Proceedings of International Business and Social Sciences and Research Conference
16 - 17 December 2013, Hotel Mariiott Casamagna, Cancun, Mexico, ISBN: 978-1-922069-38-2
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
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£
This paper examines if Australian firms engaged in a higher level of earnings management during the global financial crisis (GFC) and if there was any industry effect on firms’ earnings management. During financial crises many firms experience a systematic decline in incomes, and majority of them report a fall in earnings or losses. The onset of a financial crisis may, therefore, trigger or magnify managerial motives to engage in earnings management, attributing the reduced earnings (or losses) to the macroeconomic shocks rather than to poor managerial performance.
Earnings management has been defined in terms of the discretionary components of total accruals. We use parametric and non-parametric tests and panel data regression methods to analyse the impact of the GFC and industry effect on the discretionary accruals sample comprised of 149
Australian firms during the 2006 to 2009 period. We find that Australian firms engaged in a higher level of income-decreasing earnings management during the GFC. This finding is consistent with that of earlier research in the
Asian Financial Crisis context. We also find that firm s’ industry classification has statistically significant effect on their earnings management.
Keywords: Earnings Management; Global Financial Crisis; Discretionary accruals,
Panel data, Fixed-effect model.
JEL Classification : G32; G01; M42
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.
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
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The authors gratefully acknowledge the financial support of CPA Australia for this research project. This is through their provision of CPA Australia Global Research Perspectives Scheme grant funding.
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Assistant Professor in Banking & Finance, Faculty of Business & Government, University of Canberra
(contact author).
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Professor, Accounting, Banking & Finance, Faculty of Business & Government, University of Canberra.
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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.
1

Proceedings of International Business and Social Sciences and Research Conference
16 - 17 December 2013, Hotel Mariiott Casamagna, Cancun, Mexico, ISBN: 978-1-922069-38-2 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.
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). One reason managers involve in earnings management is based on reflecting their opportunistic incentives ( Bernard and Skinner, 1996).
A number of studies also document that earnings management is motivated by manag ers‘ 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). The consequence 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).
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
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Proceedings of International Business and Social Sciences and Research Conference
16 - 17 December 2013, Hotel Mariiott Casamagna, Cancun, Mexico, ISBN: 978-1-922069-38-2 below the earningsbased 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). Lim and Matolcsy (1999) document the existence of significant
income-reducing 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.
The incomesmoothing 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-de-
Albornoz & 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).
Tse and Tucker (2009) find that managers time their earnings announcement (with earnings shocks) to occur soon after their industry peers‘ warnings to minimize their apparent responsibility for earnings shortfalls. They conclude that the observed clustering of earnings shocks announcement is due to managerial herding behaviour. Karuna et al. (2012) examine the relationship between industry product market competition and earnings management. Their findings suggest that industry factors play a vital role in influencing the variation in extent of earnings management across firms. Interpreted together, the findings of Tse and Tucker (2009) and Karuna et al. (2012) imply that firms‘ industry identity may affect firms‘ earnings management behaviour. However, little evidence exists on the effect of industry identity on firms‘ earnings management behaviour.
In light of the above , we examine firms‘ earnings management behaviour during a
systematic financial crisis (e.g., Fiechter & Meyer 2010); and the industry effect on
the firms‘ earnings management . We focus on a single, developed economy—
Australia —a country for which there is limited coverage in the literature. Following
Miller et al. (2004), we focus on a single country context 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 these 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) Does firms‘ industry identity affect the earnings management behaviour?
We follow Chia et al. (2007) and Johl et al. (2007) in examining
firms‘ earnings management behaviour in the context of a financial crisis. However, we differ from these two studies in that we examine aggressive earnings management behaviour during a period of financial crisis in a developed economy
—Australia.
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Proceedings of International Business and Social Sciences and Research Conference
16 - 17 December 2013, Hotel Mariiott Casamagna, Cancun, Mexico, ISBN: 978-1-922069-38-2
The rest of the paper is as follows. Section 2 discusses and identifies the relevant time periods to be associated with the GFC and pre-crisis period (PCP) in Australia‘s case. Section 3 outlines the data and sample set from which the metrics in the paper are derived. Section 4 describes the methodology and basis for the construction of the models used in this study. Section 5 provides the results from the statistical analysis. Section 6 concludes the paper.
Drawing an appropriate timeline for the GFC and the 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 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 in 30 June 2004 which increased to 4347 as of 30
June2005, and to 5034 during June 2006. It soared to 6311 during June 2007 and up to 6779 during October 2007. The index began to decline thereafter, 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.
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
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Proceedings of International Business and Social Sciences and Research Conference
16 - 17 December 2013, Hotel Mariiott Casamagna, Cancun, Mexico, ISBN: 978-1-922069-38-2
disclosure requirements (Khan et al. 2008), and have also been excluded in previous
studies (Baxter & Cotter 2009; Habib 2008). Of the remaining 237 firms, 9 firms were
excluded because their annual reports were not available from the Connect Four annual report collection. 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. 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.
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 149
Panel B: Industry distribution of the sample
2009 2008 2007 2006 Total
Consumer Discretionary
Consumer Staples
Energy
Health Care
Industrials
Information Technology
Materials
Telecommunications Services
Total
Common firms for all four years
13
10
31
10
38
7
38
2
13
10
31 31 30 123
10 10 10
38
7
12
9
34
7
12
9
33
7
50
38
40
143
28
36 36 36 146
2 2 2 8
149 147 141 139 576
139 139 139 139 139
301
22
29
13
237
6
9
73
4.1 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
5

Proceedings of International Business and Social Sciences and Research Conference
16 - 17 December 2013, Hotel Mariiott Casamagna, Cancun, Mexico, ISBN: 978-1-922069-38-2 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, one-period 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
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
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Proceedings of International Business and Social Sciences and Research Conference
16 - 17 December 2013, Hotel Mariiott Casamagna, Cancun, Mexico, ISBN: 978-1-922069-38-2
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). This performance adjusted
discretionary accruals model has also been used by Lawrence et al. (2013). In each case DACs are represented by model residuals with NDAs being the explained component of the model.
Jones (1991) model:
TAC it
TA it

1
 
1
1
TA it

1
 
2

REV it
TA it

1
 
3
PPE it
TA it

1
  it
(eqn. 1)
Modified Jones model (Dechow et al., 1995):
TAC it
TA it

1
 
1
1
TA it

1
 
2

REV it
 
REC it
TA it

1
 
3
PPE it
TA it

1
  it
(eqn. 2)
Modified Jones model adjusted for firm performance (Kothari et al., 2005)
TAC it
TA it

1
 
1
1
TA it

1
 
2

REV it
 
REC it
TA it

1
 
3
PPE it
TA it

1
 
4
ROA it
  it
(eqn. 3)
Where:
TAC it
Total accruals (measured as net income – cash flow from operations) for firm i at time t
TA it-1
Total assets at the beginning of the year for firm
∆REV it
Changes in total revenue for firm i at time t i at time t
∆REC it
Changes in receivables for firm i at time t
PPE it
Property plant and equipment for firm i at time t
ROA it
Return on assets for firm i at time t
ε it
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 .
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Proceedings of International Business and Social Sciences and Research Conference
16 - 17 December 2013, Hotel Mariiott Casamagna, Cancun, Mexico, ISBN: 978-1-922069-38-2
4.2 Measuring total accruals
Total accruals are calculated by subtracting cash flows from operations (OCF) from
income before extraordinary items. Hribar and Collins (2002) argue that the
difference between net income and cash from operations is the correct measure of total accruals and that the use of a balance sheet approach may in some circumstances lead to a systematic bias in the estimated discretionary accruals.
Hence, consistent with Choi et al. (2011) and Ahmed et al. (2008) total accruals in
this paper are calculated as the difference between income before extraordinary items and tax and the OCF.
4.3 Model for examining research questions 1 and 2
To test research questions 1 and 2, 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:
DECDAC it
ABSDAC it
 
0
 
1
CRISIS
 
2
OCF it
 
3
LEV it
 
4
SIZE it
 
0
 
1
CRISIS
 
2
OCF it
 
3
LEV it
 
4
SIZE it
 
5
NEG it
 i j 

1
   it
(eqn. 4)
 
5
NEG it
 i j 

1

I i i
  it
(eqn. 5)
Where:
DECDAC it
Income decreasing or negative accruals for firm i at time t
ABSDAC it
Absolute value of discretionary accruals for firm i at time t
CRISIS Crisis dummy taking the value of 1 for the year 2008 and 2009, 0 otherwise
OCF it
LEV it
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
SIZE it
NEG it
Natural log of the market value of firm i during year
Indicator variable taking the value of one if firm i t
has negative
I i earnings for year t , 0 otherwise
Industry dummy for firm i
Industry dummies were used to test research question 2 , that is to examine the industry effect on earnings management during GFC. 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 that 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. A statistically significant p i
(the coefficient of industry dummies) would suggest that company‘s industry identification has significant impact on its earnings management behaviour.
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Proceedings of International Business and Social Sciences and Research Conference
16 - 17 December 2013, Hotel Mariiott Casamagna, Cancun, Mexico, ISBN: 978-1-922069-38-2
LEV it
, SIZE it
, NEG it
, OCF it
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 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).
5.1 Descriptive statistics
Table 2 provides descriptive statistics for alternative dependent variables and for the main independent variables used in equations 4 and 5.
Table 2: Descriptive Statistics
Variable
§
Panel A: Pooled sample
N Minimum Maximum Mean Standard
Deviation
Skewne ss
Kurtosis
DAC1
DAC2
DAC3
ABSDAC1
ABSDAC2
ABSDAC3
SIZE
NEG
576
576
576
576
576
576
576
576
-1.984
-2.581
-1.970
0.000
0.000
0.000
2.982
0.000
1.750
1.678
1.503
1.984
2.581
1.970
8.171
1.000
OCF 576 -16.759
LEVERAGE 576 0.000
114.787
52.963
CRISIS 576 0.000 1.000
§
Variable definitions are as per equations 4 to 8.
-0.028
-0.040
-0.015
0.188
0.188
0.161
5.863
0.295
0.219
0.565
0.514
0.354
0.362
0.305
0.301
0.312
0.259
0.722
0.456
4.893
2.223
0.500
-0.821 10.274
-1.455 12.581
-0.982 11.980
3.245 12.296
3.542 15.674
3.469 14.932
0.090
0.901
0.428
-1.193
22.366 526.946
22.898 540.141
-0.056 -2.004
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 t statistics.
5.2 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 the PCP).
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Proceedings of International Business and Social Sciences and Research Conference
16 - 17 December 2013, Hotel Mariiott Casamagna, Cancun, Mexico, ISBN: 978-1-922069-38-2
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.
5.3 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.
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 non-parametric Mann-Whitney test suggests that the differences are not significant for ABSDAC2 and ABSDAC3.
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Proceedings of International Business and Social Sciences and Research Conference
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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
DAC1 DAC2 DAC3 CRISIS SIZE NEG OCF LEV
1.000 0.934
**
0.947
**
0.113
*
0.287
**
-
0.131
**
-0.028 -
0.004
0.933
**
1.000 0.988
**
0.092
*
0.281
**
-
0.130
**
-0.022 -
0.002
DAC3 0.921
**
0.964
**
1.000 0.127
**
0.276
**
-
0.124
**
-0.025
CRISIS 0.097
*
0.072 0.076 1.000 -0.074 -0.026 0.048 -
-
0.010
0.070
SIZE
NEG
OCF
0.366
**
0.358
**
0.346
**
-0.085
*
1.000
-0.294
**
-0.288
**
0.254
**
0.265
**
-0.303
0.294
**
**
-0.026
-0.046
-
0.386
**
0.294
**
-
0.366
**
1.000
-0.036
-
0.083
*
-
0.021
0.028
1.000 0.001
LEV 0.328
**
0.319
**
0.322
**
-
0.174
**
0.273
**
-
0.581
**
-
0.314
**
0.293
**
1.000
** 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)
Table 4: Differences in the different proxies for earnings management
Wilcoxon signed rank tests: z-statistics Paired sample t -tests for the equality of the mean
Signed DACs
DAC1- DAC2
DAC1-DAC3
DAC2- DAC3
-0.503
-1.194
-3.274**
DAC1- DAC2
DAC1- DAC3
DAC2- DAC3
Absolute value of DACs
2.021*
-0.628
-0.743
ABSDAC1- ABSDAC2
ABSDAC1-ABSDAC3
ABSDAC2- ABSDAC3
-1.082
-2.704**
-3.591**
ABSDAC1 - ABSDAC2
ABSDAC1 - ABSDAC3
ABSDAC2 -ABSDAC3
0.468
2.255*
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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Proceedings of International Business and Social Sciences and Research Conference
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Table 5: Differences in the earnings management
Variable
§
Mean Standard deviation Independent sample t test ( t -stat)
Mann-
Whitney test (zstat)
GFC
Between the GFC and the PCP
PCP GFC PCP
DECDAC1
DECDAC2
DECDAC3
ABSDAC1
ABSDAC2
ABSDAC3
SIZE
NEG
OCF
LEV
-0.256
-0.244
-0.223
0.220
0.215
0.192
5.811
0.283
0.446
0.415
-0.165
-0.188
-0.138
0.152
0.157
0.126
5.918
0.307
-0.019
0.725
0.364
0.357
0.346
0.339
0.335
0.309
0.709
0.451
6.712
0.328
0.285
0.353
0.201
0.247
0.281
0.183
0.733
0.462
1.266
3.168
GFC vs.
PCP
-2.233**
-1.271
-2.214**
2.553**
2.081**
2.751***
-1.773*
-0.613
1.141
-1.670*
GFC vs.
PCP
-2.429
-1.545
-1.602
-2.176**
-1.631
-1.635
-2.045**
-0.614
0.275
-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.
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.
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.
5.4 Multivariate regressions results
Research question 1
Research question 1 examines if Australian firms engaged in a greater level of earnings management during the global financial crisis. To examine this question, equation 4 is estimated. The results for equation 4 are presented in 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.
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Proceedings of International Business and Social Sciences and Research Conference
16 - 17 December 2013, Hotel Mariiott Casamagna, Cancun, Mexico, ISBN: 978-1-922069-38-2
Table 6: Effect of the GFC on firms’ earnings management

Variable
§
DECDAC1 DECDAC2 DECDAC3
Intercept
CRISIS
1.254***
4.052
-0.120***
-2.861
Control variables
-0.003
1.303***
3.814
-0.095**
-2.193
-0.001
0.763***
2.740
-0.097**
-2.530
OCF
SIZE
-1.167
-0.175***
-4.109
-0.475
-0.186***
-3.814
-0.012***
-2.618
-0.123***
-2.802
NEG
LEV
0.004
0.074
-0.054
-0.009
-0.176
-0.066
-0.001
-0.016
-0.038
-1.209
CD
CS
ENERGY
-1.224
Industry dummies
-1.401
-0.368**
-2.242
-0.354**
-2.326
-0.231
-1.518
-0.408***
-0.209
-1.521
-0.405***
-0.198**
-2.278
0.024
0.357
-0.216***
HC
IND
IT
MAT
Adjusted R-square
-2.758
-0.166
-1.204
-0.342**
-2.516
-0.251*
-1.830
-0.351**
-2.493
0.1736
-2.822
-0.160
-1.204
-0.369***
-2.730
-0.252*
-1.878
-0.325**
-2.402
0.1422
-2.693
-0.082
-0.715
-0.087*
-1.810
-0.069
-1.293
-0.146**
-2.212
0.1950
F-statistic 5.323***
Durbin-Watson 1.817
 t -statistics appear under each coefficient.
4.507***
1.643
5.240***
1.808
***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.
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. Statistically significant F -statistics 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. Results are similar to
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Proceedings of International Business and Social Sciences and Research Conference
16 - 17 December 2013, Hotel Mariiott Casamagna, Cancun, Mexico, ISBN: 978-1-922069-38-2 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.
Table 7: Effect of the GFC on firms’ earnings management

Variable
§
ABSDAC1 ABSDAC2 ABSDAC3
Intercept
CRISIS
OCF
LOGMV
NEG
LEV
-0.783***
-4.341
0.072***
2.860
Control variables
0.004
1.465
0.116***
4.677
-0.037
-1.064
0.006
2.513
Industry dummies
-0.800***
-4.153
0.067**
2.523
0.002
0.844
0.120***
4.434
-0.039
-1.132
0.006**
2.486
-0.616***
-3.559
0.062***
2.757
0.009**
2.231
0.087***
3.764
-0.039
-1.143
0.005
2.456
CD
CS
ENERGY
HC
IND
IT
MAT
0.260***
2.963
0.181**
2.424
0.262***
3.279
0.105
1.482
0.284***
3.835
0.170**
2.348
0.307***
3.941
0.250***
2.960
0.161**
2.168
0.266***
3.308
0.098
1.396
0.296***
3.793
0.165***
2.271
0.290***
3.779
0.254***
2.993
0.175**
2.265
0.260***
3.124
0.115
1.543
0.219***
2.876
0.164***
2.170
0.282***
3.483
Adjusted R-square 0.1321
F-statistics 7.200***
Durbin-Watson 1.625
 t -statistics appear under each coefficient.
0.1185
6.479***
1.537
0.1511
7.604***
1.638
*** 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.
Amongst the control variables SIZE has positive and significant coefficients for all three earnings management proxies. This suggests that larger firms 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.
Pooling the cross-section and time series data increases the likelihood of violating the assumption of independence in the error terms. Panel data regressions are used to deal with the problem. Hausman specification tests suggest that a fixed-effects model is appropriate against the alternative of a random-effects model. Because the
CRISIS dummy variable controls for period effects, a further robustness test for research question 1 is performed. This is by estimating equation 4 with a one-way cross-section fixed effect. Minutti-Meza (2013) suggests that firm specific fixed effect model mitigates the unobservable firm specific characteristics that are stable over
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Proceedings of International Business and Social Sciences and Research Conference
16 - 17 December 2013, Hotel Mariiott Casamagna, Cancun, Mexico, ISBN: 978-1-922069-38-2 time and impact the outcome variable. Fixed effect model isolates the effect of the time-invariant characteristics from the predictor variables in order to assess the predictors‘ net effect. Because the cross-sectional fixed-effects model controls for any firm-specific effects, the industry dummy variables are not included in this estimation of equation 4. Results are presented in Table 8. The adjusted R-square
(in Table 8) of the fixed-effects model is higher than the ordinary least square models
(in Tables 6 and 7). The CRISIS dummy variable is positive and statistically significant for all the three proxies of the dependent variable (earnings management).
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.
Table 8: Effect of the GFC on firms’ earnings management

Variable
§
ABSDAC1 ABSDAC2 ABSDAC3
Intercept
CRISIS
OCF
SIZE
NEG
LEV
0.055
0.301
0.071***
2.829
0.003***
5.943
0.014
0.456
0.046*
1.831
-0.001
-1.451
0.027
0.131
0.060***
2.628
0.002***
3.241
0.022
0.628
0.019
0.582
-0.001*
-1.869
0.020
0.110
0.057***
1.704
0.010***
12.086
0.015
0.478
0.048
1.308
-0.000
-0.149
Adjusted R-square
F-statistics
0.2879
2.397***
0.3564
2.917***
0.2505
2.160***
Durbin-Watson 2.558
Cross-section fixed effect Chi –square
292.778***
 t -statistics appear under each coefficient.
2.530
351.882***
2.531
237.730***
*** 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 findings relating to research question 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
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Proceedings of International Business and Social Sciences and Research Conference
16 - 17 December 2013, Hotel Mariiott Casamagna, Cancun, Mexico, ISBN: 978-1-922069-38-2 findings suggest that some firms in both countries adopted income-decreasing earnings management during the GFC as compared to the PCP. Hence, the findings of the present study are consistent with earlier studies in other crisis and other country contexts.
Research question 2
Research question 2 examines the effect of firms‘ industry identity affect the earnings management behaviour. With respect to the different industry dummies, consumer discretionary (CD), ENERGY, Industrial (IND) and material (MAT) have significantly higher level of income-decreasing earnings management during the
GFC than the PCP (Table 6). Results from Model 5 (presented in Table 7) are 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 absolute values of DACs as the dependent variables. Thus ou r findings suggest that firms‘ industry identity affects their earnings management behaviour.
Our findings are consistent with Tse and Tucker (2009) who find that managers time their earnings announcement (with earnings shocks) to occur soon after their industry peers‘ warnings to minimize their apparent responsibility for earnings shortfalls. Our findings are also consistent with the findings of Karuna et al. (2012) that industry factors play a vital role in influencing the variation in extent of earnings management across firms. Our findings imply that managers herd in managing earnings when peer managers in the same industry also manage earnings. Thus earnings management by one firms in an industry will act as warnings of earnings management by other firms in the same industry.
The study is important for Australian investors, academics and policy makers. This study contributes to the earnings management literature. It extends the earnings management literature by examining whether managers engage in earnings management during a financial crisis. It also provides evidence of earnings management behaviour in a developed market, that of Australia, during the GFC.
The analysis in the paper suggests that firms engaged in more income-decreasing earnings management during the GFC (2008 and 2009) than during the PCP (2006 and 2007). This was through greater use of discretionary accruals. We also find that firms‘ industry classification has statistically significant effect on their earnings management. 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 GFC, or reflected opportunistic behaviour
— the taking of a ―big bath‖. However, this is beyond the scope of the current study.
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