the
Department's stakeholders can be confident that resources are used
efficiently and effectively” (p.24). Other agencies make reference to
management reports based on financial information derived from the
financial statements or from the related processes that produce more
timely and accurate financial information. The Social Security
Administration (2011) explains, “the [financial] statements are in
addition to the financial reports used to monitor and control
budgetary resources, which are prepared from the same books and
records (p. 40-41). Similarly, USAID (2011) identifies such reports as
“[…] quarterly financial statements, financial statements at the
operating division or program level, budget execution reports, reports
used to monitor specific activities, and reports used to monitor
compliance with laws and regulations[…]” (p. 30).
Notwithstanding positive assertions about the uses and users of
federal agency financial statements, there is at best slim evidence
that the statements themselves are useful to policy makers and
managers. Danny Werfel, comptroller of the Office of Management
and Budget, has observed, “Neither the public nor the government
decision makers appear to be looking at our standard reports, such
as our balance sheets or our net operating costs” (Federal News
Radio 2010). Interview data indicate that agency CFOs and financial
managers also see only limited usefulness to the financial for the
financial statements. One respondent said “Financial statements are
not useful if you are trying to manage daily operations and are
interested in the current status of your funds. However, financial
statements can give you reference points for some high level
historical financial information” (personal communication with author
April 8, 2012).
But if a distinction is made between financial statements and
financial information, there is some evidence for the usefulness for
policy makers and managers. Agency CFOs report that they are
preparing a variety of internal financial reports for policy makers and
managers from data that underlie the financial statements. These
reports reflect financial information that has been made more timely
AUDITED FINANCIAL STATEMENTS IN THE US FEDERAL GOVERNMENT
149
and accurate because of the improved processes, systems and
internal controls required for the financial statements. These reports
include quarterly budget execution reviews, cost analyses, resource
utilization analyses, resource management dashboards, trend
analyses and other standard reports (personal communications with
author April 8-12, 2012).
Thus, it remains doubtful that the financial statements
themselves are used much by policy makers and managers. Similarly,
development of government-specific techniques for analyzing
financial statements for governmental users appears to remain
unaddressed. But the timely and accurate financial information
associated with the processes and practices of audited financial
reporting may have increasing usefulness as new standard reports
are developed for budgetary review, cost analysis, program evaluation
and other purposes.
LOOKING AHEAD: IMPROVING USEFULNESS
GAO (2007) has urged a reevaluation of the federal
financial reporting model to address these questions:
- What kind of information is most relevant and useful for a
sovereign nation?
- Do traditional financial statements convey information in a
transparent manner?
- What is the role of the balance sheet in the federal government
reporting model?
OMB’s comptroller has identified three objectives for federal financial
reporting:
- Transparency in the nature of the government’s finances
- Sustainability in the costs of operations
- Cost effectiveness of government programs (Werfel 2011)
To serve these goals three missions are identified;
- Transparency
- Internal controls
- Decision support (Werfel 2011)
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Financial statements themselves may not be as important to
some users as the underlying financial information that is derived
from systems and processes that produce timely and accurate data.
For instance “… [program managers] do not use annual financial
statements for decision making. Program officials do care, however,
about and need the information generated from the systems used to
prepare the annual financial statements. Program officials must have
reliable, consistent, useful, accurate and timely information from
those same systems and need to know that the information is in
compliance with applicable standards” (David 1997, p. 57).
Comparisons are being made to the reporting under the American
Recovery and Reinvestment Act of 2009 which displayed a new
speed and style of financial reporting, including the posting of
recipient information on the Recovery.gov website.
Two new
approaches are being suggested for the publication of federal
financial data: the “supermarket approach” whereby the government
publishes raw data and allows users to generate the reports they
need, and the “restaurant” approach” whereby the government
produces the reports that it thinks the public and users want or need
(AGA 2010).
AGA’s Relmond Van Daniker advocates for 4-page Citizen Centric
Reports which devote one page to each of the following topics:
statement of financial stability/statement of financial position, results
of operations, sustainability, and performance” (AGA 2010, p. 3).
And finally, OMB’s Werfel argues for a larger role for agency CFOs
beyond being mere compliance officers, “[…] what is the full vision of
a CFO in the future beyond just clean audits and financial
statements? It’s an individual […] who can identify the critical risks,
financial risks and the critical business goals that the agency has.
And […] turn around and implement a data strategy to inform on
those goals and risks” (IBM 2007, p. 1).
Returning then to David’s (2002) hierarchy of needs it appears
that some financial information associated with audited financial
statements is currently useful for meeting some of these needs, while
others seem less so but could be further developed:
- Budget Information. Some agencies are reporting links to
budgetary matters and resource allocation decisions
AUDITED FINANCIAL STATEMENTS IN THE US FEDERAL GOVERNMENT
151
- Status of Funds. The management of budget execution seems
less associated with financial statement information and
reporting but the Department of Defense has placed a high
priority on achieving auditability for its Statement of Budgetary
Resources because of its immediate relevance to budget
formulation and execution (Hale 2011).
- Financial Information. Assertions by agencies that other reports
are being generated from information used for financial
statements suggest the financial information needs of the policy,
program and operating official can be satisfied at the same time
the information is gathered for financial statement purposes.
- Cost Management Information. Some agencies appear to suggest
that they are able to extract details on how resources are used to
manage their organizations, programs, projects and services from
financial information associated with the financial statements.
- Cost/Performance Information. A few agencies indicate a
relationship between financial information and performance
evaluation.
An important concluding distinction must be made between the
usefulness of financial statements and the usefulness of financial
information associated with those statements. AGA recommends that
“Research is needed to determine what kind of financial information
is being sought by various groups (that is, the public, executive
branch officials, legislative bodies, the press, and bond rating
agencies)” (AGA 2010). The CFO Council (2011) has recommended
“Congress should consider directing OMB, GAO and the Federal
Accounting Standards Board […] to evolve the financial reporting
model by examining the entire process with an eye toward how to
further improve and streamline current reporting requirements and to
better meet the needs of all stakeholders” (p. 2). In the words of one
senior Pentagon financial manager, “I am more encouraged than ever
that we can take the balance sheet and make it highly informative to
leadership so long as we interpret the data and not make them read
the statements themselves” (e-mail to the author, April 22, 2010).
CONCLUSION
Twenty years after passage of the CFO Act, most agencies of the
federal government have been able to produce annual financial
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statements and achieve and sustain unqualified audit opinions. There
is general recognition that the requirement to produce audited
financial statements has driven improvements in financial
management systems and processes and enhanced the quality and
timeliness of financial information. Clean audit opinions serve as
effective proxies for good financial management and, therefore,
contribute to the overall goal of financial accountability. (Brook,
2010). There is also some evidence that audited financial statements
have helped to reduce inefficiencies and improve mission-related
purchasing power per appropriated dollar (U.S. Marine Corps, 2008).
However making good and proper use of financial information
associated with financial statements remains a challenge. Even the
OMB seems now to recognize a less-than-dominant role for federal
financial reporting. “Financial reporting is not the only source of
information to support decision-making and accountability. Neither
can financial reporting, by itself, ensure that the government operates
as it should. “Financial reporting can, however, make a useful
contribution toward those objectives” (Office of Management and
Budget, 1993, para. 107).
This paper has examined the question of the utility of federal
financial statements. While federal agencies have successfully
developed the means to produce proprietary financial statements and
receive, in most cases, unqualified audit opinions, the goal of using
this information for policy making and management remains to be
fully achieved. Our analysis suggests that the emerging challenge in
the evolution of financial reporting in the federal government is to
develop government-specific analytical tools and identify special
reports and other information for policy makers and managers that
can be drawn from the underlying data supporting the financial
statements. Ultimately, the goals of accountability and stewardship
predominate. Through audited financial statements, “Agencies assure
Congress and the public that assets are being safeguarded, financial
results are reported accurately and timely, and performance is
measured accurately” (CFO Council, 2007, p.6).
ACKNOWLEDGEMENT
The author gratefully acknowledges the research support
provided by Sarah Martin, MBA, and LCDR Rex Aman, research
assistant and MBA student, respectively, at the Naval Postgraduate
AUDITED FINANCIAL STATEMENTS IN THE US FEDERAL GOVERNMENT
153
School. Thanks also for the technical assistance from my colleagues
in the NPS accounting faculty: Chong Wang, and Danny Matthews.
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J. OF PUBLIC BUDGETING, ACCOUNTING & FINANCIAL MANAGEMENT, 25 (1), 158-234
SPRING 2013
THE IMPACT OF THE GREAT RECESSION ON THE FINANCIAL
MANAGEMENT PRACTICES OF STATE AND LOCAL GOVERNMENTS:
PART I
Symposium Editor: Martin J. Luby
Copyright © 2013 by PrAcademics Press
J. OF PUBLIC BUDGETING, ACCOUNTING & FINANCIAL MANAGEMENT, 25 (1), 159-164
SPRING 2013
SYMPOSIUM INTRODUCTION
Martin J. Luby*
There is no doubt the Great Recession has inflicted severe
financial pain on private sector firms, individuals, non-profits and all
levels of government. It has also provided substantial “grist for the
mill” for public finance and public administration researchers who
have tried to determine the causes and consequences of the recent
financial crisis as well as develop potential preventive remedies to
mitigate the effect of such a financial event in the future. Specifically,
the public administration academic community in the United States
has produced several special journal issues and symposiums devoted
to this topic. For example, the Municipal Finance Journal has
devoted two special issues to the financial crisis’ impact on the
municipal bond market and on large municipalities’ budgets (see vol.
29 no. 4 and vol. 32 no. 1), Public Budgeting & Finance ran an entire
issue on the impact of the Great Recession on state budgets (see vol.
30 no. 1) while Public Administration Review published a symposium
examining issues related to financial market regulatory reform in light
of the global financial crisis (see vol. 69 no. 4). This journal, the
Journal of Public Budgeting, Accounting and Financial Management,
recently published a symposium entitled “Beyond the Fiscal Storm:
Surmounting Challenges of the New Public Finance” which details
new approaches governments will have to employ to deal with the
substantial fiscal challenges caused partly by pre-Great Recession
public financial management practices.
This symposium fits into this larger body of research but is
distinctive in that it focuses mostly on the public financial
-------------------------* Martin J. Luby, Ph.D., is an Assistant Professor, School of Public Service,
DePaul University. His teaching and research interests are in state and local
government capital markets, public financial management, and public
management.
Copyright © 2013 by PrAcademics Press
160
LUBY
management practices of state and local governments rather than on
subnational public revenue and public budgeting topics. These
practices include issues related to financial condition analysis,
financial statement disclosure, debt management, risk management,
and pension funding. These are areas of study that have generally
been overlooked by the academic community in its scholarly research
into the Great Recession but have had significant impact on
subnational finances drawing heavy media attention as evidenced by
the substantial press coverage of the recent bankruptcy filing by
Jefferson County, Alabama. Thus, the symposium fits nicely into the
aforementioned Journal of Public Budgeting, Accounting and
Financial Management symposium in that much of the symposium’s
descriptive analysis shows the need for such a “new public finance”
while offering some policy suggestions that could populate this
emerging public finance paradigm.
The symposium aims for a “story arc” of sorts in approaching the
study topic. That is, the symposium papers track the development of
several “innovations” in financial management practices employed by
state and local governments before the Great Recession, evaluate
the impact of the Great Recession on the usage and efficacy of these
practices, and analyze some of the responses by federal and
subnational governments related to these financial management
practices in this current era of severe fiscal stress. Specifically, the
symposium includes papers covering debt-related derivatives,
variable rate securities, municipal bond insurance, pension funding
and financial market reform. The symposium begins with an
assessment of the financial condition of state governments before
and after the Great Recession to set the context, then proceeds with
several papers detailing innovative financial management practices
and the Great Recession’s impact on these practices, and concludes
with the seminal federal response to the financial crisis as it relates
to state and local governments, the Dodd-Frank financial reform law.
The remaining paragraphs in this introductory essay provide a brief
overview of each of the papers and their overall fit into the
symposium.
The symposium begins with Kioko’s “Reporting on the Financial
Condition of the States 2002-2010” which examines the fiscal
performance and financial health of state governments in the period
before, during, and after this Great Recession. This paper sets the
SYMPOSIUM INTRODUCTION
161
general financial context for state and local governments in the
period under study in this symposium. As has been generally
reported in the financial press, Kioko finds through GASB 34 financial
statement analysis a significant weakening of most states’ finances
as measured by various financial indicators culminating in the Great
Recession period of 2008 through 2010. However, smaller states
generally outperformed larger states while most states’ liquidity
positions remain strong and debt levels sustainable.
The second paper, Moldogaziev’s “The Collapse of the Municipal
Bond Insurance Market: How Did We Get Here and Is There Life for
the Monoline Industry Beyond the Great Recession,” details the
explosion in the use of municipal bond insurance by state and local
governments in the years before the Great Recession and the causes
for the sudden collapse of the monoline bond insurance industry
during the Great Recession. Moldogaziev specifically details the
growing and substantial exposure each of monoline bond insurance
companies had to US structured finance and international finance
products in their portfolios which ultimately led to the demise of every
bond insurance firm save one as a result of the undercurrents
associated with the recent financial crisis. The paper concludes with
a set of policy implications and observations about the potential
future impact of an industry that state and local governments relied
so heavily on in the past but which now consists of only “one and a
half firms.”
Luby and Kravchuk’s “An Historical Analysis of Debt-Related
Derivatives by State Governments in the Context of the Great
Recession” provides a comprehensive and systematic analysis of the
use of financial derivatives by state governments, one of the areas of
public financial management that has come under heavy scrutiny by
the mainstream and financial press during the Great Recession. The
paper details the amount and types of debt-related derivatives used
by state governments as well as usage as a percentage of the size of
these government entities’ bond portfolios. Luby and Kravchuk found
that large, sophisticated users of debt finance were most active in
executing debt-related derivatives while many states did not employ
these financial products at all during the period of study. In addition,
while states did escalate their use of debt-related derivatives in
recent years, the level of usage did not generally comprise an overly
162
LUBY
large part of their bond portfolios thus evincing a relatively
conservative use of these financial instruments.
Denison and Gibson’s “Adjustable Rate Debt Overwhelms
Jefferson County, AL Sewer Authority: A Tale of Market Risk, False
Hope and Corruption” provides a case study on the potential severe
impacts that debt-related derivatives (as analyzed in the preceding
paper by Luby and Kravchuk) and variable rate debt can have on
state and local government finances. The paper details how the bond
finance decisions made by Jefferson County, Alabama related to the
use of auction rate securities and interest rate swaps for the purpose
of lowering the escalating cost of repairing its sewer system led the
county to financial bankruptcy in the wake of the recent financial
markets collapse. Denison and Gibson offer a number of “lessons
learned” from this case as it relates to debt limits, financial oversight,
debt-related derivatives regulation, credit rating agencies activity, and
debt finance disclosures to the public.
Seligman’s “State Pension Funding and the Great Recession of
2007 to 2009” investigates one of the most salient and currently
controversial areas of public financial management, the sustainability
of government retirement plan funding. The paper looks at state
pension funding levels, historic asset returns, and business cycle
data to analyze the performance of these funds for the period 2001
to 2009 (i.e., pre and post Great Recession). Seligman finds that,
contrary to much recent conventional wisdom, funding ratios are not
as bad as advertised and not any worse than they were in 1990 right
before the sizable improvement in pension funding seen throughout
the 1990s. This finding casts doubt on the necessity at this time of
taking more draconian actions such as closing or freezing pension
funds. Based on his findings and analysis, Seligman offers some
policy implications and specific recommendations related to plan
administration, asset allocation, and plan participant contribution
levels that would go a long way towards making sure states continue
to honor their current and future pension commitments without
having to close or freeze funds.
The symposium concludes with Johnson’s “Understanding DoddFrank’s Reach into Main Street’s Financial Market” which provides an
overview of the seminal financial regulation legislation enacted partly
as a result of some of the public financial management practices
discussed earlier in this symposium. Johnson specifically focuses on
SYMPOSIUM INTRODUCTION
163
the legislation’s “reach” into the regulation of municipal advisors and
the credit rating agencies couching Dodd-Frank in the context of its
theoretical rationale based on previous finance literature on
securities certification and monitoring. Johnson raises several
concerns related to Dodd-Frank’s impact on the municipal securities
industry including issues surrounding the supply and quality of
municipal advisors, past and future borrowing costs as a result of
under-rated municipal bonds, and increased federal intrusion into the
public financial management practices of state and local
governments.
The papers in this symposium offer a contribution to the public
financial management literature in two ways. First, the six papers in
this symposium advance our understanding of the various financial
management practices employed prior to the Great Recession and
the impact the recent financial crisis had on such practices as well as
the overall finances of state and local governments. In addition to
this descriptive information, the empirical conclusions found in these
papers offer numerous relevant policy implications for finance
managers and public financial management practices. Second, many
of the symposium papers move the public finance literature forward
across a wide range of theoretical strands such as public choice
theory, certification, financial intermediation, and fiscal federalism.
Thus, the symposium strives to achieve the primary mission of the
Journal of Public Budgeting, Accounting and Financial Management
by focusing on bridging both the theories and practices that undergird
the field of public finance, including the subfield of public financial
management.
ACKNOWLEDGEMENTS
In closing, I would like to thank the symposium authors for their
outstanding work in creating the symposium’s content and
developing and refining their individual papers. All papers included in
the symposium were submitted to full, blind peer review and selected
for publication from a larger number of papers than published. I am
also most grateful to editor Khi Thai for his support and
encouragement of this symposium from initial conception to
completion. Finally, I would like to extend a sincere thank you to the
following individuals who served as anonymous manuscript referees:
Woods Bowman, Neal Buckwalter, Beverly Bunch, Jean Harris,
164
LUBY
Rebecca Hendrick, Alfred Ho, Jonathan Justice, Josie LaPlante, Justin
Marlowe, Christine Martell, Clifford McCue, Charles Menifield,
Lawrence Miller, Beth Neary, Jun Peng, Mark Robbins, Bill Simonsen,
Dan Smith, Louis Stewart, Samuel Stone, Janey Wang, Yonghong Wu,
Wenli Yan, and Kurt Zorn. The work of these referees significantly
improved the quality of the individual manuscripts and the
symposium as a whole.
J. OF PUBLIC BUDGETING, ACCOUNTING & FINANCIAL MANAGEMENT, 25 (1), 165-198
SPRING 2013
REPORTING ON THE FINANCIAL CONDITION OF THE STATES:
2002-2010
Sharon N. Kioko*
ABSTRACT. The Governmental Accounting Standards Board (GASB) set forth
a new model for financial reporting for state and local governments when it
issued Statement No. 34 in 1999. Under the new financial reporting model,
state and local governments are now required to develop financial
statements that report on the operating activities and financial position of
the government as a whole. This study provides a review of the financial
health of state governments in the period before, during, and after the Great
Recession. Virtually every state reported revenue losses and an operating
deficit in 2009. 41 states continued to report an operating deficit in 2010.
For a vast majority of the states, their 2010 general revenues were still
below 2008 levels. Smaller governments (e.g., Alaska, Wyoming, and North
Dakota) reported robust operating and financial positions. This is partially
attributable to their natural resource base. Larger governments (e.g.,
California, Connecticut, Hawaii, Illinois, Massachusetts, and New Jersey)
consistently reported weaker operating and financial positions.
INTRODUCTION
The Governmental Accounting Standards Board (GASB) set forth a
new model for financial reporting for state and local governments
when it issued Statement No. 34, Basic Financial Statements – and
Management’s Discussion and Analysis – for State and Local
Governments in 1999. States and local governments are now
--------------------------* Sharon N. Kioko, Ph.D., is an Assistant Professor of Public Administration
and International Affairs, Maxwell School, Syracuse University, Her research
interests include examining the relevance of GASB-34 financial information
in the primary and secondary municipal bond markets.
Copyright © 2013 by PrAcademics Press
166
KIOKO
required to develop government-wide financial statements and report
on the operating activities and financial position of the government as
a whole. This paper uses the GASB-34 financial statements to report
on the operating activities and financial position of the state
governments from 2002 through 2010. This is an especially critical
period, as states recover from the worst recession since the Great
Depression. This study finds states reporting their strongest operating
and financial positions in 2004 through 2007. States reported their
worst operating and financial positions in 2009 through 2010.
Smaller governments reported robust operating and financial
positions in large part due to their rich natural resource base. Some
of the larger governments e.g., California, Connecticut, Hawaii, Illinois,
Massachusetts, and New Jersey reported deficits often (at least six
out of the nine years). Their anemic operating position subsequently
led to rating downgrades in 2010. States maintained a strong
liquidity position, albeit weakened in the post-recessionary period.
Their long-term debt levels were also at sustainable levels as well.
This study begins with a brief overview of the financial crisis and
its impact on state government revenues. It continues with a review
of the current financial reporting model and presents a set of
financial condition indicators for the 50 states for the nine-year
period. Because the mean (and median) is reported for each indicator
on an annual basis and for each state for the nine-year period, one
can compare the financial performance of an individual state to
another state and to the sector as a whole.
IMPACT OF THE GREAT RECESSION ON GOVERNMENT REVENUES
The subprime mortgage crisis was one of the first indicators of
the imminent recession. Home values peaked in mid-2006, and after
a brief rise in interest rates, home values began to free fall. By 2008,
foreclosures were endemic and the U.S. economy was in the midst of
a financial crisis. By mid-2009, unemployment levels were at an alltime high of 10.1 percent. Taxable consumption fell sharply in this
period (Boyd, 2011). States began reporting revenue losses as early
as mid-2007 (see Table 1). By 2009, tax revenues had plummeted
with states reporting five consecutive quarters of revenue losses
beginning with the last quarter of 2008 through the last quarter of
2009 (Boyd, 2011). In three out of these five quarters, revenue
losses were more than 10 percent (Dadayan and Ward, 2011).
REPORTING ON THE FINANCIAL CONDITION OF THE STATES: 2002-2010
167
Virtually every state reported revenue losses, with median losses of
more than 8 percent in 2008-09.1 An additional 25 states continued
to report revenue losses in 2009-10 (see Table 1).
The federal government responded with a number of stimulus
packages. The American Recovery and Reinvestment Act (ARRA)
specifically addressed revenue shortfalls state and local governments
were experiencing at the time. The $787 billion stimulus package
appropriated roughly $280 billion to state and local governments
through 2016. As Graph 1 illustrates, the composition of state
government revenues for 2009 and 2010 changed significantly as a
result of the unprecedented shortfalls in revenues and the influx the
federal stimulus dollars. Unfortunately, federal stimulus funds did not
sufficiently address existing budget gaps through its budget relief
program. 49 states reported operating deficits in 2009, while 41
states reported operating deficits in 2010. In fact, for a vast majority
of states, their 2010 general revenues were below their 2008 levels
and in a number of states – their 2010 general revenues were below
their 2007 levels as well.
GRAPH 1
Total Primary Government Revenue Source Share
32%
33%
33%
32%
31%
31%
31%
18%
18%
18%
18%
18%
18%
18%
49%
48%
49%
50%
51%
52%
2002
2003
2004
2005
2006
2007
General Revenues
Charges for Services
37%
41%
20%
18%
51%
44%
42%
2008
2009
2010
Operating and Capital Grants
Notes: Excluding CAFR data for New York (2002) and South Dakota,
Illinois, and Hawaii (for FY 2010).
Source: State Comprehensive Annual Financial Reports (CAFRs) and
authors calculations.
168
KIOKO
TABLE 1
Year
# of
States
Annual Percent Change in Total Primary Government Revenues by
Source and Expenses
2002-03 49*
2003-04 50
2004-05 50
2005-06 50
2006-07 50
2007-08 50
2008-09 50
2009-10 47*
General Revenues
Charges and
Services
Mean
Median Mean
7.5878
3.5899 6.1559
8.6138
6.5732 9.5103
10.0222
8.9528 8.3198
10.2313
9.5087 7.0591
7.2823
6.4906 2.1550
1.0008
0.9463 4.1593
-11.8308† -8.8543 3.2776
-10.4495† -0.6857 9.6569
Median
5.9730
8.3350
7.4067
7.6346
0.9906
2.6264
4.0637
5.5488
Operating and
Capital Grants
Mean
10.2846
7.6411
2.5218
4.3819
3.9503
3.1555
18.2912
28.7553
Median
9.7354
8.2983
2.0969
3.0373
3.4781
2.9986
17.2708
27.4465
Total Expenses
Mean
4.5001
4.2327
4.6918
5.5080
5.7600
7.8502
8.0822
6.6875
Median
4.2789
4.1658
4.1424
5.6258
5.5346
7.8391
7.9495
6.8244
Notes: * Excludes CAFR data for New York (2002) and South Dakota, Illinois,
and Hawaii (for FY 2010).
†Alaska is an extreme outlier. The mean general revenue growth for the
states excluding Alaska was -9.53 percent and 1.20 percent for 200809 and 2009-10 respectively.
Source: State CAFRs and authors calculations.
While this recession officially ended in June 2009, a vast majority
of the states will not report a positive operating position until 2012
and perhaps even through 2014. Revenue collections are higher in
most states, but the National Association of State Budget Officers
(NASBO) reports revenues for 2012 will remain below their 2008
levels by nearly $20.8 billion (NASBO, 2011). Moreover, with the
European debt crisis largely unresolved, volatility in global markets,
and the impending congressional action to reduce the federal deficit,
stability of all the revenue streams will remain a primary concern for
the governments.
THE FINANCIAL REPORTING MODEL
The Comprehensive Annual Financial Reports (CAFR) of
governments now include the government-wide financial statements
– the Statement of Net Assets and the Statement of Activities which
report the financial position and operating results of a government as
a single economic entity.
REPORTING ON THE FINANCIAL CONDITION OF THE STATES: 2002-2010
169
Before this change, financial statements spread information
among individual funds and fund types, used different measurement
foci, and different bases of accounting. Under the new financial
reporting model, the governmental funds are to be consolidated and
reported under governmental activities (i.e., activities financed
primarily by general revenues, intergovernmental revenues, and other
non-exchange or non-market transaction based revenue sources). All
enterprise funds are also to be consolidated and reported as
business-type activities (i.e., activities financed primarily by revenue
from prices charged to parties e.g., user fees, user charges, license
fees, etc.). Combined, governmental activities and business-type
activities present the operating results and financial position of the
primary government. The government-wide financial statements do
not include financial information related to a government’s fiduciary
activities (e.g., public pension funds or any other resources held in
trust). These activities are reported separately in the fiduciary fund
financial statements of the CAFR.
The government-wide financial statements also integrate a longterm view of a government’s financial position by reporting the
government’s fixed assets in addition to its cash and current financial
resources, as well as its current and long-term debt. Since these
government-wide financial statements are prepared using full accrual
accounting, revenues and expenses are now reported in the financial
year in which the transactions took place, regardless of period when
funds were transferred.
FINANCIAL CONDITION ANALYSIS
Financial condition can be broadly defined as a government’s
ability to meet its obligations on a continuing basis. In assessing the
financial condition of a government, emphasis is placed on the
government’s ability to meet its obligations within the fiscal year
(budget solvency), its ability to pay its current obligations as they
come due (cash solvency), its ability to maintain existing service
levels (service-level solvency), as well as meet outstanding obligations
in the future (long-term solvency). The goal is to assess whether
overall, a government’s financial condition is improving or
deteriorating over time and in comparison with similar governments.
A vast majority of financial condition models are based in part on
the Financial Trend Monitoring System (FTMS) that was developed by
170
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the Inter City-County Management Association (see Nollenberger,
Groves, & Valente, 2003). The FTMS has developed more than forty
indicators over three dimensions -- financial, environmental, and
organizational, with a vast majority of these measures focusing
explicitly on the financial condition of a local government.
In an effort to simplify the process of assessing the financial
condition of a local government, Kenneth Brown developed the 10Point test of financial condition (1993; also see Maher &
Nollenberger, 2009). In developing this model, Brown argued that
the 10-point test was an effective tool for assessing the financial
condition of a government without the use of analytical techniques
that are costly, time-consuming, or complex, making such
assessments difficult if not impossible.
The government-wide financial statements provide us with “new”
information that can be used to develop financial condition indicators
for the government as a whole. A vast majority of the studies that
incorporate government-wide information are either limited in scope
or duration. Chaney, Mead, and Schermann (2002) for example
report on the financial condition of two medium sized cities -Alexandria, Virginia and Corona California. While reporting on a single
local government, Mead (2006) updates the Kenneth Brown 10-point
test by incorporating information reported in the government-wide
financial statements. At the state level, Kamnikar, Kamnikar and Deal
(2009) develop three financial condition indicators for the states for
2003 and 2004 while Wang, Dennis, and Tu (2007) develop multiple
financial condition indicators for a single year - 2003.
Johnson, Kioko, and Hildreth (2012) is the only study to report on
an annual basis the financial condition of the 50 state governments
over multiple years (2002 through 2005). The authors report the
ratios for governmental activities (GA) separately from business-type
activities (BTA) as well as report the ratios for total primary
government (TPG). At the local level, Rivenbark, Roenigk, and Allison
(2010) report ratios for GA, BTA, and TPG for the Village of Pinehurst,
North Carolina. They also report indicators for fund financial health
(i.e., governmental funds and enterprise funds but exclude fiduciary
funds and internal service funds) and report the village’s financial
condition indicators relative to a local government benchmark.
REPORTING ON THE FINANCIAL CONDITION OF THE STATES: 2002-2010
171
This study sought to report on the financial condition of the 50
states over a nine-year period. It reports on the financial condition of
these governments on an annual basis, but also on a state by state
basis. The mean and the median for all states for a single fiscal year
are reported together with the mean for each state for the nine-year
period. Such information remains largely unavailable as a vast
majority of publicly available financial condition reports describe the
operating activities and financial position of the general fund and not
the government as a whole.
There is little agreement as to what dimensions of financial
condition are relevant and what should be reported when performing
financial condition of a government. As Frank and Gianakis (2010)
note, “there is no Yahoo! Finance” module for governments. This
study retained as far as possible the nomenclature that already exists
in the private sector and in a vast majority of the studies that
currently report on the financial condition of governments under the
GASB-34 financial reporting model. It reports on the financial
condition of state governments across the four dimensions (budget,
cash, service-level, and long-term solvency). The measures that are
reported here are not exhaustive nor are they widely applicable to all
governments. The objective here is to report on the financial
condition of the states using multiple indicators with greater
emphasis on simplicity. Therefore, when determining the financial
condition of a particular jurisdiction, consider tailoring the measures
in order to make them more relevant to that specific jurisdiction.
DATA COLLECTION AND ANALYSES
This study sourced CAFRs for the states from 2002 through
2010.2 For each state and for each year, data were extracted from
the government’s Statement of Activities and Statement of Net
Assets.
The Statement of Activities reports the annual cost of services by
major category (i.e., GA, BTA, TPG, and component units) alongside
program revenues to produce a net column – either net revenue or
net expense, which is then offset by general revenues to produce
change in net assets. From the Statement of Activities data was
collected on total expenses, program revenues including charges for
services and fees, operating grants and contributions, as well as
capital grants and contributions. These categories are reported
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KIOKO
separately in the Statement of Activities. Data was also collected on
the state’s general revenues, change in net assets, net assets at the
beginning of the fiscal year and net assets at the end of the fiscal
year. Each of these items was recorded by major category. Excluded
are component units which are reported in the government-wide
financial statement, but operate with significant autonomy from the
reporting government.
In the Statement of Net Assets, data was collected data on total
assets, total liabilities as well as current assets and current liabilities
if reported by the state.3 Since Net Assets are also reported in the
Statement of Activities, the only additional data collected from this
statement was the unrestricted net assets. Unrestricted net assets
report, on a cumulative basis, whether the government’s revenues
exceeded full costs of programs. It represents assets accumulated
over time, but does not necessarily represent assets that are
available and in a readily spendable form, like cash (Mead, 2001).
For simplicity, data reported on a line item basis in the CAFRs
(e.g., total tax revenues, income tax revenues) were excluded. While
these individual line items and other data are important to various
stakeholders, the objective here is to develop indicators and report
on the financial condition of the government’s on major elements,
thereby assisting in the interpretation of financial information and
providing a benchmark for which additional analysis by line item or
other financial statements can be incorporated. This study does not
incorporate information reported in the fund-based statements as
Rivenbark et al. (2010) encourages nor does it integrate any socioeconomic data as Wang et al. (2007) does. In the case of the former,
the objective was to assess the financial condition of the
governments using information reported in the government-wide
statements. These financial statements report on the financial
position and operating activities of a government on an accrual basis
of accounting using an economic resources measurement focus. With
regard to the latter, this study measures financial performance of a
government relative to its reported revenues, expenses, assets, or
liabilities in order to avoid any subjectivity that may arise from using
any socio-economic data. As Rivenbark et al. (2010) note, these
demographic factors do not represent actual financial condition and
do lend themselves to subjective interpretation. Wang et al (2007)
notes inclusion of these socioeconomic factors is questionable as
REPORTING ON THE FINANCIAL CONDITION OF THE STATES: 2002-2010
173
they may affect the financial condition, but are not financial condition
itself (p. 5).
FINANCIAL CONDITION OF THE STATES 2002-2010
A series of measures report on the budget, cash, service-level,
and long-term solvency of the states for the period 2002 through
2010. For each indictor, the mean and the median are reported. In
some instances, indicators are reported on the basis of activities e.g.,
governmental activities (GA), business-type activities (BTA), and/or
total primary government (TPG). Each indicator is reported on an
annual basis; a limited number of indicators are reported on a state
by state basis for all years, thereby reporting on the differences in
outcomes across time - and more importantly across states.
Budget solvency often refers to the government’s ability to
balance its budgets i.e., raise sufficient revenues to meet all its
expenses or consistently run moderate surpluses. It is also a measure
of inter-period equity. If governments fail to balance their current
budget, the burden is placed on future taxpayers (Rivenbark et al.,
2010). Budget solvency is estimated as a ratio of operating revenue
to expenses (Operating Revenue/Expenses). Operating revenue in
this instance is the sum of general revenues, charges for services and
operating grants and contributions but excludes capital grants and
contributions (Johnson, et al., 2012).4 Expenses are the full costs of
services in the current period. Governments should at least break
even or report moderate surpluses (i.e., a ratio greater than or equal
to 1.00).
Table 2 reports the operating position for the states over the nine
year period. In 2002, the first year CAFRs were published, only 12
states reported a GA operating surplus and only 10 states reported a
TPG operating surplus. This was the period following the September
11th terror attacks where revenue growth for a vast majority of the
states was weakened (see Table 1). However, by the end of FY 2006,
revenue growth across the states was strong. This is also reflected in
their operating position. In 2004, at least half the states reported a
TPG operating surplus and by 2006 only 9 states reported a TPG
operating deficit.
The effects of the recession were felt by a number of states as
early as 2007 where 36 states reported a TPG operating surplus – 5
174
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TABLE 2
Operating Position (Operating Revenue/Expenses)
Governmental
Activities (GA)
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
Number
of States
49*
50
50
50
50
50
50
50†
47*
Mean
Median
0.9569
0.9809
1.0156
1.0361
1.0796
1.0534
0.9950
0.9055
0.9908
0.9567
0.9841
0.9999
1.0226
1.0364
1.0181
0.9836
0.9324
0.9821
Total Primary
Government (TPG)
# of
states
whose
ratio>1
12
18
25
33
37
33
14
3
14
Mean
Median
0.9528
0.9748
1.0145
1.0413
1.0654
1.0607
0.9967
0.9091
0.9755
0.9635
0.9787
1.0022
1.0281
1.0396
1.0283
0.9818
0.9208
0.9748
# of
states
whose
ratio>1
10
13
26
35
41
36
16
1
Notes: * Excludes CAFR data for New York (2002) and South Dakota, Illinois,
and Hawaii (for FY 2010).
†Alaska is an extreme outlier here; the median is more representative of
the operating position of the states for 2009.
Source: State CAFRs and authors calculations.
less than the previous year. Growth in general revenues was 7.28
percent; substantially less than the 10.23 percent in the previous
year (see Table 1). At the heart of the crisis – FY 2009, only 3 states
reported a GA operating surplus5 and only one state - North Dakota
reported a TPG operating surplus (1.0580). Growth in operating and
capital grants was eight to nine times higher than previous years
(18.29 percent and 28.76 percent in 2008-09 and 2009-10
respectively) while growth in general revenues (i.e., tax revenues) was
significantly lower with more than half the states reporting negative
growth rates (-8.85 percent in 2008-09 and 0.69 percent in 200910). Without the ARRA funding, the financial position of the states
would have been considerably worse. For example, 48 states
reported general revenue losses in 2008-09, while 25 states reported
general revenue losses in 2009-10.6 In 41 states, their 2010 primary
government general revenues were below the 2008 levels with only
six states7 reporting general revenues greater than or equal to their
2008 TPG general revenues. As a result, grants and contributions
(operating and capital) now play a significant role. The share of grants
REPORTING ON THE FINANCIAL CONDITION OF THE STATES: 2002-2010
175
and contributions was up almost 10 percentage points from 31
percent in 2006 to 41 percent in 2010 (see Graph 1).
Table 3 reports the mean operating position for each state by GA
and TPG for the nine-year period. The table also reports the number
of years in which each state reported an operating deficit. Data is
sorted in descending order on the basis of the state’s mean TPG
operating position. For comparative purposes, the state’s ranking by
operating revenue is also included. It’s important to note that while a
government may have reported a GA operating deficit, it may report a
TPG operating surplus if its BTA’s operating surplus was sufficiently
large to cover its GA operating deficit and vice versa.8
Only 19 states reported a mean operating surplus for either TPG
or GA - though not the same states in each of these categories. In the
TPG category, the first nine states – Wyoming through Montana
reported exceptional performance with their average operation
position ratio being greater than 1.03. These states also reported an
operating deficit no more than three out of the nine years. This
however is not surprising given their size. The average operating
revenue for these states was $6.5 billion (with a range from 2 billion
up to 13 billion). The mean operating revenue for the 50 states was
$29 billion and the highest ranking state – California reporting
operating revenues of $198 billion. This was $71 billion more than
the state of New York, which is ranked second. Texas through South
Carolina reported a mean TPG operating position that was at least
greater than one. These states reported an operating deficit no more
than six out of the nine years. Also note that Texas and Pennsylvania
are the only large states (ranked of 3 and 5 respectively) to report a
mean operating surplus for the period. The remaining 31 states
reported a deficit at least three out of the nine years, with most states
reporting deficits at least two thirds of the time. For these states, their
annual operating losses ranged from 0.15 percent of operating
revenue up to 7.86 percent of operating revenue. The data also
shows three states - Wisconsin, Michigan, and Illinois, never posted a
primary government operating surplus in the nine-year period. Alaska
on the other hand reported the largest revenue loss and the lowest
GA and TPG operating position of all states (0.2391 and 0.2637
respectively). This was in large part due to large investment losses in
the Alaska Permanent Fund ($6.46 billion in 2009). In spite of this
outcome, its exceptional performance in previous years made up for
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KIOKO
TABLE 3
Operating Position by State
State
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
Wyoming
Alaska
North Dakota
Utah
South Dakota*
Nebraska
Idaho
Oklahoma
Montana
Texas
Tennessee
Iowa
West Virginia
Maine
Arkansas
Indiana
Nevada
Pennsylvania
South Carolina
Florida
North Carolina
Arizona
Colorado
Minnesota
Delaware
Alabama
Missouri
Mississippi
Virginia
Rhode Island
Kansas
Wisconsin
Ohio
Vermont
New Hampshire
New York*
Georgia
Louisiana
Oregon
Kentucky
Total Primary
Government
Years of
Negative TPG
Mean
Operating
Position
1.2811
1
1.2620
3
1.0738
2
1.0731
2
1.0451
1
1.0388
3
1.0379
3
1.0360
3
1.0313
2
1.0174
5
1.0163
3
1.0140
2
1.0112
4
1.0081
5
1.0075
3
1.0040
6
1.0030
5
1.0030
5
1.0024
5
0.9982
3
0.9960
6
0.9952
5
0.9940
4
0.9937
6
0.9930
4
0.9928
5
0.9885
5
0.9864
7
0.9857
6
0.9850
7
0.9804
6
0.9701
9
0.9686
7
0.9675
8
0.9671
7
0.9660
6
0.9660
7
0.9656
6
0.9655
6
0.9593
6
Governmental
Activities
Years of
Negative GA
Mean
Operating
Position
1.3984
1
1.2723
3
1.0948
2
1.0674
2
1.0397
1
1.0385
3
1.0445
2
1.0357
2
1.0331
2
0.9987
5
1.0164
3
1.0124
3
0.9967
6
1.0113
3
1.0073
3
1.0168
4
1.0084
4
1.0143
1
0.9985
5
0.9987
2
1.0031
5
0.9971
5
0.9824
5
0.9924
6
1.0257
2
0.9917
5
0.9915
5
0.9865
7
0.9899
6
0.9879
7
0.9492
6
0.9647
8
0.9726
9
0.9747
8
0.9673
7
0.9680
6
0.9592
8
0.9642
6
0.9536
8
0.9596
7
Rank by
Operating
Revenue
48
50
46
36
49
41
42
30
45
3
21
29
35
40
31
18
38
5
23
4
11
19
26
15
43
28
24
32
16
37
34
14
7
47
44
2
12
20
27
25
REPORTING ON THE FINANCIAL CONDITION OF THE STATES: 2002-2010
177
TABLE 3 (Continued)
State
41
42
43
44
45
46
47
48
49
50
Maryland
Michigan
Washington
Massachusetts
California
New Mexico
New Jersey
Hawaii*
Connecticut
Illinois*
Mean
0.9589
0.9561
0.9442
0.9438
0.9430
0.9351
0.9319
0.9311
0.9309
0.9214
Years of
Negative TPG
operating
Position
7
9
5
5
7
7
8
7
8
8
Mean
0.9511
0.9664
1.0009
0.9346
0.9409
0.9267
0.9329
0.9298
0.8849
0.9159
Years of
Negative GA
Operating
Position
7
9
5
6
7
8
8
7
9
8
Rank by
Operating
Revenue
17
9
13
10
1
33
8
39
22
6
Notes: * Excludes CAFR data for New York (2002) and South Dakota, Illinois,
and Hawaii (for FY 2010).
Source: State CAFRs and authors calculations.
this large loss. The state’s mean GA and TPG operating position for
the nine-year period was second to Wyoming. These were the only
states to report an operating position greater than 1.20.9
An alternative measure of budget solvency is the total margin
ratio. The traditional approach to estimating the total margin ratio10 is
Changes in Net Assets/Operating revenue. The total margin ratio is a
measure of the size of the surplus or deficit relative to its operating
revenue. One may also report this measure on a per-capita basis (see
Maher & Nollenberger, 2009; Wang, et al., 2007). The numerator change in net assets is a measure of the government’s surplus or
deficit i.e., the sum of its revenues, expenses, gains, and losses
reported in the government-wide Statement of Activities on a full
accrual basis (Mead, 2001). The measure for operating revenue
remains the same i.e., general revenues, plus charges for services,
operating grants and contributions, -- excluding capital grants and
contributions.
Table 4 reports the total margin ratio for the 50 states from 2002
through 2010. Findings here are somewhat comparable to those
reported in Table 2. In 2002, more than half the states reported
deficits, however, as revenues rebounded especially in 2004 through
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KIOKO
TABLE 4
Total Margin Ratio (Change in Net Asset/Operating revenue)
Governmental
Activities (GA)
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
# of
States
49*
50
50
50
50
50
50
50†
47*
Mean
Median
-0.0246
0.0036
0.0321
0.0522
0.0724
0.0627
0.0133
-0.1076
0.0088
-0.0181
0.0052
0.0358
0.0466
0.0602
0.0455
0.0075
-0.0403
0.0084
# of states
reporting a
negative
Change in
Net Asset
Position
27
22
14
8
4
10
22
41
17
Total Primary
Government (TPG)
Mean
Median
-0.0279
-0.0044
0.0305
0.0551
0.0758
0.0686
0.0143
-0.1085
-0.0004
-0.0156
-0.0060
0.0260
0.0419
0.0611
0.0515
0.0059
-0.0583
-0.0009
# of states
reporting a
negative
Change in
Net Asset
Position
28
28
14
7
2
9
23
44
25
Notes: * Excludes CAFR data for New York (2002) and South Dakota, Illinois,
and Hawaii (for FY 2010).
†Alaska is an extreme outlier here; the median is more representative of
the mean total margin ratio of the states for 2009
Source: State CAFRs and authors calculations.
2007, a vast majority of states reported surpluses. The mean GA total
margin ratio in this period was 3.21 percent (in 2004), 5.22 percent
(in 2005), 7.24 percent (in 2006) and 6.27 percent (in 2007) while
the mean TPG total margin ratio was 3.05 percent (in 2004), 5.51
percent (in 2005), 7.58 percent (in 2006), and 6.86 percent (in
2007). When general revenue growth was weak, the number of states
that reported a negative change in net asset position doubled to 23
in 2009 and again in 2010 to 44 states. The median GA total margin
ratio was below 1 percent in the three-year period 2008-2010. The
median TPG total margin ratio was not only below 1 percent, but also
below what the states had reported as their GA total margin ratio, an
indicator that states also reported a negative change in net asset
position (or deficit) in their BTA.
A measure often used in the private sector is the return to asset
ratio (ROA). Often, the ROA measure is used to assess an
organization’s profitability relative to its assets (i.e., a measure of
asset-use efficiency). Given the public sector context, one should
interpret the ratio differently i.e., not as a measure used to determine
REPORTING ON THE FINANCIAL CONDITION OF THE STATES: 2002-2010
179
asset-use efficiency but rather as a measure of the government’s
ability to maintain or expand its asset base. ROA is a ratio of change
in net assets to total assets (i.e., Change in Net assets/Total
Assets).11 The numerator is as reported in the Statement of Activities
while the denominator is the sum of current and long-term assets
(including capital assets) as reported in the Statement of Net Assets.
An appropriate ROA figure needs to be at least as high as the rate
of inflation, and higher if the organization needs to replace its assets.
If the ROA was greater than inflation, it meant that the government
could invest in additional assets (current and long-term). If the ROA is
greater than zero but below the rate of inflation, it meant that the
government’s “book-value” of assets was eroded. A negative ROA
ratio is an indicator of the government’s inability to maintain or
ensure growth of its assets (i.e., its cash and investments in the shortterm and non-current assets including physical assets over the long
run). Government’s that report recurring deficits will likely report a
reduction in accumulated assets - especially current assets and longterm investments. They are also more likely to report larger liabilities
including long-term obligations (e.g., debt and pension obligations).
Results are tabulated in Table 5 and Table 6.
TABLE 5
Return on Asset Ratio (Change in Net Asset/Total Assets)
Year
Number of
States
2002
2003
2004
2005
2006
2007
2008
2009
2010
49*
50
50
50
50
50
50
50
47*
Governmental
Activities (GA)
Mean
Median
-0.0116
-0.0100
-0.0056
0.0042
0.0182
0.0346
0.0311
0.0373
0.0495
0.0456
0.0384
0.0406
-0.0023
0.0047
-0.0465†
-0.0379
-0.0133†
0.0083
Total Primary
Government (TPG)
Mean
Median
-0.0146
-0.0106
-0.0108
-0.0047
0.0148
0.0261
0.0311
0.0357
0.0489
0.0454
0.0398
0.0398
-0.0007
0.0050
-0.0533†
-0.0476
-0.0185†
-0.0009
Notes: * Excludes CAFR data for New York (2002) and South Dakota, Illinois,
and Hawaii (for FY 2010). †Alaska is an outlier here; the median is more
representative of the Return on Asset Ratio for the states for 2009 and
2010
Source: State CAFRs and authors calculations.
180
KIOKO
Table 5 reports the ROA ratio for the 50 states from 2002 through
2010. Again, as revenues rebounded especially in 2006 the GA and
TPG ROA position was robust -- 4.95 and 4.89 percent respectively.
The ROA position is significantly lower in 2009 through 2010. TPG
ROA position was negative in 2009 (-4.76 percent) and 2010 (-0.09
percent).
Table 6 reports the state’s mean TPG total margin ratio as well as
the state’s mean TPG return on asset ratio. The data is sorted in
TABLE 6
Total Margin and Return to Asset Ratios by State
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
State
Wyoming
Montana
North Dakota
Utah
West Virginia
Louisiana
South Dakota*
Tennessee
Texas
Idaho
Nebraska
Arkansas
Oklahoma
South Carolina
Mississippi
Iowa
Alabama
Florida
Maine
North Carolina
Colorado
Virginia
Delaware
Arizona
Kansas
Rhode Island
Vermont
New Hampshire
Pennsylvania
Ohio
Indiana
Total
Margin
Ratio
Return on
Asset Ratio
0.1960
0.1135
0.0874
0.0766
0.0535
0.0451
0.0443
0.043
0.0413
0.0373
0.0366
0.0356
0.0333
0.0306
0.0300
0.0297
0.0288
0.0257
0.0217
0.0196
0.0192
0.0188
0.0154
0.0121
0.0054
0.0053
0.0052
0.0037
0.0028
0.0021
0.0020
0.0669
0.0689
0.036
0.0378
0.0377
0.0359
0.0245
0.0338
0.0223
0.0251
0.0219
0.0273
0.0267
0.0213
0.0247
0.0279
0.0245
0.0170
0.0239
0.0147
0.0115
0.0186
0.0109
0.0120
0.0037
0.0145
0.0078
0.0039
0.0026
0.0033
0.0034
Number of years
the state
reported a
negative Change
in Net Asset
Position
1
0
1
2
2
1
1
1
2
2
3
1
3
2
2
0
2
2
2
1
3
3
3
5
4
4
3
4
4
3
6
Rank by
Operating
Revenue
48
45
46
36
35
20
49
21
3
42
41
31
30
23
32
29
28
4
40
11
26
16
43
19
34
37
47
44
5
7
18
REPORTING ON THE FINANCIAL CONDITION OF THE STATES: 2002-2010
181
TABLE 6 (Continued)
Rank
32
33
34
35
36
37
38
39
40
41
42
43
44
45
47
46
48
49
50
State
Total
Margin
Ratio
Return on
Asset Ratio
Minnesota
Nevada
Missouri
Wisconsin
Washington
Georgia
Kentucky
Maryland
New York*
Michigan
New Mexico
Oregon
Alaska
Connecticut
Hawaii*
Massachusetts
California
Illinois*
New Jersey
0.0012
0.0011
-0.0019
-0.0020
-0.0021
-0.0079
-0.0080
-0.0136
-0.0226
-0.0303
-0.0341
-0.0371
-0.0416
-0.0500
-0.0549
-0.0549
-0.0563
-0.0671
-0.0735
0.0013
-0.0039
-0.0008
-0.0019
0.0017
-0.0074
-0.0078
-0.0112
-0.0222
-0.0426
-0.017
-0.0236
0.0418
-0.0501
-0.0253
-0.0760
-0.0723
-0.0888
-0.1064
Number of years
the state
reported a
negative Change
in Net Asset
Position
4
5
5
4
5
7
4
6
6
8
5
6
2
7
6
5
7
7
8
Rank by
Operating
Revenue
15
38
24
14
13
12
25
17
2
9
33
27
50
22
39
10
1
6
8
Notes: * Excludes CAFR data for New York (2002) and South Dakota, Illinois,
and Hawaii (for FY 2010).
Source: State CAFRs and authors calculations.
descending order on the basis of the state’s TPG total margin ratio.
For comparative purposes, the state’s ranking by operating revenue is
also included. For informative purposes, Table 6 also reports the
number of years the state reported a negative change in net asset
position.
Wyoming and Montana outperformed all other states. Wyoming,
for example reported an average annual surplus of 19.6 percent of its
revenues for the period 2002 through 2010; Montana reported an
average surplus of 11.35 percent of its revenues for the same period.
Their return on asset position was also very strong, their mean return
on asset position was 6.69 percent and 6.89 percent respectively. 19
states reported a mean total margin ratio greater than 2 percent (i.e.,
Wyoming through Maine), and 18 states reported a mean return on
asset ratio greater than 2 percent (i.e., Wyoming through Maine,
182
KIOKO
except for Florida). These states reported a negative change in net
asset position no more than three out of the nine years.
An additional 14 states reported a positive mean primary
government total margin ratio. These states reported a negative
change in net asset position more often – up to six out of the nine
years. For most of these states, their mean return on asset ratio was
positive but less than or equal to 2 percent. 17 states reported a
negative total margin ratio as well as a negative return on asset ratio.
These states reported a negative change in net asset position more
often, with some states reporting a positive change in net asset
position only once or twice in the nine-year period.12 Six states
California, Connecticut, Hawaii, Illinois, Massachusetts, and New
Jersey reported annual deficits that were on average greater than or
equal to 5 percent of their annual operating revenue.13 These states
experienced a significant erosion in “book value” of their assets or
increase in current and long-term liabilities of at least 5 percent each
year (see return on asset ratio). New Jersey (ranked 50th on both
criteria) for example reported a mean return on asset ratio that was 10.64 percent and a mean total margin ratio that was -7.35 percent.
Cash solvency refers to the government’s ability to make
payments on its bills as they come due. The current ratio is used to
estimate cash solvency (Current Assets/Current Liabilities, Table 7).
TABLE 7
Current Ratio (Current Assets/Current Liabilities)
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
Number of
States‡
25
27
27
27
25
25
25
25
25
Governmental
Activities
Mean
Median
2.1009
1.9989
1.8775
1.6675
1.8632
1.6994
1.8889
1.7138
2.0164
1.8646
2.0851
1.9347
1.9807
1.7952
1.8537
1.6432
1.8102
1.5329
Business-type
Activities
Mean
Median
7.9303
4.0960
6.3239
3.5451
8.0836
3.0911
8.5572
3.1286
7.5357
3.3434
7.5179
3.5118
7.0180
3.2366
4.1281
2.5138
3.4447
2.7856
Total Primary
Government
Mean
Median
2.4361
2.3378
2.1189
1.9770
2.0582
1.9841
2.0870
1.8801
2.1928
2.0701
2.2465
2.1533
2.1486
1.8890
1.9124
1.6876
1.8432
1.7759
Notes: ‡The number of states reported in the table is limited to states that
report current assets and current liabilities separately from other assets
and liabilities respectively.
Source: State CAFRs and authors calculations.
REPORTING ON THE FINANCIAL CONDITION OF THE STATES: 2002-2010
183
The cash ratio focuses on liquid assets available to meet current
obligations. It’s critical that a government maintain sufficient current
assets, as non-current assets are less likely to be converted into cash
quickly without significant losses in value. Under GASB-34,
governments are encouraged, but not required to separate assets
and liabilities into current and long-term groups. Table 7 only reports
the results of the 25 (sometimes 27 states) that self-report current
assets and current liabilities in the Statement of Net Assets.14
The GA current ratio was above 1.00; however for most states,
the GA current ratio was below 2.00, the benchmark often used in the
private sector. The data shows that for some states, the liquidity
problems intensified during the recession. Arizona’s GA current ratio
was 0.79 in 2009 – it reported a stronger ratio in 2010 of 0.99.
Connecticut reported a GA current ratio below the 1.00 threshold four
out of the nine years. In 2010, it reported its lowest ratio for the nineyear period at 0.79.
The current ratio for BTA is higher with significant variation across
states. As a result the median is more representative of the data.15
Governments have drawn down on their current assets - especially
unemployment reserve funds, more so than they did in 2002-03. The
median BTA current ratio was down more than 1.3 points to 2.79 in
2010, though well above the 2.00 threshold. The BTA portion of TPG
was current asset rich (approximately 25 percent of TPG current
assets); consequently, all 25 states reported a TPG ratio above
1.00.16
Long-term solvency (Unrestricted Net Assets /Expenses) refers to
the government’s ability to maintain the provision of basic
government services. The ratio is a variation of the fund balance
divided by expenses ratio (Chaney, et al., 2002). Unrestricted net
assets (UNA) is the residual component of net assets that are not
invested in capital assets (net of related debt) or restricted by any
externally (e.g., creditors) or internally (constitutional or statutory
provisions) imposed constraints. Unrestricted net assets represent
net assets accumulated and available for the provision of future
government services although they are not in cash form (Johnson, et
al., 2012). Unlike local or smaller governments, states have placed
greater restrictions on their net assets; as a result, a number of
states report a negative unrestricted net assets position.
184
KIOKO
The financial position ratio is small (or negative) as the number of
states reporting a negative unrestricted net assets position grows.
This is true indicator that these governments did not maintain large
unrestricted economic resources prior to the recession (i.e.,
unrestricted net assets, see Table 8).17 The number of states
reporting a negative total primary government unrestricted net assets
TABLE 8
Financial Position Ratio (Unrestricted Net Assets/Expenses)
Governmental Activities
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
Number of
States
49*
50
50
50
50
50
50
50
47*
Number of
States
49*
50
50
50
50
50
50
50
47*
Number of
States
49*
50
50
50
50
50
50
50
47*
Mean
Median
0.0207
0.0267
-0.0161
-0.0025
-0.0235
-0.0053
0.0113
0.0129
0.0507
0.0310
0.0562
0.0349
0.0452
0.0181
-0.0169
-0.0186
-0.0151
-0.0195
Business-type Activities
Mean
Median
0.1752
0.0630
0.1518
0.0585
0.1669
0.0686
0.2041
0.0573
0.1895
0.0525
0.2525
0.0853
0.2260
0.0750
0.1079
0.0411
0.0399
0.0453
Total Primary Government
Mean
Median
0.0313
-0.0030
-0.0073
0.0263
0.0485
0.0689
0.0574
-0.0074
-0.0015
0.0497
0.0104
0.0090
0.0377
0.0440
0.0495
0.0184
-0.0090
-0.0265
Number of states with a
negative UNA position
20
26
26
24
18
17
21
28
33
Number of states with a
negative UNA position
10
12
12
13
13
9
10
17
21
Number of states with a
negative UNA position
18
24
24
21
20
18
20
26
30
Notes: * Excludes CAFR data for New York (2002) and South Dakota, Illinois,
and Hawaii (for FY 2010).
Source: State CAFRs and authors calculations.
REPORTING ON THE FINANCIAL CONDITION OF THE STATES: 2002-2010
185
position in 2002 was 18, but that number increased to 24, before a
slight recovery in 2007. By the end of FY 2010, 30 states reported a
negative primary government unrestricted net assets position. The
median TPG financial position ratio in 2002 was 4.97 percent. In
2003 through 2004, the median TPG financial position was less than
1 percent.
While the states posted a slight recovery through 2007 (TPG
financial position ratio was 4.95 percent), the deficits reported in
2009 – 2010 eroded any such gains and for the second year in a row
more than half the states reported a negative TPG financial position.
States are reporting a negative unrestricted net assets position due in
part to problems laid bare in the Statement of Net Assets (Chaney, et
al., 2002; Johnson, et al., 2012). The treatment of non-capital debt or
debt on behalf of another government will negatively affect the
governments unrestricted net assets position.18 Recurring deficits
and mounting non-capital obligations (e.g., pension and postemployment benefit obligations) also impact the states unrestricted
net asset position negatively, in some instances, the state’s negative
unrestricted net assets position is greater than the state’s restricted
net assets i.e., if the state liabilities exceed its reported assets (net of
fixed assets), that negatively impacts unrestricted net assets. In 2010
for example, the following states reported a negative net asset
position - California (-$4.96 billion), Connecticut (-$9.39 billion),
Illinois (-$27.37 billion for 2009), Massachusetts (-$18.60 billion),
and New Jersey (-$28.97 billion).
Table 9 reports TPG financial position ratio for the states. Also
reported in Table 9 is the number of years the state reported a
negative primary government unrestricted net assets position. The
first 15 states (Wyoming through Nebraska) reported strong financial
position ratios with states maintaining unrestricted net assets
sufficient to meet 10 percent of their expenses. Alaska for example
reports a mean financial position ratio of 1.33, i.e., the state reported
an unrestricted net assets position that was sufficient to meet 133
percent of its current expenses. In 2008, the ratio was 240 percent.
The difference is the result of investment losses reported in the
Alaska Permanent Fund in 2009. The state now reports a 2010 TPG
financial position ratio of 185 percent. Wyoming also reported a
strong TPG financial position ratio. In 2006, the state maintained
186
KIOKO
TABLE 9
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Alaska
Wyoming
North Dakota
Indiana
Hawaii*
South Dakota*
Oklahoma
Utah
Delaware
Arkansas
Texas
Idaho
Montana
Colorado
Nebraska
New Mexico
Kansas
Iowa
Tennessee
New Hampshire
Georgia
Oregon
Mississippi
Alabama
Nevada
Mean
1.3352
0.6481
0.4027
0.2034
0.2018
0.1981
0.1902
0.1855
0.1736
0.1669
0.1471
0.1328
0.1221
0.1185
0.1111
0.09
0.0749
0.0712
0.0703
0.0679
0.0568
0.0453
0.0295
0.0188
0.0134
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
1
1
2
5
State
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
Virginia
Minnesota
South Carolina
Maryland
Pennsylvania
Arizona
Maine
Vermont
Washington
Michigan
Missouri
Ohio
North Carolina
Louisiana
Rhode Island
Florida
West Virginia
Kentucky
New York*
Wisconsin
California
Massachusetts
New Jersey
Connecticut
Illinois*
Mean
# of Years reporting UNA<0
State
# of Years reporting UNA<0
Total Primary Government Financial Position Ratio by State
0.0103
0.0014
0.0007
-0.0119
-0.0196
-0.0198
-0.0198
-0.0297
-0.0501
-0.0529
-0.0538
-0.0763
-0.0778
-0.0886
-0.1413
-0.1596
-0.173
-0.1797
-0.2033
-0.256
-0.3064
-0.31
-0.3963
-0.5236
-0.6013
3
5
4
5
7
6
8
9
5
8
7
9
9
7
9
9
6
9
8
9
9
9
9
9
8
Notes: * Excludes CAFR data for New York (2002) and South Dakota, Illinois,
and Hawaii (for FY 2010).
Source: State CAFRs and authors calculations.
unrestricted net assets sufficient to meet 102 percent of its current
expenses. At the end of FY 2010, the state reported a financial
position ratio of 88 percent.
An additional 13 states (i.e., New Mexico through South Carolina)
reported a positive mean TPG financial position ratio.
The
remaining22 states reported a negative mean TPG financial position
ratio. The last 10 states (Rhode Island through Illinois) reported large
REPORTING ON THE FINANCIAL CONDITION OF THE STATES: 2002-2010
187
negative TPG financial position ratio. These states also either
reported large non-capital obligations, obligations on behalf of its
local governments, or large or frequent deficits (five out of the nine
years). Some states did report a negative financial position ratio even
though they did not report recurring operating deficits (e.g., North
Carolina and Louisiana) while others did report a positive financial
position even though they reported recurring operating deficits (e.g.,
Hawaii and Indiana).
Service-level solvency is a measure of the government’s ability to
meet all expenses related to business-type activities with non-tax
revenues (i.e., Program Revenues/Expenses). Business-type activities
are usually expected to be self-supporting. Expenses should be
covered for the most part with user charges and fees, and to some
extent - operating and capital grants. Business-type activities are
generally not expected to rely on tax revenues to cover costs. In order
to more appropriately report service level solvency program revenues
(i.e., the sum of charges for services, operating grants, and capital
grants) as reported in the Statement of Activities are used to estimate
service level solvency.
The mean self-sufficiency ratios for each year and for each state
are reported in Table 10 and Table 11. For the most part, BTA
program revenues are sufficient to meet BTA expenses – except
during an economic downturn when revenues were slightly lower but
TABLE 10
Self-Sufficiency Ratio (Program Revenues/Expenses)
Year
Number of
States
2002
2003
2004
2005
2006
2007
2008
2009
2010
49*
50
50
50
50
50
50
50
47*
Business-type Activities
Mean
0.9294
0.9146
1.0051
1.1108
1.1252
1.1390
1.0428
0.8593
0.8943
Median
0.9401
0.8814
0.9738
1.0972
1.0998
1.1085
1.0225
0.8282
0.8884
Business-type Activities
Share of Total Primary
Government
14.05%
14.01%
13.32%
13.16%
12.97%
12.94%
12.75%
14.25%
17.54%
Notes: * Excludes CAFR data for New York (2002) and South Dakota, Illinois,
and Hawaii (for FY 2010).
Source: State CAFRs and authors calculations.
188
KIOKO
TABLE 11
Self-Sufficiency Ratio for the States
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
State
South Dakota*
Alaska
New Hampshire
Florida
Oklahoma
Delaware
West Virginia
Utah
Maryland
Louisiana
Virginia
Illinois*
Maine
Rhode Island
Missouri
Nebraska
Ohio
New Jersey
Michigan
Montana
Kentucky
North Carolina
Oregon
Tennessee
Pennsylvania
Ratio
2.4312
1.2947
1.2361
1.2235
1.2230
1.2224
1.2202
1.2109
1.2073
1.1990
1.1647
1.1327
1.1321
1.1218
1.1212
1.1045
1.0821
1.0776
1.0623
1.0544
1.0518
1.0431
1.0310
1.0152
1.0009
BTA
share
of
TPG
4%
5%
14%
11%
3%
16%
22%
7%
10%
4%
8%
9%
6%
26%
7%
4%
15%
11%
10%
8%
11%
6%
24%
6%
13%
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
State
Hawaii*
Wyoming
Colorado
California
Vermont
New York*
Texas
North Dakota
Kansas
South Carolina
Iowa
Mississippi
Washington
Wisconsin
Connecticut
Idaho
Minnesota
Alabama
Indiana
Massachusetts
Arizona
Georgia
New Mexico
Arkansas
Nevada
Ratio
0.9996
0.9649
0.9500
0.9180
0.9132
0.9005
0.8954
0.8879
0.8816
0.8561
0.8502
0.8467
0.8427
0.8327
0.8303
0.8274
0.8255
0.8205
0.7734
0.7511
0.7378
0.7190
0.6846
0.6505
0.4964
BTA
share of
TPG
8%
10%
30%
13%
6%
15%
27%
34%
11%
24%
26%
3%
22%
25%
21%
19%
12%
11%
5%
14%
16%
25%
24%
24%
10%
Notes: * Excludes CAFR data for New York (2002) and South Dakota, Illinois,
and Hawaii (for FY 2010).
Source: State CAFRs and authors calculations.
more importantly expenses especially those related to unemployment
benefits were higher (see 2002-2003 as well as 2009-2010). At least
57 percent or 255 out of 446 states reported a self-sufficiency ratio
less than 1.00, but only 97 states report a ratio less than 0.80.19
The data in Table 11 is sorted in descending order using the
mean self-sufficiency ratio for each state. Included in Table 11 is a
measure of the relative size of BTA i.e., BTA share of TPG.
Governments reporting a large BTA share of TPG report a significant
proportion of their activities under BTA. On average BTA expenses
were 13 percent of TPG - but with significant variation. For example
REPORTING ON THE FINANCIAL CONDITION OF THE STATES: 2002-2010
189
Colorado, Georgia, Iowa, North Dakota, Rhode Island, and Wisconsin
reported a BTA expense share of more than 25 percent; another
seven states reported a BTA expense share of more than 20 percent.
If business-type activities are a larger portion of TPG, its imperative
that these activities are self-sufficient, so as not to draw down on
general revenues that would be used to support governmental
activities.
The first 25 states (South Dakota through Pennsylvania) reported
a mean self-sufficiency ratio greater than 1.00. The remaining 25
states report a mean self-sufficiency measure of less than 1.00, for
some states e.g., Nevada, that measure was below 0.50, while for six
other states, the measure was below 0.80.
Long-term solvency is a measure of a government’s ability to
meet its long-term obligations as they come due. Two measure are
used to report long-term solvency – (i) debt to asset ratio and (ii)
liability to asset ratio. Table 12 and 13 report the TPG long-run
solvency ratios.
The debt measure includes all outstanding long-term debt
reported by the state for the fiscal year as reported in the required
statistical information (RSI) section of the CAFRs. The measure
TABLE 12
Long Run Solvency Ratio (Debt/Assets, Liability/Assets)
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
Total Primary Government Debt To
Asset Ratio
Number of
Mean
Median
States
0.2052
0.1810
47‡
0.2366
0.2022
49‡
0.2455
0.1982
49‡
0.2364
0.1969
50
0.2292
0.1912
50
0.2305
0.1801
50
0.2389
0.1962
50
0.2570
0.2164
50
0.2585
0.2223
46‡
Total Primary Government Liability to
Asset Ratio
Number of
Mean
Median
States
0.4165
0.3232
49*
0.4451
0.3555
50
0.4591
0.3905
50
0.4612
0.3857
50
0.4514
0.3966
50
0.4515
0.3931
50
0.4638
0.4150
50
0.5064
0.4425
50
0.5272
0.4550
47*
Notes: ‡Excludes debt data for New York (2002), Connecticut (2002),
Missouri (2002-2004), Hawaii, Illinois, Rhode Island, and South Dakota
(2010) * Excludes CAFR data for New York (2002) and South Dakota,
Illinois, and Hawaii (for FY 2010).
Source: State CAFRs and authors calculations.
190
KIOKO
therefore includes all general obligation debt as well as revenue debt,
certificates of participation, capital leases etc. As Mead (2006) notes,
general obligation debt is no longer the dominant financing
mechanism. Therefore reporting only general obligation debt would
understate the government’s long-term obligations. The second
measure - liability to asset ratio, is a ratio of the government’s
liabilities as a percent of reported assets. The liability measure
incorporates the government’s short- term and long-term obligations.
It includes accounts payables, accrued liabilities, as well as pension
and post-employment obligations and all the long-term debt.
Debt issuance was significantly lower following the financial crisis
(up only 2.86 percent, 2007-08). However, issuance in the
subsequent years was robust, in part due to federal programs (e.g.,
the Build America Bonds programs). Growth in long-term debt was
4.28 percent and 6.49 percent in 2008-09 and 2009-10
respectively. This is also evident in the debt to asset ratio. The
median debt to asset ratio is up from a low of 0.1801 in 2007 to a
high of 0.2328 in 2010, the highest debt to asset ratios for the nineyear period. This is also evident when one examines the median
liability to asset ratio which in 2007 was 0.3931. In 2010, the
median liability to asset ratio was 0.4550.
Table 13 reports the mean debt to asset ratio as well as the
mean liability to asset ratio for the states for the period. Also included
in Table 13 is the Debt Share of Liabilities, as well as the state’s rank
based on its operating revenue. The state’s debt share of liabilities is
a measure of the state’s long-term debt as a percent of its liabilities
i.e., what proportion of its long-term obligations are bonded. In New
Jersey for example, 71 percent of its liabilities are in the form of
outstanding bond issues while in Nebraska, only 3 percent of its
liabilities are in the form of a long-term debt issue. To illustrate the
implications of converting a long-term obligation into a bonded
security, consider the case of the Illinois. In 2003, the state issued a
$10 billion pension obligation bonds. In doing so, the state converted
a long-term obligation to its pension fund to a general obligation debt.
The states debt to asset ratio was significantly higher following the
issue - 0.6110 in 2003 compared to 0.4280 in 2002. Its debt share
of liabilities was also somewhat higher - 0.4851 up from 0.3665 in
2003.
REPORTING ON THE FINANCIAL CONDITION OF THE STATES: 2002-2010
191
TABLE 13
Debt and Liability Ratios by State
State
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
New Jersey
Connecticut‡
Illinois‡
Rhode Island‡
Massachusetts
California
New York‡
Hawaii‡
Nevada
Maryland
Wisconsin
Oregon
Louisiana
South Carolina
Georgia
New Hampshire
Kansas
Delaware
Florida
Mississippi
Washington
Michigan
Arizona
Minnesota
Utah
Vermont
Ohio
North Dakota
Pennsylvania
Kentucky
Virginia
Colorado
Texas
West Virginia
North Carolina
Iowa
New Mexico
Maine
Arkansas
Oklahoma
Missouri‡
South Dakota‡
Idaho
Indiana
Alabama
Montana
Tennessee
Debt to Asset
Ratio
Liability to
Asset Ratio
0.8676
0.7494
0.6669
0.6661
0.5556
0.5341
0.3925
0.3561
0.3329
0.3249
0.3210
0.3139
0.2804
0.2763
0.2558
0.2444
0.2429
0.2408
0.2291
0.2210
0.2180
0.2156
0.2150
0.2066
0.2027
0.1951
0.1926
0.1768
0.1742
0.1704
0.1670
0.1658
0.1429
0.1420
0.1407
0.1366
0.1346
0.1321
0.1289
0.1164
0.1004
0.0859
0.0738
0.0727
0.0678
0.0576
0.0504
1.2463
1.1181
1.4175
0.8322
1.2292
0.8693
0.6561
0.4535
0.5549
0.5254
0.5941
0.4895
0.3843
0.4491
0.4072
0.3928
0.3240
0.3906
0.4493
0.3455
0.6912
0.4866
0.3452
0.5106
0.2661
0.4596
0.6759
0.5003
0.4852
0.3799
0.4524
0.2900
0.3040
0.5437
0.3606
0.2798
0.2693
0.3568
0.2725
0.2622
0.1899
0.1820
0.2063
0.3057
0.1631
0.2035
0.1335
Debt as a
percent of
Liabilities
71%
67%
47%
81%
45%
62%
60%
79%
60%
62%
54%
64%
73%
62%
62%
63%
75%
62%
51%
64%
31%
45%
62%
41%
76%
43%
28%
35%
36%
45%
37%
57%
47%
27%
39%
49%
50%
37%
47%
46%
48%
47%
36%
24%
41%
29%
38%
Rank by
Operating
revenue
8
22
6
37
10
1
2
39
38
17
14
27
20
23
12
44
34
43
4
32
13
9
19
15
36
47
7
46
5
25
16
26
3
35
11
29
33
40
31
30
24
49
42
18
28
45
21
192
KIOKO
TABLE 13 (Continued)
State
48
49
50
Alaska
Wyoming
Nebraska
Debt to Asset
Ratio
Liability to
Asset Ratio
0.0301
0.0084
0.0051
0.1208
0.3409
0.1473
Debt as a
percent of
Liabilities
30%
3%
3%
Rank by
Operating
revenue
50
48
41
Notes: ‡Excludes debt data for New York (2002), Connecticut (2002),
Missouri (2002-2004), Hawaii, Illinois, Rhode Island, and South Dakota
(2010).
Source: State CAFRs and authors calculations.
For a vast majority of the states, their debt to asset ratio was
below 0.30 and at least 25 states report their liability to asset ratio to
be below 0.45. For five states – California, Connecticut, Illinois,
Massachusetts, and New Jersey, their liability to asset ratio was
reported to be greater than 1 more than once in the nine-year period.
In other words the state’s reported liabilities exceed its reported book
value of assets. This is in part due to recurring deficits that eroded
the government’s unrestricted net assets position and mounting noncapital obligations or long-term debt for which there is no
corresponding asset. Ohio, Pennsylvania, and Texas are but few of
the larger states that report a debt to asset ratio that is below the
mean (0.1926, 0.1742, and 0.1429 respectively).
OUTLOOK ON THE FINANCIAL CONDITION OF STATE GOVERNMENTS
This study reports on the financial condition of the states using
GASB-34 information currently reported in the government’s CAFRs. It
develops key indicators of financial condition and contributes to
broader measures of fiscal health (e.g., credit ratings) and financial
performance (e.g., Government Performance Project Grades).
This study also reports on the financial condition of states
following the worst recession since the Great Depression. Virtually
every state reported an operating deficit in 2009, and 41 states
reported an operating deficit in 2010. This study found smaller states
outperforming larger states. This is partially attributable to their
natural resource base (e.g., Alaska, North Dakota, and Wyoming).
Three large states (Florida, Pennsylvania, and Texas) reported strong
fiscal performance prior to the recessionary period while six others
REPORTING ON THE FINANCIAL CONDITION OF THE STATES: 2002-2010
193
consistently reported operating deficits (e.g., California, Connecticut,
Hawaii, Illinois, Massachusetts, and New Jersey). For these states,
their anemic fiscal performance led to rating downgrades in the postrecessionary period.20 For most governments, their liquidity remained
strong, albeit weakened in 2008 through 2010. Even though a
municipal debt crisis has been widely speculated, states report
sustainable long-term debt levels as their long-term debt obligations
did not exceed their reported assets or operating revenue.21
Even though this recession officially ended in June 2009, a vast
majority of the states will not report a positive operating position until
2012 and perhaps even through 2014. Their fiscal performance and
long-term fiscal health will be determined by a number of factors.
First –robust growth in tax revenues. State tax collections have grown
each quarter since the beginning of 2010. This growth has been
driven by a recovering economy as well as changes in tax policy
(Dadayan and Ward, 2011). In some states, double-digit growth in
revenues was reported in the first two quarters of 2011, though it’s
not expected to be sustainable over the long run (Boyd, 2011).
Moreover, even with this growth in tax collections, revenues for a vast
majority of the states remain below their pre-recession peak levels
(NASBO, 2011). Second – their fiscal health will depend upon their
ability to close budget gaps as federal stimulus dollars are exhausted.
Over the medium-term, states will need to address budget gaps that
will emerge once Congress acts to reduce its deficit. Third – their
long-term fiscal health will depend upon their ability to restore
depleted reserve funds and resolve their long-term obligation funding
issues. For a few states, there will be greater urgency to address their
underfunded pensions and unfunded other post-employment benefit
obligations. States will need to address retirement benefits for
current and future employees and ensure annual contributions to
pension and other post-employment benefit programs are met
consistently overtime.
ACKNOWLEDGEMENTS
The author would like to thank the Special Issue Editor – Martin
Luby, two anonymous reviewers, and gratefully acknowledges the
outstanding research assistance provided by Neal Buckwalter, Biff
Jones, and Christopher Bianchi. Any remaining errors are the sole
responsibility of the author.
194
KIOKO
NOTES
1.
The only states to report growth in general revenues in 2008-09
were North Dakota (6.01 percent) and Montana (2.05 percent).
2.
New York 2002 financial statements do not reflect changes from
GASB Statement 34; South Dakota did not publish their 2010
CAFR in time for the initial data collection. Hawaii and Illinois did
not have their 2010 CAFR available as of Aug 1, 2011.
3.
GASB does not require governments to classify assets or
liabilities into current (short-term) and non-current (long-term)
groups. To avoid any subjectivity, current assets and current
liabilities are as reported in the CAFRs.
4.
Capital grants and contributions were approximately 2 to 3
percent of total revenues for either GA or TPG. The only states to
report capital grants and contributions outside of this range were
Alabama (4.3 percent), Alaska (9.2 percent), Louisiana (7.7
percent), and Mississippi (4.2 percent).
5.
Indiana and Maine reported a GA operating position of 1.0004
and 1.0125 respectively; North Dakota reported a strong GA
operating position of 1.12.
6.
The only states to report growth in general revenues in 2008-09
were North Dakota (6.01 percent) and Montana (2.05 percent).
7.
North Dakota and Wyoming were the only states to report
significantly higher revenues in 2010 compared to 2008 levels
(14 and 15 percent respectively). Alaska’s 2010 primary
government general revenues were 9 percent higher than the
2008 levels but more than 16 percent lower than its 2007
levels. Alabama, New Hampshire, and West Virginia 2010
primary government general revenues were only marginally
greater than their 2008 (0.7, 0.05, and 0.4 percent
respectively). Only 7 states in 2010 reported their primary
government’s general revenues to be higher than their 2007
levels. They include Iowa (1.4 percent), Kansas (0.5 percent),
Missouri (1.4 percent), North Dakota (37 percent), Oregon (4.3
percent), West Virginia (2 percent) and Wyoming (9.7 percent).
8.
While BTA operating position is not reported, the data shows that
out of the 50 states, at least 29 states reported a mean
REPORTING ON THE FINANCIAL CONDITION OF THE STATES: 2002-2010
195
operating surplus for the nine-year period. There is significant
volatility in BTA outcomes within and between states.
9.
Consider the following: the operating position for Alaska’s is
estimated to be 1.260 operating position. This would translate to
an average annual surplus of $2,520. Wyoming’s operating
position of 1.2811. This would translate to an average surplus of
$1,681 per capita. New York’s average annual operating deficit
per capita for the period would be $231 per capita while that of
Georgia would be $138 per capita, nearly half that of New York
even though both states reported an operating position of
0.9660. The per capita basis makes subjective interpretation of
a fiscal measure using a socio-economic variable. New York’s
deficit is not significantly larger than that of Georgia nor is
Alaska’s surplus significantly greater than that of Wyoming given
the government’s operating revenues. A per capita measure
would give that impression, even though these differences do
not actually exist.
10. Margin ratios (gross margin or total margin ratios) generally
focus on size of an organization profit or loss relative to its
revenues.
11. An alternative measure is Change in Net Assets/Net Assets
(Johnson, et al., 2012; Mead, 2006; Rivenbark, et al., 2010).
Since a number of states (California, Connecticut, Illinois,
Massachusetts, New Jersey and Rhode Island) report a negative
change in net asset position as well as a negative net asset
position (resulting in a large positive ratio), this ratio is not
estimated in this study. While one may exclude these
observations from reported descriptive statistics, the results that
would have been reported would be biased upwards especially in
2008 through 2010.
12. New Jersey, Illinois, and Michigan reported a positive change in
net asset position only once in the nine-year period while
California, Hawaii, and Connecticut reported a positive change in
net asset position twice in the nine-year period.
13. The state reformed its transportation system by creating a new
entity – Massachusetts Department of Transportation
(MassDOT) which in effect merged several entities including the
Highway Department, Registry of Motor Vehicles, Massachusetts
196
KIOKO
Turnpike Authority, Massachusetts Port Authority, as well as the
Massachusetts Bay Transportation Authority. In FY 2010, the
state reported transfers to MassDOT of $8.9 billion and a
negative change in net position of $10.4 billion. The ROA for the
state for FY 2010 was -44.05 percent and its total margin ratio
for the year was -26.30; without the transfers to MassDOT, the
state’s fiscal picture would have been much better, although its
mean ratio for the nine-year period would more likely still have
been negative.
14. The following states report the current assets separately –
Arizona, Arkansas, California, Colorado, Connecticut, Delaware,
Florida, Indiana, Iowa, Kansas, Maine, Massachusetts, Michigan,
Minnesota, Mississippi, New Hampshire, New Jersey, New
Mexico, Oklahoma, Oregon, Pennsylvania, Rhode Island, South
Carolina, Texas, Vermont, West Virginia, and Wyoming.
15. For the nine-year period the BTA current ratio for Mississippi was
37.72, while Kansas reported a current ratio of 26.08. Vermont
and Maine reported a current ratio of 16.60; Indiana was 14.93,
while Oklahoma was 13.68. Rhode Island also reported a large
current ratio of 6.78.
16. BTA expenses are approximately 14 percent of TPG while BTA
assets are approximately 20 percent of TPG. For the 25 states,
their BTA current assets are approximately 25 percent of TPG
current assets, with some states reporting significantly higher
proportions (e.g., Iowa and Oregon report a BTA current asset
share of 42 percent; Colorado, Connecticut, Kansas, Texas and
West Virginia report BTA current asset share greater than 34
percent).
17. One must keep in mind that the expenses may be met using
restricted assets; therefore the measure reported here is but a
rough estimate (Chaney, et al., 2002).
18. Generally debt sold to finance capital assets is deducted from
the value of the asset and reported in the line “invested in
capital assets, net of related debt”. However, debt used to
finance non-capital assets, or used to provide financial
resources for other governments (e.g., school districts or public
authorities) will have the liability shown as a direct reduction in
UNA
REPORTING ON THE FINANCIAL CONDITION OF THE STATES: 2002-2010
197
19. While not a critical measure for GA since these are financed
primarily through tax revenues, the data shows that program
revenues (charges for fees, operating grants, and capital grants)
cover at least 46 percent of total expenses.
20. Of these five states, Standard and Poor’s downgraded California
from an A+ to an A-, downgraded Illinois from AA to A+, and
downgraded New Jersey from an AA to an AA-.
21. The mean debt to asset and debt to operating revenue ratios for
2009 were 0.2592 and 0.3181 respectively, with Connecticut,
Hawaii, New Jersey, Illinois, and Massachusetts reporting ratios
greater than 0.60. For a vast majority of states, their debt to
asset and debt to operating revenue ratios were below 0.40.
REFERENCES
Boyd, D. (2011, October). “The Long Road to State Fiscal Recovery.”
The Land Lines: 20-23.
Brown, K. W. (1993). “The 10-Point Test of Financial Condition:
Toward an Easy-to-Use Assessment Tool for Smaller Cities.”
Government Finance Review, 9 (6): 21-26.
Chaney, B. A., Mead, D. M., & Schermann, K. R. (2002). “The New
Governmental Financial Reporting Model.” The Journal of
Government Financial Management, 51 (1): 26-31.
Dadayan, L., & Ward, R. B. (2011). PIT, Overall Tax Revenues Show
Strong Growth in Second Quarter. Albany, NY: The Nelson A.
Rockefeller Institute of Government.
Frank, H. A., & Gianakis, G. A. (2010). “What Hath the GASB Wrought?
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Government Financial Officers.” Journal of Budgeting, Accounting
& Financial Management, 22 (2): 178-204.
Johnson, C. L., Kioko, S. N., & Hildreth, W. B. (2012). Governmentwide Financial Statements and Credit Risk. . Public Budgeting and
Finance, 32(1): 80-104
Maher, C. S., & Nollenberger, K. (2009). “Revisiting Kenneth Brown's
‘10-Point Test’”. Government Finance Review, 25 (5): 61-66.
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Mead, D. M. (2001). An Analyst's Guide to Government Financial
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Management (pp. 383-419). Boca Raton, FL: Taylor & Francis.
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Survey of the States. Washington, DC: National Governors
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Nollenberger, K., Groves, S. M., & Valente, M. G. (2003). Evaluating
Financial Condition: A Handbook for Local Governments.
Washington DC: International City/County Management
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Rivenbark, W. C., Roenigk, D. J., & Allison, G. S. (2010).
“Conceptualizing Financial Conditon in Local Government.”
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J. OF PUBLIC BUDGETING, ACCOUNTING & FINANCIAL MANAGEMENT, 25 (1), 199-233
SPRING 2013
THE COLLAPSE OF THE MUNICIPAL BOND INSURANCE MARKET: HOW
DID WE GET HERE AND IS THERE LIFE FOR THE MONOLINE INDUSTRY
BEYOND THE GREAT RECESSION?
Tima Tilek-uulu Moldogaziev*
ABSTRACT. This paper addresses risk exposures in insured portfolios of
monoline firms for 1995-2010. US public finance exposures decreased
dramatically during the period, while US structured finance and international
finance exposures grew significantly. By 2007, combined non-public finance
insured exposures were from 30% to over 80% of financial guaranty
portfolios. Regulatory and statutory requirements, however, did not change
as rapidly. Capital reserve requirements were adequate to withstand the
public finance default rates but not the structured risks in asset-backed
debt, CDOs or CDSs. As a result, the monoline industry today is a one-and-ahalf-firm business with uncertain future. Municipal bond market insurance
penetration was about 5% in 2010 guarantying about $27 billion in new
money issued. This is a far smaller industry than the one that existed in
2007 prior to the Great Recession.
INTRODUCTION
This paper surveys the insured portfolios of financial guaranty
firms to gauge the risk exposures by three portfolio areas from 1995
to 2010. I study the choices in domestic public finance, domestic
structured finance, and international finance portfolios of bond
insurance firms. A comprehensive survey of insured portfolios helps
to answer the question – “How did we get here?” Further answers to
------------------------------------* Tima T. Moldogaziev, Ph.D, is an Assistant Professor of Financial
Administration in the Public Administration Program, University of South
Carolina. His teaching and research interests are in financial management,
financial institutions and intermediation, fixed income markets and pricing,
and state and local government finance.
Copyright © 2013 by PrAcademics Press
200
MOLDOGAZIEV the same question stem from a review of statutory environments for
monoline firms, their capital reserve requirement rules, and the
premiums earned during the period under review. I then discuss the
post-recession state of affairs in the bond insurance market and the
efforts that we ought to take to reconfigure the market. Based on the
findings of the paper, I outline a set of policy relevant conclusions and
observations.
The recession of 2007-08 was a crisis of catastrophic proportions
for the financial guaranty industry. Nowadays, the municipal bond
insurance market remains in a very fragile state since the Great
Recession. All, except two monoline firms – AGC and BHAC2, are
under bankruptcy, regulatory administration, legal challenges,
commutation disputes or reorganization. Of the nine major monoline
insurance firms that existed prior to the crisis, seven discontinued
writing financial guaranty business (ACA, Ambac, CIFG, FGIC, MBIA,
Radian, and XLCA) and the two remaining firms (AGC and FSA) have
merged to form a single entity in 2010. The State of New York invited
Berkshire Hathaway to rescue the industry by early 2008, which Mr.
Buffet promptly executed by establishing Berkshire Hathaway
Assurance Corporation (BHAC). However, BHAC has written very little
business in 2009 and 2010; it also lost its triple-A rating by the fourth
quarter of 2010. Therefore, the monoline insurance industry in the
US is effectively a one-and-a-half-firm business in the post-Great
Recession era. The prospects are grim in the bond insurance market;
questions have been legitimately raised whether it can still be called
an industry. This gives us a pause to reassess what happened in the
monoline insurance industry, why the entire industry has collapsed,
and what policy implications to municipal credit enhancement
regulation we should draw from the recent financial crisis.
Starting with the inception of Ambac in 19713, state and local
governments have increasingly relied on monoline municipal bond
insurance to obtain better access to capital and to increase
marketability of their debt issues. Monoline firms were initially limited
in their business choices only to municipal securities instruments and
could not branch out to other types of insurance services such as
auto, health, catastrophic insurance and so on. The idea was to limit
the municipal fixed-income bond insurance business from the
uncertainties of other markets by building entry/exit walls by product
type; hence, the term ‘monoline’ or single-product insurance market.
THE COLLAPSE OF THE MUNICIPAL BOND INSURANCE MARKET
201 Municipal debt insurance contracts or financial wraps underwritten by
monoline firms act as a third-party guarantee to honor any remaining
coupon interest and principal when the issuing entity is not able to do
so for any reason. An insured bond is assigned the rating of the
insuring firm (i.e. triple-A rating for almost all of the monoline insurers
before the Great Recession), and hence, enjoys the benefits
associated with any such credit enhancement. This form of risk
intermediation became so popular that in 2007 almost 60% of the
long-term new money issued in the municipal securities market had
obtained monoline guarantees. Markets were so confident in the
financial soundness of these insurance firms that no serious doubts
were ever voiced about the ability of these companies to withstand a
crisis of epic proportions until the first quarter of 2008. Godfrey and
York are an illustration of such optimism:
Insured bonds seem to have suffered unfairly from various
degrees of skepticism about their ability to perform as well as
natural Triple-A’s. For example, they have endured the capitaladequacy allegation that insurers do not have sufficient
resources to meet their obligations when the economic chips
are down. […] Insured bonds are even stronger than natural
Triple-A’s because of the extra measure of protection. […] We
proved that by applying severe economic pressure (italics
added) to both naturals and insured bonds. A diversified
portfolio of natural Triple-A bonds probably would not make it
through a Great Depression without defaults. Insured bonds,
on the other hand, should do so without any ultimate defaults
(Godfrey & York, 1994, p. 124).
Similarly, Standard and Poor’s has stated by late 2007 that every
firm in the industry, with the exception of CFIG, had stable ratings
outlooks (Green and Smith, 2007, October 29). However, during the
Great Recession it became apparent that the risk exposures of
monoline firms in structured and international finance were
overwhelming; despite the more secure trends in the municipal debt
sector. In retrospect, we saw that the monoline insurance industry
might not have been monoline enough, and consequently, the firms
were not as resilient as we expected them to be.
A number of studies provide a careful review of the public finance
side of the exposure in the municipal bond insurance industry – its
purposes, organizational structures, and functions. Comprehensive
202
MOLDOGAZIEV reviews of the monoline firms’ public finance sector roles are offered
by Justice and Simon (2002), Godfrey and York (1994), Satz and
Perry (1993), Hirtle (1987), and Feldstein (1983)4. Of these, only
Hirtle briefly mentions the structured finance products that the
municipal bond insurance firms began to insure in 1980s. CDIAC5
(2002) provides a detailed roadmap for practitioners and public
officials about the benefits and costs of the insurance selection
decision for municipal borrowers. Perkins and Quinn (2001) analyze
and summarize the statutory bases under which the insurance firms
operate in the United States. Denison (2009) and Kwiatkowski
(2009) provide a discussion of the most recent trends (i.e. failures) in
the monoline industry.
On the topic of structured finance risk exposure of financial
guaranty firms, I am aware of just two studies in the academic
literature. Kotecha (1998) and Drake and Neale (2011) discuss the
‘wraps’ that monoline insurers provide for structured finance
products. This low level of interest from the academic researchers
appears very hard to justify especially given that non-public finance
insurance exposures grew significantly for every monoline firm since
the mid-1990s. For example, anywhere between 10%-50% of total
insured portfolios of financial guaranty firms were in the structured
finance products at any given time since mid-1990s. At the same
time, the financial guaranty industry was aware of the sizable
structured finance risk exposure of the ‘muni’ bond insurers.
Greenwald writes in the business insurance practitioners’ periodical
that in the first 3 quarters of 1999 “[…] bond insurers guaranteed
$104 billion in asset- and mortgage-backed securities, collateralized
bond and loan obligations, and international transactions; compared
with the $95 billion insured in the municipal market […]” (2000, p. 3).
Clearly, the muni bond insurers were not strictly in the municipal bond
market any more. Drake and Neale (2011) make the first empirical
effort to understand the structured finance portfolios of monoline
insurance firms. They study the insured products and the investment
decisions of financial guaranty firms. Their scope, however, is limited
to the structured finance risk exposure and focuses on one firm –
MBIA. I study complete insured portfolios of all the financial guaranty
firms from 1995 to 2010.
THE COLLAPSE OF THE MUNICIPAL BOND INSURANCE MARKET
203 THE BOND INSURANCE LITERATURE
The literature on bond insurance and its purpose originates from
four main theoretical traditions. Denison (2003; 2009) classifies the
field by information asymmetry, market segmentation, and credit
enhancement based discussions. I add the theory of tax arbitrage by
Nanda and Singh (2004) to this list, which pertains to the role of
bond insurance in the municipal tax-exempt debt market. Thakor
(1982) argues that insurance functions as a signal to the markets to
alleviate information asymmetries. By choosing a specific level of
insurance coverage, the borrowers are signaling their default
probabilities to the markets. Thakor assumes the possibility that the
buyers of insurance can choose the level of coverage that they desire
and that their premiums reflect such selected levels of insurance as
signals of individual default risk. However, insurance for a municipal
security is complete (it guarantees both the interest and principal);
differentiation by any level of protection is infeasible.
Yet, the signaling benefit may still be present due to an insurance
firm’s decision to insure the debt issue. Kidwell et al. opine that “If
the insurer views the default characteristics of the municipality the
same as the market does, then the insurance premium should exactly
offset any reduction in the risk premium imposed by the market. Any
further reduction in yields can be attributed to the benefits of
signaling that have accrued to the issuer” (Kidwell et al., 1987, p.
303). However, Hsueh and Liu (1990; 1992) do not believe that
insurance contracts may function as self-certification signals. They
argue that “complete debt insurance is not an informative signal and
the purchase of debt insurance in and of itself cannot provide any
signaling benefit to borrowers” (Hsueh and Liu, 1990, p. 692). This
full coverage, the authors believed, did not give the issuers of
municipal debt any ability to choose levels of coverage, and hence,
insurance contracts could not function as a signaling tool in the
municipal bond markets.
Denison (2001) extends empirical research on bond insurance by
looking at aggregate quarterly insured municipal bond volumes and
risk premiums. He tests the theory of market segmentation and finds
that the shifts on the supply side of the market are significant for the
yield differential between the triple-A vs. Baa securities. Denison
writes that “[The] results demonstrate that ceteris paribus, a 1%
increase in the percentage of new issue municipal bonds using
204
MOLDOGAZIEV insurance decrease the average yield spread ratio by nearly 0.01. [A]
0.01 decrease in the spread ratio combined with a yield of 6% on Aaa
bonds translates to a reduction in the yield spread differential of
approximately six basis points” (Denison, 2001, p. 407). Finally,
Denison (2009) believes that bond insurance provides the market
with high investment grade securities while the supply of naturals is
limited.
The most studied role of insurance is its credit enhancement use
and obtaining any benefits associated with such an enhancement.
Cole and Officer (1981) and Bland (1987) discovered that the
borrowing costs of municipalities are significantly lower for
guaranteed bonds compared to non-insured long-term debt
obligations. They also discovered that bond insurance was the most
‘profitable’ for municipalities with lower credit ratings; and it did not
matter much whether insurance was purchased during stable or
volatile market conditions. These results confirmed the results from
an earlier simulated study on bond guarantees and their effects on
borrowing costs by Joehnk and Minge (1976). They conclude that:
“The historical performance of guaranteed bonds suggests that the
market recognizes enhancement of agency ratings, due to the
addition of the guaranty, as a reflection of improved investment
quality” (Joehnk & Minge, 1976, p. 14-15). However, Braswell et al.
(1982) do not find any effect of insurance on borrowing costs and
report that no significant evidence can be substantiated for the claim
on bond insurance benefits.
Scholars have also been interested in finding whether bond
insurance is acting as a tax arbitrage instrument for the municipal
securities products. Nanda and Singh (2004) not only discovered that
bond insurance has a more significant effect on borrowing costs for
the lowest rated securities, but also that bond insurance provides tax
arbitrage in case of a default by the issuer of a tax-exempt bond.
Municipal bond investors, among other important reasons, demand
securities with a tax-exempt status. Nanda and Singh (2004) state
that the tax arbitration powers of municipal bond insurance are of
particular significance for longer-duration muni bonds. By agreeing to
insurer a bond, the insurance firms are set to become the ‘issuers’ of
tax-exempt securities; i.e. they become suppliers of tax-exempt bonds
for the remaining maturities should the muni issuer fail to service
debt. Nanda and Singh (2004) also find that the distribution of bond
THE COLLAPSE OF THE MUNICIPAL BOND INSURANCE MARKET
205 insurance is not monotonic, i.e. just a small number of bond offerings
with very low or very high ratings have purchased an insurance
contract. The bulk of the bond insurance volume is related to the
municipal securities rated between triple-B to upper double-A ranges.
As seen from this review, previous academic literature
concentrated heavily on the public finance side of the monoline
insurance industry. While one may expect that the theoretical
traditions explaining the role of bond insurance for municipal markets
may extend to the structured finance products, no empirical evidence
exists to confirm such expectations. (However, of the four theoretical
traditions, the tax arbitration hypothesis, would not apply to the nonpublic finance products as the bulk of structured finance exposure
was in non-tax exempt securities.) Nevertheless, no studies
comparing the roles of bond insurance for public vs. structured
finance securities exist in the academic literature. Similarly, there are
no studies comparing risk exposures of municipal securities versus
structured finance instruments. Were these two sectors sufficiently
similar to be under the same umbrella of monoline guaranty? Only
one paper looked at the structured finance risk exposure of financial
guaranty insurance, however, concentrating on a single firm (Drake &
Neale, 2011). As a result, we know very little about portfolio
compositions of monoline firms, proportions of risk exposure by type,
and/or whether, given the risk exposures, there are adequate
mechanisms in place to keep the firms solvent in the event of
defaults. This work studies the portfolio structures, proportions of
exposure by sector type, trends within and between firms. The hope is
to improve our understanding of the bond insurance industry beyond
the traditional public finance side of the picture. That in turn may help
us better understand why the industry has collapsed and what to do
in the future, if any future indeed exists for the industry.
THE GREAT RECESSION AND THE MONOLINES
Numerous authoritative accounts have already been written
about the Great Recession. There appears to be a consensus that the
recent financial crisis had its roots in the mortgage markets,
especially the subprime bottom of the market (Diamond & Rajan,
2009; Ashcraft & Schuermann, 2008; Demyanyk & Van Hemert,
2008; Benmelech & Dlugosz, 2009; Jaffee, 2009; Dwyer & Tkac,
2009; Longstaff, 2010; Acharya et al., 2009). While we know, mostly,
206
MOLDOGAZIEV in which sector the troubles brewed, there is no consensus on what
exactly had caused the crisis itself. Whalen (2008) singles out the
market regulators in Washington for their perceived “odious public
policy partnership” with mortgage market participants, lack of
regulation (or willingness to regulate) in the securitized derivatives
markets, and ‘faulty’ accounting standards requiring ‘mark-to-market’
requirements. Ashcraft and Schuermann (2008) identify seven major
informational frictions in the mortgage securitization process starting
from the mortgage originator; adverse selection and moral hazard
conditions for the arranger, asset manager, rating agencies, and
servicers; and informational asymmetries between the third-party
investors and each of the listed financial intermediaries.
Jaffee (2009) puts the blame on the investment banking and
government sponsored pass-through entities (also known as GSEs –
government sponsored enterprises). Longstaff (2010) and Acharya et
al. (2009) believe that the overreliance between the money markets
and mortgage backed securities markets, as well as the subsequent
illiquidity and cross-market contagion shocks had caused the Great
Recession. In addition, Acharya et al. (2009) believe that instead of
risk sharing, the financial institutions involved in the securitization
process, have kept a bulk of the risk (mostly tail-risk) on their own
books. Demyanyk and Van Hemert (2008) and Benmelech and
Dlugosz (2009) suggest that the quality of mortgage market
securities had monotonically deteriorated from early 2000s (and
perhaps the securitizers and ratings firms knew about it). This asset
quality deterioration was a result of mortgage and derivatives market
opacity (Diamond & Rajan, 2009; Gallegati et al. 2008), fueled by the
perverse payment schemes for the market participants that lead to
market herding and excessive tolerance/ignorance of tail risks
(Rajan, 2005; Acharya et al., 2009; Dwyer & Tkac, 2009).
Surprisingly, the mainstream finance literature does not discuss
the role the financial guaranty industry has played in the asset
securitization process that lead to the crisis. There is silence despite
the implosion of the entire industry; the meltdown of financial
guaranty insurance has gone largely unnoticed in the literature so far.
Few exceptions are Denison (2009), Kwiatkowski (2009), Drake and
Neale (2011), and Martell and Kravchuk (2010). Drake and Neale
(2011) find that MBIA had exposure to structured finance products on
both ends – it insured structured finance products and invested
THE COLLAPSE OF THE MUNICIPAL BOND INSURANCE MARKET
207 assets in structured financial securities. Thus, their argument is that
instead of being misled, the bond insurance firms may have flocked
and hurried to insure the generally very risky structured financial
products6 and invested in the same securities. In this line of thought,
the actions of monoline firms may not have been any different from
the actions taken by the banks, ratings agencies, or other financial
intermediaries – they too participated in the profits frenzy. If that is
indeed the case, then the insurance industry must be included in the
monitoring pool to prevent any future financial crises. In addition, any
current or future effort to revive the bond insurance industry and then
regulate it appropriately may take note of negative trends, if any.
Total premiums earned in the bond insurance industry appear to
mimic the general upward-sloping trend of the securitization era. In
terms of dollar volumes, MBIA and Ambac, with FSA having posted
mid-range earnings, lead the pack. Figure 1 plots annual change in
premiums earned for the industry. There is a considerable spike in
earnings from 2001 to 2008, with AGC remaining the only firm that
continued earning premiums beyond that. We see that it was not
unusual for the monoline firms to post annual increases in premiums
earned in excess of 25% during this period, with ACA, Radian, and
FGIC going especially ‘ballistic’. It may be hard to apply the term
‘innocent bystander’ to the monoline industry in lieu of these
premiums earned regarding their role in the years prior to the Great
Recession.
DATA
I collected data on insured portfolios for nine bond insurance
firms (the entire industry) for the period of 1995 to 2010 inclusive.
Monoline firm daily public finance insured volumes are obtained from
Bloomberg, which gives over 1.4 million bond-series level individual
observations. The public finance data are aggregated to quarterly
levels to match the data on non-public finance sectors in the portfolio.
I then collected data on quarterly volumes for structured finance and
international finance portfolios of monoline insurers from the S&P
ratings analyses, the SEC-10K forms and quarterly financial reports
for monoline firms that the insurers filed for the period of 1995-2010
(unfortunately, this information cannot be disaggregated further as
finer frequencies of reports do not exist). The SEC-10K forms also
provide information on quarterly premiums earned and balances of
statutory capital reserve requirements.
208
MOLDOGAZIEV FIGURE 1
Percentage Annual Change of Industry Total Premiums Earned from
1995 to 2010 by Insurer: All Insured Product Types – Public,
Structured, and International Finance
Source: Form SEC-10K and quarterly financial reports for all
insurance firms.
STATUTORY ENVIRONMENTS AND REGULATION
The laws governing the monoline insurance industry are state
specific. Each bond insurance firm is subject to regulation by the
insurance department of the state in which it is domiciled. Perkins
and Quinn (2001) provide a detailed summary of these state specific
THE COLLAPSE OF THE MUNICIPAL BOND INSURANCE MARKET
209 laws for a select number of states. These state specific laws are
supposed to be harmonized based on the 1986 Financial Guaranty
Insurance Model Act of the National Association of Insurance
Commissioners (for details see Drake and Neale [2011]; and Perkins
and Quinn [2001]), yet there is a great deal of variation in how states
approach regulation. However, New York insurance department
(NYID) became de facto monoline insurance industry regulator due to
Appleton Rule. This rule stipulates that regardless of domicile status,
any financial guaranty firm that wants to write business in the state of
New York must be licensed in New York and must remain monoline
(as defined by Article 69 Financial Guaranty Insurance Corporations
[FGICs] adopted in 1989) outside the borders of the state. If a firm
writes multiline insurance outside of New York, it would lose its
license for any business activity within the state (see §6904 Article
69; and Drake and Neale [2011]). All financial guaranty firms in the
US operated in New York and were subject to NYID regulation.
On the regulation of insurance firms, Borch (1974) writes that the
regulatory laws must achieve two objectives. The first objective is to
write policy that creates fair contracts as well as clearly defined
products. The rules must define the boundaries of activities: the limits
of what is permissible and monitored. The second objective is to
“[make] it reasonably certain that the company is able to fulfill its
obligations under the insurance contracts it has concluded… Rules of
this kind must require that the company has sufficiently large capital
reserves” (Borch, 1974, p. 398). Solvency of insurance firms with
regards to defined insurance products and fields of activity are then
an important task for the regulators.
Nine sections of Article 69 of the New York statute on FGICs
provide definitions, financial and organization requirements, reserve
levels, limitations, forms and rates, reinsurance and transition
provisions, applicability of other relevant laws, and relevance of
security funds for the monoline firms. This article defines monoline
beyond the municipal bond market securities and includes a variety
of asset-backed securities (ABSs) and collateralized debt obligations
(CDOs). Consequently, Article 69 permits industry exposure to risks
beyond the traditional municipal bond market and widens the
definition of monoline to include structured non-municipal finance
products. In 1997, monoline firms received permission to underwrite
credit default swaps (CDS) based on the opinion of New York
210
MOLDOGAZIEV Department of Insurance subject to a few relaxed rules (see NY
Circular Letter No. 19, 2008, p. 6; Drake and Neale, 2011, p. 34).
(This line of business was also becoming a staple product of the
mainstream multi-line insurance firms.) Finally, insurance guaranties
for CDO-squared products were permitted in 2004. Lack of regulation
for the special purpose vehicles (SPVs) that the monoline firms
established to sell CDSs, unusually large and punishing triggers in the
CDS contracts, uncertainty of squared risks, and, as we now know,
excessive exposure to securitized products in the mainstream
financial markets may have eventually brought the industry down.
The New York Insurance Department has already conceded that
monitoring and limitations on non-public finance products was
insufficient, however, it has not yet banished the securitized products
from the financial guaranty markets outright (NY Circular Letter No.
19, 2008).
An important rule for insurance firm regulation is to require
capital reserves. Diamond (1984) believes that requirements that
lead to higher levels of own equity for financial intermediaries correct
for some of the perverse incentives that the firm may have in its
business decisions. Klein (2009) believes that the capital reserve
requirements for insurance firms ought to be stricter for new and
lesser-known products. Yet, the reserve requirements alone cannot
solve the solvency conundrum. A careful monitoring mechanism must
be in place for the reserve requirements to work. This author further
suggests that ‘early-warning’ mechanisms must be in place to study
solvency issues not only within a specified domain but also in crossmarket conditions. Did the monoline regulation put the necessary
mechanisms in place in the financial guaranty industry?
In order to maintain business, financial guaranty firms must have
a minimum capital of $75 million and a policyholders’ surplus equal
to $65 million. NY Circular Letter No. 19 (2008) states that:
‘Capital’ is defined by Insurance Law § 107(a)(12) to mean,
when used in reference to a stock insurance company, the
aggregate par value of all classes of shares of capital stock
issued and outstanding. “Surplus to Policyholders” is defined
by Insurance Law § 107(a)(42) to mean the excess of total
admitted assets over the liabilities of an insurer, which is the
sum of all capital and surplus accounts minus any impairment
thereof” (2008, p. 4).
THE COLLAPSE OF THE MUNICIPAL BOND INSURANCE MARKET
211 Section 6903, paragraphs 1-4, of Article 69 on FGICs established
the rules on required contingency reserves in excess of capital and
surplus to policyholders’ levels. Paragraph 3, subsection B (subsubsections i-v), spell out the levels of contingency reserves required
as a percentage of principal guaranteed for public finance products.
The range is 0.55% to 2.5% depending on risk category. Similarly,
Paragraph 4, subsection 4 (sub-subsections i-v) establish levels for
structured finance products. The contingency levels here fall in the
range of 1%-2.5%. The NYID expected that these levels captured the
differences in risk exposure. The total statutory capital reserve
requirement is a function of principal guaranteed and adjusted for
risk in the insured portfolio pool in excess of capital and surplus
accounts. I find that public and structured finance insured portfolios
of monoline firms were assumed to have the same levels of default
risk.
Table 1 provides data on statutory capital requirements for nine
financial guaranty firms, where available, for 1995-2010. It is obvious
TABLE 1
Statutory Capital Requirements as a Percent of Total Principal
Guaranteed in the Portfolio, by Insurer: 1995-2010
Year
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
ACA
Ambac
0.29
5.72
0.21
5.39
0.80
0.78
1.22
0.84
1.23
1.58
1.18
2.89
1.15
2.01
0.99
2.27
0.88
2.17
0.86
1.54
0.86
1.51
0.88
5.22
0.81
137.58 0.84
0.89
1.32
AGC
1.33
1.35
0.98
1.04
1.34
1.62
1.02
0.84
0.82
0.81
0.89
0.90
0.86
1.23
FSA
0.69
0.57
0.47
0.63
0.68
0.69
0.70
0.71
0.71
0.73
0.93
1.02
0.99
1.04
1.18
CIFG
FGIC
-3.43
-1.46
0.62
1.68 0.27
0.90 0.80
1.39 0.80
2.65 0.85
4.25 0.89
11.89 1.09
1.12
1.27
1.45
1.44
1.63
1.49
1.41
MBIA
0.48
0.39
0.07
0.94
1.06
1.12
1.02
1.12
1.09
1.09
1.08
1.08
1.04
1.03
0.93
Radian XLCA
1.67
1.47
1.35
1.29
1.65
1.88
1.49
2.28
1.69
1.62
1.22
1.58
1.85
1.35
1.53
2.43
3.06
4.93
6.02
16.50
Source: Form SEC-10K and quarterly financial reports for all
insurance firms.
212
MOLDOGAZIEV that the monoline industry has largely conformed to the reserve
requirement levels – with niche firms ACA and Radian and foreign
crossovers CIFG and XLCA – having higher levels of reserves than
other remaining ‘more secure’ firms. Did these capital reserve levels
maintain solvency of the industry? In hindsight, it is obvious that the
assumption of equal levels of risk for public vs. structured finance
risk exposures was faulty. The size of capital requirements did not
reflect the true levels of risk by product type largely because the
definition of ‘monoline’ was stretched beyond any sensible
boundaries. This misclassification fails the theoretical and empirical
prescriptions (as discussed earlier) of any effective regulation by
Borch (1974), Diamond (1984), and Klein (2009).
PORTFOLIO COMPOSITIONS
In this part, I report the findings about insurance risk exposures in
public, structured and international finance exposures in the financial
guaranty industry. The data suggest that exposure to structured and
international finance risks has increased during the period under
study for every firm in the industry but one. FGIC was the only firm
that decreased the share of structured finance volume in its total
insured portfolio. In 2007, between 20% (FGIC) to 80% (ACA) of total
insurance contracts were outside the usual municipal securities
products.
Public Finance Insured Portfolios
From Figure 2 we can learn about specific municipal portfolio
tastes of monoline insurers. The figure plots complete U.S. public
finance insurance exposure by industry/purpose categories for select
financial guaranty firms. The niche firms ACA and Radian (which had
the ratings of single-A and double-A respectively before the crisis)
concentrated mostly in riskier public finance industries. ACA’s
portfolio has exploded in hospital and higher education insurance
contracts since its inception in 1997.
Radian’s public finance portfolio also shot up significantly starting
from 2002, with general obligation bonds leading the way and
hospital bonds coming second in the insured portfolio. For six other
insurance firms (CIFG, MBIA, Ambac, FSA, FGIC, and XLCA) the growth
in insured portfolios was primarily in more secure public finance
industries such as general obligation, special tax-backed, and public
THE COLLAPSE OF THE MUNICIPAL BOND INSURANCE MARKET
213 utility debt. From these plots, we can observe that insured U.S. public
finance portfolios grew significantly for all bond insurance companies
FIGURE 2
U.S. Structured Finance Portfolios by Select Insurance Firms (Ambac,
FGIC, and MBIA), 1995-2010
214
MOLDOGAZIEV Note: Plots for Remaining Six Firms Are Available Upon Request.
Source: Bloomberg.
with the exception of AGC. In fact, for AGC its portfolio was constant
from 1997 to 2003, then declined until 2007, and grew exponentially
from 2007 on.
I find that data reveal evidence of very strong statutory
convergence in the U.S. public finance portfolios of monoline insurers
as expected. Table 2 shows that all firms except AGC exhibit
correlated portfolio compositions prior to the Great Recession. The
U.S. public finance portfolio correlation coefficients range between
0.67 (for Radian and CIFG) and 0.99 (for MBIA and FGIC), excluding
AGC. The mean and the median correlation scores for the industry are
0.58 and 0.91 respectively including AGC, or 0.91 and 0.93 excluding
it.
Are there any signs of converging trends in the financial guaranty
industry toward the riskier products in the public finance portfolios? I
reclassify the insured U.S. public finance portfolios by high-risk
purpose issues as described in Peng and Brucato (2004). Figure 3
shows that there are three groupings of monoline firms separated by
risk tastes in the U.S. public finance products. ‘High risk taste’ firms
with about 40% or greater of their public finance portfolios in riskier
public finance debt; ‘moderate risk taste’ firms with about 30% of
their public finance portfolios in riskier products, and “low risk taste”
THE COLLAPSE OF THE MUNICIPAL BOND INSURANCE MARKET
215 TABLE 2
Pearson Correlations Matrix, Insured Portfolio Volumes by Insurer;
U.S. Public Finance, 1995-2010 (Quarterly)
1 ACA
2 Ambac
3 AGC
4 FSA
5 CIFG
6 FGIC
7 MBIA
8 Radian
9 XLCA
1
2
3
1.00
0.91 1.00
-0.34 -0.60 1.00
0.92 0.99 -0.59
0.88 0.95 -0.89
0.96 0.98 -0.46
0.95 0.98 -0.51
0.83 0.91 -0.51
0.88 0.98 -0.92
4
5
1.00
0.96
0.98
0.98
0.79
0.97
1.00
0.89
0.92
0.67
0.97
6
7
1.00
0.99
0.90
0.94
8
1.00
0.92 1.00
0.95 0.81
firms with 20% or less of their insured portfolios in the riskier public
finance debt issues.
Table 3 provides correlation scores of high-risk purpose public
finance exposures of bond insurers. These results provide no clearcut evidence of convergence in the industry for riskier public finance
products; the mean and median correlation coefficients are 0.44 and
0.65 respectively. The so-called niche firms, ACA and Radian,
specialized in insuring high-risk municipal debt. At the same time
firms like FSA and FGIC, as well as two newcomers XLCA and CIFG,
concentrated their insurance exposure to lower risk securities.
TABLE 3
Pearson Correlations Matrix, Insured High Risk Debt Purpose
Volumes by Insurer; U.S. Public Finance: 1995-2010 (Quarterly)
1 ACA
2 Ambac
3 AGC
4 FSA
5 CIFG
6 FGIC
7 MBIA
8 Radian
9 XLCA
1
1.00
0.97
0.13
0.92
0.92
0.35
0.62
0.87
0.90
2
1.00
-0.30
0.91
0.98
0.79
0.64
0.89
0.97
3
1.00
0.19
-0.90
-0.76
0.35
-0.19
-0.91
4
1.00
0.80
0.20
0.55
0.87
0.83
5
1.00
0.89
-0.57
0.66
0.98
6
1.00
0.13
0.61
0.91
7
8
1.00
0.43 1.00
-0.61 0.77
216
MOLDOGAZIEV FIGURE 3
High Risk Purpose U.S. Public Finance Exposure: 1995-2010
Structured Finance Insured Portfolios
A recent study by Drake and Neale (2011) finds that the monoline
insurance industry’s collapse had its main roots in the structured
finance portfolios of insurance firms7. They find that MBIA’s troubles
brewed both in the insured structured finance portfolio choices as
wells as the firm’s investment decisions. Due to data limitations, my
study looks at the insured portfolio side only. However, I have
sufficient data to study the insured structured finance exposures for
each firm in the financial guaranty industry. The data reveal that
starting from late 1990s the structured finance exposures grew many
folds for each bond insurance firm. The top four structured finance
THE COLLAPSE OF THE MUNICIPAL BOND INSURANCE MARKET
217 categories generally are insurance contracts for mortgage backed
securities, home equity loans, commercial asset backed securities,
and ‘other’ structured finance products. Category ‘other’ includes
credit default swap insurance contracts among other miscellaneous
purposes. In Figure 4, I plot the U.S. structured finance portfolio
volumes for select insurers for the period of 1995-2010. Ambac and
MBIA appear to have gone for commercial ABS, while mortgage ABS
were prominent for Ambac and FGIC. In addition, FGIC appears to
have packed on ‘other’ types of structured products of which swaps
are the biggest share; home equity loans are a distant third choice for
all three financial guaranty firms. Furthermore, the correlation matrix
of insured U.S. structured finance portfolios (quarterly volumes) in
Table 4 appears to suggest that there is a strong level of convergence
in the industry in this sector of the market. Except for ACA, the
correlations are in the range of 0.62 (between FGIC and Ambac) to
0.98 (between FSA and Ambac). The mean and median correlation
FIGURE 4
U.S. Structured Finance Portfolios by Select Insurance Firms (Ambac,
FGIC, and MBIA), 1995-2010
218
MOLDOGAZIEV Note: Plots for remaining six firms are available upon request.
Source: Form SEC-10K and firm quarterly financial reports.
THE COLLAPSE OF THE MUNICIPAL BOND INSURANCE MARKET
219 scores in the data are 0.82 and 0.88 respectively including ACA, and
0.85 and 0.89 excluding this niche firm.
TABLE 4
Pearson Correlations Matrix of Insured Portfolio Volumes by Insurer;
U.S. Structured Finance, 1995-2010 (Quarterly)
1 ACA
2 Ambac
3 AGC
4 FSA
5 CIFG
6 FGIC
7 MBIA
8 Radian
9 XLCA
1
1.00
0.51
0.62
0.44
0.98
0.93
0.42
0.86
0.95
2
1.00
0.90
0.98
0.90
0.61
0.94
0.79
0.93
3
1.00
0.88
0.87
0.81
0.94
0.86
0.94
4
5
1.00
0.73
0.65
0.91
0.75
0.85
1.00
0.95
0.74
0.95
0.98
6
7
8
1.00
0.63 1.00
0.93 0.72 1.00
0.91 0.83 0.90
Source: Form SEC-10K and quarterly financial reports for all
insurance firms.
I now asses the ratios of U.S. structured finance products in
monoline insurance firm aggregate portfolios; the ratios for select
firms are depicted in Figure 5. The data suggest that by 2007-08 the
share of structured finance products in the insured portfolios has
increased for five firms – ACA, AGC, Ambac, FHIC, and Radian. Two
firms – CIFG and XLCA (now Syncora), held by foreign parent
companies – entered the monoline insurance market from the
structured finance product insurance side to public finance insurance
business. For them, the share of U.S. structured finance exposure has
decreased. MBIA’s (now NPFG) U.S. structured finance exposure has
stayed constant around 20% of the aggregate insured portfolio. One
strong survivor of the Great Recession – FSA (now AGM) – appears to
be the only major monoline insurance firm that has shrank its
exposure to structured finance products by half from 1995 to 2010.
The findings in this section suggest that the risk exposures for
monoline insurance firms were well beyond the public finance
products. While there was anecdotal evidence that this was occurring
220
MOLDOGAZIEV FIGURE 5
Product-type Exposure Shares of Insured Portfolios for Select Insurers
(ACA, MBIA, and FSA); U.S. Public Finance, U.S. Structured Finance,
and International Finance (International Public and Structured
Products Combined) Ratios, 1995-2010
THE COLLAPSE OF THE MUNICIPAL BOND INSURANCE MARKET
221 Note: Plots for remaining six firms are available upon request.
Source: Bloomberg, form SEC-10K and firm quarterly financial reports.
since the early 1990s, I provide detailed evidence for this
phenomenon for all bond insurance firms in the industry8. I find that
by 2007 the insurance exposure of monoline firms to structured
finance risks in their portfolios was anywhere between 20% (MBIA
and FGIC) to over 80% (ACA). By overlooking this part of insured
portfolios, especially in light of high correlations in insured risks, it
would be impossible to assess the true risk exposures of bond
insurance firms. The structured finance risks were concentrated in
the types of products that exploded during the Great Recession –
mortgage ABS, home equity loans, swaps, and even commercial ABS
securities.
International Finance Insured Portfolios
What was the exposure to non-US market insured risks in the
monoline industry? In Figure 5 (in the previous section), one can
observe that international risk exposure was the lowest compared to
U.S. public finance and U.S. structured finance exposures. All
monoline insurance firms (except for ACA and XLCA, perhaps even
222
MOLDOGAZIEV FGIC) appear to have experienced an increase in insured
international portfolios. In 2007 the ratios ranged anywhere between
about 0.00-0.40. However, for major players in the industry such as
Ambac, FSA, MBIA, and AGC, 10%-20% of their portfolios appear to
have been exposed to international risks as of 2007. When taken as
a whole, I do not find any evidence of convergence in trends in the
international finance portfolios within the industry. Table 5 provides
the matrix of correlation coefficients for international finance
exposures in the monoline insurance industry; where ACA and Ambac
appear to be the odd firms, though Ambac being closer to the pack.
TABLE 5
Pearson Correlations Matrix of Insured Portfolio Volumes by Insurer,
International Finance (International Public and Structured Finance
Combined), 1995-2010 (quarterly)
1 ACA
2 Ambac
3 AGC
4 FSA
5 CIFG
6 FGIC
7 MBIA
8 Radian
9 XLCA
1
1.00
0.78
0.37
0.64
-0.91
-0.01
0.48
0.14
-0.83
2
3
1.00
0.55
0.89
-0.82
0.32
0.86
0.43
-0.29
1.00
0.87
0.94
0.93
0.83
0.96
0.96
4
1.00
0.87
0.65
0.96
0.77
0.92
5
1.00
0.97
0.83
0.94
0.98
6
1.00
0.65
0.95
0.95
7
8
1.00
0.80 1.00
0.85 0.98
Source: Form SEC-10K and quarterly financial reports for all insurance firms.
However, a closer look at the international structured finance
exposures in insured portfolios reveals that the industry (and every
firm in the industry) has seen a significant growth in international
structured finance exposure relative to public finance exposure. I plot
these volumes for select firms in Figure 6 below. The data suggest
that there was an industry-wide growth in international structured
finance exposure, with FGIC picking up some exposure in 2004 and
on.
THE COLLAPSE OF THE MUNICIPAL BOND INSURANCE MARKET
FIGURE 6
223 International Finance (combined international public and structured
finance) Portfolios by Select Insurance Firms (Ambac, AGC, and
MBIA), 1995-2010
224
MOLDOGAZIEV Note: Plots for remaining six firms are available upon request.
Source: Bloomberg, form SEC-10K and firm quarterly financial reports.
The correlation coefficients of insured international structured
finance portfolios in Table 6 provide strong evidence of convergence
in insured volume trends in the monoline industry. AGC appears to be
a moderate outlier in the industry; however, even with AGC included
in the pool, the mean and median correlation scores are 0.83 and
0.88 respectively. Unfortunately, no sufficient data is available on the
breakdown of international structured finance portions by product
lines. Hence, only an aggregate view of international structured risk
exposure is possible here. Acharya et al. (2009) reviewed the
structured financial risk exposures in the rest of the world and
concluded that the trends were very similar to the ones in the US.
Consequently, it is quite plausible to expect that there was a similar, if
not greater, level of harmful risk mix in international structured
finance portfolios of the bond insurance firms.
THE MONOLINE INDUSTRY AFTER THE GREAT RECESSION
Is there life for the monoline industry beyond the Great
Recession? No definitive answer is available to satisfy this question
THE COLLAPSE OF THE MUNICIPAL BOND INSURANCE MARKET
225 TABLE 6
Pearson Correlations Matrix of Insured Portfolio Volumes by Insurer,
International Structured Finance, 1990-2010 (Quarterly)
1 ACA
2 Ambac
3 AGC
4 FSA
5 CIFG
6 FGIC
7 MBIA
8 Radian
9 XLCA
1
1.00
0.83
0.55
0.89
0.97
0.97
0.88
0.82
0.88
2
3
4
5
6
7
8
1.00
0.87 1.00
0.99 0.80 1.00
0.81 -0.03 0.90 1.00
0.91 0.68 0.96 0.98 1.00
0.96 0.84 0.99 0.85 0.89 1.00
0.79 0.59 0.88 0.86 0.93 0.88 1.00
0.82 0.33 0.89 0.99 0.97 0.90 0.92
Source: Form SEC-10K and quarterly financial reports for all insurance firms.
today. Only one firm, albeit with two independent arms - AGC and
AGM – is active in the bond insurance market after the Great
Recession. The second firm – BHAC – has written very little new
business in 2010. Standard & Poor’s and Moody’s downgraded both
of these bond insurance firms to AA+ in October 2010 and Aa3 in
December 2009 respectively. The ratings firms appear to think that
the bond insurance industry is likely to remain in its current weak
form and is unlikely to achieve the pre-recession penetration levels in
financial guaranty insurance. Only a little over 5% of the muni issuers
purchased insurance in 2010 from AGC and AGM (about 1,700
issues valued a little shy of $27 billion). Only one arm of Assured
Guaranty – AGC is still active in insuring structured finance products.
With these dynamics in place, can we still call the monoline industry
an industry? Can other firms such as Ambac and MBIA rejoin the
financial guaranty business? What needs to happen for the monoline
industry to start actively functioning again?
The current sorry state of affairs is a result of assuming that the
monoline insurance industry was indeed monoline. The assumption
was partly based on a premise that the risks and products in the nonpublic finance portion of the insured portfolios were generally similar
to the public finance side of the insured portfolio. The assumption
was also partially built on an expectation that international risks were
226
MOLDOGAZIEV similar to domestic risks. The hope was that the knowledge of the
public finance side of the bond insurance business would extend to
the structured finance securities. Based on this expectation, no need
for special type of regulation was assumed to exist. These
assumptions and expectations must be adjusted to reality. How
realistic would an expectation that a triple-A rated municipal general
obligation bond is identical to (and has the same default rate) a tripleA rated mortgage backed asset or a triple-A rated credit default swap
be? Even within public finance exposure, how realistic would an
assumption that a single-A US sub-sovereign bond is the same (and
has the same default rate) as a similarly rated Italian or Greek subsovereign debt be? The definition of monoline must be tightened in
the regulatory guidelines and statutes.
One option for the future is to create a truly monoline industry,
the option that some firms are already taking voluntarily. Assured
Guaranty Municipal and National Public Finance Guaranty have both
opted to stick with not just the public finance insurance only, but also
to concentrate on one domestic market at a time. Other monoline
firms that want to survive and rejoin the industry are attempting to
commute their structured finance exposures – ACA, NPFG, CIFG,
Radian, and Syncora. It is not clear how Ambac emerges from
bankruptcy, but a true public finance only firm is a possibility.
Contrary to predictions of Godfrey and York (1994), the markets have
learnt that the US municipal debt issuers have had very low default
rates during the Great Recession. State and local governments relied
on bond insurance in the past; they continue purchasing bond
insurance today. They need monoline firms that can provide
insurance protection and remain solvent.
Another option is to have realistic assumptions about the nonpublic finance products should the regulators decide to keep the
definition of monoline intact. Asset-backed securities have default
risks far greater than the current range of 0.5%-2.5% during severe
economic crises. Unregulated and little known derivatives, such as
credit default swaps or tranches and squared tranches have default
rates even greater than that. Consequently, the statutory reserve
requirements for structured finance products and even international
public finance products must go up. Better yet, since the credit
default swaps are like catastrophic risks with severe losses (see
Rajan, 2005; Acharya et al., 2009) they should be regulated as
THE COLLAPSE OF THE MUNICIPAL BOND INSURANCE MARKET
227 catastrophic risk insurers. The simplest and wisest approach would
be banning them from the bond insurance market altogether.
Regardless of whether the first or second option is to be taken,
there is a need for greater transparency in the bond insurance
industry. Monoline insurance firms must provide data not only on
their public finance products but structured finance products as well.
Data on off-the-book contracts should have been on the books (re
swaps). Equally important, information on insurance premiums must
be public. A recent study by Johnson and Moldogaziev (2011) has
found that insurance premiums have informational value beyond the
value of traditional underlying credit ratings. Market efficiency may
improve when the market participants know bond insurance
premiums for public and non-public finance securities. Today we can
access information only at the aggregate premium levels. When bond
insurance premiums are public, the profit-chasing incentives of
financial guaranty firms will be easier to detect. Therefore, premiums
should reflect the risks, which in turn should be reflected in statutory
reserve requirements.
The New York Department of Insurance has produced a list of
best practices for the monoline industry (NY Circular Letter No. 19,
2008). However, these are largely cosmetic in nature and do not
adequately address the failures of monoline industry monitoring. The
market correction for the monoline industry was very dramatic; the
regulatory changes for the industry should be as dramatic. Finally,
currently New York is a de facto regulator of the financial guaranty
industry. However, the services of bond insurance firms are
demanded all over the domestic market. Should there be a move
toward a federal statutory framework, which would reflect the true
scale of activities of bond insurance firms before the Great
Recession? Lessons learnt are many. A separate matter is whether
the bond insurance firms and regulators decide to avoid the previous
mistakes that brought the industry on its knees.
CONCLUSION
The monoline insurance industry is going through the toughest
times. The firms have ventured into products that were very different
from the traditional public finance debt instruments; structured
finance risks and international finance risks have brought the
industry down. The literature and the regulatory statutes have ignored
228
MOLDOGAZIEV this shift instead opting to assume that the non-public finance
products were identical to public finance products in all aspects. In
the meantime, the non-public finance exposures grew significantly
but the financial guaranty industry was still regulated as if it remained
a low-risk public finance insurance provider. By 2007, 20% (MBIA) to
80% (ACA) of insured portfolios were tied to US structured finance
contracts. Similarly, exposures to international finance were 7% (ACA)
to 40% (CIFG). Clearly, the industry was not monoline enough in the
original meaning of the word.
For the state and local governments, the role and use of bond
insurance is still the same. Municipal governments continue
purchasing bond insurance where necessary; however, the levels of
bond insurance market penetration shrank 10 times – from 50% in
2007 to just over 5% in 2010. Bond insurance post-Great Recession
will come at a price. Competition in the industry is non-existent – a
choice is between Assured Guaranty Corporation or Assured Guaranty
Municipal – in either case the underlying firm is the same. Cost of
insurance may have gone up as benefits received should have gone
down. Currently, active bond insurance firms have the highest rating
of AA+, hence, as previous literature would predict, cost of borrowing
will not necessarily be at that level but lower.
Bond insurance firms appear to be exercising self-regulation by
banishing or limiting exposure beyond the public finance securities.
Almost all firms are in commutation talks or legal disputes in their
attempts to retire structured finance contracts. With time, more firms
may return to the industry; but self-regulation, as we learnt it, is not
the most reliable approach to market regulation. A functioning
regulatory and monitoring system is important to avoid calamities in
the future. Solvency reserve requirements should reflect actual risks
and real-time portfolio composition data should be reported and
monitored. Only a properly risk adjusted solvency approach will help
to build again the monoline industry on what is now a ‘one-and-a-halffirm’ market. The monoline insurance industry is not completely dead
today. However, a lot depends on the choices that the firms and
regulators take, including whether there is any life for the industry
beyond the Great Recession. It is highly unlikely that a current AA+
rated bond insurance market will ever achieve the pre-recession
penetration levels above 50%.
THE COLLAPSE OF THE MUNICIPAL BOND INSURANCE MARKET
229 NOTES
1. The bond insurance industry and the scholars that study it use
terms bond insurance, monoline insurance, financial guaranty,
and bond wrap interchangeably. This paper is in line with that
practice.
2. All abbreviations explained in the Appendix section Table A1.
3. Although we consider Ambac to be the earliest bond insurance
company, The Daily Bond Buyer published an article about a
financial guaranty firm in its 1897 issue (April 3), p. 1). The article
is titled “Municipal Bond Insurance” and discusses activities of a
firm called First Municipal Bond Insurance Company of America.
The descriptions suggest this firm was the first prototype of a
bond insurance firm in the market.
4. Minge (1974) provides the earliest of these reviews.
5. Abbreviation stands for the California Debt and Investment
Advisory Commission.
6. This same phenomenon of ‘race-to-the-bottom’ may have
happened in the public finance securities portfolios too. However,
with very few defaults by the municipal issuers observed during
the Great Recession (or since the WW II indeed), the public
finance portfolios of insurance firms are not believed to be the
main source of the industry’s collapse.
7. Failures in securitization chains and credit default swaps are well
document; see the earlier section on the causes of the Great
Recession.
8. The only article written on the topic by Drake and Neale (2011)
concentrates primarily on MBIA.
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APPENDIX SECTION
TABLE A1
Abbreviations for Monoline Insurance Names and Years Established
Abbreviation
ACA
Ambac
AGC
BHAC
FSA
CIFG
FGIC
MBIA
Radian
XLCA
Full Name, year formed
American Capital Access, 1997
American Municipal Bond Assurance Corporation, 1971
Assured Guaranty Corporation, 1988
Berkshire Hathaway Assurance Corporation, 2007
Financial Security Assurance, 1985; now Assured
Guaranty Municipal (AGM)
CDCI IXIS Financial Guaranty, 2002
Financial Guarantee Insurance Company, 1983
Municipal Bond Insurance Association, 1973; now
National Public Finance Guarantee Corporation (NPFG)
Radian, 1999
XL Capital Assurance, 2000; now Syncora
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Governmental Accounting Manuscripts should be e-mailed to:
Don Deis, DBA, Professor, Governmental Accounting Section Editor
E-mail: [email protected].
Healthcare & Nonprofit Organizations Manuscripts should be e-mailed to:
Dana Forgione, Healthcare & Nonprofit Organizations Manuscripts, Section
Editor. E-mail: [email protected].