Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
Effects of Audit Quality on Earnings Management during
the Global Financial Crisis: An Empirical Analysis of
Australian Companies
Abu Taher Mollik, Monir Mir, Ronald McIver§ and M Khokan Bepari£
This paper empirically investigates the effects of audit quality on earnings
management behaviour of Australian firms, if any, during the global
financial crisis (GFC). In addressing this issue, we integrate several
branches of the recent literature: earnings management behaviour and the
GFC and audit quality. Earnings management, being represented by the
discretionary components of total accruals was estimated based on the
Jones (1991) and modified Jones (Dechow, Sloan, & Sweeney, 1995;
Kothari, Leone, & Wasley, 2005) models. Audit quality is represented in
use of a Big-N audit firm and through audit committee characteristics. We
used parametric and non-parametric tests to identify differences in
earnings management for firms audited by Big-4 and non Big-4 firms. We
also used panel data regression methods to analyse the impact of audit
quality and the GFC on the discretionary accruals. The sample comprised
of an unbalanced panel of 149 firms for the period of 2006 to 2009. We find
that although Australian firms engaged in a higher level of incomedecreasing earnings management during the GFC, with the exception of
audit committee independence, audit quality, in general, did not have an
impact in mitigating this behaviour. This contrasts with earlier research
conducted on companies in Singapore and Malaysia, which examines the
impact of audit quality in light of the Asian Financial Crisis.
JEL Classification: G32; G01; M42
Keywords: Earnings Management; Global Financial Crisis; Audit Committee; Audit
Quality
1. Introduction
Empirical evidence suggests that firms engage in aggressive earnings
management during periods of financial crisis (Chia et al. 2007; Johl et al. 2007).
During financial crises many firms experience a systematic decline in incomes, and a
majority will report a fall in earnings (or losses). Thus, the onset of a financial crisis
may trigger (or magnify) managerial motives to engage in earnings management
(Kim & Yi 2006). Management may attribute reduced earnings (or losses) to the
macroeconomic shock rather than to poor managerial performance. Detection of
such activities by investors requires that a firm‘s financial reports accurately
communicate
changes
in
its
underlying
economic
position.

Assistant Professor in Banking & Finance, Faculty of Business & Government, University of Canberra
(contact author).

Associate Professor, Accounting, Banking & Finance, Faculty of Business & Government, University of
Canberra.
§
Lecturer in Financial Economics, Centre for Applied Financial Studies (CAFS), School of Commerce,
Division of Business, University of South Australia.
£
Sessional Academic staff in Accounting and Finance in the University of Canberra.
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
An unqualified audit opinion suggests that a firm‘s financial accounting practices
are such that investors can rely on the firm‘s financial statements to determine its
economic position. However, when a large number of companies report falls in
profits (or losses) following release of unqualified audit opinions, both scrutiny of
auditors‘ practices and their perceived responsibilities may increase (Sikka 2009).
Thus, events associated with financial crises may increase the focus on the
importance of audit quality and practice (Fargher & Liwei 2008). This is especially
the case where auditor choice/audit quality is recognised as having a significant
effect in constraining managers‘ earnings management behaviour during these
periods (Chia et al. 2007; Johl et al. 2007).
Audit quality is often related to the perceived quality of the external audit provider
(i.e., use of an auditor chosen from the Big-4, -5 or -6). However, legislation and
best-practice resolution undertaken as a result of major financial collapses in the
United States and European Union (e.g., Enron, WorldCom, Parmalat) reinforce the
importance of corporate governance for audit quality (Leung & Horwitz 2010). Listing
rules on major exchanges such as the NYSE and NASDAQ also highlight the
importance of the structure of the audit committee (Klein 2002). Thus, instead of
simply taking the use of a Big-N (i.e. N = 4, 5 or 6) audit firm as a proxy for audit
quality, internal control mechanisms related to the audit committee should also be
considered.
In light of the above, there are two main motivations for this paper. First, to
integrate recent branches of the literature that discuss: earnings management
behaviour in the presence of systematic financial crisis (e.g., Fiechter & Meyer
2010); earnings management, audit quality and firm-specific corporate governance
attributes (e.g., Klein 2002; Xie et al. 2003; Shen & Chih 2007); and the impact of
corporate governance attributes on the firm‘s response to financial crisis (e.g., Leung
& Horwitz 2010). Second, this paper seeks to focus on a single, developed
economy—Australia—a country for which there is limited coverage in the literature.
Following Miller et al. (2004), this second driver reflects an attempt to avoid
methodological and modelling problems frequently associated with multi-country
studies. These arise in the forms of omitted and noisy variables, and differences in
the cross-country effects of key factors, potentially complicating or preventing a clear
identification of core concepts and an in-depth understanding of the event (problem)
being examined.
In line with its dual motivations, the present study embraces two research
questions:
(i) Did Australian firms engage in a greater level of earnings management
during the global financial crisis?
(ii) Where identified, did audit quality (broadly defined) have a discernible
effect in mitigating any earnings management behaviour?
We follow Chia et al. (2007) and Johl et al. (2007) in examining firms‘ earnings
management behaviour and role of audit quality in constraining earnings
management in the context of a financial crisis. However, we differ from these two
studies in several important ways. First, we examine aggressive earnings
management behaviour during a period of financial crisis in a developed economy—
Australia. Second, instead of simply taking the use of a Big-N audit firm as a proxy
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
for audit quality, we also consider internal control mechanisms related to the audit
committee.
The rest of the paper is as follows. Section 2 provides a brief review of relevant
literature. This deals with the impact of audit quality, the audit committee, and
financial crises on earnings management. The hypotheses to be tested in this paper
are also established at this point. Section 3 discusses and identifies the relevant time
periods to be associated with the GFC and pre-crisis period (PCP) in Australia‘s
case. Section 4 outlines the data and sample set from which the metrics in the paper
are derived. Section 5 describes the methodology and basis for the construction of
the models used in this study. Section 6 provides the results from the statistical
analysis. Section 7 concludes the paper.
2. Theory of Earnings Management, Financial Crisis and Audit
Quality
2.1. Earnings management defined
Earnings management has remained a widely-researched area in accounting for
the last two decades. In the accounting literature a variety of terms are synonymous
with earnings management: ‗creative accounting‘, ‗cooking the books‘, ‗earnings
manipulation‘, ‗accounts manipulation‘, ‗income smoothing‘, etc. Consensus on the
exact definition of earnings management is lacking. However, Healy and Wahlen
provide a comprehensive definition:
―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 & Wahlen 1999 p. 368).
This definition suggests that earnings management requires both motivation and
opportunity.
2.2. Motivation and opportunity in earnings management
Diverse managerial motives for earnings management exist, as supported in prior
research. These motives include: political cost minimization; financing cost
minimization; managerial wealth maximization; debt contracts; compensation
agreements; equity offerings; insider trading; reductions in the likelihood of wealth
transfers; obtaining import relief; decreasing earnings during union negotiations;
decreasing earnings in periods preceding management buyouts; and mergers (Watts
& Zimmerman 1978; Jones 1991; Woody 1997; Louis 2004). However, managerial
decisions to engage in earnings management are normally based on opportunistic
reactions in response to incentives created by specific economic and financial
conditions (e.g., economic downturn, or an unexpected fall in earnings/unexpected
loss).
A result of earnings management is that a firm‘s financial reports may not
accurately communicate its underlying economic position. This is due to deliberate
choices by management regarding financial reporting methods, estimates, and
disclosures (Healy & Wahlen 1999). As a consequence, a high or heightened level of
examination of managerial judgements regarding the company‘s financial reporting
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
practices may result in identification of managerial earnings management. For
example, Lim and Matolcsy (1999) document the existence of significant incomereducing earnings management in Australian companies in the presence of price
controls (established by the Australian government in the early 1970s) when
subjected to high levels of scrutiny.
A number of studies also document that earnings management is motivated by
managers‘ compensation contracts. There are few other circumstances where both
the incentive and ability to manage earnings are as closely connected (i.e., through
bonuses). Thus, the general proposition tested in these studies is whether firms with
accounting-based bonus plans are more likely to adopt accounting methods that
increase reported earnings (Watts & Zimmerman 1978). For example, Healy (1985)
observes that managers will manage earnings upward if unmanaged earnings are
between the level required to trigger the bonus (the lower bound) and that which
gives the maximum bonus (the upper bound).
2.3. Practice and outcome with earnings management
In the accounting literature, observed managerial earnings management
behaviours have been widely explained using both the ‗big bath‘ and ‗income
smoothing‘ hypotheses.
The ‗big bath‘ hypothesis (Healy 1985) implies that when the firm‘s current period
earnings are unexpectedly low—depriving managers of the opportunity to receive a
bonus or meet pre-specified targets—managers will clear off future potential
expenses by matching additional discretionary charges to current earnings,
worsening the reported financial outcome. According to this hypothesis, the
information content of a negative move in current earnings is the same regardless of
the scale of the fall in earnings. Healy (1985) shows that if current earnings are
below the earnings-based bonus plans‘ floor, managers have incentives to lower
earnings further using negative discretionary accruals. Empirical evidence shows
that a managerial bonus plan is not the only situation when managers apply the big
bath. It may also be applied during management changes (Strong & Meyer 1987;
Wells 2002; Masters-Stout et al. 2008) and accounting policy changes (Beatty &
Weber 2006).
The income-smoothing hypothesis implies that managers‘ accounting choices are
driven by their desire to remove (manage) large earnings fluctuations around predetermined target earnings levels. Thus, income smoothing reflects the propensity of
managers to choose accounting policies that increase (decrease) reported earnings
when unmanaged current period earnings are below (above) target earnings (Gill-deAlbornoz & Alcarria 2003). Income smoothing has been linked to dividend stability
and higher share prices, reflecting managers‘ efforts to signal positive private
information about the firm‘s future performance (Wang & Williams 1994).
2.4. Audit quality in earnings management
Corporate governance mechanisms have been widely recognised as constraining
factors in earnings management (Klein 2002; Xie et al. 2003; Baxter & Cotter 2009;
Lin & Hwang 2010). The role of audit quality in constraining earnings management
behaviour in different countries has also been studied, although the empirical
findings of these studies are mixed. While many support the claim that use of a Big4
Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
4/5/6 auditor reduces earnings management (Lin et al. 2006; Hutchinson et al. 2008;
Baxter & Cotter 2009), some find no significant constraining effect of such audit
quality measures (e.g., Davidson et al. 2005).
2.5. Financial crises and earnings management
Financial crises provide examples of events for which managers‘ motivation to
engage in earnings management may be greater. Simultaneously, auditors may face
additional legal challenges where external parties suffering losses associated with a
financial crisis perceive negligence or mistake on the auditor‘s part. So, in the
presence of financial crises, auditors may have an incentive to be more cautious in
curbing opportunistic behaviour by management (via qualification of the audit). To
the extent that auditors‘ performance matters in constraining managers‘ earnings
management behaviour, audit quality is expected to have a significant negative
association with it.
Chia et al. (2007) examine the earnings management behaviour of serviceoriented companies and the impact of choice of auditor in curbing earnings
management during the 1997 Asian financial crisis in Singapore. Their findings
suggest that service-oriented companies in Singapore engage in income decreasing
earnings management, and only Big-6 audit firms are able to significantly constrain
such earnings management behaviours. Similarly, in the context of the Asian
financial crisis, Johl et al. (2007) examine auditor reporting behaviour in the
presence of aggressive earnings management in Malaysia. Their findings suggest
that Big-5 audit firms in Malaysia qualify audit reports more frequently than their nonBig-5 counterparts in the presence of high levels of abnormal accruals.
The focus in the above research is on use of a Big-N firm as the main measure of
audit quality. Limited research in the context of the 2008 to 2009 GFC justifies
additional research, including that in the Australian context. This is in order to better
determine and possibly generalise the impact of audit quality—including those
aspects likely to be associated with firm‘s governance attributes—in limiting earnings
management behaviour during periods of financial crisis. This study proposes the
following hypotheses:
H1:
H2:
On average, Australian companies engaged in a higher level of earnings
management during the 2008-2009 GFC.
Companies audited by highly ranked audit firms engaged in a lower level of
earnings management during the 2008-2009 GFC than companies audited
by lower ranked audit firms.
3. Defining the Global Financial Crisis and the Pre-Crisis Period
Drawing an appropriate timeline for the GFC and PCP in the Australian context is
troublesome, given the GFC of 2008 and 2009 started in the US. The GFC began in
the US market in mid-2007 but its impact was felt more broadly across the globe
from the second quarter of 2008.
The Australian financial market reflected little impact of the US sub-prime
mortgage crisis during 2007 (Xu et al. 2011). Grosse (2010) suggests that the US
financial crisis began to develop into a GFC from March 2008, with the first major
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
event indicative of a spill-over effect being the near failure and then Bank of America
purchase of Countrywide Financial in January 2008. Following this, the crisis spread
around the world, as is apparent in the near shutdown of the inter-bank lending
market from the end of March 2008. This continued well into 2009, with positive GDP
growth returning for many countries affected by the crisis in the second half. Thus
suggests that the years 2008 and 2009 be considered as the GFC period whereas
2006 and 2007 are considered as the pre-crisis period, PCP.
Taking into consideration these issues, in the Australian context the GFC and the
PCP are defined based on existing literature (Sidhu & Tan 2011; Xu et al. 2011;
Spear & Taylor 2011) and movements in the ASX All Ordinaries Index. The ASX All
Ordinaries Index was 3546 by June 30 2004, 4347 as of June 30 2005, and 5034
during June 2006. It soared to 6311 during June 2007 and up to 6779 during
October 2007. The index began to decline after this period, dipping to 5333 in June
2008 and then to 3297 by February 2009. At the end of June 2009, the index was
3948 (Yahoo finance).
In Australia, the unemployed rate began to rise dramatically from October 2007.
The reserve bank of Australia began to cut interest rates from 7.25 per cent in
September 2008, to three per cent in 2009. Based on this and consistent with other
studies (e.g., Mahmood et al. 2010), this paper therefore utilises 2006 to 2007 as the
PCP period and 2008 and 2009 as the GFC period.
4. Data and Sample
For the sample the 301 firms included in the S&P ASX 300 list as at September
30 2009 were identified. These firms represent 80+ per cent of the market
capitalisation of ASX listed companies (S&P, 2007). Banks, other financial
institutions, trust companies and utility companies were excluded because these
companies have different legal and regulatory reporting requirements. This resulted
in the elimination of 64 companies. Financial firms have different corporate
governance structures and disclosure requirements (Khan et al. 2008), and have
also been excluded in previous studies (Baxter & Cotter 2009; Habi 2008). Of the
remaining 237 firms, 9 firms were excluded because their annual reports were not
available from the Connect Four annual report collection. The existence of an audit
committee could not be determined for six firms (either because these firms do not
have an audit committee or they have not mentioned such a committee in their
annual reports). A further 73 firms were excluded either because the required
variables for the calculation of the proxies for earnings management—discretionary
accruals (DACs)—were not available, or they belong to an industry with less than 10
firms (the calculation of DACs requires that there be at least 10 firms in the industry).
The above processes left a sample comprised of 149 firms. These firms were then
traced back from 2009 to 2006 to be included in the final sample, which consists of
576 firm-year observations. Observations are 149, 147, 141 and 139 for the years
2009, 2008, 2007 and 2006, respectively. Financial accounting data has been
collected from the Datastream Advance data base. Audit quality data has been
collected from companies‘ annual reports, sourced from the Connect 4 data base.
Table 1 outlines the sample selection procedure and the industry distribution of the
final sample.
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
Table 1: Sample selection procedure and industry distribution of the sample
Panel A: Sample selection procedure
Firms included in the S&P ASX 300 index as at September 30 2009
Less: GICS 60: A-REIT
Less: GICS 65: Financial –x-A-REIT
Less: GICS 50: Utilities companies
Remaining firms
Required data not available
Annual report not available via the Connect Four data base
Less: Financial data not available to calculate the proxy for earnings management
(DAC) or there are less than 10 firms in the industry
Total firms remaining in sample
Panel B: Industry distribution of the sample
2009
2008 2007 2006
Consumer Discretionary
13
13
12
12
Consumer Staples
10
10
9
9
Energy
31
31
31
30
Health Care
10
10
10
10
Industrials
38
38
34
33
Information Technology
7
7
7
7
Materials
38
36
36
36
Telecommunications Services
2
2
2
2
Total
149
147
141
139
Common firms for all four years
139
139
139
139
301
22
29
13
237
6
9
73
149
Total
50
38
123
40
143
28
146
8
576
139
5. Research methods
5.1 Proxies for earnings management—discretionary accruals
Prior research has used a variety of measures of DACs to proxy earnings
management. Since DACs are not observable, many proxies and estimation
techniques have been suggested in the literature to capture the DACs. For example,
Healy (1985) uses total accruals as the proxy for DACs, whereas DeAngelo (1986)
uses the change in total accruals as the proxy for DACs. Jones (1991) uses a more
sophisticated approach to estimate DACs. This is to decompose total accruals into
explained (non-discretionary accruals, or NDAs) and unexplained components (the
DACs) via regression methods.
More commonly, total accruals are assumed to be the sum of both DACs and
NDAs. To determine NDAs, total accruals are regressed on changes in revenue or
sales during the year, the firm‘s property, plant and equipment, and current, oneperiod lag and lead cash flow from operations. The unexplained portion of total
accruals from this regression is considered as providing a measure of DACs.
Dechow et al. (1995) evaluate the relative performance of five DAC models
including those of Healy (1985), DeAngelo (1986), the industry model proposed by
Dechow and Sloan (1991), the Jones (1991) model, and their own proposal for a
Modified Jones model. The performance of the five models is evaluated using four
samples: (a) a random sample; (b) a sample of firm-years experiencing extreme
financial performance; (c) a sample of firm-years with artificially induced earnings
management; and (d) a sample of firm-years for which the SEC alleged earnings
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
were overstated. Dechow et al. (1995) conclude that the Jones (1991) and Modified
Jones models perform better than the other models tested. Guay et al. (1996)
provide a similar assessment of the five DAC models, finding that only the Jones and
Modified Jones models appear to have potential to provide reliable estimates of
DACs.
Consistent with the evidence provided by Dechow et al. (1995) and Guay et al.
(1996), it follows that the Jones (1991) and Modified Jones models are the models
most frequently used in estimating DACs in a wide range of previous studies
(e.g.,DeFond & Jiambalvo 1994; Dechow et al. 1995; Subramanyam 1996; Beneish
1997; Becker et al. 1998; Francis et al. 1999; Bartov et al. 2000; Krishnan 2003;
Davidson et al. 2005; Kothari et al. 2005; Chia et al. 2007; Ahmed et al. 2008; Baxter
& Cotter 2009; Iqbal & Strong 2010). Within this literature a cross-sectional version
of the Modified Jones model is common. The absolute value of DACs in the Modified
Jones‘ (1991) model serves as a proxy for accrual-based earnings management
(consistent with Francis et al. 1999; Krishnan 2003).
A time-series approach would normally be expected in estimating firm-specific
accruals, which is assumed to capture the accruals-generating process. Indeed the
original Jones model (1991) requires 10 time series observations for each sample
firm for reliable measures of NDAs and DACs. Thus, data non-availability is one
problem for this model, as is survivorship bias (DeFond & Jiambalvo 1994; Rees et
al. 1996). Additionally, a firm-specific approach needs to assume that the process
generating the accruals is constant over time (Richardson 2000, p. 331). However,
this assumption is likely to be violated during periods of changing economic
conditions—especially those associated with a crisis period such as the GFC
(Ahmed et al. 2008). For these reasons the literature suggests that cross-sectional
versions of the Jones and Modified Jones models will have better power in
decomposing accruals into NDAs and DACs and thus in detecting DACs than the
time-series versions (Subramanyam 1996; Jeter & Shivakumar 1999; Bartov et al.
2000).
In light of the above, three alternative specifications are used to estimate DACs:
the cross-sectional version of Jones (1991) model; the Modified Jones model
proposed by Dechow et al. (1995); and, as proposed by Kothari et al. (2005), a
Modified Jones model with an alternative control variable—‗return on assets‘
(ROA)—used to capture firm performance (see also Choi et al. 2011). In each case
DACs are represented by model residuals with NDAs being the explained
component of the model.
Jones (1991) model:
TACit
REVit
PPEit
1
 1
 2
 3
  it
TAit 1
TAit 1
TAit 1
TAit 1
Modified Jones model (Dechow et al., 1995):
TACit
REVit  RECit
PPEit
1
 1
 2
 3
  it
TAit 1
TAit 1
TAit 1
TAit 1
8
(eqn. 1)
(eqn. 2)
Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
Modified Jones model adjusted for firm performance (Kothari et al., 2005)
TACit
REVit  RECit
PPEit
1
(eqn. 3)
 1
 2
 3
  4 ROA it  it
TAit 1
TAit 1
TAit 1
TAit 1
Where:
TACit
TAit-1
∆REVit
Total accruals (measured as net income – cash flow from operations) for
firm i at time t
Total assets at the beginning of the year for firm i at time t
Changes in total revenue for firm i at time t
∆RECit
Changes in receivables for firm i at time t
PPEit
ROAit
εit
Property plant and equipment for firm i at time t
Return on assets for firm i at time t
Error term (discretionary accruals component) for firm i at time t
The residuals from equations 1, 2 and 3 provide the three alternative measures of
DACs used in this paper (hereafter identified as DAC1, DAC2 and DAC3,
respectively).
DACs can be either positive or negative, as discretionary accruals can be used
either to conceal poor performance or to save current earnings for a future time
period (Gul et al. 2003). Negatively-signed DACs represent income-decreasing
discretionary accruals (DECDACs), while positive-signed discretionary accruals are
considered as income-increasing discretionary accruals. Absolute values of the
DACs from the modified Jones‘ (1991) model serve as proxies for accruals-based
earnings management (Francis et al. 1999; Krishnan 2003), with the absolute values
of the residuals from equations 1, 2 and 3 (hereafter ABSDAC1, ABSDAC2 and
ABSDAC3, respectively) providing alternative proxies for earnings management in
this paper.
5.2 Model for examining Hypothesis 1
To test Hypothesis 1, we identify the sub-sample of firms with income-decreasing
discretionary accruals. The effect of the GFC on earnings management is examined
using the following cross-sectional models:
j
DECDACit   0  1CRISIS   2OCFit  3 LEVit   4 SIZEit  5 NEGit   i I i  it
i 1
(eqn. 4)
j
ABSDACit   0  1CRISIS   2OCFit  3 LEVit   4 SIZEit  5 NEGit   i I i  it (eqn. 5)
i 1
Where:
DECDACit
ABSDACit
CRISIS
OCFit
LEVit
Income decreasing or negative accruals for firm i at time t
Absolute value of discretionary accruals for firm i at time t
Crisis dummy taking the value of 1 for the year 2008 and 2009, 0
otherwise
Cash flow from operations for firm i during year t deflated by the lag of
total assets
Leverage ratio for firm i during year t measured as total liabilities to
total debt
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
SIZEit
NEGit
Ii
Natural log of the market value of firm i during year t
Indicator variable taking the value of one if firm i has negative
earnings for year t, 0 otherwise
Industry dummy for firm i
Industry dummies are defined as: Consumer Discretionary (CD); Consumer
Staples (CS); Energy (Energy); Home Construction (HC); Industrial (IND);
Information Technology (IT); Materials (MAT); and Telecommunication Services
(TS). Separate industry estimation also permits the coefficients to reflect systematic
variations in economic and accounting environments across industries (Barth et al.
1999).
A statistically significant 1 (the coefficient of the crisis variable) would imply firms
displayed different earnings management behaviour during the GFC period as
compared to the PCP. Equations 4 and 5 are estimated to examine if firms engage in
an increasing level of income decreasing earnings management during the GFC
compared to the PCP.
5.4 Model for examining Hypothesis 2
To examine the effect of audit quality on earnings management the following
cross-sectional models will be used:
ABSDACit   0  1BIG 4it   2CRISISit  3 ACSIZEit   4 ACINDit  5 ACMEETit
j
 6 ACFINACit  7CFOit  8 LEVit  9 SIZEit  10 NEGit   i I i  it
(eqn. 6)
i 1
Where:
ABSDACit
BIG4it
ACSIZEit
ACINDit
ACMEETit
ACFINAC it
CRISIS * BIG4
Absolute value of discretionary accruals for firm i during
year t
Indicator variable taking the value of 1 for firm i if the firm
has employed one of the big 4 audit firms during year t, 0
otherwise
Number of members in the audit committee of firm i during
year t
Proportion of independent directors in the audit committee
of firm i during year t
Number of meeting of the audit committee of firm i during
year t
Number members in the audit committee of firm i during
year t with accounting and finance expertise
BIG-4 and CRISIS interaction
A statistically significant coefficient for any interaction term will imply that a
particular variable had a different mitigating effect on earnings management during
the GFC than for the PCP. The ACSIZE, ACIND, ACMEET and ACEXP variables
are included to capture internal control aspects of audit quality. LEVit , SIZEit , NEGit ,
and Ii are used as control variables, given previous studies have found these
variables to be significantly related to the firm‘s level of accruals, which may
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influence the level of DACs as well. The models used in the present study are
modified from Baxter and Cotter (2009), Johl et al. (2007) and Chia et al. (2007).
6. Results and discussion
6.1 Descriptive statistics
Table 2 provides descriptive statistics for alternative dependent variables and for
the main independent variables used in equations 4 to 8.
Table 2: Descriptive Statistics
Variable§
DAC1
DAC2
DAC3
ABSDAC1
ABSDAC2
ABSDAC3
SIZE
NEG
OCF
ACSIZE
BIG-4
ACFINAC
ACMEETING
ACIND
LEVERAGE
CRISIS
§
N
Minimum
576
576
576
576
576
576
576
576
576
576
576
576
576
576
576
576
-1.984
-2.581
-1.970
0.000
0.000
0.000
2.982
0.000
-16.759
0.000
0.000
0.000
0.000
0.000
0.000
0.000
Panel A: Pooled sample
Maximum
Mean
Standard
Deviation
1.750
-0.028
0.354
1.678
-0.040
0.362
1.503
-0.015
0.305
1.984
0.188
0.301
2.581
0.188
0.312
1.970
0.161
0.259
8.171
5.863
0.722
1.000
0.295
0.456
114.787
0.219
4.893
7.000
3.237
0.888
1.000
0.831
0.375
5.000
1.377
0.978
14.000
3.944
1.930
1.000
0.519
0.500
52.963
0.565
2.223
1.000
0.514
0.500
Skewne
ss
-0.821
-1.455
-0.982
3.245
3.542
3.469
0.090
0.901
22.366
1.248
-1.771
0.346
1.058
-0.077
22.898
-0.056
Kurtosis
10.274
12.581
11.980
12.296
15.674
14.932
0.428
-1.193
526.946
3.866
1.141
-0.228
2.115
-2.001
540.141
-2.004
Variable definitions are as per equations 4 to 8.
The means of the signed DACs are negative for all three proxies of earnings
management. The means of the absolute value of DACs are 0.188, 0.188 and 0.161,
for ABSDAC1, ABSDAC2 and ABSDAC3, respectively. These measures compare to
the means of the absolute value of DACs of 0.18 found by Baxter and Cotter (2009)
and 0.156 found by Davidson et al. (2005) for the Australian market. The skewness
and kurtosis statistics suggest that some variables are not normally distributed.
Therefore to remove any heteroskedasticity problems arising out of the non-normal
distributions, all regressions are estimated with White-adjusted standard errors and tstatistics.
6.2 Correlation coefficients
Table 3 presents the correlation coefficients between the dependent and
independent variables used in each of the models. The Pearson‘s correlation
coefficients are shown to the upper right of the diagonal, and the Spearman‘s Rho
coefficients are shown to the lower left of the diagonal. The correlations between the
three proxies for earnings management (ABSDAC1, ABSDAC2 and ABSDAC3) are
positive and statistically significant. This implies that these proxies are capturing
similar phenomena. The CRISIS dummy variable is positively correlated with all
three measures of earnings management. This provides initial support for the
proposition that the absolute levels of DACs were high during the GFC (compared to
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the PCP). Most variables related to audit quality are positively correlated with the
earnings management proxies.
OCF has a negative correlation (although not significant) with all three measures
of earnings management. This implies that if OCF increases, firms‘ discretionary
accruals decrease. Of particular note is the fact that, with the exception of OCF and
NEG, the independent variables are not highly correlated to each other. This reduces
concerns about multicollinearity problems impacting the precision of estimated
coefficients.
6.3 Univariate test results
Table 4 presents univariate test results for differences in the three measures of
earnings management. Results for both the signed DACs and absolute values of
DACs (ABSDAC) are reported. For the signed DACs, DAC1 and DAC2 and DAC1
and DAC 3 are not significantly different in terms of Wilcoxon signed-rank tests.
Results from the paired sample t-tests for equality of the means suggest that DAC1
and DAC2 are significantly different.
For the ABSDACs, both the non-parametric Wilcoxon signed-rank tests and the
parametric paired-sample t-tests suggest no statistically significant difference
between ABSDAC1 and ABSDAC2. However, ABSDAC3 is significantly different
from each of ABSDAC1 and ABSDAC2. This may suggest that ABSDAC3 has
different distributional properties than ABSDAC1 and ABSDAC2, and that the mean
of ABSDAC3 is significantly different from the respective means of ABSDAC1 and
ABSDAC2.
Panel A of Table 5 provides univariate parametric and non-parametric test results
for differences in the variable means of firms audited by Big-4 and non-Big-4
auditors.
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Table 3: Correlation coefficients between variables§
(Pearson correlation coefficients to the upper right of the diagonal and Spearman’s Rho coefficients to the lower left
of the diagonal)
DAC1
DAC2
DAC3
ACSIZE
0.116*
ACMEE
T
0.143**
BIG-4
ACFINAC
ACIND
SIZE
NEG
OCF
LEV
-0.028
CRISI
S
0.113*
0.121**
0.112*
0.287**
0.131**
0.130**
0.124**
0.194**
0.292**
0.328**
0.294**
0.177**
-0.026
-0.028
0.366**
1.000
-0.036
0.004
0.002
0.010
0.041
0.075
0.022
0.028
0.020
0.070
0.021
0.028
DAC1
1.000
0.934**
0.947**
DAC2
0.933**
1.000
0.988**
0.127**
0.146**
0.112*
0.108*
-0.053
0.092*
0.281**
DAC3
0.921**
0.964**
1.000
0.158**
0.150**
0.113*
0.103*
-0.025
0.127**
0.276**
ACSIZE
0.190**
0.206**
0.225**
0.223**
0.305**
0.320**
-0.056
-0.036
0.374**
ACMEET
0.254**
0.254**
0.239**
0.266**
1.000
0.217**
0.208**
0.061
0.017
0.338**
BIG-4
0.289**
0.270**
0.291**
0.348**
0.287**
1.000
0.310**
0.147**
0.063
0.407**
ACFINAC
0.184**
0.169**
0.166**
0.343**
0.211**
0.337**
1.000
0.230**
0.014
0.265**
ACIND
0.107*
0.101*
0.109*
0.039
0.061
0.201**
0.221**
1.000
0.037
0.163**
CRISIS
0.097*
0.072
0.076
-0.005
0.017
0.074
0.008
0.037
1.000
-0.074
SIZE
0.366**
0.358**
0.346**
0.399**
0.344**
0.436**
0.292**
0.190**
-0.085*
1.000
NEG
-0.294**
-0.288**
-0.303**
-0.247**
-0.292**
-0.311**
0.254**
0.265**
0.294**
0.114**
0.200**
0.238**
0.177**
0.128**
-0.026
OCF
0.382**
0.233**
0.386**
0.294**
LEV
0.328**
0.319**
0.322**
0.171**
0.172**
0.261**
0.275**
0.193**
0.174**
1.000
-0.046
0.273**
0.581**
0.314**
-0.022
-0.025
-0.071
-0.067
-0.026
-0.035
-0.029
0.048
0.083*
1.000
0.293*
** Significant at the 1 per cent level; * Significant at the 5 per cent level.
§
Variable definitions are as per equations 3 to 8. Note that DACs in the above refers to the absolute values of DACs (ABSDACs)
13
*
0.001
1.000
Proceedings of 9th Asian Business Research Conference
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Table 4: Differences in the different proxies for earnings management
Wilcoxon signed rank tests: z-statistics
DAC1- DAC2
DAC1-DAC3
DAC2- DAC3
ABSDAC1- ABSDAC2
ABSDAC1-ABSDAC3
ABSDAC2- ABSDAC3
Paired sample t-tests for the equality of the
mean
Signed DACs
-0.503
DAC1- DAC2
2.021*
-1.194
DAC1- DAC3
-0.628
-3.274**
DAC2- DAC3
-0.743
Absolute value of DACs
-1.082
ABSDAC1 - ABSDAC2
0.468
-2.704**
ABSDAC1 - ABSDAC3
2.255*
-3.591**
ABSDAC2 -ABSDAC3
2.376*
** Significant at the 1 per cent level; * Significant at the 5 per cent level.
§
Variable definitions are as per equations 3 to 8.
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Table 5: Differences in the earnings management
§
Variable
Mean
Standard deviation
Independent
sample ttest (t-stat)
MannWhitney
test (zstat)
Panel A: Between the clients of Big-4 auditors and non-Big-4-auditors
Big-4
Other
Big-4
Other
Big-4 vs.
Big-4 vs.
Other
Other
DECDAC1
-0.236
-0.079
0.349
0.135
-2.680***
-4.245***
DECDAC2
-0.242
-0.073
0.375
0.121
-2.627***
-4.282***
DECDAC3
-0.204
-0.063
0.307
0.087
-2.607***
-3.617***
ABSDAC1
0.208
0.094
0.317
0.181
3.239***
-5.624***
ABSDAC2
0.209
0.088
0.331
0.161
3.313***
-5.634***
ABSDAC3
0.180
0.080
0.275
0.150
3.235***
-5.282***
ACSIZE
3.325
2.795
0.876
0.815
5.379***
-6.307***
ACMEET
4.131
3.010
1.856
2.029
5.231***
-6.728***
ACFINAC
1.469
0.927
0.979
0.844
5.075***
-4.995***
ACIND
0.534
0.453
0.499
0.500
1.456
-1.316
SIZE
5.976
5.330
0.694
0.586
8.555***
-8.222***
NEG
0.232
0.587
0.423
0.494
-7.311**
-6.756***
OCF
0.073
0.942
0.852
11.777
-1.592
-4.613***
LEV
0.491
0.939
0.348
5.364
-1.808*
-3.933***
Panel B: Between the GFC and the PCP
GFC
PCP
GFC
PCP
GFC vs.
GFC vs.
PCP
PCP
DECDAC1
-0.256
-0.165
0.364
0.285
-2.233**
-2.429
DECDAC2
-0.244
-0.188
0.357
0.353
-1.271
-1.545
DECDAC3
-0.223
-0.138
0.346
0.201
-2.214**
-1.602
ABSDAC1
0.220
0.152
0.339
0.247
2.553**
-2.176**
ABSDAC2
0.215
0.157
0.335
0.281
2.081**
-1.631
ABSDAC3
0.192
0.126
0.309
0.183
2.751***
-1.635
BIG-4
0.837
0.824
0.369
0.381
0.412
-0.412
ACSIZE
3.206
3.269
0.751
1.013
-0.837
-0.108
ACMEET
4.062
3.819
1.907
1.949
1.491
-1.733
ACFINAC
1.391
1.363
1.015
0.939
0.340
-0.197
ACIND
0.537
0.500
0.499
0.500
0.891
-0.891
SIZE
5.811
5.918
0.709
0.733
-1.773*
-2.045**
NEG
0.283
0.307
0.451
0.462
-0.613
-0.614
OCF
0.446
-0.019
6.712
1.266
1.141
0.275
LEV
0.415
0.725
0.328
3.168
-1.670*
-4.173***
*** Significant at 1 the per cent level; ** Significant at the 5 per cent level; * Significant at the 10 per
cent level.
§
Variable definitions are as per equations 3 to 8.
Firms audited by Big-4 auditors have larger means for each of the three measures
of earnings management (ABSDAC1, ABSDAC2 and ABSDAC3) than the firms
audited by non-Big-4 auditors. The means of all the audit committee-related
variables are also higher for the firms audited by Big-4 auditors than for the firms
audited by non-Big-4 auditors. Similarly, firms audited by Big-4 auditors seem to
engage in larger income-decreasing earnings management (evidenced in the mean
statistics for DECDAC1, DECDAC2 and DECDAC3) than firms audited by non-Big-4
auditors. A likely reason for this is that firms audited by Big-4 auditors are generally
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larger in size and in their scale of operations than firms audited by non-Big-4
auditors, as is evident in the proxy for firm size (SIZE).
It is important to note that about 58.7 per cent of the firms audited by non-Big-4
auditors have negative earnings, whereas only 23.2 per cent of the sample of firms
audited by Big-4 auditors have negative earnings. The means of the leverage and
OCF variables are higher for the sample of firms audited by non-Big-4 auditors than
the sample of firms audited by Big-4 auditors. The parametric independent sample ttests and non-parametric Man-Whitney Test z-statistics suggest that the differences
between the two samples are significant for all the variables. Thus it appears that the
firms audited by the Big-4 auditors have a higher level of discretionary accruals, are
larger in size, have larger audit committees, have more audit committee related
activities (meeting), have more professionally-expert members in the audit
committee, have a lower frequency of negative earnings, and have lower levels of
leverage than firms audited by non-Big-4 auditors.
Panel B of Table 5 presents the univariate parametric and non-parametric tests
results for the differences in the variables between the GFC and PCP. The means of
all the three proxies of earnings management (ABSDAC1, ABSDAC2 and
ABSDAC3) are higher during the GFC those during the PCP. The parametric
independent sample t-test suggests that the difference in the proxies of earnings
management between the GFC and PCP are significant, although the nonparametric Mann-Whitney test suggests that the differences are not significant for
ABSDAC2 and ABSDAC3.
Similar results emerge when the mean statistics of DECDAC1, DECDAC2 and
DECDAC3 are compared for the GFC and PCP. Firms engaged in a higher level of
income-decreasing earnings management via negative DACs. However, the nonparametric tests suggest that the increase in the level of income-decreasing earnings
management during the GFC is not statistically significant. This contrasts with the
results of the independent sample t-tests, which suggest that the differences in the
income-decreasing earnings management between the GFC and the PCP are
significant for two of the three proxies of earnings management.
None of the audit quality- and committee-related variables have significantly
different means between the GFC and PCP. Thus, we conclude there was no
significant change in audit quality in terms of activities and formation of audit
committee between the GFC and the PCP. With respect to the other control
variables, the differences between the GFC and the PCP in the means of SIZE and
LEV are significant. The significantly higher SIZE during the PCP is consistent with
the decline in firms‘ market value during the GFC as compared to the PCP. The
significantly lower mean for leverage during GFC as compared to the PCP implies a
decline in debt financing during the GFC relative to that undertaken during the PCP.
6.4 Multivariate regressions results
Hypothesis 1
Hypothesis 1 states that: ―On average, Australian companies engaged in a higher
level of earnings management during the 2008-2009 GFC.” To examine the
hypothesis, equation 4 is estimated. The results for equation 4 are presented in
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Table 6. The model is significant at the 1 per cent level (F-statistic values are
significant at the 1 per cent level for all the three proxies of earnings management).
The Durbin-Watson statistics are within the 1.50 to 2.50 range, which suggests that
no serious autocorrelation problem is present. To remove heteroskedasticity, the
model has been estimated with White adjusted standard errors and t-statistics.
Table 6: Effect of the GFC on firms’ earnings management
Variable§
DECDAC1
DECDAC2
DECDAC3
Intercept
1.254***
1.303***
0.763***
4.052
3.814
2.740
CRISIS
-0.120***
-0.095**
-0.097**
-2.861
-2.193
-2.530
Control variables
OCF
-0.003
-0.001
-0.012***
-1.167
-0.475
-2.618
SIZE
-0.175***
-0.186***
-0.123***
-4.109
-3.814
-2.802
NEG
0.004
-0.009
-0.001
0.074
-0.176
-0.016
LEV
-0.054
-0.066
-0.038
-1.224
-1.401
-1.209
Industry dummies
CD
-0.368**
-0.354**
-0.198**
-2.242
-2.326
-2.278
CS
-0.231
-0.209
0.024
-1.518
-1.521
0.357
ENERGY
-0.408***
-0.405***
-0.216***
-2.758
-2.822
-2.693
HC
-0.166
-0.160
-0.082
-1.204
-1.204
-0.715
IND
-0.342**
-0.369***
-0.087*
-2.516
-2.730
-1.810
IT
-0.251*
-0.252*
-0.069
-1.830
-1.878
-1.293
MAT
-0.351**
-0.325**
-0.146**
-2.493
-2.402
-2.212
Adjusted R-square
0.1736
0.1422
0.1950
F-statistic
5.323***
4.507***
5.240***
Durbin-Watson
1.817
1.643
1.808

t-statistics appear under each coefficient.
***Significant at the 1 per cent level; **Significant at the 5 per cent level; * Significant at the 10 per
cent level.
§
Variable definitions are as per equations 3 to 8.
The coefficients of the CRISIS test variable are significant and negative for all
three proxies for income-decreasing earnings management. The statistically
significant and negative coefficients of the CRISIS dummy variables suggest that the
sample firms engaged in more income-decreasing earnings management during the
GFC than in the PCP. Amongst the control variables, SIZE is negatively and
significantly associated with earnings management. This suggests that larger firms
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Proceedings of 9th Asian Business Research Conference
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have a higher level of income-decreasing earnings management. This may be due to
the higher level of operating activities for larger firms than for smaller firms.
Table 7: Effect of the GFC on firms’ earnings management
Variable
ABSDAC1
ABSDAC2
ABSDAC3
Intercept
-0.783***
-0.800***
-0.616***
-4.341
-4.153
-3.559
CRISIS
0.072***
0.067**
0.062***
2.860
2.523
2.757
Control variables
OCF
0.004
0.002
0.009**
1.465
0.844
2.231
LOGMV
0.116***
0.120***
0.087***
4.677
4.434
3.764
NEG
-0.037
-0.039
-0.039
-1.064
-1.132
-1.143
LEV
0.006
0.006**
0.005
2.513
2.486
2.456
Industry dummies
CD
0.260***
0.250***
0.254***
2.963
2.960
2.993
CS
0.181**
0.161**
0.175**
2.424
2.168
2.265
ENERGY
0.262***
0.266***
0.260***
3.279
3.308
3.124
HC
0.105
0.098
0.115
1.482
1.396
1.543
IND
0.284***
0.296***
0.219***
3.835
3.793
2.876
IT
0.170**
0.165***
0.164***
2.348
2.271
2.170
MAT
0.307***
0.290***
0.282***
3.941
3.779
3.483
Adjusted R-square
0.1321
0.1185
0.1511
F-statistics
7.200***
6.479***
7.604***
Durbin-Watson
1.625
1.537
1.638
§

t-statistics appear under each coefficient.
*** Significant at the 1 per cent level; ** Significant at the 5 per cent level; * Significant at the 10 per
cent level.
§
Variable definitions are as per equations 3 to 8.
The results from the estimation of equation 5 using the absolute value of DACs
(ABSDAC1, ABSDAC2 and ABSDAC3) as the dependent variable are presented in
Table 7. These are similar to those obtained from the use of the income-decreasing
earnings management models. The coefficients of the CRISIS dummy variables are
positive and statistically significant for all three proxies for earnings management.
Amongst the control variables SIZE has positive and significant coefficients for all
three earnings management proxies. The results for the industry dummies are also
similar to those reported in Table 6, excepting that the signs of the coefficients are
positive. This is consistent with prior expectations, because of the use of the
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Proceedings of 9th Asian Business Research Conference
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absolute values of DACs as the dependent variables. Statistically significant Fstatistics suggest that the models are significant at the 1 per cent level. The values
for the Durbin-Watson statistics suggest that there is no serious autocorrelation
problem with any of the models.
The above analysis suggests that the sample firms engaged in more incomedecreasing earnings management via discretionary accruals during the GFC than
during the PCP. The results of the multivariate regressions corroborate those of the
univariate parametric and non-parametric tests presented in Table 5. These findings
are also consistent with Hypothesis 1, so we do not reject it.
The findings relating to Hypothesis 1 are also consistent with those in the existing
literature. For example, Chia, et al. (2007), examine a sample of firms in Singapore
in the context of 1997 Asian financial crisis and find that their sample firms engage in
income-decreasing earnings management during the Asian crisis period. Similarly, in
the context of 1990 Persian Gulf crisis, Han and Wang (1998) investigated whether
firms that expect increases in earnings resulting from product price increases use
accounting accruals to reduce earnings. Their findings suggest that oil firms that
expected to profit from the oil price increase during the Gulf crisis used accounting
accruals to reduce their reported quarterly earnings. van Zalk (2010) examines the
effect of the 2008-2009 GFC on earnings management for a sample firms‘ applying
the earnings distribution approach proposed by Burgstahler and Dichev (1997) in the
context of the code law France and common law UK. The findings suggest that some
adopted income-decreasing earnings management. Hence, the findings of the
present study are consistent with earlier studies in other crisis and other country
contexts. We, therefore, do not reject Hypothesis 1.
Hypotheses 2
Hypothesis 2 states that ―Companies audited by highly ranked audit firms
engaged in a lower level of earnings management during the 2008-2009 GFC than
companies audited by lower ranked audit firms‖.
To examine the effect of audit quality and audit committee on earnings
management, equations 6 is estimated. The results for equation 6 are presented in
Table 8.
As is evident from the results in Table 8, only the audit committee-related variable,
ACIND, has a statistically significant and negative coefficient for all the three proxies
of earnings management. This suggests that firms with a higher proportion of
independent directors in the audit committee have a significantly lower level of
earnings management via DACs. The coefficient for Big-4 is not significant for any of
the three specifications. Thus, appointment of a Big-4 (highly ranked auditor) does
not seem to have been associated with a lower level of earnings management via
DACs in the Australian market.
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Proceedings of 9th Asian Business Research Conference
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Table 8: Effect of audit quality on firms’ earnings management
Variable§
ABSDAC1
ABSDAC2
ABSDAC3
Intercept
-0.501***
-0.512***
-0.374***
-3.983
-3.895
-3.323
CRISIS
0.072***
0.067**
0.061**
2.669
2.394
2.543
Big-4
0.021
0.019
0.025
0.508
0.448
0.691
ACSIZE
-0.006
0.001
0.013
-0.374
-0.022
0.864
ACIND
-0.060**
-0.082***
-0.046*
-2.111
-2.753
-1.798
ACMEET
0.005
0.005
0.004
0.678
0.605
0.529
ACFINAC
0.021
0.022
0.016
1.271
1.271
1.140
OCF
0.004**
0.003
0.010***
2.309
1.535
4.098
SIZE
0.114***
0.118***
0.078***
5.012
4.994
3.836
NEG
-0.027
-0.028
-0.024
-0.788
-0.783
-0.788
LEV
0.006
0.006
0.006
1.091
1.026
1.135
CD
-0.027
-0.048
-0.013
-0.545
-0.945
-0.269
CS
-0.113*
-0.145**
-0.095*
-1.875
-2.313
-1.702
Energy
-0.026
-0.041
Excluded
-0.583
-0.892
Excluded
HC
-0.181***
-0.201***
-0.153***
-3.223
-3.431
-2.941
IND
Excluded
Excluded
-0.046
Excluded
Excluded
-1.150
IT
-0.109*
-0.127**
-0.095*
-1.792
-2.001
-1.742
MAT
0.029
-0.007
0.025
0.695
-0.154
0.697
TEL
-0.260**
-0.272***
-0.240**
-2.282
-2.287
-2.438
Adjusted R-square
0.1340
0.1260
0.1520
F-statistics
5.276***
5.003***
5.515***
Durbin-Watson
1.712
1.632
1.823
Highest VIF
1.880
1.883
1.887

t-statistics appear under each coefficient.
*** Significant at the 1 per cent level; ** Significant at the 5 per cent level; * Significant at the 10 per
cent level.
§
Variable definitions are as per equations 3 to 8
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
The univariate tests results presented in Table 5 indicate that the clients of Big-4
auditors have significantly higher income-decreasing DACs and absolute values of
DACs than clients of the non-Big-4 auditors. The univariate tests results also
indicated that the clients of Big-4 auditors are significantly larger firms than the
clients of non-Big-4 auditors. Thus, as discussed previously, it is likely that the higher
level of DACs measured for the clients of Big-4 auditors simply relates to the larger
average size of these firms.
The coefficients of ACSIZE, ACMEET and ACFINAC from equation 6 are also not
statistically significant. Hence, firms‘ audit committee size, audit committee activity
(in terms of the number of meetings) and audit committee members‘ accounting and
finance backgrounds do not appear to constrain firms‘ earnings management via
DACs. Similar to equations 4 and 5, the coefficients of the CRISIS variable is
significant and positive, supporting the claim that firms engaged more in earnings
management via DACs during the GFC than during the PCP.
These findings in this paper are consistent with prior Australian studies examining
the effect of audit quality and the audit committee on earnings management. For
example, Baxter and Cotter (2009) examine the effect of the audit committee on
earnings quality and find that none of the audit committee characteristics except the
committee members‘ accounting and finance background is associated with a lower
likelihood of earnings management. However, amongst the two proxies they use for
earnings management, they find that the audit committee members‘ accounting and
finance expertise is not associated with lower level of DACs when estimated using a
modified Jones model. Similarly, Davidson et al. (2005) examine the role of firms‘
internal governance structures in constraining earnings management. They find
evidence that the audit committee members‘ independence and board members‘
impendence are associated with a lower likelihood of earnings management via
DACs, when DACs are measured using the modified Jones model. However, they
fail to find any impact from auditor choice and audit committee characteristics in
constraining the earnings management via DACs.
The findings of the present study are also consistent with recent evidence for a
sample of firms with earnings restatements in the U.S.A. Lin et al. (2006) find a
significant negative association between the size of the audit committee and
earnings restatement. However, they do not find any significant association between
the other four audit committee characteristics (independence, financial expertise,
activity, and stock ownership) and earnings restatement.
In contrast to the above, and in relation to earnings management during a period
of financial crisis, the findings of the present study are not consistent with those of
Chia et al. (2007) and Johl et al. (2007). In the case of Singapore, Chia et al. (2007)
find that the appointment of one of the Big-6 (now Big-4) auditors significantly
constrained earnings management via discretionary accruals during the 1997 Asian
financial crisis. However, Chia et al. (2007) do not examine the role of audit
committee characteristics in constraining earnings management. Johl et al. (2007)
examine audit qualification in Malaysia in the period surrounding the 1997 Asian
Financial Crisis. They find that the then Big-5 auditors qualified the audit reports
more frequently than their non-Big-5 counterparts in the presence of a high level of
abnormal accruals. Johl et al. (2007) do not find any such differentiation during the
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Proceedings of 9th Asian Business Research Conference
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pre-crisis period. However, the comparability of Johl et al. (2007) with the present
study is limited, as Johl et al. do not directly examine the association between audit
quality and earnings management proxied by DACs. Rather they examine the
association of audit qualification in the annual reports with the presence of
discretionary accruals.
Based on the above results, and giving consideration to the prior literature cited,
we reject Hypotheses 2.
7. Conclusions
The study is important for Australian investors, academics and policy makers. It
examines the impact of audit quality on earnings management, and therefore
financial reporting quality, in the Australian context. Also, whether audit quality
constrains managers‘ opportunistic behaviours during a financial crisis. It also has
implications for how post-crisis corporate governance reform, if entered into, might
proceed.
While many earnings management studies have examined the effects of different
corporate governance and audit quality variables on earnings management, the
empirical evidence has proven to be inconsistent (Lin & Hwang 2010). This study
contributes to both the earnings management and corporate governance literature. It
extends the earnings management literature by examining whether managers
engage in earnings management during a financial crisis. It extends the corporate
governance literature by examining whether audit quality has any discernible effect
on constraining opportunistic managerial behaviour regarding earnings management
during periods of financial crisis. It also provides evidence of earnings management
behaviour in a developed market, that of Australia, during the global financial crisis.
The analysis in the paper suggests that the firms in the sample engaged in more
income-decreasing earnings management during the global financial crisis (2008
and 2009) than during the pre-crisis period (2006 and 2007). This was through
greater use of discretionary accruals. An objective of future research should be to
examine whether these income-decreasing discretionary accruals were justified by
prudence, in light of uncertainties regarding the impact of the global financial crisis,
or reflected opportunistic behaviour—the taking of a ―big bath‖. However, this is
beyond the scope of the current study.
Only one audit quality variable, the proportion of independent member of the audit
committee had an impact in reducing the level of earnings management over this
period. Thus, firms with a higher proportion of independent directors in the audit
committee have a significantly lower level of earnings management via discretionary
accruals. This supports the importance of ensuring a role for independent directors
on boards.
Of significance in the Australian context is that appointment of a Big-4—a
commonly used indicator of audit quality—what not significant in reducing the level of
earnings management via discretionary accruals. This may reflect a number of
factors, including an overall high level of quality in audit services in the Australian
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
market outside the Big-4 auditors. Again, this is a matter for future research, being
outside the scope of the current study.
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
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