Embed Size (px)
Citation preview
1
Regulatory monitoring and financial reporting quality: Evidence
from the University Sector
Working paper: Not to be quoted without the permission of the authors
Lei Tao†
Margaret J. Greenwood
School of Management
University of Bath
† Corresponding author: Lei Tao, University of Bath School of Management, University of
Bath, Claverton Down, Bath BA2 7AY. E-mail: [email protected]
2
Regulatory monitoring and financial reporting quality: Evidence from the
University Sector
Abstract
General purpose financial statements increasingly contribute to the performance
assessment and regulation of organisations in receipt of public funding as a means of
protecting public money and services. This paper investigates the impact of regulatory
monitoring and control on financial reporting quality in the University sector. Our
setting is English universities, independent not-for-profit entities, which are subject to
monitoring and control by the sector regulator which provides a significant proportion
of their funding. Using data for the period 2002-2011, we conduct both univariate and
multivariate analysis and find evidence that financial reporting quality increases with
the strength of regulatory monitoring, but that this benefit is more than offset by
earnings management when the achievement of financial breakeven, a key performance
indicator, is threatened. These findings contribute to the limited literature on public
sector and not-for-profit financial reporting quality, and to the wider literature on the
influence of monitoring and control. They also have relevance first, to Governments
and other organisations providing funding to NFP entities and second, to other
stakeholders who use financial statements to make judgements about not for profit
performance.
Key words: External monitoring, financial reporting quality, not-for-profit, universities,
regulation.
3
Regulatory monitoring and financial reporting quality: evidence from the
University sector
1. Introduction
With the aim of generating greater efficiency and effectiveness in service
delivery, public sector reform over the last thirty years has delivered increasing levels
of managerial discretion to public service providers. However, the pursuit of ‘freedom
to manage’ has been tempered by the desire to improve accountability and the need to
protect public money and services from mismanagement and fraud (Hood, 1991, 1995).
In response there has been a growth of ‘light touch’ forms of regulation in which
performance monitoring, particularly financial performance, plays a significant part in
assessing where public money or services may be ‘at risk’. Such performance
monitoring is a feature of the provision of much public funding whether to public, not
for profit or private sector firms and can be found for example, in the provision of
healthcare and education institutions as well as in the regulated industries such as water.
As general purpose financial statements are increasingly being used as a basis
for the performance evaluation and regulation of entities in receipt of public funding,
the quality of financial reporting in these sectors is of growing significance. In a general
sense Dechow et al. (2010, p. 344) for example argue that financial reporting quality is
important because:
Higher quality earnings provide more information about the features of a firm’s
financial performance that are relevant to a specific decision made by a specific
decision-maker. (Dechow et al., 2010, p.344)
4
In this paper we investigate two questions of concern to regulators and other
stakeholders. First, does the exercise of monitoring and control functions lead to better
financial reporting quality and thus more effective regulation? Second, does the threat
of failure to achieve key financial objectives lead to lower financial reporting quality
and to what extent might this interact with the influence of the monitoring and control
function?
We use the setting of English universities, which are independent not for profit
entities largely but not wholly dependent on public funding, to investigate these
questions over a period of ten years from 2002-2011. The sector regulator, the Higher
Education Funding Council for England (HEFCE), provides a significant, but varying,
proportion of University funding and exercises a monitoring and control function
which, if the assessment of financial sustainability is poor, triggers progressive
intervention mainly in the form of increased scrutiny but ultimately with the potential
for the withdrawal of funding. Other funding providers, such as students, do not have
the resources or capability to exercise similar levels of monitoring and control.
Prior literature based in agency theory, and largely conducted in the private
sector, provides evidence that principals investing in monitoring activities aimed at
agent-principal goal alignment are associated with better financial reporting quality. A
much larger literature however suggests that financial reporting quality is impaired
when self-interested managers act opportunistically to avoid costly intervention and to
meet targets. In this paper we extend these literatures into the not-for-profit sector
where the presence of multiple stakeholders, goal ambiguity and a weaker, more
amorphous incentive framework raise questions about the generalisability of findings
5
from private sector studies, and where the literature and thus our understanding of
financial reporting quality is, as a consequence, more limited.
In this study, we use discretionary accruals as a proxy for financial reporting
quality (Dechow et al., 2010) and, drawing a parallel with the literature which explores
the influence of institutional shareholdings on financial reporting quality, we use the
proportion of funding sourced from HEFCE as a proxy for the influence of external
monitoring and control. We find that the influence of monitoring and control has a
beneficial impact on financial reporting quality but that this benefit is overridden if the
achievement of financial breakeven, a key regulatory threshold is threatened.
These findings contribute to the limited literature on the determinants of
financial reporting quality in the public and not for profit sectors; to the literature on
the influence of external monitoring and control on financial reporting quality and for
the first time provides evidence of the interaction of financial objective achievement
and external monitoring.
This paper proceeds as follows: Section 2 reviews prior literature; Section 3
provides a brief overview of the institutional setting; Section 4 provides the basis for
our hypotheses and describes our research method; Section 5 reports our findings and
Section 6 comprises a discussion of the findings and concludes with their implications
for policy and for future research.
2. Prior literature
For the purposes of this paper we consider the literature on financial reporting
quality as falling into two strands: that which explores the factors which contribute to
6
good financial reporting quality, particularly the role of monitoring and control as a
means of reducing agency costs, and a much larger strand which investigates the factors
which impair it, for example, by creating incentives for opportunistic behaviour. The
majority of this literature has been performed in a private sector setting but, with the
advent of public sector reforms which aim to enhance both performance and
accountability through marketization and the strengthening of incentives in publicly
funded entities, an agency based exploration of the factors influencing financial
reporting quality in these sectors is now developing.
Agency theory predicts that the ability of managers to opportunistically manage
reported financial performance is constrained by the effectiveness of external
monitoring by stakeholders who have the resources and capability to monitor, discipline
and influence the managers of reporting entities (Monks and Minow, 1995). Further,
corporate governance mechanisms that act to mitigate agency costs often require the
disclosure of information which renders opportunistic management of financial
performance more challenging because of the need to avoid detection. To date the
influence of three groups of external stakeholders has been investigated: institutional
shareholders; security analysts and finally, tax authorities. Chung et al (2002) and Mitra
and Cready (2005) argue that the lower liquidity of large shareholdings fosters concern
for an understanding of underlying profitability and an interest in long term, rather than
short-term performance. They proceed to show that large institutional shareholdings
improve financial reporting quality. Further, Irani and Oesch (2013) argue that
information intermediaries undertake private information production to inform
shareholders and to facilitate the detection of opportunistic managerial rent seeking
behaviour and show that the extent of analyst coverage is positively associated with
7
financial reporting quality. Finally, Desai et al (2007) argue that tax authorities provide
a monitoring mechanism which acts to the benefit of shareholders as stricter tax
enforcement, e.g. through tax audits, makes it harder for managers to extract private
benefits. Hanlon et al (2014) go on to demonstrate that the ex-post probability of a tax
audit is associated with better financial reporting quality.
In summary there is growing evidence that external monitoring has a beneficial
impact on financial reporting quality. This literature has yet to be tested for
generalisability to the not for profit and public sectors.
The second, more substantial, strand of the literature investigates the factors
which impair financial reporting quality by incentivising the management of reported
financial performance.
Earnings management occurs when managers use judgment in financial
reporting and in structuring transactions to alter financial reports to either mislead
some stakeholders about the underlying economic performance of the company or to
influence contractual outcomes that depend on reported accounting numbers’ (Healy
and Wahlen, 1999, p.365).
Earnings have for example been found to be managed in order to avoid
regulatory intervention (Kanagaretnam et al. 2004; Lobo & Yang 2001; Alali & Jaggi
2011), to secure/retain government contracts and to avoid political attention arising
from high profitability (Key 1997; Makar et al. 1998).
The extension of this literature into the public and not for profit sectors is a more
recent phenomenon. Concerns about goal ambiguity, a weaker, more amorphous
8
incentive framework and a less clear agency based theoretical explanation for the form,
content and use of financial statements have historically constrained its development
but, in the context of the increased marketization and corporatisation of public services
(Hood 1991, 1995), a number of studies have sought to extend our understanding of
financial reporting quality in these sectors. At present, these are however limited in
their scope. A general finding is that of loss avoidance and small surplus reporting in
response to political and regulatory incentives (Jegers, 2012; Ballantine et al., 2007;
Hofmann, 2007; Omer and Yetman, 2003; Omer and Yetman, 2007; Leone and Van
Horn, 2005). Other than this, reported financial performance has also been found to be
managed with a view to generating income in the form of grants (Pilcher and Van der
Zahn, 2010) and donations (Jones & Roberts 2006; Krishnan et al. 2006) and to signal
competence (Ferreira et al. 2013; Pilcher and Van Der Zahn, 2010). Issues relating to
the influence of external monitoring and control have yet to be investigated.
In this paper we contribute to the limited literature on the determinants of
financial reporting quality in the public and not for profit sectors; to the literature on
the influence of external monitoring and control on financial reporting quality and by
investigating the interaction of external monitoring with earnings management to avoid
regulatory intervention.
3. Institutional and regulatory setting
The setting for this paper is the University sector. Universities represent an
interesting setting because internationally, although they largely operate as independent
not-for-profit entities, they also operate in the for-profit and public sectors. Whatever
sector they operate in, however, universities are dependent on public funding to a
9
greater or lesser extent, particularly with respect to student fees and funding for
research, with very few receiving no public funding at all. In England, in the period of
our study, for example, Universities received about half of their funding from public
sources with over a third coming from the main funding body and sector regulator, the
Higher Education Funding Council in England (HEFCE).
Most OECD Universities are independent autonomous entities but to the extent
that they depend on public funding have, over the last thirty years or so, been subject
to public sector reforms which aim to deliver enhanced efficiency, better performance
and improved accountability. In the higher education sector the application of the
doctrines of New Public Management, a generic term which captures the general
features of these reforms (Hood, 1991, 1995) has resulted in Universities becoming less
dependent on government funding and in the increased marketization of the higher
education sector. These changes have been accompanied by an increasingly managerial
culture within higher education institutions (Clark 1997; Ferlie et al. 2008; Deem et al.
2007.
3.1 The English setting
In England, as elsewhere, the three main generators of university income are
teaching and research activities, with endowments and other sources making up the
balance. The sector regulator (HEFCE) is the single biggest funder of teaching and
research in the UK distributing about £4bn annually of public funds which account for
over 30% of English universities’ revenue. Academic teams generate additional
research income (16%) by applying for research grants from the (seven) research
councils and other research sponsors, whilst tuition fees levied on individual students,
10
at rates of between £3000 and £20000 per student, generate another 35% of university
income. This funding structure confers considerable power and influence on HEFCE.
HEFCE’s primary aims, in addition to investing funds in higher education, are
to ensure accountability to both students and the public for their use. Funds are provided
subject to a number of conditions which are incorporated into a formal Financial
Memorandum. The performance of the institution is then subject to a risk based
regulatory regime in which intervention is proportionate to the assessed risk to financial
sustainability on the one hand and quality of teaching and research on the other.1 This
system relies heavily on financial statements and other forms of financial reporting. The
extent of financial information collected is considerable and includes five year forecasts
of performance and detailed activity based cost information primarily, the latter being
collected to ensure that funds allocated by HEFCE are used for the intended purposes
and that there is no cross subsidisation between teaching and research. As the HEFCE
funding system is largely a capitation system based on student numbers those
institutions heavily reliant on HEFCE funding can be vulnerable to a fall in student
numbers, a real risk in the competitive environment in which universities operate.
The HEFCE assessments of institutional risk are not published nor are the
metrics for assessing the risk to financial sustainability because of first, the need to
protect the interests of existing students which may be adversely affected if, in a
competitive market for new students, a University’s position is further destabilised
through publication of poor risk assessments and secondly, to avoid the potential for
1 www.hefce.ac.uk/
11
opportunistic gaming of the metrics (Baylis et al., 2015). Notwithstanding this the
reported surplus/deficit is known to be a key indicator with the expectation of a surplus
being incorporated into the Financial Memorandum between HEFCE and each
university and with consecutive deficits being identified as a cause for concern. Gaming
which flatters current year performance at the expense of future years, e.g. through the
inflation of discretionary accruals is discouraged through the requirement for five year
forecasts, the credibility of which is assessed with reference to past performance. An
integral part of the regulatory regime is the possibility of an institutional audit, with a
number of universities being selected randomly for audit and others being triggered by
the results of the risk assessment exercise.
The focus of the risk assessment regime is largely the effective management of
downside risk with those institutions whose performance is deemed to be satisfactory
being subject to little more than the requirement to submit annual returns and the
potential for a ‘random’ audit. However, if a university is assessed as being at risk,
HEFCE adopts a number of interventionary strategies based on the exercise of ‘soft
power’ 2 backed up by the power to change the accounting officer and to remove
funding. The accounting officer is normally the vice-chancellor but this need not be the
case. However, a change in accounting officer or the removal of funding are generally
considered to be a ‘nuclear option’ (reference the news article on my desk) only to be
exercised in extreme circumstances. Instead, intervention is progressive involving
‘conversations’ in which HEFCE engages in a conversation with the Governing Body,
2 Interview with former HEFCE senior executive.
12
and particularly the audit committee3, to facilitate a return to a satisfactory financial
position. This may entail written correspondence, meetings, formal presentations of
recovery plans etc.4
Overall the number of institutions which are deemed to be ‘at risk’ is low. The
main influence of HEFCE’s regulatory regime is for most institutions therefore the
extent of scrutiny to which their financial and other performance is subjected.
4. Hypothesis development
Prior to investigating the main question as to the influence of monitoring and
control on the financial reporting quality of not for profit entities we first investigate
whether, consistent with previous research in not for profit and public entities, English
universities manage reported financial performance to report small surpluses.
Prior research in the public and not for profit sectors suggests that deficits are
associated with CEO turnover, (Eldenburg and Krishnan, 2008; Ballantine et al., 2008;
Brickley and Van Horn, 2002) and that the reporting of losses is avoided through the
management of accruals (Ballantine et al., 2007). Further, the literature suggests that
these entities also avoid the reporting of large surpluses in order to demonstrate
efficiency and effectiveness in delivering services to important stakeholder groups
(Connolly and Hyndman, 2003; Verbruggen and Christiaens, 2012), to avoid questions
regarding their charitable status (Krishnan et al., 2003) and to avoid discouraging
3 Ibid. 4 The effectiveness of this form of engagement, and the power of HEFCE to influence institutional policy
and strategy, was illustrated in an interview with a senior executive of HEFCE with an example of a
member of a University executive team who was ultimately dismissed as a consequence of such
discussions.
13
donations and endowments (Frank et al., 1989). We therefore hypothesise that
universities manage their financial performance, through the management of
discretionary accruals, to avoid report small surpluses close to zero.
H1: Universities manage reported financial performance to report small
surpluses close to zero.
Public funding represents a significant proportion of university revenue which
in England over the period of our study amounts to approximately 50%. Consistent with
New Public Management reforms which aim to enhance both performance and
accountability, these funds are often provided subject to terms and conditions,
compliance with which is monitored and with non-compliance triggering sanctions. In
England, HEFCE, as the university sector regulator which provided, over the period of
our study, about 35% of university revenue in the form of a block grant, has the power,
resources and capability of ensuring that funds are used effectively for the purposes for
which they are provided, as outlined in the Financial Memorandum issued by HEFCE
to each university. In contrast, research councils, although monitoring individual
research grant awards which are made on the basis of research excellence, do not have
the same level of interest in overall institutional performance and so awards are made
subject to terms and conditions relating only to the use of the grant and are generally
not conditional on the health of the institution as a whole. Finally, the many thousands
of students who provide about 35% of University revenue have little power or capacity
on an individual basis to monitor and influence the performance and financial
sustainability of their University.
14
There is no prior literature in the public or not-for-profit sectors on the influence
of external monitoring and control on financial reporting quality but the private sector
literature provides evidence that external monitoring is associated with better financial
reporting quality. The literature which investigates the influence of institutional
shareholders in particular provides the basis for a comparison with the university sector
in that a parallel can be drawn between the relative power that can be exercised by
HEFCE in its funding of English universities and that exercised by institutional
shareholders in the funding of private sector firms. In the private sector literature
institutional shareholders, are characterised as sophisticated investors who not only
have the incentive to monitor and analyse reported financial performance but also, as
compared with individual investors with smaller holdings, have both the resources and
capability of processing and analysing firm information (Mitra and Cready, 2005; Lim
et al., 2013). These studies have found financial reporting quality increases with
increasing institutional share ownership.
In our study of English universities we draw a parallel between institutional
shareholders and the sector regulator, HEFCE, which has the motive, power, resources
and capability for the external monitoring of universities. We therefore expect that
HEFCEs monitoring activities will have a greater influence on those institutions which
are most dependent on its funding. Further, those universities which have a higher
proportion of HEFCE funding are by definition those with lower levels of research
funding. Universities with a strong research profile are generally those with the highest
reputations, attracting the best students. Those with high levels of HEFCE funding are
therefore represented in general by universities with lower reputations who are, as a
consequence, more vulnerable in a competitive higher education sector, to changes in
15
student numbers and therefore HEFCE sourced revenue. Thus the higher the levels of
HEFCE funding the more likely that HEFCE scrutiny is going to be increased and the
potential for disguising underlying performance reduced.
On the basis of the above analysis and consistent with prior literature we
therefore hypothesise:
H2: The higher the influence of regulatory monitoring, as proxied by the
proportion of university revenue provided by HEFCE, the higher the financial reporting
quality.
In HEFCE’s regulatory regime there are, however, clear incentives for
universities to avoid reporting a deficit, a key indicator which could trigger regulatory
intervention. Therefore, consistent with prior literature which shows that in both the
private, the public and the not for profit sectors the management of financial
performance is particularly acute when the achievement of financial breakeven is
threatened, we hypothesise:
H3: Accruals management to report a small surplus is particularly acute when
the achievement of financial breakeven is threatened.
Finally, although we predict that financial reporting quality will be better when
there is heavier reliance on HEFCE funding, we also expect a disproportionate number
of these institutions to have poor financial performance and for financial breakeven to
be under threat. Given the particularly heavy potential costs associated with HEFCE
intervention for these institutions, we predict that their responses will be to avoid
intervention if at all possible. We therefore predict that the benefits of monitoring to
16
financial reporting quality will be more than offset when the proportion of HEFCE
funding is high and financial breakeven is threatened.
H4: The benefits of external monitoring to financial reporting quality will be
more than offset by the management of accruals when the achievement of financial
breakeven is threatened.
5. Method
In this study we use discretionary accruals as a measure of financial reporting
quality. In the University setting staff costs account for about 60% of revenue and are
vulnerable to management through the judgement applied to accruals for, inter alia,
employment tax liabilities, holiday pay, travel expenses, redundancy and termination
costs, recruitment costs and sickness and maternity pay, etc. The accruals related to
staff costs are not directly observable but are captured by aggregate accruals models.
Consistent with Leone and Van Horn (2005) and Ballantine et al. (2007) we therefore
adopt an aggregate accruals model for the estimation of discretionary accruals.
We apply the model of Dechow and Dichev (2002) which is based on cash flows
and which allows for the reversing out of accruals, and which generally has greater
explanatory power than those models based on Jones (1991). We adapt this model as
recommended by McNichols (2002), and applied by Francis et al. (2005), to
accommodate changes in revenue and the level of PPE (Equation 1). Discretionary
accruals are taken as the residual from this model.
17
Equation 1:
∆𝑊𝐶𝑖𝑡
𝑇𝐴𝑖𝑡−1= 𝛼0 + 𝛼1 (
𝐶𝐹𝑂𝑖𝑡−1
𝑇𝐴𝑖𝑡−1) + 𝛼2 (
𝐶𝐹𝑂𝑖𝑡
𝑇𝐴𝑖𝑡−1) + 𝛼3 (
𝐶𝐹𝑂𝑖𝑡+1
𝑇𝐴𝑖𝑡−1) + 𝛼4 (
∆𝑅𝐸𝑉𝑖𝑡
𝑇𝐴𝑖𝑡−1)
+ 𝛼5 (𝑃𝑃𝐸𝑖𝑡
𝑇𝐴𝑖𝑡−1) + 𝜀𝑖𝑡
Where: ∆𝑊𝐶𝑖𝑡 = is calculated as the change in non-cash current assets from
time t-1 to time t, minus the change in cash and minus the change in current liabilities
for entity i; 𝐶𝐹𝑂𝑖𝑡 represents cash flow from operations; ∆𝑅𝐸𝑉𝑖𝑡 is the change in
revenue from time t-1 to time t; 𝑃𝑃𝐸𝑖𝑡 is property, plant and equipment at time t; 𝜀𝑖𝑡 is
the residual, a measure of discretionary accruals. All variables are scaled by lagged total
assets (Dechow and Dichev 2002).
5.1 Hypothesis 1: Small surplus reporting
We investigate Hypothesis 1 by applying the model adopted by Leone and Van
Horn (2005) (equation 2) where discretionary accruals are modelled as a function of
pre-discretionary performance, of last year’s performance and of last year’s
discretionary accruals:
Equation 2:
𝐷𝐴𝑖𝑡 = 𝛼0 + 𝛼1𝑆𝐵𝐷𝐴𝑖𝑡 + 𝛼2𝑆𝑢𝑟𝑝𝑙𝑢𝑠𝑖𝑡−1+ 𝛼3𝐷𝐴𝑖𝑡−1 + 𝜀𝑖𝑡
Where 𝑆𝐵𝐷𝐴𝑖𝑡 is the surplus before discretionary accruals of institution i in
period t divided by total assets in period t-1; 𝑆𝑢𝑟𝑝𝑙𝑢𝑠𝑖𝑡−1 is the surplus of institution i
in period t-1 divided by total assets in period t-2; and 𝐷𝐴𝑖𝑡−1 is the estimate of
discretionary accruals of institution i in period t-1 divided by total assets in period t-2.
18
A negative co-efficient 𝛼1 on the pre-managed surplus would be consistent with
universities managing financial performance such that small surpluses are reported
(Hypothesis 1). Last year’s discretionary accruals 𝑆𝑢𝑟𝑝𝑙𝑢𝑠𝑖𝑡−1 is included in the model
because, according to Kothari et al. (2005), there is a positive relation between past
performance and discretionary accruals for the present period. Thus, they expect 𝛼2 to
be positive. Finally, they also consider the variable 𝐷𝐴𝑖𝑡−1 in the regression to control
for the probability of autocorrelation in discretionary accruals.
5.2 Hypothesis 2: External monitoring and control
To test whether financial reporting quality increases with the proportion of
HEFCE funding, we introduce an interaction effect into equation 2 as follows:
Equation 3:
𝐷𝐴𝑖𝑡 = 𝛼0 + 𝛼1𝑆𝐵𝐷𝐴𝑖𝑡 + 𝛼2𝑆𝑢𝑟𝑝𝑙𝑢𝑠𝑖𝑡−1+ 𝛼3𝐷𝐴𝑖𝑡−1 + 𝛼4 𝑆𝐵𝐷𝐴𝑖𝑡 ∗ 𝐻𝐸𝐹𝐶𝐸𝑖𝑡 + 𝜀𝑖𝑡
Where HEFCE is the excess proportion of revenue derived from HEFCE
relative to the yearly median. Given that we expect that a higher proportion of HEFCE
funding will increase financial reporting quality by reducing the level of discretionary
accruals we predict a positive co-efficient on 𝑆𝐵𝐷𝐴𝑖𝑡 ∗ 𝐻𝐸𝐹𝐶𝐸.
To provide further evidence to support our hypothesis regarding the impact of
monitoring and control on financial reporting quality we further investigate the impact
of other sources of funding, (research, tuition and other) which do not exercise the same
levels of monitoring and control as HEFCE. We predict that there will be no significant
relationship between discretionary accruals and the proportion of funding drawn from
these sources. We adapt Equation 4 as follows:
19
Equation 4
𝐷𝐴𝑖𝑡 = 𝛼0 + 𝛼1𝑆𝐵𝐷𝐴𝑖𝑡 + 𝛼2𝑆𝑢𝑟𝑝𝑙𝑢𝑠𝑖𝑡−1+ 𝛼3𝐷𝐴𝑖𝑡−1 + ∑ 𝛼𝑗 𝑆𝐵𝐷𝐴𝑖𝑡 ∗ 𝑆𝑜𝑢𝑟𝑐𝑒𝑗𝑖𝑡 + 𝜀𝑖𝑡
Where Source = the proportion of funding from research councils, students and
others, respectively.
5.3 Hypothesis 3: Small loss avoidance
As a first test of hypothesis 3, we plot the distribution of reported and pre-
discretionary surplus as applied in Burgstahler and Dichev (1997). In the frequency
distribution of cross-sectional reported income, a higher than expected number of
institutions reporting income in the interval immediately to the right of zero, may be
seen as evidence of institutions’ tendency to report small surpluses. In the same manner,
a small number of institutions reporting earnings in the interval immediately to the left
of zero would indicate an avoidance of the reporting of deficits. In order to test the
significance of observed discontinuities, we calculate the Z statistic following the
method adopted by Burgstahler and Dichev (1997). The underlying assumption is that
the frequency distribution of cross-sectional income is smooth in the absence of
financial performance management
We further investigate the avoidance of small loss reporting by investigating the
presence of discontinuities in the regression of discretionary accruals. Equation 2
assumes a linear relationship between discretionary accruals and the pre-discretionary
surplus. However, given prior research findings and the loss aversion signalled in
HEFCEs regulatory regime, we predict that Universities which experience a small
discretionary surplus are subject to particularly strong incentives to avoid reporting a
small deficit and that discretionary accruals will therefore be more positive than
20
otherwise predicted by equation 2. This will be evidenced by a discontinuity in the
regression as follows:
Equation 5:
𝐷𝐴𝑖𝑡 = 𝛼0 + 𝛼1𝑆𝐵𝐷𝐴𝑖𝑡 + 𝛼2𝑆𝑢𝑟𝑝𝑙𝑢𝑠𝑖𝑡−1+ 𝛼3𝐷𝐴𝑖𝑡−1 + 𝛼5 𝑆𝐵𝐷𝐴𝑖𝑡 ∗ 𝑆𝐷𝑒𝑓𝑖𝑡 + 𝜀𝑖𝑡
Where 𝑆𝐷𝑒𝑓𝑖𝑡 is a dummy variable equal to 1 when the pre-managed deficit is
less than 1% and 0 otherwise. We predict a negative co-efficient on the interaction term
𝛼5 as evidence that Universities adopt more aggressive accruals management when the
achievement of financial breakeven is threatened and the probability of costly
intervention by HEFCE is increased.
5.4 Hypothesis 4: Interaction of external monitoring and small loss avoidance
Finally we investigate the extent to which external monitoring and control
mitigates the strong incentive to avoid reporting a loss when financial breakeven is
threatened. To investigate Hypothesis we combine the interaction of the pre-
discretionary surplus with both a small pre-managed deficit (<1% of assets) and the
excess of HEFCE funding over the annual median as follows:
Equation 6
𝐷𝐴𝑖𝑡 = 𝛼0 + 𝛼1𝑆𝐵𝐷𝐴𝑖𝑡 + 𝛼2𝑆𝑢𝑟𝑝𝑙𝑢𝑠𝑖𝑡−1+ 𝛼3𝐷𝐴𝑖𝑡−1 + 𝛼4 𝑆𝐵𝐷𝐴𝑖𝑡 ∗ 𝐻𝐸𝐹𝐶𝐸𝑖𝑡
+ 𝛼5 𝑆𝐵𝐷𝐴𝑖𝑡 ∗ 𝑆𝐷𝑒𝑓𝑖𝑡 + 𝛼6 𝑆𝐵𝐷𝐴𝑖𝑡 ∗ 𝑆𝐷𝑒𝑓𝑖𝑡 ∗ 𝐻𝐸𝐹𝐶𝐸𝑖𝑡 + 𝜀𝑖𝑡
21
5.5 Sample and data
Data from the financial statements of all English Universities5 for the period
from 2001-2002 to 2010-2011 was collected from the Higher Education Statistics
Association (HESA) database generating a total of 1115 university-year observations.
The requirement for lagged and leading variables for the modified version of Dechow
and Dichev (McNichols, 2002; Francis et al., 2005) model that we use as a our primary
estimator of discretionary accruals reduces the sample to 999 observations and this
reduced further to 886 observations for our multivariate analysis.
6. Findings
6.1 Descriptive statistics
Table 1 sets out the context for our study. Over the 10 year period of this study
the mean revenue of Universities for £145m growing from £107m in 2001 to £186m in
2011. The comparable figure for mean assets is £229m with mean staff costs of £82m,
representing 56% of mean revenue. The mean reported surplus amounts to just £3m,
2.1% of revenue.
INSERT TABLE 1 HERE
6.2 Estimation of discretionary accruals
In this paper we use discretionary accruals as a measure of financial reporting
quality. We estimate discretionary accruals as being the difference between the actual
and expected value of accruals based on two principal models: the Dechow and Dichev
5 Except the University of Buckingham which receives no public funding.
22
(2002) model as adapted by Francis et al. (2005) and the modified Jones model (Jones,
1991; Dechow et al., 1995). The results of these estimations (Appendix 1) show the
expected positive, negative and positive associations between the change in accruals
and lagged, contemporaneous and leading cash flows for both Model 1, which defines
the change in working capital as being the change in non-cash current assets less the
change in current liabilities, and Model 2 which additionally includes depreciation and
the change in long term provisions.(insert references) Consistent with prior research,
the explanatory power of the Dechow and Dichev (2002) model (Models 1 and 2) has,
greater explanatory power as compared with the modified Jones model and Model 2
has greater explanatory power (15.6%) than Model 1 (14.2%). Model 2 is therefore
adopted as the primary estimator of discretionary accruals for the purposes of our
investigations.
6.3 Hypothesis 1: Small surplus reporting
We investigate small surplus reporting by applying the Leone and Van Horn
(2005) model to our sample of English Universities. The results are shown in Table 2
for Models 1 and 2 (the primary focus of investigation). Table 2 shows that for Model
2 the association between discretionary accruals and the pre-discretionary surplus
(EBDA) is highly negative (-0.625, p=0.000), consistent with the use of discretionary
accruals to report small surpluses. Similar results are obtained for Model 1. The results
suggest that Universities reduce both surpluses and deficits on average by over 60%
through the use of discretionary accruals.
INSERT TABLE 2 HERE
23
6.4 Hypothesis 2: External monitoring and control
Table 3 Model A shows the results of the investigation of the effect of
monitoring and control, (as proxied by the proportion of revenue represented by
HEFCE funding), on financial reporting quality, (as proxied by discretionary accruals).
The co-efficient on the pre-managed surplus (SBDA) is negative as before but has risen
to 0.806 (p=0.000). As predicted, the co-efficient on the interaction term
SBDA*HEFCE is positive (0.0152, p=0.003) indicating that the management of
reported financial performance reduces with the level of HEFCE funding. For every
1% that HEFCE funding is increased above the annual median level the association
between the pre-discretionary surplus and discretionary accruals reduces by 1.5%.
These findings suggest that an underlying surplus/deficit is reduced by 80%
through the use of discretionary accruals but that this impact reduced by 1.5% for every
% of HEFCE funding beyond the annual median. For a university with 12%6 funding
above the annual median the reduction in the underlying surplus/deficit would be 62%.
To provide further evidence to support our hypothesis regarding the impact of
monitoring and control on financial reporting quality Models B and C show the effect
of introducing other sources of funding into the regression. These other sources are
tuition related revenue, research related revenue and other revenue, including
endowment income. In Model B the baseline source of funding is Tuition related
revenue. There is no significance attached to the coefficients on Research or Other. In
Model C the baseline is Research funding and there is no significance attached to the
6 12% is the standard deviation of the level of HEFCE funding.
24
coefficients on tuition related income or other income. In both cases however the results
for HEFCE funding are similar to those for Model A.
In summary, Table 3 provides support for Hypothesis 2 in that the proportion
of HEFCE funding, a proxy for the influence of external monitoring and control, is
positively associated with financial reporting quality (Hypothesis 2).
INSERT TABLE 3 HERE
6.5 Hypothesis 3: Small loss avoidance
As a first stage of the investigation into the avoidance of small losses we analyse
the distribution of both reported and pre-discretionary performance. Figure 1 shows the
frequency distribution first of the reported surplus (Panel A) and of pre-discretionary
surplus (Panel B) both scaled by lagged total assets), with histogram interval widths of
0.01 for scaled surpluses/deficits ranging from -0.20% to 0.20% of lagged total assets.
The pre-discretionary surplus distribution is flatter and more dispersed than the reported
surplus and exhibits no discontinuity around zero, in contrast with the distribution of
the reported surplus where the standardized difference (Z-statistic) for the interval
immediately to the left of zero is -3.59, and for the interval immediately to the right of
zero it is 6.41, both significant at the 1% level. These findings are consistent with
Hypothesis 3: that Universities avoid the reporting of small losses.
INSERT FIGURE 1 HERE
To test that the above results are not due to discontinuities in the deflator (see
for example, Durtschi & Easton 2005) the distribution of non-scaled reported
25
surpluses/deficits is also investigated and qualitatively similar results (not tabulated)
are obtained.
Table 4 reports the results from Equation 5 which investigates whether
Universities adopt more aggressive accruals management in order to report a small
surplus when the achievement of financial breakeven is threatened. As for Tables 2 and
3 the coefficient on the underlying surplus is negative (-0.621, p =0.000) and, as
predicted, the coefficient on the interaction is also negative, and large at -0.699
(p=0.003). These results suggest that a university would reduce an underlying deficit
by 62% unless the underlying deficit was small (less than 1%), when the reduction
would be 132% (0.622 + 0.699), thereby transforming the underlying deficit into a
small surplus.
INSERT TABLE 4 HERE
In summary our findings, consistent with Hypothesis 3, provide evidence that
the use of discretionary accruals to reduce reported deficits is particularly marked when
the achievement of financial breakeven is threatened and, in this instance, discretionary
accruals are used to transform a small deficit into a small surplus.
6.6 Hypothesis 4: Interaction of external monitoring with small loss avoidance
Table 5 shows the results of Equation 6. The coefficient on the pre-managed
surplus is similar to that in Table 4 (-0.81, p=0.000) as is the interaction with the
proportion of HEFCE funding (0.0155, p= 0.002). The interaction term on the small
pre-managed deficit however loses its significance as compared with Table 3. The
combined interaction term of small deficits and HEFCE funding is negative (-0.057,
26
p=0.003). These results indicate that, consistent with Table 3, underlying small deficits
are associated with a higher level of discretionary accruals and further, that these
increase at the rate of 5.7% with every % of excess HEFCE funding. So a university
with excess HEFCE funding of 12% would reverse out the underlying surplus (deficit)
at the rate of 62% (as for Table 3). However, for universities with small deficits of less
than 1%, the reversal is 130%7, resulting in the reporting of a small surplus.
INSERT TABLE 5 HERE
In summary Table 5 indicates that the beneficial impact of monitoring is more
than offset by the imperative of loss avoidance and that financial reporting quality is
reduced for universities where financial breakeven is threatened.
7. Discussion and conclusions
This paper investigates the impact of monitoring and control on financial
reporting quality in the distinctive not for profit setting of English Universities. These
institutions derive a significant proportion of their revenue from the sector regulator,
the Higher Education Funding Council (HEFCE) which operates a risk based regulatory
regime of progressive monitoring and intervention to ensure that public money and
student interests are protected from managerial opportunism. An assessment of
organisational financial sustainability, of which financial breakeven is a key indicator,
is a significant part of this regime as is the incidence of risk based institutional audits.
7 130% is derived from the regression results as follows: (-0.808 + (12*(0.0155-0.057) = -1.302
27
Using data collected over a ten year period from 2002-2011 we conduct both
univariate and multivariate analysis to investigate the influence on financial reporting
quality of: first, external monitoring and control activities; second, an underlying threat
to a key financial objective and third, the interaction of these two influences.
Consistent with prior literature we find that external monitoring and control
activities have a beneficial impact on financial reporting quality and that an underlying
threat to the achievement of a key financial objective is associated with poorer financial
reporting quality. Finally we find that the imperative of achieving key financial
objectives overrides the beneficial impact of external monitoring and control such that
those universities with an underlying small deficit will reverse this out with
discretionary accruals in order to report a small surplus, rather than a small loss.
The finding that external monitoring has a beneficial impact on financial
reporting quality is consistent with prior private sector research and provides initial
evidence that these findings are generalizable into the public and not-for-profit sectors.
We also provide initial evidence that the imperative to achieve financial objectives has
an adverse impact on financial reporting quality which more than offsets the beneficial
impact of monitoring and control activities. Although appropriate research settings may
be scarce, further research into this interaction would be beneficial in developing our
understanding of this type of interaction and the conditions which determine the
dominant influence.
The findings will also be of interest to regulators of public sector and not-for-
profit entities, and to other service commissioning bodies such as Central Government
Departments, Local Authorities and the commissioners of health services. First, they
28
provide evidence to support their reliance on financial statements for the purposes of
performance evaluation under conditions associated with monitoring and control
activities; and secondly, that this reliance may need to be tempered when entities report
performance close to financial thresholds of regulatory significance. The finding that
financial reporting quality is poorer when the achievement of such thresholds is
threatened has implications for the ways in which risk is assessed when entities report
performance just above such thresholds. This is relevant, for example, in the English
NHS where the sector regulator has, in recent years, adopted a regulatory regime where
intervention in the form of additional scrutiny with, ultimately the power to remove the
Board and Governing Body, has been linked to performance against a number of
financial objectives.
29
8. Tables
Table 1: Descriptive statistics for English Universities 2002-2011:
Obs. No. Mean
% mean
revenue
Std.
devn. Med. Min. Max. Skewness Kurtosis
£000 £000 £000 £000 £000
Total revenue 1115 144,759 100 148,228 106,783 3,484 1,251,484 2.94 15.40
Total assets 1115 229,386 158 326,079 148,315 328 3,366,234 4.98 35.64
Staff cost 1115 81,056 56 78,398 61,595 16 570,927 2.40 10.58
Surplus 1115 3,495 8,167 1,502 -58,810 94,704 3.13 21.75
Funding council grant 1115 51,842 36 41,254 42,691 568 257,815 1.67 6.32
Student tuition fees 1115 41,032 28 33,401 33,696 405 247,275 1.42 6.26
Research funding 1115 22,462 16 45,624 5,006 0 372,256 3.75 20.22
Other income 1115 27,093 19 45,102 16,017 0 619,860 7.88 87.26
Endowment 1115 2,155 1 5,063 815 0 58,856 6.24 50.52
30
Table 2: Small surplus reporting
Robust standard errors in parentheses, clustered by university
*** p<0.01, ** p<0.05, * p<0.1
𝐷𝐴𝑖𝑡 = 𝛼0 + 𝛼1𝑆𝐵𝐷𝐴𝑖𝑡 + 𝛼2𝑆𝑢𝑟𝑝𝑙𝑢𝑠𝑖𝑡−1+ 𝛼3𝐷𝐴𝑖𝑡−1 + 𝜀𝑖𝑡
Where SBDAit is the surplus before discretionary accruals of institution i in period t divided by total
assets in period t-1; Surplusit−1 is the surplus of institution i in period t-1 divided by total assets in
period t-2; and DAit−1 is the estimate of discretionary accruals of institution i in period t-1 divided
by total assets in period t-2.
VARIABLES Discretionary accruals
Model 1 Model 2
SBDA -0.573*** -0.625***
(0.150) (0.130)
Lagged reported surplus 0.0859 0.0962
(0.0670) (0.0582)
Lagged discretionary accruals -0.109*** -0.0610*
(0.0321) (0.0333)
Constant 0.00803** 0.00979***
(0.00311) (0.00299)
Year Control Yes yes
Observations 886 886
R-squared 0.451 0.530
31
Table 3: Influence of external monitoring and control
Discretionary accruals (Model 2)
VARIABLES Model A Model B Model C
SBDA -0.806*** -0.836*** -0.803***
(0.0424) (0.0528) (0.0446)
SBDA*HEFCE 0.0152*** 0.0189** 0.0142***
(0.00506) (0.00808) (0.00419)
SBDA*Tuition -0.00435
(0.00442)
SBDA*Research 0.00512
(0.00460)
SBDA*Other 0.00486 0.000124
(0.00779) (0.00550)
Lagged surplus 0.0889 0.0889 0.0888
(0.0570) (0.0562) (0.0564)
Lagged disc. accruals -0.0532* -0.0503 -0.0513
(0.0312) (0.0316) (0.0319)
Constant 0.0138*** 0.0141*** 0.0141***
(0.00253) (0.00260) (0.00262)
Year effects Yes Yes Yes
Observations 886 886 886
R-squared 0.603 0.605 0.605
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
𝐷𝐴𝑖𝑡 = 𝛼0 + 𝛼1𝑆𝐵𝐷𝐴𝑖𝑡 + 𝛼2𝑆𝑢𝑟𝑝𝑙𝑢𝑠𝑖𝑡−1+ 𝛼3𝐷𝐴𝑖𝑡−1 + ∑ 𝛼𝑗 𝑆𝐵𝐷𝐴𝑖𝑡 ∗ 𝑆𝑜𝑢𝑟𝑐𝑒𝑗𝑖𝑡 + 𝜀𝑖𝑡
Where SBDAit is the surplus before discretionary accruals of institution i in period t divided by total
assets in period t-1; Surplusit−1 is the surplus of institution i in period t-1 divided by total assets in
period t-2; and DAit−1 is the estimate of discretionary accruals of institution i in period t-1 divided
by total assets in period t-2. Source is the proportion of funding from research councils, students and
others, respectively.
32
Table 4: Small loss avoidance
Disc. Accs
VARIABLES Model 2
Pre-discretionary surplus (SBDA) -0.621***
(0.129)
1.smalldeficits#c.SBDA -0.699***
(0.232)
Lagged surplus 0.100*
(0.0591)
Lagged discretionary accruals -0.0670**
(0.0332)
Constant 0.0101***
(0.00291)
Year effects Yes
Observations 886
R-squared 0.534
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
𝐷𝐴𝑖𝑡 = 𝛼0 + 𝛼1𝑆𝐵𝐷𝐴𝑖𝑡 + 𝛼2𝑆𝑢𝑟𝑝𝑙𝑢𝑠𝑖𝑡−1+ 𝛼3𝐷𝐴𝑖𝑡−1 + 𝛼5 𝑆𝐵𝐷𝐴𝑖𝑡 ∗ 𝑆𝐷𝑒𝑓𝑖𝑡 + 𝜀𝑖𝑡
Where SBDAit is the surplus before discretionary accruals of institution i in period t divided by total
assets in period t-1; Surplusit−1 is the surplus of institution i in period t-1 divided by total assets in
period t-2; and DAit−1 is the estimate of discretionary accruals of institution i in period t-1 divided
by total assets in period t-2. 𝑆𝐷𝑒𝑓𝑖𝑡 is a dummy variable equal to 1 when the pre-managed deficit is
less than 1% and 0 otherwise.
33
Table 5: Interaction of external monitoring and small loss avoidance
VARIABLES Discretionary accruals
SBDA -0.808*** (0.0429) SBDA*HEFCE 0.0155*** (0.00500) SDef*SBDA 0.970 (0.697) SDef*SBDA*HEFCE -0.0567*** (0.0185) Lagged surplus -0.0536* (0.0314) Lagged discretionary accruals 0.0143*** (0.00263) Constant -0.808*** (0.0429) Year Control Yes Observations 886 R-squared 0.606
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
𝐷𝐴𝑖𝑡 = 𝛼0 + 𝛼1𝑆𝐵𝐷𝐴𝑖𝑡 + 𝛼2𝑆𝑢𝑟𝑝𝑙𝑢𝑠𝑖𝑡−1+ 𝛼3𝐷𝐴𝑖𝑡−1 + 𝛼4 𝑆𝐵𝐷𝐴𝑖𝑡 ∗ 𝐻𝐸𝐹𝐶𝐸𝑖𝑡 + 𝛼5 𝑆𝐵𝐷𝐴𝑖𝑡
∗ 𝑆𝐷𝑒𝑓𝑖𝑡 + 𝛼6 𝑆𝐵𝐷𝐴𝑖𝑡 ∗ 𝑆𝐷𝑒𝑓𝑖𝑡 ∗ 𝐻𝐸𝐹𝐶𝐸𝑖𝑡 + 𝜀𝑖𝑡
Where 𝑆𝐵𝐷𝐴𝑖𝑡 is the surplus before discretionary accruals of institution i in period t divided by total
assets in period t-1; 𝑆𝑢𝑟𝑝𝑙𝑢𝑠𝑖𝑡−1 is the surplus of institution i in period t-1 divided by total assets in
period t-2; and 𝐷𝐴𝑖𝑡−1 is the estimate of discretionary accruals of institution i in period t-1 divided
by total assets in period t-2. 𝑆𝐷𝑒𝑓𝑖𝑡 is a dummy variable equal to 1 when the pre-managed deficit is
less than 1% and 0 otherwise. HEFCE represents the excess proportion of HEFCE funding as % of
total income.
34
9. Figures
Figure 1: Distribution of reported and pre-managed financial performance (scaled by lagged total assets)
Notes: The distribution interval width, is calculated using as 2(IQR)n-1/5, is 0.01. The first interval to the right of zero contains all observations in the interval [0, 0.01), the
second interval contains [0.01, 0.02) and so on. Frequency is the number of observations in a given interval.
Degeorge et al. (1999) determine bin widths according to 2(IQR)N-1/3, where IQR is the sample interquartile range and n is the number of available observations. This method
proposes a bin size just above 1%. The resultant distributions are similar to those above.
2 14
14
68
129
1216
1820
25
31
34
49
67
58
86
79
6668
56 55
36
3032
23 22
8
35
8
4 46
4
05
01
00
15
02
00
Fre
qu
ency
-.18 -.16 -.14 -.12 -.08 -.06 -.04 -.02 .02 .04 .06 .08 .12 .14 .16 .18-.2 -.1 0 .1 .2
Surplus before abnormal accruals (%)
1 14
2 2 25
10 812
29
37
56
155
143
137
126
106
74
48
39
33
19
1311
75 4 5
3 3 2 2
05
01
00
15
02
00
Fre
qu
ency
-.18 -.16 -.14 -.12 -.08 -.06 -.04 -.02 .02 .04 .06 .08 .12 .14 .16 .18-.2 -.1 0 .1 .2
Reported Surplus (%)
Panel A: Reported surplus Panel B: Pre-managed surplus
35
10. References
Alali, F. & Jaggi, B., 2011. Earnings Versus Capital Ratios Management: Role of Bank
Types and SFAS 114. Review of Quantitative Finance and Accounting, 36(1),
pp.105–132.
Ballantine, J., Forker, J. & Greenwood, M., 2007. Earnings Management in English
NHS Hospital Trusts. Financial Accountability & Management, 23(4), pp.421–
440.
Ballantine, J., Forker, J. & Greenwood, M., 2008. The Governance of CEO Incentives
in English NHS Hospital Trusts. Financial Accountability & Management, 24(4),
pp.385–410
Baylis, R., Greenwood, M. & Tao, L., 2015. Regulatory Incentives and Accruals
Quality in Public Healthcare Organisations. Working Paper.
Brickley, J.A. & Van Horn, R.L., 2002. Managerial Incentives in Nonprofit
Organizations: Evidence from Hospitals. Journal of law and Economics, 45(1),
pp.227–249.
Burgstahler, D. & Dichev, I., 1997. Earnings Management to Avoid Earnings
Decreases and Losses. Journal of Accounting and Economics, 24.
Chung, R., Firth, M., & Kim, J. B., 2002. Institutional Monitoring and Opportunistic
Earnings Management. Journal of Corporate Finance, 8(1), 29-48.
Clark, B.R., 1997. Creating Entrepreneurial Universities: Organizational Pathways of
Transformation. Issues in Higher Education.
36
Connolly, C., & Hyndman, N., 2003. Performance Reporting by UK Charities:
Approaches, Difficulties and Current Practice. Institute of Chartered Accountants
of Scotland.
Dechow, P. M., & Dichev, I. D., 2002. The Quality of Accruals and Earnings: the Role
of Accrual Estimation Errors. The Accounting Review, 77(s-1), 35-59.
Dechow, P., Ge, W. & Schrand, C., 2010. Understanding Earnings Quality: A Review
of the Proxies, their Determinants and their Consequences. Journal of Accounting
and Economics, 50(2), pp.344-401.
Deem, R., Hillyard, S. & Reed, M., 2007. Knowledge, Higher Education, and the New
Managerialism: The Changing Management of UK Universities, New York:
Oxford University Press.
Degeorge, F., Patel, J. & Zeckhauser, R., 1999. Earnings Management to Exceed
Thresholds. The Journal of Business, 72(1), pp.1–33.
Desai, M. A., & Dharmapala, D., 2009. Corporate Tax Avoidance and Firm Value. The
Review of Economics and Statistics, 91(3), 537-546.
Durtschi, C. & Easton, P., 2005. Earnings Management? The Shapes of the Frequency
Distributions of Earnings Metrics Are not Evidence Ipso Facto. Journal of
Accounting Research, 43(4), pp.557–592.
Eldenburg, L.G. & Krishnan, R., 2008. The Influence of Ownership on Accounting
Information Expenditures. Contemporary Accounting Research, 25(3), pp.739–
772.
37
Ferlie, E., Musselin, C. & Andresani, G., 2008. The Steering of Higher Education
Systems: A Public Management Perspective. Higher Education, 56(3), pp.325–
348.
Ferreira, A., Carvalho, J. & Pinho, F., 2013. Earnings Management Around Zero: A
motivation to local politician signalling competence. Public Management
Review, 15(5), pp.657–686.
Francis, J., LaFond, R., Olsson, P., and Schipper, K., 2005. The Market Pricing of
Accruals Quality. Journal of Accounting and Economics, 39 (2), 295–327.
Frank, R.G., Salkever, D.S. & Mitchell, J., 1989. Market forces and the public good:
competition among hospitals and provision of indigent care, National Bureau of
Economic Research.
Hanlon, M., Hoopes, J. L., & Shroff, N., 2014. The effect of tax authority monitoring
and enforcement on financial reporting quality. The Journal of the American
Taxation Association, 36(2), 137-170.
Healy, P.M. & Wahlen, J.M., 1999. A review of the earnings management literature
and its implications for standard setting. Accounting horizons, 13(4), pp.365–
383.
Hofmann, M.A., 2007. Tax-motivated expense shifting by tax-exempt associations.
Journal of the American Taxation Association, 29(1), pp.43–60.
Hood, C., 1991. A Public Management for All Seasons? Public Administration, 69(1),
pp.3–19.
38
Hood, C., 1995. The “new public management” in the 1980s: Variations on a theme.
Accounting, Organizations and Society, 20(2-3), pp.93–109.
Irani, R. M., & Oesch, D., (2013). Monitoring and corporate disclosure: Evidence from
a natural experiment. Journal of Financial Economics, 109(2), 398-418.
Jegers, M., 2012. Do Nonprofit Organisations Manage Earnings? An Empirical Study.
VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations,
24(4), pp.953–968.
Jones, C.L. & Roberts, A.A., 2006. Management of Financial Information in Charitable
Organizations: The Case of Joint‐Cost Allocations. The Accounting Review,
81(1), pp.159–178.
Jones, J.J., 1991. Earnings management during import relief investigations. Journal of
accounting research, 29(2), pp.193–228.
Kanagaretnam, K., Lobo, G.J. & Yang, D.H., 2004. Joint Tests of Signaling and Income
Smoothing through Bank Loan Loss Provisions. Contemporary Accounting
Research, 21(4), pp.843–884.
Key, K., 1997. Political cost incentives for earnings management in the cable television
industry. Journal of accounting and economics, 23, pp.309–337.
Kothari, S.P., Leone, A.J. & Wasley, C.E., 2005. Performance matched discretionary
accrual measures. Journal of Accounting and Economics, 39(1), pp.163–197.
Krishnan, R., Yetman, M. & Yetman, R., 2006. Expense misreporting in nonprofit
organizations. The Accounting Review, 81(2), pp.399–420.
39
Krishnan, R., Yetman, M. & Yetman, R., 2003. Financial disclosure management by
nonprofit organizations. Available at SSRN 319581, (319).
Leone, A.J. & Van Horn, R.L., 2005. How do nonprofit hospitals manage earnings?
Journal of Health Economics, 24(4), pp.815–837.
Lim, C. Y., Ding, D. K., & Charoenwong, C., 2013. Non-audit fees, institutional
monitoring, and audit quality. Review of Quantitative Finance and Accounting,
41(2), 343-384.
Lobo, G.J. & Yang, D.-H., 2001. Bank managers’ heterogeneous decisions on
discretionary loan loss provisions. Review of Quantitative Finance and
Accounting, 16(3), pp.223–250.
Makar, S., Alam, P. & Pearson, M., 1998. Antitrust Merger Investigations and the
Quality of Reported Earnings. Journal of Applied Business …, 14(4), pp.89–100.
Martin, B. & Whitley, R., 2010. The UK Research Assessment Exercise: a case of
regulatory capture. In Reconfiguring knowledge production : changing authority
relationships in the sciences and their consequences for intellectual innovation.
Oxford: Oxford University Press, p. 51－80.
McNichols, M.F., 2002. Discussion of the quality of accruals and earnings: The role of
accrual estimation errors. The accounting review, 77(s-1), pp.61–69.
40
Meulen, B. Van der, 2007. Interfering governance and emerging centres of control. In
R. Whitley & J. Gläser, eds. The Changing Governance of the Sciences: The
Sociology of the Sciences Yearbook. Dordrecht: Springer Netherlands, pp. 191–
203.
Mitra, S., & Cready, W. M. 2005. Institutional stock ownership, accrual management,
and information environment. Journal of Accounting, Auditing & Finance, 20(3),
257-286.
Monks, R. & Minow, N. 1995. Corporate Governance. Cambridge, Massachusetts:
Blackwell.
Omer, T.C. & Yetman, R.J., 2003. Near zero taxable income reporting by nonprofit
organizations. Journal of the American Taxation Association, 25(2), pp.19–34.
Omer, T.C. & Yetman, R.J., 2007. Tax misreporting and avoidance by nonprofit
organizations. Journal of the American Taxation Association, 29(1), pp.61–86.
Pilcher, R., and Van Der Zahn, M., 2010. Local Governments, Unexpected
Depreciation and Financial Performance Adjustment. Financial Accountability &
Management, 26 (3), 299-324.
Universities UK, (2014). The impact of universities on the UK economy. London.
Verbruggen, S. & Christiaens, J., 2012. Do Non-profit Organizations Manage Earnings
toward Zero Profit and Does Governmental Financing Play a Role? Canadian
Journal of Administrative Sciences / Revue Canadienne des Sciences de
l’Administration, 29(3), pp.205–217.
41
Whitley, R., 2007. Changing governance of the public sciences. In R. Whitley & J.
Gläser, eds. The changing governance of the sciences. Dordrecht: Springer
Netherlands, pp. 3–27.
42
11. Appendices
Appendix 1: Accrual models regression results.
Model 1 Model 2 Model 3 Model 4
VARIABLES ΔWC
As Model 1 but
including change
in long term
provisions and
depreciation
Jones Model Modified Jones
Model
CFOit-1 0.147* 0.0903
(0.0792) (0.0657)
CFOit -0.270*** -0.322***
(0.0914) (0.0923)
CFOit+1 0.149*** 0.0744
(0.0508) (0.0553)
ΔREVit -0.0248*** -0.0222*** -0.0278***
(0.00478) (0.00651) (0.00714)
PPE -0.0360*** -0.0463*** -0.0468*** -0.0473***
(0.00926) (0.0117) (0.0128) (0.0121)
ΔREVit- ΔRECit -0.173
(0.197)
Constant 0.0180** -0.00362 -0.0117 -0.0133
(0.00891) (0.0102) (0.0102) (0.00942)
Year Control Yes Yes Yes Yes
Observations 999 999 999 999
R-squared 0.142 0.156 0.106 0.105
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
∆𝑊𝐶𝑖𝑡
𝑇𝐴𝑖𝑡−1= 𝛼0 + 𝛼1 (
𝐶𝐹𝑂𝑖𝑡−1
𝑇𝐴𝑖𝑡−1) + 𝛼2 (
𝐶𝐹𝑂𝑖𝑡
𝑇𝐴𝑖𝑡−1) + 𝛼3 (
𝐶𝐹𝑂𝑖𝑡+1
𝑇𝐴𝑖𝑡−1) + 𝛼4 (
∆𝑅𝐸𝑉𝑖𝑡
𝑇𝐴𝑖𝑡−1) + 𝛼5 (
𝑃𝑃𝐸𝑖𝑡
𝑇𝐴𝑖𝑡−1) + 𝜀𝑖𝑡
𝛥𝑊𝐶𝑖𝑡 = 𝛥𝐶𝐴𝑖𝑡−𝛥𝐶𝐴𝑆𝐻𝑖𝑡−𝛥𝐶𝐿𝑖𝑡
Where ΔWCit is the change in working capital accrual, ΔCAit means the change in current assets,
ΔCASHit presents the change in cash in hands, ΔCLit stands for the change in current liabilities, TAit−1
is the total asset, CFOit is the operating cash flow, ∆REVit represents change of total income from
year t to year t-1, PPEit is the property, plant and equipment in year t and εit is the residual.
The Jones Model used is: 𝐴𝐶𝐶𝑖𝑡
𝑇𝐴𝑖𝑡−1
= 𝛼1 (1
𝑇𝐴𝑖𝑡−1
) + 𝛼2 (∆𝑅𝐸𝑉𝑖𝑡
𝑇𝐴𝑖𝑡−1
) + 𝛼3 (𝑃𝑃𝐸𝑖𝑡
𝑇𝐴𝑖𝑡−1
) + 𝜀𝑖𝑡
Where ACCit means total accruals in year t, ∆REVit represents change of total income from year t to year
t-1, PPEit is the property, plant and equipment in year t. The error term from the equation can be
treated as a measure of discretionary accruals.
The Modified Jones Model used is 𝐴𝐶𝐶𝑖𝑡
𝑇𝐴𝑖𝑡−1
= 𝛼𝑖 [1
𝑇𝐴𝑖𝑡−1
] + 𝛽1𝑖 [∆𝑅𝐸𝑉𝑖𝑡 − ∆𝑅𝐸𝐶𝑖𝑡
𝑇𝐴𝑖𝑡−1
] + 𝛽2𝑖 [𝑃𝑃𝐸𝑖𝑡
𝑇𝐴𝑖𝑡−1
] + 𝜖𝑖𝑡
Where ∆RECit represents the changes in receivables.
