1  

THE TREND IN THE EARNING MANAGEMENTS IN RELATION TO THE

IMPROVEMENTS IN ACCOUNTING STANDARDS

Juan Carlos Navarro

Mariluz Mate(*)

Departamento de Economía Financiera y Contabilidad

Universidad Politécnica de Cartagena

Área temática: A) Información financiera y Normalización contable

Keywords: IFRS, earnings management, accounting quality, international accounting.

JEL: M41

88a

2  

THE TREND IN THE EARNING MANAGEMENTS IN RELATION TO THE

IMPROVEMENTS IN ACCOUNTING STANDARDS

Abstract

This paper studies the temporal trend of earnings management over the last decade in

a sample of German listed companies. To get this purpose, we analyse discretionary

accruals of firms yielded from 2001 to 2010 applying the Seemingly Unrelated

Regression (SUR) methodology and highlighting the mandatory character of IFRS from

2005 on. Our results indicate that as companies have incentives to adopt IFRS, the

level of earnings management decreases for a certain period of time. However, we find

no evidence about higher financial reporting quality in mandatory IFRS adopters after

IFRS adoption.

3  

Introduction

Since 2005, all European Union (EU) listed companies have been required to

prepare their consolidated financial statements in accordance with IFRS. Regulators

expect the IFRS to enhance comparability and improve transparency of financial

reporting. Although a number of companies voluntarily adopted IFRS, starting in 2005,

a large number of companies had to mandatorily adopt IFRS, therefore, it is difficult to

predict their behavior.

If regulators expect IFRS to increase the quality of financial reporting, different

factors may affect it. Thus, IFRS of higher quality than local Generally Accepted

Accounting Principles (GAAP) may reduce earnings management if there are

managerial incentives for that. Both Christensen et al. (2008) and Ahmed et al. (2013)

indicated that the application of IFRS involves substantial discretion and this discretion

can be used to manage earnings or to convey information, depending on managerial

incentives. In fact, Capkun et al. (2013) claim that IAS/IFRS changed substantially from

the pre-2005 voluntary adoption period to the post-2005 mandatory adoption period, in

such a way that they find an increase in earnings management after 2005, which they

attribute to increased firms’ flexibility coupled with the lack of implementation guidance.

The aim of this paper is to study, some years after IFRS adoption, whether

these new standards have affected earnings management. To this end, we use a

sample of German listed firms. In Germany, a number of years before 2005, firms

could draw up their financial statements under international accounting standards,

namely IAS or USGAAP. Thus, we can compare the behavior of new IFRS adopters

(from 2005 on) relative to voluntary adopters (prior to 2005). To get this purpose we

apply Seemingly Unrelated Regression (SUR) methodology which allows us to analyze

the temporal evolution of the coefficients of the model.

Our results suggest that as companies have incentives to adopt IFRS, the level

of earnings management decreases for a certain period of time. For instance, firms

4  

voluntarily adopting IFRS before 2001 exhibit less earnings management than the rest

up to 2003; then, companies voluntarily adopting IFRS between 2001 and 2004, show

less earnings management up to 2007. However, we do not find evidence that

mandatory adopters have decreased earnings management after 2005.

Our contribution is two-fold. Firstly, using the Seemingly Unrelated Regression

(SUR) methodology, we analyse the effect of IFRS adoption on earnings management.

This methodology allows us to follow year by year the evolution of earnings

management and to determine the relationship between discretionary accruals and

different IFRS adopters. Secondly, we distinguish two categories of voluntary adopters

companies: those with incentives to voluntarily adopt IFRS even before knowing that

IFRS would be mandatory, and those which adopted IFRS before they were

compulsory.

This study is structure in different sections. Second section shows the

background of our study. The third section exposes the methodology. Section four

presents the main results and finally, we conclude.

2. Background and research question

The German setting and the change from German GAAP to international GAAP

Germany provides a unique study setting. Since 1998, the German commercial code

(Handelsgesetzbuch – HGB) has allowed listed firms to report consolidated financial

statements under accepted international accounting standards. Although before this

date several German entities presented IAS or US-based financial statements, starting

in 1998 a growing number of firms began to use international standards. Two factors

have contributed to this situation: companies were no longer required to disclose two

sets of consolidated statements (HGB and either IAS or US GAAP), and new stock

exchange segments required the application of either IAS or US GAAP.

5  

Both IFRS and US GAAP are viewed as capital-market-oriented standards

(Weibenberger et al. 2004), so that investors can understand the adoption of either of

them as a step towards higher transparency. In fact, both sets of accounting standards

are based in an Anglo-American common-law tradition (Hail et al. 2010).

Although the first IAS was published in 1975, the International Accounting

Standards Committee (IASC) undertook the Comparability Project in 1987, becoming

effective in 1995. However, the existence of a number of deficiencies led to the “Core

Standards Project”, which included important revisions. Specifically, 12 IAS were

substantially revised from 1998 to 2001. Later, from 2003 to 2004, 23 IAS/IFRS were

additionally revised or issued1. Since 2005, IFRS have been adopted by all listed

companies in the EU, although we still do not know clearly the effects of such adoption

on financial reporting quality. Following Barth et al. (2008:472), the claim that the

application of IAS is associated with higher accounting quality may not be true due to at

least two reasons. Firstly, IAS may be of lower quality than domestic standards; and

secondly, even if IAS are of higher quality, the institutional framework could offset any

improvements. In this sense, Pope and McLeay (2011) indicate that the consequences

of IFRS adoption and the quality of implementation are not uniform in Europe, and

depend on incentives and local enforcement.

Germany is a continental European country with a code-law system.

Traditionally, accounting literature has found that disclosure is weak in Germany (La

Porta et al. 1998; Hope 2003). Regarding enforcement, the evidence is mixed. La Porta

et al. (1998) indicated that the quality of enforcement is highest in German and

Scandinavian countries; more recently Kaufman et al. (2007) have obtained similar

results. However, we can also find contradictory evidence both in Hope (2003) and in

La Porta et al. (2006). HGB is considered to be lower quality than IFRS (Leuz and

Verrecchia 2000). There are important differences between HGB and IAS. Following

                                                            1 Limited revisions or amendments are not included.

6  

Hung and Subramanyam (2007), the German accounting system is stakeholder-

oriented and tax-driven. Thus, Germany possesses a conservative accounting system

(Garcia Lara and Mora 2004; Van Tendeloo and Vanstraelen 2005). Continental

countries like Germany are characterized by an understatement of equity (balance

sheet conservatism), that is, the prevalence of the prudence principle to treat assets

and liabilities. This implies accounting practices such as LIFO, accelerated depreciation

or anticipation of losses.

Related literature and research question

Whereas it is widely believed that IFRS have improved in quality during recent

years due to the revision process and the issuing of new standards, we do not know to

what extent the application of IFRS has led to an improvement of financial reporting

quality.

Van Tendeloo and Vanstraelen (2005) studied whether voluntary adoption of

IFRS by German listed companies is associated with lower earnings management.

From 1999 to 2001, they compared the levels of earning management between firms

reporting under IFRS and German GAAP. Their findings suggest that IFRS adopters do

not present lower earnings management compared to firms reporting under German

GAAP. Gontcharov and Zimmermann (2007) analysed the levels of earnings

management for German firms presenting financial statements under IAS, US-GAAP or

German-GAAP. They only found evidence on significantly lower levels of earnings

management for firms reporting under US-GAAP. Jeanjean and Stolowy (2008) found

that earnings management did not decline in France, Australia and the UK the two first

years after the adoption of IFRS in 2005. In sum, an important part of the accounting

literature finds that financial reporting quality depends mainly on firms’ incentives or

institutional structures, so that the role of accounting standards is not significant.

7  

On the other hand, Barth et al. (2008) studied firms applying IFRS from 21

countries between 1994 and 2003. As firms could have adopted IFRS in response to

changed incentives, they attempted to control for all factors related to voluntary IFRS

adoption. They found an improvement in accounting quality after IFRS adoption. For

the period 1998-2005, using a similar methodology, Christensen et al. (2008) examined

earnings management and timely loss recognition to assess the impact of incentives on

accounting quality changes related to IFRS adoption. They compared earnings

management of pre-2005 adopters to post-2005 adopters. They find a decrease in

earnings management for the former while the latter increased earnings management.

Studies indicating that IFRS do not improve financial reporting quality only study

a short time period, such as Van Tendeloo and Vanstraelen (2005) or Gontcharov and

Zimmermann (2007). Consequently, they would not detect a relationship between

improvements in the quality of IFRS and earnings management, if one existed.

Moreover, at that time, the IASB had only developed the first phase of their substantial

reform.

More recently, several studies analyse the consequences of IFRS adoption.

Ahmed et al. (2013) analyzed firms from 20 countries during the pre-IFRS period

(2002-2004) and post-IFRS period (2006-2007). They compared earnings

management of firms that adopted IFRS for the first time in 2005 to firms from non-

IFRS countries. In this case, firms that adopted IFRS in 2005 presented greater

earnings management relative to the control firms in the post-adoption period.

Contradictorily, they also find that both adopters and control firms exhibit a significantly

lower likelihood of reporting small positive earnings in the post-adoption period relative

to the pre-adoption period, although they attribute this difference to general economic

trends.

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Capkun et al. (2013) studied a sample partitioned into three segments: early

adopters (transitioned to IAS/IFRS from 1993-2004), late adopters (transitioned from

2005-2009) and mandatory adopters (firms from countries where early adoption was

not allowed). They claim that IAS/IFRS changed substantially from the pre-2005

voluntary adoption period to the post-2005 mandatory adoption period. They find an

increase in earnings management after 2005, which they attribute to increased firms’

flexibility coupled with the lack of implementation guidance. Instead, Navarro-Garcia

and Madrid-Guijarro (2014) found that, during the period 1998-2006, the improvement

of accounting standards quality significantly reduces the level negative discretionary

accruals of German listed firms.

In the same vein, Doukakis (2014) used a sample from 22 European countries

between 2000 and 2010. He employs differences-in-differences methodology to control

for the behavior of mandatory adopters vs. voluntary adopters. He concludes that IFRS

adoption had no significant impact on either real or accrual-based earnings

management.

In this paper, we study a sample of German listed firms that adopted IFRS in

different circumstances. Early adopters had incentives to voluntary adopt IFRS even

before knowing that IFRS would be mandatory2, that is, before 2001. Late adopters

employed IFRS after 2000 but before 2005, that is, before they were compulsory.

Finally, mandatory adopters applied IFRS only when they were in force, that is, starting

in 2005. In this manner, we distinguish two types of pre-2005 adopters since they may

have different incentives. As a consequence, our research question is:

RQ: Does IFRS adoption affect earnings management of the German listed

firms?

                                                            2 In June 2000, the EU Commission announced that they were intended to achieve that all publicly traded Community companies prepare their consolidated statements in accordance with IAS, at the latest by 2005. 

9  

3. Sample and methodology

In order to develop our study we obtained available information from Amadeus

database supplied by Bureau van Dijk. This information is completed with the cash

flows from the operations obtained from the Statement of Cash Flows reported by each

firm in their financial data. In addition, data about the type of auditor and the accounting

normative used before 2004 are hand-collected from the financial statements of each

company. In this sense, we find companies which applied IFRS before 2005, together

with other companies USGAAP or HBG. Following previous methodology (Van

Tendeloo and Vanstraelen, 2005), we exclude firms with negative equity and/or whose

ratio of total accruals to total assets was greater than one. Financial institutions were

also excluded because of the specific characteristics of this activity sector. Finally, we

get accessible information for 108 German listed firms over the period 2001-2010.

Estimating accruals management

We examine the accounting quality of German listed companies through the

study of earnings management, for which we use discretionary accruals as a proxy.

Previous literature has developed different models to estimate discretionary accruals

(Jones 1991; Dechow et al. 1995; McNichols, 2000). In this paper, we use the Jones

Cash Flow model which is a variation of Jones’ model suggested by Shivakumar

(1996), and it is based on the fact that the measure of discretionary accruals are likely

to be misspecified for firms with extreme levels of performance (Dechow et al. 1995;

Kasznik 1999; Jeter and Shivakumar 1999). In this regard, Chan et al. (2004) indicate

that the Jones Cash Flow model is a better model to detect earnings management than

the Jones model. Despite the advantages of this model, it tends to incur in

misspecification bias due to the existence of atypical behaviors in companies (Kothari

et al., 2012). In order to overcome this limitation, we estimate (1) including specific

unobservable heterogeneity for each company which is constant over time

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(with temporal periods). This term considers firms’ specific characteristics,

which could be generating wrong estimations. With this effect, Shivakumar’s model

(1996) is expressed as in (1):

(1)

where is total accruals in scaled by lagged total assets ( ). Total Accruals are

calculated as the difference between profit before taxes and the cash flow from

operations in . measures the change in sales in scaled by lagged total

assets ( ), PPEit is net property, plant, and equipment scaled by lagged total

assets, and is the operating cash flow scaled by lagged total assets ( ).

represents the unobservable heterogeneity of the model which could be considered as

fixed o random and is the error term. Abnormal accruals are the difference between

the actual and the predicted value of total accruals. The following Table 1 shows the

correlation among the dependent variable (TA) of the model (1) and the explicative

variables. The signs are expected according to the literature.

[Insert Table 1 here]

Finally, model (1) is estimated through Panel data methodology which considers

the double spatio-temporal dimensions and, therefore, all the available information is

considered in this process. The estimation of (1) concludes about the existence of

heterogeneity into the sample which is collected through the fixed effects model3.

In order to test the adequacy of the Cash Flow Jones’ model in comparison with

alternative specifications, we undertake the comparison developed by Alcarria and Gill

de Albornoz (2004) which compares one of the most applied discretionary accruals

specifications, the Modified Jones’ model, with the Cash Flow Jones model (Dechow et

                                                            3 Hausman’s tests=6.7143 (p-value=0.008)

11  

al., 1995). With the aim of differentiate the behavior between models, the authors

analyze the estimated discretionary accruals in terms of their dispersion. This result is

based on the theoretical reasoning that insufficient specifications tend to disperse the

discretionary accruals’ distributions under extreme values (Jeter and Shivakumar,

1999). Following this reasoning, we compute the descriptive statistics for the

discretionary values in both models obtaining lower variability in the discretionary

accruals estimation of the Cash Flow model4.

Determinants of Earnings Management

Incentives to increase or decrease earnings management are ample (Van

Tendeloo and Vanstraelen 2005). In order to examine the main determinants of the

level of earnings management and to test whether the improvements of quality of IFRS

have significantly affected the quality of the financial information reported by listed

German firms, we propose the following model (2)

(2

)

DEit : discretionary earnings management proxied by the absolute value of the

discretionary accruals calculated as the residuals of the Cash Flow Jones’ model

estimation.

BIGit : indicator variable equal to 1 if the auditor is a Big 4 audit firm and 0 otherwise.

SALESGit: change in sales in year t, measured in logarithmic terms.

SIZEit : total amount of assets in natural logarithm.

                                                            4 We get a standard deviation of 0.087 for the Cash-Flow model while a value of 0.989 is obtained for the Modified version of Jones model. Moreover, the Cash Flow model presents less dispersion than the modified Jones’ model when extreme values for the cash flow and the profitability variables are studied.

12  

ROAit : return on assets, defined as the ratio profit before taxes and interest over total

assets.

Leverageit : total liabilities divided by total assets in year t.

Liabilitiesincrit : indicator variable equal to 1 if there is a debt increase higher than 10%

during the year t.

Capitalincrit : indicator variable equal to 1 if there is a capital growth higher than 10%

during the year t.

Model (2) is estimated through SUR methodology (Zellner, 1962). This is a

multi-equational estimation procedure which allows us to estimate (2) defining one

equation for each temporal period. This method provides us information about the

temporal change in the coefficients of the model testing significant differences among

them. Moreover, SUR estimation assumes the existence of association among the

residuals of the different equations which, in our case, represent temporal

interdependences between discretionary accruals (see Table 2)

[Insert Table 2 here]

Significant temporal correlations between discretionary accruals are provoked

by the temporal inertia in the abnormal accruals’ adjustment process (Kothari, 2012).

As a difference with previous studies, we take into account this effect into the model

through SUR methodology.

Based on the aim of this study, we define two variables to test the effects

provoked by the change in the accounting normative before it was compulsory.

Specifically, we distinguish between two kinds of companies:

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(1) companies which applied IFRS before 2001. For these companies, we define the

dummy variable which takes the value of one if the company ( ) applied IFRS

before 2001 and zero otherwise.

(2) Companies which applied IFRS between 2001 and 2004. For these firms, the

variable takes the value of one if the company ( ) applied the IFRS between

2001 and 2004 and zero otherwise5.

Finally, in order provide additional information about discretionary accruals, we

estimate model (2) applying an alternative dependent variable which allow us to identify

the differences between the positive and negative values. With this aim, we define as

dependent variable, the dichotomy variable which takes the value of one if the

company ( ) present a positive discretionary accrual’s value in the year ( ) and zero

otherwise. In this case, each equation in the SUR specification corresponds to a

probabilistic model where we are determining the influence of different variables on the

probability of having positive discretionary accruals in relation to the negative result.

4. Results

Some descriptive statistics about discretionary accruals are shown in Table 3.

Although the absolute discretionary accruals are lower during the 2005-2010 period

than that between 2001-2004, this is not statistically significant. We can observe that

this is mainly due to a decrease in negative discretionary accruals, because positive

discretionary accruals have even increased while negative discretionary accruals have

decreased. This is particularly true for early adopter and late adopter firms respectively,

which show a statistically significant difference between both studied periods. In the

same vein, the results show that German listed firms are traditionally conservative,

                                                            5 We consider alternative definitions for EARLY and LATE variables adding to the companies which have adopted USGAAP normative. The results were analogous.

14  

since almost every year negative discretionary accruals are higher than positive

discretionary accruals.

[Insert Table 3 here]

Table 4 analyses through SUR methodology the relationship between absolute

discretionary accruals and different independent variables. Chi and R statistics

corroborates the global significance of the model for each year. Breusch Pagan test

confirms our hypothesis about the existence of autocorrelation among the residuals of

the different equations, and therefore, the adequacy of applying SUR methodology in

this case.

As for the importance of the type of auditor, we find that an auditor belonging to

a big audit firm was important to reduce earnings management up to 2005. Afterwards,

some circumstances could explain a different behaviour, such as lack of incentives

among firms or lack of knowledge on how to implement the new standards. Another

finding is that, in general terms, there is an inverse relationship between absolute

discretionary accruals and SIZE, ROA or LEVERAGE.

We have also found a different behaviour when firms have financing needs.

Thus, during the last years (2007, 2009 and 2010) firms increasing their debt also

increased earnings management. However, from 2001 to 2004, firms with a capital

increase underwent a decrease in earnings management. It seems that during the

recent crisis, German listed companies have preferred bank to equity financing.

Table 4 also shows a different behaviour of early and late adopters regarding

mandatory adopters. Before 2004, early adopters presented lower earnings

management than the rest. However, during 2006 and 2007, those with lower earnings

management were late adopters. In the end, all categories have a similar level of

earnings management, that is, we find no statistically significant differences among

them. Therefore, early adopter companies had incentives to prepare high-quality

15  

financial reporting before 2004, while late adopters had incentives mainly during 2006

and 2007. This is consistent with firms improving earnings management to the extent

that they have incentives. We must remember that, after 2005, only a few new

standards have been released or revised. Thus, during the period 2008-2010, the level

of earnings management was similar for early, late and mandatory adopters.

[Insert Table 4 here]

On the other hand, Table 5 contributes to establish a difference between

positive and negative values. Companies which tend to exhibit positive discretionary

accruals are audited by big audit firms. Also, high growth, bigger, more profitable, and

low leveraged companies tend to engage in positive discretionary accruals.

Furthermore, early adopter firms often have positive discretionary accruals, while late

adopters usually relate to negative discretionary accruals.

[Insert Table 5 here]

Conclusions

This paper studies the effect of IFRS adoption on German listed firms.

Specifically, the aim of this paper is to study, some years after IFRS adoption, whether

these new standards have affected earnings management. Germany provides a

constant institutional environment in which there has been an important change from

domestic standards to IFRS. To this end, we use the SUR methodology, while we

distinguish two types of voluntary adopters (early and late) vs. mandatory adopters.

The results suggest that as companies have incentives to adopt IFRS, the level

of earnings management decreases for a certain period of time. Although there seem

to be a general trend to decrease earnings management over time, we do not find

16  

statistically significant evidence that mandatory adopters have decreased earnings

management after 2005. The only strong evidence is that as companies have

incentives to adopt IFRS, the level of earnings management decreases for a certain

number of years. This is the case for voluntary adopters.

These results are subject to limitations. Firstly, we analysed a sample of quoted

German firms. However, Germany is the best country to carry out a single-country

research, since a long time before 2005, firms could prepare their financial statements

under international accounting standards. Therefore, we can obtain a large number of

voluntary adopter companies; furthermore, we achieve a constant institutional

framework in this manner. On the other hand, since we have only considered one

measure of earning management (i.e. discretionary accruals), other alternative

methodologies should be implemented in the future.

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18  

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Table 1: Correlation among variables in Cash Flow Jones model

6

0.2241***

(0.000)

-0.3331***

(0.000)

-0.2523***

(0.000)

0.1745***

(0.000)

(***) significant at 1%

                                                            6 is an explicative variable introduced in the Modified Jones model proposed by Dechow et al. (1995) and which adjusts the sales by the trade debtors in order to avoid including non discretionary accruals into the model associated with the shifting revenues from future periods (Alcarria and Gil de Albornoz, 2004).

21  

Table 2: Discretionary accruals temporal correlations

2010-

2009

2009-

2008

2008-

2007

2007-

2006

2006-

2005

2005-

2004

2004-

2003

2003-

2002

2002-

2001

Bivariate

correlati

on

0.214

**

(0.048

)

0.255

**

(0.034

)

0.194

*

(0.05

6)

0.176

*

(0.06

2)

0.277

**

(0.032

)

0.395*

**

(0.001)

0.383

**

(0.002

)

0.367

**

(0.004

)

0.363

**

(0.004

)

P-values in brackets. (**) significant at 5%. (***)Significant at 1%

22  

Table 3: Descriptive statistics of discretionary accruals

Panel A: Absolute Discretionary Accruals (N=108 for each T)

2010 2009 2008 2007 2006 2005 2004 2003 2002 2001

Mean 0.041

6

0.041

6

0.033

2

0.032

6

0.039

3

0.037

8

0.035

7

0.038

7

0.047

5

0.043

9

Medi

an

0.034

7

0.036

8

0.025

3

0.030

4

0.035

7

0.030

4

0.029

5

0.030

6

0.037

5

0.039

2

St

Dev

0.034

4

0.031

0

0.025

9

0.022

3

0.029

3

0.027

4

0.029

3

0.032

3

0.033

1

0.036

6

Panel B. Absolute discretionary accruals: Early (N= 20 for each T)

2010 2009 2008 2007 2006 2005 2004 2003 2002 2001

Mean 0.034

0

0.039

9

0.038

3

0.031

0

0.048

9

0.041

8

0.033

6

0.029

4

0.050

1

0.048

9

Medi

an

0.030

2

0.037

9

0.037

0

0.031

1

0.036

6

0.027

1

0.024

3

0.021

3

0.038

3

0.036

5

St

dev.

0.022

3

0.025

8

0.029

1

0.026

1

0.036

2

0.023

9

0.033

5

0.030

7

0.046

1

0.044

2

Panel C. Absolute discretionary accruals: Later (N=38 for each T )

2010 2009 2008 2007 2006 2005 2004 2003 2002 2001

Mean 0,032

9

0,038

4

0.032

6

0.030

9

0.031

7

0.033

6

0.038

5

0.038

1

0.040

3

0.040

1

Medi

an

0,028

2

0.030

5

0.038

1

0.028

9

0.035

7

0.028

9

0.029

5

0.022

6

0.035

8

0.031

8

St

dev.

0.023

8

0.030

3

0.024

0

0.024

1

0.033

4

0.023

6

0.033

5

0.029

1

0.037

2

0.038

5

Panel D. Absolute discretionary accruals: Mandatory (N=50 for each T )

2010 2009 2008 2007 2006 2005 2004 2003 2002 2001

Mean 0,051

3

0,044

6

0.032

8

0.033

6

0.042

7

0.052

7

0.032

0

0.048

2

0.052

9

0.054

7

Medi

an

0,035

3

0.042

3

0.025

1

0.032

3

0.040

0

0.042

6

0.025

8

0.050

4

0.045

1

0.041

6

St

dev.

0.042

1

0.031

1

0.028

2

0.019

8

0.031

8

0.042

8

0.024

4

0.037

3

0.036

3

0.035

6

Panel E: Positive Discretionary Accruals

23  

2010 2009 2008 2007 2006 2005 2004 2003 2002 2001

Mean 0.038

1

0.038

0

0.031

6

0.036

4

0.043

7

0.045

9

0.031

2

0.034

4

0.034

6

0.034

8

Medi

an

0.030

9

0.029

9

0.026

8

0.033

3

0.040

8

0.040

8

0.023

0

0.030

6

0.036

7

0.035

5

St

Dev.

0.032

3

0.030

2

0.026

1

0.022

6

0.034

0

0.038

8

0.028

9

0.030

8

0.027

1

0.030

1

Panel F: Negative Discretionary Accruals

2010 2009 2008 2007 2006 2005 2004 2003 2002 2001

Mean -

0.043

9

-

0.044

4

-

0.033

8

-

0.023

1

-

0.038

2

-

0.032

9

-

0.039

2

-

0.041

3

-

0.051

1

-

0.052

3

Medi

an

-

0.038

9

-

0.037

9

-

0.025

3

-

0.020

0

-

0.032

0

-

0.031

8

-

0.033

3

-

0.032

5

-

0.041

0

-

0.044

4

St

Dev.

0.036

0

0.031

0

0.256

1

0.018

7

0.028

9

0.029

7

0.030

5

0.031

0

0.038

5

0.037

8

Panel G: Average values discretionary accruals

Absolute

discretionary

accruals

Positive discretionary

accruals

Negative

discretionary

accruals

Earl

y

Late Manda

tory

Early Late Manda

tory

Earl

y

Late Manda

tory

2001-2004 0.04

0

0.03

9

0.046 0.03

2

0.03

2

0.033 -

0.04

6

-

0.04

0

-0.049

2005-2010 0.03

3

0.03

2

0.040 0.04

0

0.03

6

0.042 -

0.03

6

-

0.03

2

-0.043

Average

Differences test

0.05

3

(0.8

19)

0.53

6

(0.4

65)

0.084

(0.722)

6.55

3**

(0.01

3)

0.60

0

(0.4

44)

1.277

(0.261)

1.29

7

(0.2

59)

2.34

5*

(0.0

98)

0.971

(0.327)

p-values in brackets. (**) significant at 5%; (***) significant at 1%

24  

25  

Table 4: SUR estimation results by Maximum Likelihood (ML). Dependent variable: DE (2)

2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 Intercept 0.0164**

(0.042) 0.0228*** (0.001)

0.1301*** (0.009)

0.0436** (0.043)

0.1583*** (0.000)

0.1579*** (0.000)

0.1079*** (0.002)

0.0899***(0.002)

0.0919***(0.000)

0.0966***(0.000)

0.0035 (0.582)

0.0089 (0.243)

0.0062 (0.420)

0.0146 (0.119)

0.0128 (0.109)

-0.0162** (0.047)

0.0054 (0.458)

-0.0177**(0.021)

-0.0151* (0.069)

-0.0165* (0.057)

0.0224* (0.096)

0.0528* (0.071)

0.0759*** (0.002)

-0.0184 (0.312)

0.0542*** (0.004)

0.0938*** (0.000)

0.0907*** (0.001)

0.0126 (0.562)

0.0670***(0.003)

0.0701***(0.003)

-0.0034* (0.078)

-0.0526***(0.009)

-0.0118** (0.032)

-0.0036** (0.019)

-0.0029 (0.161)

-0.0014 (0.347)

-0.0002 (0.882)

-0.0014 (0.514)

-0.0032* (0.072)

-0.0085**(0.068)

-0.0763** (0.015)

-0.0836* (0.064)

-0.1055***(0.002)

0.0129 (0.725)

-0.0921***(0.002)

-0.0841** (0.030)

-0.0914***(0.001)

-0.0516* (0.096)

-0.0353 (0.315)

-0.0502* (0.093)

-0.0627 (0.208)

-0.0115 (0.126)

-0.0850***(0.007)

0.0253 (0.328)

0.0124 (0.741)

-0.1140** (0.027)

-0.0810* (0.056)

-0.0249 (0.425)

-0.0748* (0.071)

-0.0784* (0.069)

0.0237*** (0.003)

0.0190** (0.015)

-0.0079 (0.261)

0.0182*** (0.002)

0.0039 (0.712)

-0.0111 (0.133)

0.0070 (0.386)

0.0070 (0.413)

0.0076 (0.338)

0.0080 (0.325)

-0.0112 (0.160)

-0.0098 (0.450)

-0.0037 (0.629)

0.0027 (0.735)

-0.0023 (0.743)

-0.0030 (0.682)

-0.0225** (0.020)

-0.0215**(0.045)

-0.0253**(0.033)

-0.0226**(0.030)

Early -0.0034 (0.687)

0.0079 (0.745)

0.0005 (0.940)

0.0068 (0.320)

-0.0141 (0.373)

-0.0156 (0.202)

-0.0044 (0.559)

-0.0159* (0.070)

-0.0239**(0.019)

-0.0201**(0.012)

Late -0.0011 (0.260)

-0.0104 (0.186)

-0.0065 (0.289)

-0.0150***(0.008)

-0.0315** (0.016)

0.0005 (0.947)

- - - -

Post-estimation proofs Chi-square 39.780***

(0.000) 34.901*** (0.000)

49.152*** (0.000)

33.374*** (0.000)

47.321*** (0.000)

50.246*** (0.000)

40.012*** (0.000)

44.814***(0.000)

47.562***(0.000)

46.879***(0.000)

R squared 0.4351 0.3461 0.3091 0.3749 0.3803 0.3276 0.4512 0.4002 0.4032 0.4286 Breusch Pagan test 385.633*** (0.000) p-value in brackets. (*) significant at 10%; (**) significant at 5%; (***) significant at 1%

26  

Table 5: SUR estimation results through ML method. Dependent variable: DE (2)

2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 Intercept 0.7368***

(0.005) 0.6052*** (0.000)

0.7642*** (0.006)

0.8371*** (0.000)

0.9425***(0.000)

0.9595*** (0.000)

0.2367 (0.492)

0.6688***(0.009)

0.6208***(0.000)

0.6568***(0.000)

0.0419 (0.734)

-0.0754 (0.570)

-0.0011 (0.994)

0.0875 (0.3841)

0.3289* (0.016)

0.2221** (0.025)

0.2270** (0.029)

0.2228** (0.021)

0.2422** (0.019)

0.2152** (0.031)

0.5209* (0.082)

0.5009* (0.078)

0.4000 (0.140)

0.9216*** (0.000)

0.7895** (0.027)

0.8463** (0.006)

0.6688** (0.031)

0.7566** (0.023)

0.7441** (0.021)

0.6156** (0.034)

0.1009** (0.014)

-0.0038 (0.911)

0.0901** (0.017)

0.0714** (0.032)

0.0586* (0.091)

-0.0081 (0.692)

0.0627* (0.070)

0.0772** (0.030)

0.0109 (0.234)

0.0801** (0.025)

0.3466* (0.065)

0.0779 (0.821)

0.0666 (0.922)

0.7532*** (0.000)

0.0407 (0.543)

0.8965** (0.006)

0.5964** (0.022)

0.6228** (0.009)

0.5335** (0.034)

0.6052** (0.012)

-0.6032 (0.000)

-0.6195***(0.000)

-0.6611***(0.002)

-0.6958***(0.006)

-0.4842**(0.046)

-0.4231** (0.048)

-0.5057**(0.041)

-0.5848* (0.070)

-0.3093 (0.192)

-0.2991 (0.287)

0.0003 (0.9974)

-0.2945***(0.002)

-0.2437***(0.008)

-0.2801***(0.004)

0.1082 (0.271)

-0.1776* (0.098)

-0.0146 (0.107)

-0.0758 (0.322)

-0.0833 (0.379)

-0.0798 (0.325)

0.2246* (0.074)

0.1990 (0.191)

0.2174* (0.095)

0.0369 (0.783)

0.1058 (0.395)

0.2571** (0.037)

0.0914 (0.436)

0.0789 (0.220)

0.0312 (0.757)

0.2014* (0.098)

Early -0.0204 (0.802)

-0.0465 (0.735)

-0.0438 (0.821)

0.4731*** (0.001)

0.0391** (0.029)

0.0392** (0.037)

0.0166* (0.062)

0.0137* (0.071)

0.0090* (0.093)

0.0072 (0.124)

Late -0.0328 (0.828)

-0.2359* (0.084)

-0.2514* (0.082)

-0.4424** (0.001)

-0.4356**(0.030)

-0.0230 (0.228)

- - - -

Post-estimation proofs

27  

Chi-square 15.491* (0.089)

23.914** (0.004)

36.632*** (0.000)

35.881*** (0.001)

41.021***(0.000)

44.012*** (0.000)

41.982***(0.000)

38.651***(0.000)

24.031***(0.000)

29.875***(0.000)

R square 0.4546 0.2681 0.1436 0.2853 0.4313 0.3169 0.2636 0.3153 0.3110 0.3256 Breusch Pagan test 88.987*** (0.000) p-value in brackets. (*) significant at 10%; (**) significant at 5%; (***) significant at 1%