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Erasmus University RotterdamErasmus School of EconomicsMaster Accounting, Auditing and Control

“Investor protection: a motive for not cooking the books?”

A study on the impact of investor protection on income smoothing

“I didn’t cook the books. Instead, I incorporated them into a tasty tuna noodle casserole!”

July 28th 2010

Author: Zeenat N.S. William

Supervisor: Dr C.D. Knoops

Co-reader: prof. dr M.A. van Hoepen RA

PROLOGUE

The last couple of months were spent writing this thesis, the final element of the master

Accounting, Auditing and Control. During this period I received support from several

people and I would like to take this opportunity to show my gratitude.

First of all I wish to thank my supervisor Dr. C.D. Knoops for his feedback, critical view

and for motivating me at times. I also greatly appreciate the quick responses to my

questions and the fact that I could always stop by his office to brainstorm while enjoying a

cup of coffee or plain water.

My sincere gratitude goes out to my parents and brother who stood by me during this

period. Their words of wisdom, support and motivation have been of priceless value.

Thanks for loving me and having faith in me.

Further, I would like to thank my friends who stood by me and supported me in whichever

way; sometimes support came in the form of doing something fun or in the form of wise

words.

At last but definitely not least, I would like to thank PricewaterhouseCoopers for

providing me with a fun place to write my thesis in peace.

Thanks to all, for making this process bearable!

Hereby I proudly present to you five months of hard work.

- Zeenat William

2 | M a s t e r t h e s i s a c c o u n t i n g , a u d i t i n g a n d c o n t r o l

ABSTRACT

The focus of this thesis lies on the relationship between the occurrence of income

smoothing and the level of investor protection in a country. I will investigate whether

income smoothing is more pervasive in low investor protection countries than in high

investor protection countries. My expectations, which are based on previous studies, are to

find that income smoothing is more pervasive in low investor protection countries. In

order to measure both real income smoothing and artificial income smoothing two

measures, developed by Leuz et al. (2003), are used. From these two measures the

aggregate income smoothing score is derived. The investor protection variables used are

obtained from Djankov et al. (2007).

Several hypotheses are stated in this thesis, the most important one being that there exists

a relationship between the occurrence of income smoothing and the level of investor

protection in a country are researched by using statistical analysis in the form of

correlation matrices and a regression model. The statistical output however, does not

confirm any of the hypotheses formulated even though previous research provided

evidence that in countries with a high level of investor protection income smoothing is less

pervasive than in countries with low investor protection. This can be explained by the

population and sample. Also, the second income smoothing measure used has a broad

range and causes a significant change in the aggregate income smoothing score for certain

countries.

Keywords: earnings management, income smoothing, investor protection


TABLE OF CONTENTS

Prologue....................................................................................................................2

Abstract.....................................................................................................................3

Table of Contents......................................................................................................4

1.1 Research Question..........................................................................................8

1.2 Structure..........................................................................................................9

2 Earnings Management......................................................................................11

2.1 The Difference between Earnings Management and Fraud.........................12

2.2 Income Smoothing........................................................................................13

2.2.1 Incentives for Smoothing Earnings............................................................15

2.3 Conclusion.....................................................................................................16

3 Accruals Methodology......................................................................................17

3.1 Accruals.........................................................................................................17

3.2 The Jones Model...........................................................................................18

3.2.1 Concerns regarding the Jones Model.......................................................20

3.3 The Modified Jones Model............................................................................21

3.4 Conclusion.....................................................................................................22

4 Other Models....................................................................................................23

4.1 Burgstahler and Dichev.................................................................................23

4.2 The Imhoff and Eckel Model.........................................................................24

4.3 Conclusion.....................................................................................................25

5 Corporate Governance......................................................................................26

5.1 Investor Protection........................................................................................28

5.1.1 Anti-self-dealing index...............................................................................29

5.2 Conclusion.....................................................................................................30

6.3 Common-law vs. Code-law............................................................................35

Barth, Landsman and Lang (2008)...........................................................................36

Jeanjean and Stolowy (2008)..................................................................................37

7 Studies on Earnings Management and Investor Protection.............................39

7.1 Earnings Management and Investor Protection...........................................39

Leuz, Nanda and Wysocki (2003)............................................................................39

Cahan, Liu and Sun (2008).......................................................................................41

Wright, Shaw and Guan (2006)...............................................................................42

Nabar and Boonlert-U-Thai (2007)..........................................................................43


7.2 Conclusion.....................................................................................................43

8 Hypotheses and Research Design.....................................................................45

8.1 Hypotheses....................................................................................................45

8.2 Measuring investor protection.....................................................................47

8.3 Measuring income smoothing......................................................................48

8.3.1 Measuring real smoothing.........................................................................49

8.3.2 Measuring artificial smoothing..................................................................51

8.3.3 Measuring total income smoothing..........................................................51

8.4 Sample selection...........................................................................................52

8.5 Earnings Management Pre-IFRS and Post-IFRS.............................................54

9 Empirical study: Influence of investor protection on Income Smoothing.......55

9.1 Introduction...................................................................................................55

9.2 Hypotheses....................................................................................................55

9.3 Sample...........................................................................................................56

9.4 Descriptive statistics......................................................................................56

9.5 Income smoothing measures........................................................................58

9.6 Cluster analysis..............................................................................................60

9.7 Statistical analysis..........................................................................................63

9.7.1 Real income smoothing and artificial income smoothing.........................63

9.7.2 Aggregate income smoothing...................................................................64

9.7.2.1 Correlation matrix.................................................................................65

9.7.2.2 Regression analysis...............................................................................66

9. 8 Model assumptions.......................................................................................67

9.9 Results and Discussion..................................................................................70

10 Empirical study: Income smoothing in the pre-IFRS and post-IFRS period. .74

10.1 Introduction...............................................................................................74

10.2 Income smoothing in the pre-IFRS period.................................................75

10.3 Income smoothing in the post-IFRS period...............................................77

10.4 Statistical analysis......................................................................................78

10.4.1 Correlation matrix pre-IFRS period........................................................78

10.4.2 Regression analysis pre-IFRS period......................................................78

10.4.3 Correlation matrix post-IFRS period......................................................79

10.4.4 Regression analysis post-IFRS period.....................................................79

10.5 Results and Conclusions............................................................................79

11.1 Limitations.................................................................................................81


11.2 Suggestions for further research...............................................................82

12 Thesis Conclusion..........................................................................................83

References...............................................................................................................85

Appendix A Schematic overview of previous studies........................................90

Appendix B Variables downloaded from Worldscope......................................92

Appendix C Hierarchical K-Means cluster analysis...........................................93

Appendix D Scatter Plot.......................................................................................94

Appendix E Residuals Statistics..........................................................................95

Appendix F Correlation Matrix and Model Summary Real Smoothing.............96

Appendix G Correlation Matrix and Model Summary Artificial Smoothing.....97

Appendix H Correlation Matrix and Model Summary pre-IFRS Period.............98

Appendix I Correlation Matrix and Model Summary post-IFRS Period.............99

Appendix J Earnings Management Measures from Leuz et al. (2003).............100


1 INTRODUCTION

Several researchers posit that the occurrence of earnings management in a country will be

influenced by the level of investor protection in that country (Cahan et al. 2008, Leuz et al.

2003). Earnings management, which was at the base of several accounting scandals

(Enron: see Catanach and Catanach, 2003), has been researched and discussed extensively

in prior studies (Beneish 1999, Kinnunen et al. 1995). It can be described as a strategy of

generating accounting earnings, which is accomplished through managerial discretion over

accounting choices and operating cash flows (Phillips et al. 2003) as well as production

and investment decisions that reduce the variability of earnings. Several academic studies

have argued that there is a relation between weak corporate governance and poor financial

reporting, financial information fraud and earnings management (Dechow et al. 1996,

Beasly et al. 1999, Cohen et al. 2004). This has led to legislators and standard setters

setting more strict regulations regarding corporate governance. In the U.S. for instance, the

collapse of large firms such as Enron Corporation and WorldCom has caused the

government to enact the Sarbanes-Oxley Act (SOX) in 2002 (Brown et al. 2004). SOX set

new or enhanced standards for all U.S. public company boards, management and public

accounting firms in order to improve issues such as corporate governance.

The classical principal-agent problem describes the difficulties that arise due to

asymmetric information between the principal and the agent. The separation of corporate

managers from outside investors involves an inherent conflict; the two parties may not

have the same interests. The objective of the principal-agent relationship is that the agent,

who is hired by the principal, acts in the interests of the principal; this however is not

always how it folds out in practice. That is why corporate control mechanisms have been

developed over the years. These mechanisms are the means by which managers are

disciplined to act in the investors’ interests. Control mechanisms include both internal

mechanisms, such as managerial incentive plans, director monitoring, and external

mechanisms, such as outside shareholder or debt holder monitoring, the market for

corporate control, competition in the product market, the external managerial labour

market, and securities laws that protect outside investors against expropriation by

corporate insiders (Bushman and Smith, 2003). Investor protection, which is an external

corporate control mechanism, defines the laws and regulations to observe, safeguard and

enforce the rights and claims of a person in his role as an investor (Djankov et al. 2007).


This includes advice and legal action. According to Boonlert-U-Thai, Meek and Nabar

(2006) accounting quality is of importance to financial statement users. They argue that

managerial discretion is an important determinant of accounting quality, and that the

degree to which this discretion is abused by managers depends on the extent to which

investors are protected by law. Not all countries have the same level of investor protection,

it differs between countries.

Thus, the extent to which controlling managers and other insiders can manage earnings

depends on the level of investor protection in that specific country. Without adequate

investor protection, as Boonlert-U-Thai, Meek and Nabar (2006) acknowledge, there

would be little appreciation for the need for good quality accounting information. It is

argued that managers who engage in forms of earnings management probably have

different incentives to do so.

1.1 RESEARCH QUESTION

The purpose of this research is to study the impact of external corporate governance,

especially investor protection, on the occurrence of earnings management. In the spirit of

foregone discussion the following research questioned surfaced:

“Does a strong investor protection environment reduce the incidence of income smoothing

in countries?”

For good understanding this thesis will first discuss the manifestation that is earnings

management. The different forms it comes in and the manner in which it is applied in

practice will be addressed. The title gave away that the focus of this thesis lies on income

smoothing, one of the many forms of earnings management; therefore I will give extensive

insight on this.

People do what they do for a reason; the same goes for managers who engage in earnings

management. Without a certain motive, managers would not manage earnings since it is

perceived by society as unethical. Therefore it is interesting to look at what pushes

managers to cooking the books. Over the years several researchers have established

models to measure earnings management. The most common used models and their pros

and cons will be discussed.


Investor protection is another main variable in my research. To fully comprehend the

adventurous journey called my master’s thesis, it is helpful to know what this variable

entails.

The goal of aforementioned journey is to determine to what extent investor protection

affects the other main variable, earnings management, in this research. Several researchers

have done research on this topic; their findings will be highlighted.

As of January 1st 2005 all European Union countries have adopted International Financial

Reporting Standards (IFRS). Even outside the EU they are widely used nowadays.

However, this was not always the case. IFRS were adopted to harmonize global

accounting standards. Some proponents have argued that IFRS adoption will increase

investor protection and improve accounting quality. Later on I will discuss why IFRS are

so generally accepted in recent days. After having examined the effect of investor

protection on the occurrence of income smoothing, the influence of IFRS on income

smoothing will be examined. I will do this by measuring the level of income smoothing in

the pre-IFRS period (2000-2004) and the post-IFRS period (2005-2008).

1.2 STRUCTURE

In order to answer the research question this thesis will first discuss the sub-questions to

ultimately formulate an answer to the main research question.

Chapter 2 discusses the phenomenon that is earnings management as well as income

smoothing. It will also elaborate on the distinction between earnings management and

fraud. And the incentives which motivate managers to engage in income smoothing will

be discussed.

Chapter 3 will shed light on several models which can be used to measure earnings

management in general. In order to fully understand the models accruals will be discussed

first. The concerns regarding the Jones model will be mentioned as well.

Chapter 4 provides insight on other non-accrual models which can be used to detect

earnings management. It will also discuss the Imhoff and Eckel model which focuses

specifically on income smoothing.


Chapter 5 discusses the theoretical background on corporate governance and specifically

investor protection. The proxy for investor protection that I will use for the empirical

research will also be discussed here.

Some researchers are of the opinion that IFRS has an influence on the level of investor

protection. In order to get a better understanding of IFRS and the discussion surrounding it

chapter 6 will focus on IFRS and its implementation in several countries. The views of

proponents and opponents will be addressed here as well.

Chapter 7 will provide a detailed literature study on the relationship between earnings

management and investor protection.

Chapter 8 presents the hypotheses as well as the research design. In order to obtain the two

income smoothing measures several calculations have to be made. The formulas used will

also be presented here.

Chapter 9 discusses the statistical analysis and results for the first leg of this research; the

influence of investor protection on income smoothing.

Chapter 10 presents the empirical study for the second leg of this research; income

smoothing in the pre-IFRS and post-IFRS period.

Chapter 11 addresses the limitations regarding this research. Also, suggestions for future

research are presented in this chapter.

Chapter 12 reviews the literature and statistical analysis and concludes.


2 EARNINGS MANAGEMENT

The definition that best captures the essence of earnings management is the following1:

“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 number” (Healy and Wahlen 1999, stated in

Ronen and Yaari 2008). Earnings management is a phenomenon that exists all over the

globe, but evidence has proved that the level of earnings management will differ among

countries (Braun and Rodriguez 2008). Some researchers are of the opinion that earnings

management can be prevented if the reason for engaging in earnings management is

known and how it is done (Erickson, Hanlon and Maydew 2006). There are several ways

to engage in earnings management2, but the largest account that has been used for earning

management is the recognition of revenues and expenses (Dechow, Sloan and Sweeney

1996). Often earnings management is carried out to smooth earnings. Management can

use the availability of accounting discretion to understate earnings in years of good

performance and create reserves for years of less good performance, this is called income

smoothing. Several studies suggest that earnings management can be limited by well-

designed corporate governance structures (Dechow, Sloan and Sweeney 1996, Cornett et

al. 2006).

Dividend-paying firms tend to manage earnings upward when their earnings would

otherwise fall short of expected dividend levels. This behaviour is evident only in firms

with positive debt and is more aggressive prior to the Sarbanes-Oxley Act, in high-payout

firms, in firms whose CEOs receive higher dollar dividends and have higher pay-

performance sensitivities (Daniel et al. 2008). Moreover, this earnings management

1 Ronen and Yaari (2008) discuss three categories of earnings management: beneficial, pernicious and neutral earnings management. They argue that defining earnings management as a non-truth-telling strategy marks it as pernicious, which means that earnings management is immoral even when it does not involve fraud (p.29). Yet earnings management can be beneficial, which enhances the transparency of reports, or neutral, which is manipulation of reports within the boundaries of compliance with bright-line standards. At the end, they came to the conclusion that the definition by Healy and Wahlen (1999) best describes earnings management.

2 Ronen and Yaari (2008) acknowledge that there are several methods to engage in earnings management (p.32). Which are: choosing between treatment which are allowed under GAAP; the decision on the timing of the adoption of a new standard; a judgment call when GAAP requires estimates; classification of items as above or below the line of operating earnings in order to separate persistent earnings from transitory earnings; structuring transactions to achieve desired accounting outcomes; timing the recognition of expenses and revenues; real production and investment decisions; managing the transparency of the presentation; managing the informativeness of earnings.


behaviour appears to significantly impact the likelihood of a dividend cut. Research done

by Daniel, Denis and Naveen implied that managers treat expected dividend levels as an

important earnings threshold. If reported earnings are an important dividend threshold,

managers have the incentive to manage earnings upwards to avoid dividend cuts when

reported earnings would otherwise be below expected dividend levels (e.g., see Watts and

Zimmerman, 1986). In other words, even though managing earnings does not alter the

firm’s capacity to pay dividends by generating additional cash, managing earnings upward

still affects the firm’s ability to pay dividends by allowing the firm to circumvent

constraints imposed by the firm’s debt covenants (Daniel et al. 2008).

2.1 THE DIFFERENCE BETWEEN EARNINGS MANAGEMENT AND FRAUD

Earnings management can be either within or outside the boundaries of the law and

standards. Earnings management within the law involves the actions which are allowed

within the rules, but which are not necessarily within the spirit of the rules. This however

means that the lines of the law are not crossed. GAAP generally allow for a certain degree

of interpretation. Manipulation that is outside the law constitutes fraud and is illegal

(Stolowy and Breton 2004). Examples of fraudulent actions are recording unearned or

uncertain revenue and recorded fictitious inventory. Falsified documents or deleted or

forged transactions are also considered to be fraudulent actions (Stolowy and Breton

2004). The actions which can be considered as earnings management, such as income

smoothing and big-bath accounting, normally remain within the boundaries of the law.

Managing earnings does not give a fair representation of the financial position and

therefore would not fit in the “fair representation zone” in the figure below. Manipulation

however, is not fraud and therefore not illegal.

Dechow and Skinner (2000) make a distinction between actions which violate GAAP and

actions which are within GAAP. Within GAAP there are three categories: conservative

accounting, neutral accounting and aggressive accounting. They distinguish between

fraudulent actions and actions which are aggressive, but still acceptable. This thesis will

focus on non-fraudulent actions.

The figure below is obtained from Stolowy and Breton, 2004.


2.2 INCOME SMOOTHING

As mentioned earlier, managers engage in income smoothing to reduce the amount of

variation in periodic earnings over time. Income smoothing can be seen as a particular

form of earnings management (Stolowy and Breton 2004). To exist, this form of

manipulation needs the firm to make large enough profits. A naturally smooth income

stream simply implies that the income generating process inherently produces a smooth

income stream (Eckel 1981).

There are two types of intentional smoothing. Real smoothing involves making production

and investment decisions that reduce the variability of earnings. Artificial or cosmetic

smoothing is achieved through accounting choices. So, management is responsible for the

occurrence of both real and artificial smoothing.

Eckel (1981) provided a clear overview of the types of income smoothing; the figure is

displayed below.


Income smoothing

Intentional smoothing byManagement

Artificialsmoothing

Realsmoothing

Fraud Fraud Manipulation Manipulation Fair representation

Natural smoothing

Researchers have several models at their disposal when it comes to detecting income

smoothing. The accrual models (see chapter 3) as well as some other models are

applicable. Over the past decades several other models have been created to detect income

smoothing. The Imhoff and Eckel model, which will be discussed in chapter 4, and the

Albrecht and Richardson model, are two of such models.

Due to the discretion permitted by accounting policies managers have the flexibility to

adjust reported earnings. Real smoothing is less transparent and therefore harder to detect

(Ewert and Wagenhofer 2005). Prior research proved that managers, to their own benefit,

distort earnings to give a wrong impression (Healy 1985). For example, managers will

manipulate the numbers to increase their remuneration, to keep their jobs, to gain tax

advantages and so on (Stolowy and Breton 2004). There is another reason why managers

would engage in income smoothing. That is to communicate private information about

future earnings to investors; Ronen and Sadan (1998) argue that income smoothing

enhances the ability of external users to predict future income. Studies done by

Subramanyam (1996) as well as Tucker and Zarowin (2006) provided evidence that

managers use discretionary accruals to smooth income. He also provides evidence which

implies that income smoothing communicates information about future benefits. So,

managers smooth earnings with both current and future relative performance in mind

(DeFond and Park 1997). Thus, income smoothing can be seen as both positive and

negative. On the one side, the negative perspective is misleading the users of the financial

reports and on the other side it will provide the investors of the company with information

about future earnings (Ronen and Sadan 1980). The following two definitions will

describe the positive and negative effect of income smoothing. The positive aspect of

income smoothing is described by Ronen & Sadan (1981). They state that income

smoothing is ‘’a deliberate management’s attempt to signal useful information to users of

financial reports’’.

The negative aspect of income smoothing can be described as follows: ‘’ the process of

manipulating the time profile of earnings reports to make the reported income stream less

variable, while not increasing reported earnings over the long run’’ (Fudenberg and

Tirole, 1995, pp. 75). More about managers’ incentives will follow in the next section.


2.2.1 INCENTIVES FOR SMOOTHING EARNINGS

The classical perception people have of income smoothing is that of an opportunistic

management tool to manipulate financial statements. Ronen and Sadan (1980), however,

propose that income smoothing is not as evil as one might think. Manipulating accounts is

indeed a managerial activity (Dye 1988). But managers will engage in income smoothing

to either opportunistically gain private benefits at the expense of shareholders (Cahan, Liu

and Sun 2008) or to efficiently communicate private information about future earnings to

outsiders (Beidleman 1973, Ronen and Sadan 1980, Wang and Williams 1994). So,

smoothed earnings may enable external users to predict future income (Ronen and Sadan

1980). Suppose that it is common knowledge that whenever there is a change in

depreciation policy this is done to boost earnings until the bad time passes. The reader

then knows that when the firm changes a depreciation policy and publicizes it, he can

undo the change to calculate the underlying truth. This shows how managers can

communicate private information to outsiders through income smoothing.

Managers may or may not believe in market efficiency. If they do not, they may attempt to

smooth earnings. Stolowy and Breton (2004) listed some of the private benefits that can

encourage managers to engage in income smoothing. The following are some the of main

incentives: managers may manipulate numbers to reduce the cost of capital, to decrease

the overall risk of the company to minimize income tax, to satisfy the external demand of

existing shareholders, to increase their own remuneration, to avoid violating the debt

covenants or incurring political costs. Fudenberg and Tirole (1995) argue that managers

who are concerned about their job security will be more likely to engage in income

smoothing.

Depending on the incentives of the manager, earnings can be managed either upwards or

downwards. For example, if the manager’s compensation is based on earnings, he will be

motivated to increase earnings, so that he will receive a higher compensation. On the other

hand, if the manager’s objective is to minimize income tax, he will be more encouraged to

manage earnings downwards (Scholes et al. 1992)

Cahan et al. (2008) found evidence that managers in weak investor protection countries

are more likely to use income smoothing to gain private benefits, while managers in high


investor protection countries are more likely to use income smoothing to provide outsiders

with private information about expected earnings.

2.3 CONCLUSION

Earnings management is a strategy used by the management of a company to deliberately

manipulate the company's earnings so that the figures match a pre-determined target.

There is however a difference between earnings management and fraud. Income

smoothing is just one of the many forms of earnings management. As mentioned, income

smoothing can be seen as a way for management to diminish the variability of income

numbers. Managers’ incentives can vary; they may engage in income smoothing to

enhance their own benefits or to communicate private information about future earnings to

outsiders. Depending on managers’ incentives, earnings can be managed either upwards or

downwards.


3 ACCRUALS METHODOLOGY

In this chapter the accruals prediction models will be discussed. The models used to detect

earnings management can either be executed in a time-series3 or a cross-sectional setting4.

Both a time-series model and a cross-sectional model use a three-step approach to break

down total accruals into discretionary and non-discretionary accruals (Dechow et al.

1995). Prior research has used various accrual prediction models to detect earnings

management. The discretionary accruals can be estimated from these accrual models.

However, not all models are equally accurate (Dechow et al.1995, Kang and

Sivaramakrishnan 1995).

Over the years several researchers have attempted to model normal accruals. Ronen and

Sadan (1981), Healy (1985), DeAngelo (1988) and Dechow and Sloan (1991) made

significant contributions to the development of accrual models. But the Jones model is by

far the most important contribution to the accruals prediction models. In order to get a

good grasp of the accruals models, it is necessary to have some knowledge about accruals.

3.1 ACCRUALS

In order to identify earnings management in general most researchers have approached the

issue by detecting discretionary accruals. To fully understand how earnings management

is detected, one should have certain knowledge of accruals.

Accruals arise when there is a discrepancy between the timing of cash flows and the

timing of accounting recognition of the transaction (Ronen and Yaari 2008). Normally,

reported revenues must equal total cash inflows and total accruals must equal zero over a

firm’s lifetime. Accruals can be divided into three categories: accruals that result from

normal accruals (non-discretionary accruals), accruals that result from managed earnings

(discretionary accruals) and reversals. Non-discretionary accruals are accruals that arise

from transactions made in the current period that are normal for the firm given its

performance level and business strategy, industry conventions, macro-economic events

3 A time-series model involves a sequence of data, typically measured at successive times, at often uniform time intervals. The objective of this type of model is to predict future events based on past events. Time-series models enable researchers to determine whether reported earnings for a series of years would have been more or less smooth if a particular variable had been excluded as a determinant of income (Dechow et al. 1995).4 Cross sectional models focus on the relationship between different variables at a certain point in time. In contrast to time-series models these models relate to how variables affect each other at the same time.


and other economic factors (Ronen and Yaari 2008). These are in contrast to discretionary

accruals, which arise from transactions made or accounting treatments chosen to manage

earnings (Ronen and Yaari 2008). There is also another group of accruals, known as

reversals, which are accruals originating from transactions made in previous periods. Most

empirical research ignores reversals, because they cannot be observed (Gillan 2006). A

problem with measuring earnings management is that a change in total accruals can be

caused by both a change in non-discretionary accruals and the accumulation of

discretionary accruals. Therefore it is of importance that researchers understand what can

be expected of normal accruals, so that the managed accruals can be identified.

3.2 THE JONES MODEL

One of the most discussed models is the Jones model. This model was developed by Jones

while she was examining U.S. firms during import relief investigations by the U.S.

International Trade Commission. The Jones model divides the time series of a company’s

earnings into two periods, the estimation period and the event period. An assumption of

this model is that the firm does not manage its earnings before the event, so discretionary

accruals amount to zero in the estimation period.

Phase 1: in the estimation period the normal accruals are calculated by applying the

following equation:

NDA¿

A¿−1=

TA¿

A ¿−1=αi [ 1

A ¿−1 ]+ β1 i [ Δ REV ¿

A ¿−1 ]+ β2 i[ PPE¿

A¿−1 ]+ε¿ (Eq .1)

Where

NDA = normal accruals

TA = total accruals;

A = assets;

REV = revenues;

PPE = gross property, plant, and equipment;

ε = error term;

i = index for firm, i=1, 2… N.

T = index for the period (year) in the estimation period,

t = 1, 2… T.


Δ = change in a given variable.

The regression produces estimates of the coefficients α i , β1 i , β2 i . The PP&E (gross

property, plant and equipment) coefficient is negative, because PP&E determines the

depreciation expense. Most researchers agree that the coefficient on change in sales should

be positive (Ronen and Yaari, 2008, p. 405), because of the fact that there is a relationship

between changes in accounts receivable and accounts payable.

Phase 2: The parameters α i , β1 i , β2 i are put into Eq.1, in order to ultimately calculate the

discretionary accruals.

Total accruals can be derived from the financial data of the firm, TAip.

TAip=( Δtotal current assets−Δcas h )− ( Δtotal current liabilities−Δs hort term debt−Δtaxes payable )−depreciation expense (Eq .2)

Normal accruals are estimated from the change in sales and PP&E, deflated by the

beginning-of-the-period assets, given the coefficients estimated in Eq. 1:

T Aip

A ip−1= αi[ 1

A ip−1 ]+β1 i[ Δ REV ip

A ip−1 ]+ β2 i[ PPE ip

A ip−1 ](Eq .3)

The abnormal accruals are fully equated with discretionary accruals. The discretionary

accruals can be calculated by deducting the normal accruals from the total accruals:

uip=TA ip−T Aip(Eq .4)

Where

TA = Total accruals

TÂ = Normal accruals

3.2.1 CONCERNS REGARDING THE JONES MODEL


A number of researchers have questioned the Jones model, among which Ronen and Yaari

(2008). They placed question marks on the assumption that firms do not engage in

earnings management in the estimation period. They wonder whether it is realistic to

assume that earnings have not been managed. In order to find out whether earnings

management during the estimation period taints the tests, they conducted simulations of

the Jones model assuming that firms manage depreciation during estimation periods. Their

results confirm that if earnings management occurs during the estimation period it will

indeed contaminate the tests. Their evidence, however, proves that the Jones model is

efficient in testing whether there is an existence of earnings management (Ronen and

Yaari, 2008, p. 410).

Some research questions the validity of equating abnormal accruals with discretionary

accruals. Healy stated that variations in accruals can be a result from changing business

conditions and change in strategy and operating decisions rather than from earnings

management (Healy 1996).

Another concern regards the sample size. A small sample increases the chance of a type II

error, which occurs when there’s an erroneous acceptance of the null hypothesis that firms

do not manage earnings (Ronen and Yaari, 2008, p. 415). Jones used a small sample of 23

firms in her study. According to Bartov et al. (2002) the median number of observations

for estimating normal accruals is 140 in a cross-sectional analysis compared to 8

observations in a time-series analysis.

Incomplete and contaminated data also pose as a threat. An error in the calculation of

total accruals can lead to an erroneous measurement of discretionary accruals. Researchers

cannot always prevent this from happening, since they rely on public data.

Several researchers have developed accruals models in order to try to improve the Jones

Model5. Many researchers make comparisons with tests using the Jones model and the

Modified Jones model (Kothari et al. 2005, Holthausen et al. 1995); from which I

concluded that it is an important alternative to the Jones model. Therefore, this paper will

only discuss the modified Jones model by Dechow, Sloan and Sweeney in the next

section.

3.3 THE MODIFIED JONES MODEL

5 Ronen and Yaari (2008) extensively discuss the modifications on the Jones model, which are the modified Jones model of Dechow, Sloan and Sweeney (1995), the forward-looking model of Dechow, Richardson and Tuna (2003), the performance adjusted models (Kang and Sivaramakrishnan (1995); Dechow and Dichev (2002); Kothari, Leone and Wasley (2005)) and the synthesis model of Ye (2006).


In the previous section it was mentioned that a small sample increases the chance of a type

II error in the results of the Jones model. The focus, however, does not lay on this type of

error, but on the type I error. This is an erroneous rejection of the null hypothesis that the

firm does not manage earnings. Major concern lays with this type I error because it

incorrectly indicates that earrings have been managed (Ronen and Yaari, 2008, p. 433).

Because of these errors there was need for improvement of the Jones model. Dechow,

Sloan and Sweeney (1995) developed the modified Jones model. The difference between

the Jones model and the modified Jones model is the treatment of accounts receivable. In

the estimation period the same procedure is used as in the standard Jones model. The

difference between these two models becomes apparent in the second stage (the event

period) when estimating normal accruals. The normal accruals are computed by

multiplying the estimated coefficient of the change in sales by the change in cash sales

instead of the change in sales. So the change in accounts receivable is deducted from the

change in revenues.

The normal accruals in the event period are calculated by applying the following equation:

NDA ip=α i[ 1A ip−1 ]+ β1 i[ ΔREV ip−Δ ARip

A ip−1 ]+ β2 i[ PPE ip

A ip−1 ](Eq .5)

Where

NDA ip= normal, non-discretionary accruals of firm i in period p;

Aip−1= lagged assets of firm i;

REV = revenues;

AR = accounts receivable;

PPE = PP&E;

Δ = change;

β1 i = the coefficient of total revenues in the estimation period. It is estimated

from the regression of accruals on ΔREV i andPPEi.


Evidence provided by Ronen and Yaari proves that both models detect attempts to manage

earnings. The modified Jones model, however, is less susceptible to type II error than the

Jones model.

3.4 CONCLUSION

This chapter discussed the several models available to researchers. Generally, the Jones

and Modified Jones model are the most common models used to measure earnings

management. These models are not used to measure income smoothing, however. The

Imhoff and Eckel model is mostly used to measure income smoothing, but there are

alternatives. These alternatives will not be further elaborated on in the remainder of this

thesis. The aforementioned model will be discussed in the following chapter.


4 OTHER MODELS

In the previous chapter the accruals models were discussed. There are however, many

models to measure earnings management without the help of accruals. This chapter will

focus on only one of such non-accruals models. This is the model developed by

Burgstahler and Dichev and will be the first one to be discussed. The second model to be

discussed is the Imhoff and Eckel model, which is a framework developed especially to

detect or identify income smoothing.

4.1 BURGSTAHLER AND DICHEV

Several studies showed that managers have incentives to report systematic increases in

earnings and positive earnings (Barth et al. 1995). Burgstahler and Dichev provide

evidence that managers avoid reporting decreases in earnings and losses. With the help of

pooled cross-sectional distributions they showed that the frequencies of small earnings

decreases and small losses are abnormally low, while the frequencies of small earnings

increases and small positive earnings are abnormally high. This is evidence that earnings

are being managed. According to Burgstahler and Dichev (1997) about 8-12 % of firms

with small pre-managed earnings decreases manipulate earnings in order to report

earnings increases, and 30-44% of firms with small pre-managed losses manage earnings

to report positive earnings (Burgstahler and Dichev, 1997, p. 101). Earnings are

manipulated through both cash flow from operations and adjustments in working capital.

Earnings management to avoid earnings decreases is likely to be reflected in cross-

sectional distributions of earnings changes in the form of unusually low frequencies of

small earning decreases and unusually high frequencies of small earnings increases. The

same goes for small losses and small positive earnings. This is an alternative way to

determine whether earnings are being manipulated.

A discussion followed after Burgstahler and Dichev (1997) pointed out that the

discontinuities in earnings distributions around zero is evidence of earnings management.

Even though there are researchers who acknowledge the findings by Burgstahler and

Dichev (Jacob and Jorgensen 2007), there are also opponents. Durtschi and Easton (2004),

for instance, say that the discontinuities cannot be solely explained by earnings

management. They also provide evidence that there are other factors contributing to the

discontinuities, such as scaling and sample selection (Durtschi and Easton 2004, 2009).


Dechow, Richardson and Tuna (2003) and Beaver, McNichols and Nelson (2003) mention

that discontinuity in earnings distribution may be driven by other factors, such as

exchange listing requirement and different tax treatment of profits and losses.

4.2 THE IMHOFF AND ECKEL MODEL

The great majority of researchers examine ex post data in order to identify income

smoothing behaviour. Those researchers examining ex post data have assumed the same

conceptual framework; if the variability of normalized earnings generated by a specified

expectancy model is lessened by the inclusion of a potential smoothing variable utilized

by the firm, then the firm has 'smoothed income' (Eckel 1981).

Eckel (1981) developed a conceptual framework in which he suggests that firms can

choose accounting variables in order to minimize the variability of their net income. The

study by Eckel was based on earlier work by Imhoff (1977). Imhoff (1977) was the first

one that suggested that income smoothing could be measured by comparing the variance

of sales to the variance of income. The type of income smoothing behaviour that Eckel is

attempting to identify is artificial smoothing. Because of the fact that real smoothing

represents an underlying economic reality it is not relevant to his study.

In an attempt to distinguish firms that smooth their income artificially from real smoothing

firms, he wanted to create a measure that can be used to do empirical research on this

topic. He stated that a firm is classified as an income smoother when the coefficient of

variation for the one-period change in sales in is greater than the coefficient of variation

for the one-period change in income; this is the first part of the testing procedure. In

symbols:

CV ΔS>CV ΔI (Eq .6)

Where:

CV ΔS=√Variance ΔSMeanValueΔS

(Eq .7)


CV ΔI=√Variance ΔI

MeanValue ΔS(Eq .8)

If the variance of ∆S throughout the years in relation to the mean ∆S of the industry is

greater than the variance of ∆I throughout the years in relation to the mean ∆I of the

industry, a firm can be identified as an income smoother.

The second part of the dual testing procedure then is to determine whether or not the

firm’s CV ΔI

CV ΔSis significantly less than the industry average. If this indeed is the case, the

firm would be identified as an income smoother (Eckel 1981). I will not elaborate on the

model of Eckel, because it is not relevant for my further research.

4.3 CONCLUSION

Burgstahler and Dichev argued that by looking at pooled cross-sectional distributions they

could determine whether earnings were being manipulated or not. Burgstahler and

Dichev’s method however, was considered highly controversial, because several

researchers proved that other factors contributed to the discontinuities.

Before Eckel a number of researchers had conducted studies; however, the conceptual

framework for most of these studies tended to be similar with differences limited to the

sample of firms, the expectancy model used, the time-frame studied, or the soothing

objects and smoothing variables considered (Eckel 1981). Therefore he developed his own

model which is widely used in the field of detecting income smoothing. These two

models, however, will not be discussed in the remainder of this thesis, because I will be

using the measurements developed by Leuz et al. (2003). The objective is to determine

whether income smoothing decreases in high investor protection countries while using the

anti-self-dealing index. Also, I will be looking at a pre-IFRS and post-IFRS period.


5 CORPORATE GOVERNANCE

According to Gillan and Starks (1998), corporate governance can be defined as the system

of laws, rules and factors that control the operations of a company. Corporate governance

can be divided into two distinct groups, internal and external corporate governance (Gillan

2006). Management, who is required to act in the interests of stakeholders, and the board

of directors are the basic parts of internal governance. External corporate governance is

concerned with the company’s need to raise capital; shareholders and debt holders provide

firms with capital. Laws and regulations as well as markets are elements of external

governance that are of most interest to researchers (Gillan 2006). The distinction between

internal and external governance defines the fact that there is a separation in publicly

traded firms between the individuals that manage the firm’s capital and the ones that

provide capital. Gillan (2006) divides external corporate governance further into five

groups:

i. Law/Regulation: federal law, self regulatory organizations:

The impact of law on shareholder wealth; the impact of the SOX implementation

on governance and wealth

ii. Markets (1): capital markets, the market for corporate control, labor markets,

product markets

Capital markets: Gillan discusses the importance of ownership structure to

corporate governance. For example, concentrated institutional ownership

moderates executive compensation (Hartzell and Starks 2003, stated in Gillan

2006).

Market for corporate control: Looks at the association between aspects of

governance and the market for corporate control. For example, CEOs of firms that

are acquired receive compensation in line with what they would have eraned had

they remained in the CEO position (Hartzell et al 2004, stated in Gillan 2006).

Labor markets: This particular group focuses on CEOs, board members and

members of senior executive teams. For instance, both managers and board

members’ behavior is regulated by labor market forces and reputation concerns.

Good performance can lead to better career opportunities (Fama and Jensen 1983,

stated in Gillan 2006).


Product markets: This category looks at product market competition and its

relation to different aspects of corporate governance, including compensation

structure and CEO turnover. E.g. researchers found that the frequency of CEO

turnover is greater in more competitive industries (DeFond and Park 1999, stated

in Gillan 2006).

iii. Markets (2) Capital Market Information:

This group focuses on the connection between capital market information

providers and corporate governance. For example, certain parties such as securities

analysts provide the market with information about firms.

iv. Markets (3) Accounting, Financial and Legal services: services from parties

external to the firm:

The focus of this group lies on the link between the market for services and

corporate governance. A good example is the relationship between firms and their

external auditor.

v. Private sources of External Oversight: media and external lawsuits:

The media and external lawsuits are the two private sources of external oversight.

The media takes in a primary role in reporting on firms’ corporate behavior. This

media oversight urges managers and directors to employ “socially correct

behavior”.

The relationship between earnings management, especially income smoothing, and

corporate governance is often the result of the principal-agent relationship between owners

and managers (Ronen and Yaari 2008). Managers are hired to act in the interests of the

owners; this however is not always how it turns out in reality. Managers tend to act in their

own interest, at the expense of the other stakeholders. Therefore, owners and regulators

are challenged to implement a system that obliges managers to act in the interests of all

stakeholders and make the “right decisions”. In this thesis the focus will lie on laws and

regulations which are in place to protect investors against the selfish behaviour of

managers and other controlling insiders.


5.1 INVESTOR PROTECTION

Investor protection refers to how well investors are protected by law from expropriation

by managers and controlling shareholders of firms (Cahan et al. 2008). Previous research

has established that earnings smoothing is less prevalent in strong investor-protection

countries (Leuz et al. 2003, Boonlert-U-Thai et al. 2006). A country’s laws and

regulations provide the underlying framework upon which its financial system functions.

The legal system establishes clear ownership rights, contract laws, commercial codes, and

bankruptcy procedures. Uncertainty is also reduced through effective enforcement. Such a

legal environment minimizes information asymmetry and investor uncertainty. Because

the legal and regulatory environment largely determines the reliability of publicly-

available information, information asymmetry will be lower for firms operating in stronger

investor protection environments (Brockman and Chung 2008). Rules can come from

different sources such as company, security, bankruptcy, takeover and compensation laws.

When investors finance a firm, they face the risk that they will not see returns on their

investments because controlling shareholders or managers expropriate them. This is one of

the reasons why investors should be protected. Examples of actions they should be

shielded against include insiders who steal profits; insiders who sell the assets, the output

or additional securities they own in the firm to another firm they own, below market

prices; overpaying executives; insiders who install unqualified family members in

managerial positions (La Porta et al. 2000). It comes down to the fact that outside

investors should be protected against insiders who rather benefit themselves than return

the money to investors. Investors obtain certain rights or powers that are protected through

laws and regulations. Voting for directors, receiving dividends on pro-rata terms,

participation in shareholders meetings are some of the protected shareholders rights.

Several researchers have studied corporate governance and its relationship to the legal

protections afforded shareholders and creditors. The introduction of the Sarbanes-Oxley

Act demonstrates that law and regulation do have influences on corporate governance

(Linck et al 2005, Cohen et al. 2005). To a large extent shareholders and creditors finance

firms because their rights are protected by the law. Laws are necessary to shield investors

from insiders’ selfish behaviour, but enforcement of laws is equally important as their

contents. India and Pakistan are examples of countries where the investor protection laws

are strong, but the de facto strength is weak. Without a good functioning legal system

which protects investors, external finance would not work well (La Porta et al. 1997).


In most countries laws and regulations are enforced in part by market regulators, in part by

courts and in part by market participants. The extent, to which these laws protect investors

however, can vary between countries because of differences in corporate law and the

enforcement of the law (La Porta et al. 1998). Prior research documents greater financial

transparency in countries with strong investor protection (Bhattacharya et al. 2003).

The earlier discussed index by La Porta et al. (1998) is not the only index available to

researchers. Other indexes have been developed over the years.

5.1.1 ANTI-SELF-DEALING INDEX

Djankov et al. (2007) developed the anti-self-dealing index, which relies on the same basic

dimensions of corporate law, but defines them with more precision. This index is

calculated for 72 countries based on legal rules prevailing in 2003. These 72 countries are

classified by their legal origin. Both the original and the revised anti-director rights indices

summarize the protection of minority shareholders in the corporate decision-making

process, including the right to vote. The index covers the following six areas: (1) vote by

mail; (2) obstacles to the actual exercise of the right to vote (i.e., the requirement that

shares be deposited before the shareholders’ meeting); (3) minority representation on the

board of directors through cumulative voting or proportional representation; (4) an

oppressed minority mechanism to seek redress in case of expropriation; (5) preemptive

rights to subscribe to new securities issued by the company; and (6) the right to call a

special shareholder meeting (Djankov et al. 2007).

Djankov et al. (2007) examine several areas of law relevant to the transaction and

summarize them with two indices: an index of public enforcement and an index of the

private control of self-dealing. The latter index can be divided into two components; an

index of ex ante private control of self-dealing and an index of ex post private control of

self-dealing.

According to Djankov et al. (2007) the first major area that the law can seek to regulate is

the approval process. The law can also seek to regulate the approval process by mandating

extensive disclosure by the company and the related party. Data on approval requirements

and immediate disclosures is summarized through an ex ante private control of self-

dealing.


Djankov et al. (2007) consider the ease with which minority shareholders can prove

wrongdoing to be the second major area that the law can regulate. The disclosure

requirements after the transaction is approved and the ease of proving wrongdoing are

combined into an index of ex post private control of self-dealing.

The anti-self-dealing index is then created by averaging the indices of ex ante and ex post

private control of self-dealing.

5.2 CONCLUSION

Investor protection is a form of external corporate governance. The objective is to protect

investors from expropriation by controlling insiders. These insiders tend to put their own

interests before those of the shareholders. In order to prevent this from happening

legislators need to put mechanisms in place which will keep managers from behaving

opportunistically. Laws regarding investor protection are not necessarily the same in

different countries. Djankov et al. (2007) acknowledged these differences and developed

investor protection indices for 72 countries. Previous work by La Porta et al. (2000) is

considered to be outdated and flawed. Therefore this research will use the anti-self-dealing

index.


6 INTRODUCTION OF IFRS

With the globalization of financial markets in sight, regulators came up with the notion

that adopting common standards for financial reporting would lead to harmonization of

corporate accounting practices. The International Financial Reporting Standards (from

here on IFRS), which were formerly known as International Accounting Standards (IAS),

were chosen as the common language for financial reporting by Europe and several other

countries. Many countries have agreed to require or allow adoption of IFRS. Japan, for

instance, will converge to IFRS in 2011. Brazil, Canada, China and India have all set

formal timelines by which they wish to converge to IFRS (Jeanjean and Stolowy 2008).

Table 2 gives an overview of the IFRS adopters. As of January 1st, 2005 all firms listed in

the European Union are required to prepare their consolidated financial statements

conform the International Financial Reporting Standards.

One of the aspects that welcomed IFRS is the fact that IFRS are the product of one

independent, private-sector body, and have arisen in response to demand, from capital

markets and not as a result of specific political initiatives by governments (Whittington

2005).

IFRS are more “principles-based” than “rules-based”. Principles-based systems issue

generic accounting standards. This means that not every controversial issue is discussed

but ambiguity about major processes as record keeping and measurement is kept

(Carmona and Trombetta 2008). Nelson (2003) stated that “principles-based” refers to

fundamental understandings that inform transactions and economic events. This is in clear

contrast to “rules-based” standards which include specific criteria, examples, scopes,

restrictions, exceptions etc. According to Carmona and Trombetta (2008) the global

acceptance of IFRS resulted from its openness and flexibility. Both of which are

fundamental in accommodating diverse institutional settings and traditions (common-law

and civil law) under shared standards.


6.1 IFRS IN EUROPE

In contrast to current standards, the original International Accounting Standards were

descriptive and provided several alternative treatments (Van Tendeloo en Vanstraelen

2005). As a result comparison between countries was made difficult. For this reason and

the additional flexibility of IAS, many criticized these standards. In cooperation with the

international Organization of Securities Commission (IOSCO) the IASC made substantial

revisions to the IAS, which are nowadays widely used (Carmona and Trombetta 2008).

The European Parliament issued a regulation (1606/2002/EC) which requires all EU listed

firms to prepare consolidated financial statements based on IFRS by 2005. However, it

was allowed to prepare the subsidiary financial statements in accordance with IFRS before

the mandatory introduction in 2005. In some countries, among which Germany, Austria,

Belgium, France, Italy and Switzerland, it was possible for firms to adopt IFRS before the

mandatory introduction.

IAS (International Accounting Standards), which are the predecessors of IFRS, were

issued by the International Accounting Standards Committee (IASC) during the period

1973 until 2000. The successor body to the IASC, The International Accounting Standards

Board (IASB), issued International Financial Reporting Standards, which include

standards issued not only by the IASB but also by the IASC, some of which have been

amended by the predecessor6. The board of IASC formed the Standing Interpretations

Committee (SIC) in 1997. It was founded with the objective of developing interpretations

of International Accounting Standards (IASs) to be applied where the standards are silent

or unclear. The International Financial Reporting Interpretations Committee replaced the

former Standing Interpretations Committee (SIC) in March 2002. IFRS enclose all

standards and interpretations.

Countries with diverse national cultures equally applied the IFRS standards. In Europe

countries with rules-based systems (Germany) as well as principles-based systems (the

UK) have adopted IFRS. At the same time, a common-law country like the UK and code-

law countries like Belgium and the Netherlands have also adopted these standards. This

global acceptance of IFRS results from its principles-based nature as well as its openness

and flexibility.

6 The IASB recognizes standards issued by the IASC.


6.2 PROPONENTS VS. OPPONENTS

Academics and practitioners have argued exhaustively about the actual benefits of

mandatory adoption of new standards across countries. There are arguments which are in

favour of IFRS adoption and back up the idea that IFRS would improve accounting

quality. But there are also arguments to the contrary.

As mentioned earlier, companies in the European Union are obliged to prepare their

consolidated financial statements in accordance with IFRS starting January 1st 2005. The

European Commission had the following arguments for mandating one set of accounting

rules across the entire EU (European Union 2002):

1. The establishment of a single set of internationally accepted high quality financial

reporting standards (compared to the many different local standard in force). The

key target of this harmonization is firms listed on financial markets.

2. To contribute “to the efficient and cost-effective functioning of the capital market”.

The Commission’s goal is to protect investors, by maintaining (or increasing)

confidence in the financial markets, which would then reduce the cost of capital for

firms in the EU.

3. To increase the overall global competitiveness of firms within the EU and thereby

improve the EU economy.

Proponents of adopting IFRS argued that common standards will improve the

comparability of financial statements of firms across different countries. This would result

in more efficient competition between international funds. The proponents however, are

basing these benefits on the assumption that mandatory use of IFRS increases

transparency and reporting quality. Several studies show that this is not necessarily the

case. Callao et al. (2007) investigated whether IFRS increased the usefulness of financial

statement information in Spain. Their findings show that local comparability of financial

statements has diminished after the adoption of IFRS. Their results also showed that the

relevance of financial reporting did not improve after adopting IFRS. Even though there’s

no significant short-term improvement, Callao et al. acknowledge that improved value

may be gained in the medium to long-term. Ewert and Wagenhofer’s (2005) research


shows that tightened accounting standards increase the quality of earnings, but it also

increases real and accounting earnings management. Panaanen and Lin (2009) found

evidence which indicates that accounting quality has not improved but worsened after the

mandatory adoption of IFRS in Europe.

Research by Hail and Leuz (2006) showed that the documented benefits of first-time

voluntary adopters were short-lived; it is possible that these benefits can be enjoyed for a

short period of time for first-time mandatory adopters as well.

Proponents who argue that IFRS adoption will increase transparency say so because IFRS

reduce the amount of reporting discretion relative to many local GAAP and, in particular,

push firms to improve their financial reporting (Jeanjean and Stolowy 2008). As

mentioned above, Ewert and Wagenhofer 2005 found evidence to support this argument.

Daske et al. (2008) found evidence that market liquidity increases around the time of the

introduction of IFRS and that firms’ cost of capital decreases.

They also discovered that only in countries where firms have incentives to be transparent

and where there is a strong legal enforcement, the capital-market benefits occur.

Reducing the reporting discretion however, can restrain firms from making information

public through their financial statements (Watts and Zimmerman 1986). Another argument

of opponents is that a single set of standards might not represent the different national

institutional settings and traditions. This is the main reason why different accounting

standards emanated in the first place.

Another frequently used argument by proponents is the increased comparability of

financial statements after adoption of IFRS. This assumption is based on the notion that

IFRS reporting will decrease costs for investors to compare firms across markets and

countries (Armstrong et al. 2006). So, common use of IFRS would enable investors to

differentiate between lower and higher quality firms. The ease of comparison persuades

managers to reduce earnings management. Having common standards could facilitate

cross-border investment and the integration of capital markets (Jeanjean and Stolowy

2008). As a result of this, firms’ investor base could be expanded (Merton 1987).

Opponents are doubtful about the notion that IFRS alone is sufficient in improving

informativeness or comparability. As long as firms have different reporting incentives,

reporting behaviour is expected to differ (Leuz and Oberholzer-Gee 2006, Ball et al.


2003). The application of accounting standards involves considerable judgement and the

use of private information. Consequently, IFRS provide firms with substantial discretion,

just like any other set of accounting standards. To what extent managers make use of this

discretion depends on firm-specific characteristics (reporting incentives and operating

characteristics), and countries’ legal institutions. Countries’ legal institutions take in a

primary role in explaining accounting quality after IFRS adoption. Strict enforcement

regimes and institutional structures provide strong incentives for high-quality financial

reports after the introduction of IFRS reporting (Jeanjean and Stolowy 2008).

6.3 COMMON-LAW VS. CODE-LAW

Historically, legal systems, combined with other political and economical differences,

created much diversity within accounting systems. As a result meaningful comparison of

financial reports across borders was difficult. Europe is the origin of many legal systems:

English, German, French and Scandinavian, and thus, prior to harmonization, there were

extremely diverse, country-specific accounting systems (Soderstrom and Sun, 2007).

Rules differ systematically by legal origin, which can be English, French, German, or

Scandinavian. English law is common-law, made by judges and subsequently incorporated

into legislature. French, German, and Scandinavian laws, however, are part of the scholar

and legislator-made civil-law tradition (La Porta et al. 1997). Legal rules from the several

traditions differ in content as well as in the history of their adoption. In the protection

against expropriation by insiders, common-law countries protect both shareholders and

creditors the most, French civil-law countries the least, and German civil-law and

Scandinavian civil-law countries somewhere in the middle.

Common-law arises from individual action in the private sector (Ball et al. 2000). Legal

rules in the common-law system are usually made by judges, based on precedents and

inspired by general principles such as fairness (La Porta et al. 2002, p.1158; La Porta et al.

2000, p.9). Judges are expected to rule on new situations by applying these general

principles even when specific conduct has not yet been described or prohibited in the

statutes. Accounting standards in common-law countries are mostly set by private

organizations such as the FASB in the United States. The purpose of these standard setters

is to satisfy investor needs for information (Soderstrom and Sun 2007).


While common-law systems allow for individual judgement, laws in code-law systems are

made by legislatures; it allows governments to control setting and interpretation of laws.

Accounting standards in these countries are a part of commercial law instituted by courts

and are therefore primarily influenced by governmental priorities. In code-law countries

judges are not supposed to go beyond the statutes and apply fairness opinions (La Porta et

al. 2002).

So when a corporate insider finds a loophole in the statutes which allows him to

expropriate outside investors can do so without fear of an adverse judicial ruling.

Common-law countries have the strongest protection of outside investors, which includes

shareholders as well as creditors (La Porta et al. 2000). Previous research provides

evidence that the magnitude of earnings management is on average higher in code-law

countries with low investor protection rights, compared to common-law countries with

high investor protection rights (Leuz et al., 2003).

6.4 PREVIOUS STUDIES

BARTH, LANDSMAN AND LANG (2008)

These researchers examined the accounting quality before and after the introduction of

IFRS for a sample of 327 firms that voluntarily adopted IAS between 1994 and 2003.

According to Barth et al. (2008) improvement of accounting quality is possible if the

standard setters succeed in limiting management’s opportunistic discretion in determining

accounting amounts by removing allowable accounting alternatives. More stringent

enforcement could also lead to an increase of accounting quality. They do acknowledge

however, that there are two limitations to their assumption that financial statements based

on IAS are of higher quality than those based on domestic standards. The first one is that

the domestic standards may be of higher quality than IAS. Secondly, weak enforcement of

standards could lead to lower quality. Their evidence showed that firms which adopted

IAS engaged less in earnings management compared to the pre-adoption period. They also

found evidence showing more timely loss recognition and more value relevance of

accounting amounts. Barth et al. (2008) suggested that their findings could party be

attributed to differences in firms’ incentives as well as the varying economic environments

of the sample firms.


Barth et al. (2008) included in their sample firms which voluntarily adopted IFRS (this

was possible in some countries such as Germany). Therefore their study suffers from a

sample selection bias, because only firms which saw an advantage in the adoption of IFRS

would actually adopt it voluntarily. Van Tendeloo and Vanstraelen (2005) investigated

whether firms in Germany that voluntary adopted IFRS showed lower earnings

management. Their results indicated that there is no difference in earnings management

behaviour of IFRS-adopters and companies reporting under German GAAP. This research,

like the study by Barth et al. (2008), suffers from a sample bias.

JEANJEAN AND STOLOWY (2008)

Contrary to the earlier discussed study by Barth et al. (2008), Jeanjean and Stolowy (2008)

did research on the effect of the mandatory introduction of IFRS standards on accounting

quality. They specifically looked at earnings management. These researchers employed

the Burgstahler and Dichev (1997) methodology. So irregularities in distributions will be

an indication of earnings management. This is one of the caveats of their paper, but

according to them using another model was not an option because adoption of IFRS was at

the moment of their research too recent to provide enough data for researchers. Australia,

France and the UK were included in their study, because all three countries first adopted

IFRS in 2005. Australia however is a non-European common-law country, whereas France

is a code-law European country and the UK a common-law European country. Their

evidence showed that after the transition to IFRS, the pervasiveness of earnings

management increased in France (code-law) and remained stable in the UK (common-law)

and Australia (common-law). Concluding, the evidence shows that sharing rules is not a

sufficient condition to create a common reporting language. Management incentives and

national institutional factors play a key role in setting financial reporting standards. This

conclusion overlaps with the argument by Ball et al. (2003) that the adoption of a common

set of standards is a necessary condition for high quality information, but not necessarily a

sufficient one.

6.5 SUMMARY

This chapter discussed the adoption of IFRS in order to harmonize the global accounting

practices. More than 100 countries have agreed to either allow or require companies to


prepare their financial statements in accordance with IFRS. Even though many

practitioners and regulators expect that the adoption of these standards will lead to

increased comparability and transparency of financial statements, there are experts who

are doubtful. Several studies have attempted to research the effect, but there is no

unanimous outcome. There are however expectations that in the long-run the effects will

be more valuable.

From the results of previous studies one can conclude that in order to conduct proper

research on the link between IFRS adoption and earnings management the legal tradition

of countries must be taken into account. Jeanjean and Stolowy (2008) detected an increase

in earnings management in France, a code-law country. La Porta et al. (1998), who were

the first to conduct research on the legal system’s effect on a country’s financial system,

found that common-law countries have better accounting systems and better protection of

investors than code-law countries. Mandatory or voluntary adoption can also influence the

outcome of IFRS adoption. Barth et al. (2008) did research on voluntary adoption of IFRS

in Germany and found that earnings management decreased. This is in clear contrast to the

results from Jeanjean and Stolowy, who conducted research on the mandatory adoption of

IFRS. The difference between the results can be caused by firms’ incentives; it is likely

that only firms which saw an advantage in the IFRS adopted it voluntarily (Leuz and

Verrechia 2000).


7 STUDIES ON EARNINGS MANAGEMENT AND INVESTOR PROTECTION

7.1 EARNINGS MANAGEMENT AND INVESTOR PROTECTION

Research identified investor protection as a key factor in earnings management activities

(La Porta et al. 2000). Studies showed that earnings management will decrease in a strong

investor protection environment, because strong protection constrains insiders’ private

control benefits. Also, managers in weak investor protection countries will use earnings

management for more opportunistic motives (Cahan et al. 2008). This is in contrast to

managers in strong investor protection countries, who want to communicate private

information about future earnings to outsiders (Leuz et al. 2003, Cahan et al. 2008).

Company insiders can use their control over the firm to gain personal benefits at the

expense of stakeholders (Watts and Zimmerman 1990). They can choose accounting

methods that are beneficial to their interests. Since insiders have this advantage over

outsiders, they need to mask/cover these private control benefits, because if detected

outsiders will take disciplinary actions (Leuz et al. 2003). This is an incentive for

managers to mislead stakeholders through earnings management.

LEUZ, NANDA AND WYSOCKI (2003)

A number of studies have conducted research on the relationship between earnings

management, specifically income smoothing, and investor protection. Leuz, Nanda and

Wysocki (2003) were among the first ones to present evidence that the level of outside

investor protection determines the quality of financial information reported to outsiders.

They examined the differences in earnings management in 31 countries. Prior to their

research there were studies that identified investor protection as a key factor in corporate

policy choices (La Porta et al. 2000). For this reason they decided to test the influence of

investor protection on earnings management. They reasoned that insiders’ private control

benefits can be limited by strong and well-enforced outsider rights. Thus insiders will be

less motivated to manage earnings. Managers have incentives to manage earnings so that

their private control benefits will be concealed from the outside world and the true

performance of the company will not be revealed. In their study the researchers suggest

that the occurrence of earnings management is higher in countries with weak legal

protection of outsiders, than in countries with strong outsider rights protection. In order to

investigate to which extent this proposition is accurate, they performed an analysis and


created four proxies that capture the extent to which insiders use their accounting

discretion to conceal their firm’s performance. The following proxies were used:

i. The country’s median ratio of firm-level standard deviation of operating income

and operating cash flow

ii. The country’s Spearman correlation between change in accruals and change in

operating cash flow

iii. The country’s median ratio of absolute value of accruals to absolute value of

operating cash flow

iv. The country’s ratio of the number of small profits to small losses where small is

less than 1 percent of lagged total assets

Since earnings management manifests itself in different forms, Leuz et al. (2003) designed

these four proxies to capture various earnings management practices, including income

smoothing. The first two proxies (i and ii) measure earnings smoothing and the latter two

(iii and iv) measure earnings discretion. An aggregate earnings management score was

constructed to mitigate potential measurement error; the four earnings management

measures were ranked and aggregated. This score is the average of a country’s earnings

smoothing score and earnings discretion score.

In order to provide evidence on the patterns in earnings management among countries, the

31 countries were classified in three categories: 1. Outsider economies with large stock

markets, dispersed ownership, strong investor rights, and strong legal enforcement; 2.

Insider economies with less-developed stock markets, concentrated ownership, weak

investor rights, but strong legal enforcement and, 3. Insider economies with weak legal

enforcement.

Leuz et al. (2003) used the institutional variables from La Porta et al. (1997, 1998), as

their primary measure of investor protection, among which are the following: legal origin,

legal tradition, outside investor rights, legal enforcement, importance of the equity market,

ownership concentration and the disclosure index. This index measures the inclusion or

omission of 90 accounting items in firms’ 1990 annual reports, and hence captures firms’

disclosure policies at the country level. Now that the general set-up and methodology is

explained, the findings of this study will be presented.

Their empirical results reveal that investor protection plays an important role in

international differences in corporate earnings management. The evidence suggest that


firms in countries with developed equity markets, dispersed ownership structures, strong

investor rights, and legal enforcement engage in less earnings management. So, the

pervasiveness of earnings management is increasing in private control benefits and

decreasing in outside investor protection. The researchers argue that managers have

incentives to manage earnings and that these incentives will vary with the level of investor

protection in the country. Leuz et al. (2003), however, have one concern; the influence of

other institutional variables, which are correlated with investor protection, on earnings

management. One specific factor that is a cause for concern is the influence of accounting

rules and firms’ ownership structures on earnings management. They admit that these

institutional factors are complementary and therefore difficult to isolate.

CAHAN, L IU AND SUN (2008)

The objective of this study is to investigate whether investor protection influences the

efficient communication of private information through income smoothing. They posit

that income smoothing increases earnings informativeness more for firms in countries

with strong investor protection than for firms in countries with weak investor protection.

Earnings informativeness refers to the informativeness of current and past earnings about

future earnings and cash flows (Tucker and Zarowin 2005). As Leuz et al. (2003) proved,

the level of earnings management varies between high and low investor protection

countries. Cahan et al. (2008) examine whether the incentives for earnings management

varies between high and low investor protection countries. Leuz et al. (2003) examined the

level of earnings management in different countries and argued that managers have

incentives to engage in earnings management. Cahan et al. (2008) coincide with this

statement and expand the underlying motive for earnings management. They are of the

opinion that there are two interpretations regarding the underlying motive for earnings

management; opportunism is the primary motivation in both low and high investor

protection countries or earnings management is motivated by opportunism in low investor

protection countries, but is motivated by the efficient communication of private

information in high investor protection countries. In order to investigate this view they

examined the informativeness of income smoothing.

Since this study focuses on income smoothing alone and not earnings management as a

whole, Cahan et al. (2008) only used the second proxy defined by Leuz et al. (2003). They

calculated the Spearman correlation between changes in total accounting accruals and


changes in operating cash flows and used the negative correlation as a measure of income

smoothing. In order to measure investor protection they only used the legal enforcement

index from La Porta et al. (1998), contrary to Leuz et al. (2003) who used all 9 variables

from La Porta et al. (1998). The legal enforcement index is measured as the mean score

across three legal variables used in La Porta et al.: (1) the efficiency of the judicial

system, (2) an assessment of rule of law and (3) the corruption index.

By examining data from 44 countries, they came to the same conclusion as Leuz et al.

(2003); the incidence of earnings management increases (decreases) in countries with

weak (strong) investor protection. They found that the underlying motive for earnings

management indeed differs between high and low investor protection countries: managers

in weak investor protection countries are more likely to use income smoothing for

opportunistic reasons while managers in strong investor protection countries are more

likely to use income smoothing to convey their private information about future earnings.

They also found evidence to substantiate their hypothesis that income smoothing improves

earnings informativeness more greatly for firms in countries with strong investor

protection than in countries with weak investor protection.

WRIGHT, SHAW AND GUAN (2006)

The objective of this study is to extend the work by Leuz et al. (2003) and to investigate

whether Leuz et al.’s country clusters are overly broad. In contrast to the other studies

discussed in this chapter, Wright, Shaw and Guan use the modified Jones model to detect

earnings management. The U.K and the U.S. share similar legal environments, large stock

markets, dispersed corporate ownership and strong investor rights (La Porta et al. 1997).

So, they should be categorized in the same country cluster. According to theories provided

by Leuz et al. (2003) earnings management should be similar and infrequent in both

countries and their evidence supported this theory. However, this study by Wright, Shaw

and Guan shows that their theory does not hold up. The evidence indicates that managers

of MBO (management buy-outs) firms in the U.S. managed earnings to a greater extent

than their colleagues in the U.K. Even though there are numerous similarities between the

U.K. and the U.S., there are various cultural and organizational differences between these

two countries as well (Hofstede 2001).


There is one major limitation to Wright et al. study; they limited their sample to one

specific group, management buy-outs (MBO’s). The researchers acknowledge that there is

a possibility that earnings management in the U.K. would be different in other settings.

NABAR AND BOONLERT-U-THAI (2007)

The two first discussed studies focused on the legal origin differences and corporate

governance in earnings management in an international context. Nabar and Boonlert-U-

Thai investigated the role of both investor protection and national culture in explaining

cross-national differences in earnings management. They examined if and how culture

influences managers’ earnings management behavior. Leuz et al. (2003) admitted that

other factors may also contribute to earnings management, Nabar and Boonler-U-Thai

proposed and substantiated that national culture is one of these other factors.

In their analysis they used the aggregate earnings management score developed by Leuz et

al. (2003) as the proxy for earnings management. Their measurement for culture consists

of four cultural variables developed by Hofstede (1980, 2001).

The evidence from their analysis shows that earnings management is influenced by both

investor protection and culture. Their evidence confirms the findings from Leuz et al.

(2003); earnings management is low in countries with high outside investor protection.

Earnings management is high in high uncertainty-avoidance (one of the Hofstede proxies)

countries and low in English-speaking countries. Also, their evidence proved that earnings

management is influenced by both investor protection and culture. National culture affects

managers’ earnings discretion but not earnings smoothing.

To measure investor protection, two institutional variables defined by La Porta et al.

(1998) were used; outside investor rights and legal enforcement. The aggregate earning

management scores in this study differ slightly from the ones reported by Leuz et al.

(2003), because this sample consists of 30 countries whereas the sample used by Leuz et

al. consisted of 31 countries. Both studies, however, yield the same empirical results.

7.2 CONCLUSION

Leuz et al. (2003) argued that managers have incentives to manage earnings to obscure the

firm’s true performance in order to hide their private control benefits from outsiders.


Investor protection reduces management’s ability to gain private benefits at the expense of

outside shareholders; therefore the level of investor protection in a country will influence

the incentives of managers. All four studies provided conclusive evidence that in countries

with a strong investor protection environment, the incidence of earnings management is

lower than in countries with weak investor protection. Cahan et al. (2008) argued and

proved that opportunism is not always the reason for engaging in earnings management;

their findings indicate that earnings management can be motivated by the efficient

communication of private information in high investor protection countries (Cahan et al.

2008). Nabar et al. (2007) proved that there are indeed other factors contributing to

earnings management, such as culture.


8 HYPOTHESES AND RESEARCH DESIGN

This chapter will explain the research design in detail. The objective of the empirical

research is to determine whether firms in high investor protection countries engage less in

income smoothing. In the previous chapters several studies were discussed, which focused

on the relationship between investor protection and earnings management. These studies

however, focused on all forms of earnings management whereas I will only measure the

level of income smoothing. In most of these studies the La Porta index was used as a

measurement for investor protection, this index however is based on legislation dating

from 1993, which is outdated. Hence, my research. The following research question lies at

the base of this thesis:

“Does a strong investor protection environment reduce the incidence of income smoothing

in countries?”

I expect the results of my empirical research to show that income smoothing indeed

decreases due to a strong investor protection environment, because previous research

indicated that investor protection is an important factor in explaining income smoothing.

This chapter is divided in several sections, some with subsections. In the first section the

hypotheses will be formulated. It will also contain my expectations for the empirical

research, which are based on previous studies discussed throughout the chapters of this

thesis. The second section will address the measurement of investor protection. As

mentioned earlier, the La Porta indices were widely used to measure investor protection. I,

however, will use a revised and more recent index, which is the anti-self-dealing index

defined by Djankov et al. (2007). Section three will focus on the measurement of income

smoothing. Followed by a fourth section, in which I will elaborate on the sample selection

process. Finally, a summary will be given.

8.1 HYPOTHESES

Real smoothing involves decisions that reduce the volatility of economic earnings, while

artificial smoothing involves both overstatement and understatement of economic

earnings: low earnings are overstated and high earnings are smoothed out. Since accruals

management does not reduce the firm’s value as much it seems more appealing to insiders

than real smoothing. On the other hand, real smoothing has the added benefit that it is less

transparent and thus much harder to detect and deter (Ewert and Wagenhofer, 2005). In


the study by Leuz et al. (2003) four different measures were defined in order to capture

management of earnings. Real income smoothing is one of the forms earnings

management manifests itself in, so it was necessary to determine a measure to capture this

form of managing earnings. The measure is a country’s median ratio of the firm-level

standard deviation of operating earnings divided by the firm-level standard deviation of

cash flow from operations. Low values of this measure show that insiders use their

accounting discretion to smooth income. The statistics of this measure in the study by

Leuz et al. (2003) show that earnings are smoother in Continental Europe and Asia than in

Anglo-American countries. Even though I will be using another index than the one used

by them, I expect to find the same outcome. Based on the previous discussion I formulated

the following hypothesis:

H1: Real income smoothing is more pervasive in countries with weak investor

protection than in countries with strong investor protection

Income smoothing consists of two types of smoothing, real and artificial. In previous

research (Leuz et al. 2003) the pervasiveness of artificial smoothing in several countries

has been researched. The measure which was used to get an indication of this form of

earnings management is the contemporaneous Spearman correlation between changes in

accounting accruals and changes in operating cash flows. The empirical results showed

that earnings smoothing is more pervasive in countries like Greece and Japan than in

countries like Japan and the U.S. In their research Greece and Japan are considered low

investor protection countries, based on the indexes from La Porta et al. (1998). This

research, however, will use another index to classify countries as either high or low

investor environment countries. After having compared the La Porta index and the anti-

self-dealing index that I will be using, I foresee no major changes in the classification of

low and high investor protection countries. So, I expect my results not to differ

significantly from the results by Leuz et al. (2003).

The foregone discussion led me to formulate the following hypothesis:

H2: Artificial income smoothing is more pervasive in weak investor protection

countries


The aforementioned components of income smoothing will determine the aggregate

measure for income smoothing.

H3: Total income smoothing is negatively related to private enforcement and public

enforcement

H4: Real income smoothing in the post-IFRS period is low in countries with weak

investor protection

H5: Artificial income smoothing in the post-IFRS period is more pervasive in weak

investor protection countries

Today, there is enough data available to do a comparison between pre-IFRS period and a

post-IFRS period in levels of income smoothing. This will show whether IFRS adoption

has reduced the smoothing of earnings.

8.2 MEASURING INVESTOR PROTECTION

In the available literature several researchers have established indices to measure investor

protection. Leuz et al. (2003) used the La Porta index in their study, which focused on the

relationship between earnings management and investor protection. The indices from La

Porta et al. (1998) were based on laws in force around 1993 and are available for 49

countries. Their anti-director rights index, however, has been criticized by several

researchers for its ad hoc nature, for mistakes in its coding, and most recently for

conceptual ambiguity in the definitions of some of its components (Pagano and Volpin,

2005).

There is an alternative index that can be used; the more recent and revised anti-self-

dealing index defined by Djankov, La Porta, Lopez-de-Silanes and Shleifer (2007). This

index generally works better than the former anti-director rights index (Djankov et al.

2007).


The following facts have led me to believe that the anti-self-dealing index is suitable for

my research:

i. This index is more applicable for my research, because it is based on legal rules

prevailing in 2003, in contrast to the anti-director rights index which is based on

rules in effect around 1993.

ii. The investor protection indices are constructed for 72 countries, which enables me

to conduct research on the 31 countries my thesis will focus on.

iii. Djankov et al. (2007) clearly state that their index is based on controllers of

companies who act in their own interest at the expense of other investors, but who

follow the law regarding disclosure and approval procedures. This means that the

index is based on interests that are relevant for earnings management. They do not

address cases of corporate crime.

8.3 MEASURING INCOME SMOOTHING

As I mentioned earlier, insiders can conceal changes in the economic performance of the

firm in two ways:

1. By their real operating decisions (real smoothing) and,

2. By their financial reporting choices (artificial smoothing)

For both forms of smoothing, measurements have been developed by Leuz et al. (2003),

which I will use in my statistical analysis. For managers and other controlling insiders,

real smoothing has the added benefit that it is less transparent and thus much harder to

detect and deter than artificial smoothing (Ewert and Wagenhofer, 2005). Real smoothing

involves making production and investment decisions so that the variability of earnings

can be reduced.

The analytical literature offers insights into the incidence of artificial smoothing in

principal–agent relationships. When the agent knows current earnings and has an

imperfect signal on future earnings, the agent smoothes the first-period report around the

future signal when he is restricted to communicating only the current period’s outcome.


The smoothing objects are the variables whose variations over time are to be controlled (Kamin & Ronen, 1978; pp. 141, 145). The objects are chosen by the management regarding aimed at their incentives for income smoothing. Empirical studies which investigated income smoothing show that the most used object is income.

8.3.1 MEASURING REAL SMOOTHING

Real income smoothing measures the degree to which insiders reduce the variability of

reported earnings by altering the accounting component of earnings. The measure is a

country’s median ratio of the firm-level standard deviation of operating earnings divided

by the firm-level standard deviation of cash flow from operations. The equation is as

follows:

IS1=σ (operating earnings)σ (operating cash flow )

(Eq .9)

The following calculations will enable me to calculate the outcome for the measure of real

income smoothing (IS1):

Operating Earnings= Op erating IncomeAverageTotal Assets

(Eq .10)

Cas h Flow Component=(Operating Income−Accruals)Average Total Assets

(Eq .11)

Cash flow from operations is computed indirectly by deducting the accrual component

from earnings. This is done because of unavailability of direct information on firms’ cash

flows in many countries.

As mentioned earlier, the measure is the country’s median ratio of the firm-level standard

deviation of operating earnings divided by the firm-level standard deviation of cash flow

from operations. First, the standard deviation of operating earnings divided by the standard

deviation of operating cash flow is calculated for each firm per country. Afterwards the

median of all the firm-level ratios is calculated and this number is the country measure.


The financial variables are scaled by the lagged value of total assets, because of the large

differences in median firm size. By doing so a comparison across firm size can be made.

When calculating the total earnings, the operating income is used as a measure. Distinctive

about this income is that items that only appear once, the so-called non-recurring items,

are excluded by this income. This results only in elements of income that are related to the

ordinary operations of a business. These non-recurring items are not related to the daily

operations of a firm. By taking the income from continuing operations you are able to

better compare the earnings from consecutive sample years, because the non-recurring

items ‘disturb’ the annual earnings.

Before being able to calculate the cash flow component, the accruals need to be

determined first. For this purpose, Dechow (1995) uses the following equation:

Accruals=(∆ CA ¿−∆ Cas h¿)− (∆ CL¿−∆ STD¿−∆ TP¿ )−Dept¿(Eq .12)

Where

ΔCA = change in current assets

ΔCASH = change in cash

ΔCL = change in current liabilities

ΔSTD = change in debt included in current liabilities (short term debt)

ΔTP = changes in income taxes payable

Dept = depreciation and amortization expense

The purpose of this equation is to calculate the change in earnings by taking current assets

as starting point. Since accruals measure the difference between earnings and the cash

flow, it is interesting to measure the difference between the changes in both elements. In

order to calculate the accruals, the change in cash (ΔCASH) has to be excluded from the

current assets. The change in debt included in current liabilities (ΔSTD) is excluded from

accruals since this is related to financing transactions and therefore is no part of the

operating transactions. The change in income taxes payable (ΔTP) is excluded since this

also is not a part of the operating income. Finally, the depreciation and amortization

expenses are also excluded. If a firm does not report information on taxes payable or

short-term debt, the change in both variables is assumed to be zero.


8.3.2 MEASURING ARTIFICIAL SMOOTHING

If insiders want to smooth earnings they can also do so by smoothing earnings artificially.

The discretion permitted by accounting policies introduces flexibility that allows managers

to adjust reported earnings to produce a smoother income stream. Managers or controlling

shareholders are in the position to accelerate the reporting of future revenues or delay the

reporting of current costs to hide poor current performance. Previous research has shown

us that as long as managers have discretion over accounting choices, they smooth reported

income and the rate of growth in income (Tucker and Zarowin, 2006).

Accounting accruals buffer cash flow shocks and result in a negative correlation between

changes in accruals and operating cash flows. Large magnitudes of this correlation

indicate smoothing of reported earnings that does not reflect a firm’s underlying

performance. Therefore, Leuz et al. (2003) used the contemporaneous Spearman

correlation between changes in total accounting accruals and changes in operating cash

flows as a measure for earnings smoothing.

The measure for artificial income smoothing (IS2) that I will be using is the following:

IS2=ρ (∆ accruals , ∆ cash flow¿operations)(Eq .13)

8.3.3 MEASURING TOTAL INCOME SMOOTHING

An overall summary measure of income smoothing can be constructed for each country.

For both income smoothing measures, countries are ranked such that a higher score

suggests a higher level of earnings management. This means that the outcomes of the real

smoothing measure (IS1) and the artificial smoothing measure (IS2) for all 31 countries

will be ranked. Both rankings will then be averaged, which will result in the aggregate

income smoothing measure. This score will determine the level of smoothing in a country,

i.e. a high score indicates a high level of income smoothing, while a low score indicates a

lower level of income smoothing in that particular country.

To test the last hypothesis, I will employ the multiple regression stated below.


Y t=( β0+β1 X1+β2 X2+β3 X3 )+εi(Eq .14)

Where Y t = Aggregate income smoothing for year t

X1= Ex ante private control of self-dealing

X2 = Ex post private control of self-dealing

X3 = Public enforcement

ε i = Error term represents unexplained variation in the dependent variable

The dependent variable, aggregate income smoothing, can be predicted by three

independent variables. Two of these variables form the anti-self-dealing index. This index

consists of the average of two indexes, which are the ex ante private control of self-dealing

and ex post control of self-dealing. The third variable that I will use is the public

enforcement index from Djankov et al. (2007). These indices have been defined by

Djankov et al. (2007) for 72 countries classified by their legal origin.

8.4 SAMPLE SELECTION

The sample will consist of observations in 31 countries, in the period 2000 till 2008. The

countries which will be included in this study are the same 31 countries investigated by

Leuz et al.(2003). In order to be included in the sample they must have at least 300 firm-

year observations for a number of accounting variables, such as total assets, sales, net

income and operating income. Each firm must have income statement and balance sheet

information for at least three consecutive years. The data is drawn from the Thomson One

Banker database. Banks and financial institutions are excluded from the empirical

research. The final sample consists of 21.297 non-financial firms. The firms included

operate in different industries which can be identified by the following codes.

Table 1

IBC Code Industry


1000 Basic Materials

3000 Consumer Goods

5000 Consumer Services

4000 Health Care

2000 Industrials

9000 Technology

6000 Telecommunications

7000 Utilities

The 31 countries that will be used are selected for two main reasons. First, because my

research focuses on complementing and extending the research of Leuz et al. (2003),

therefore the same 31 countries they included in their sample are selected. Secondly, all of

these countries are hyperinflation-free in the sample period. High inflation may affect the

income smoothing measurements used.

Because of the large sample, it is useful to cluster the companies so that the patterns in

earnings management among countries can emerge. By employing a country cluster

analysis the countries can be categorized in three clusters, each containing countries with

similar legal and institutional characteristics:

1. Countries with high investor protection;

2. Countries with medium investor protection,

3. Countries with low legal enforcement.

8.5 EARNINGS MANAGEMENT PRE-IFRS AND POST-IFRS

As of January 1st 2005 all European Union member countries have adopted IFRS. These

standards reduce the amount of reporting discretion and therefore improve transparency

and comparability. Also, managing earnings will be more difficult and if done, easier to

detect. In order to test this argument made by proponents of IFRS, I will be looking at the


level of income smoothing in the pre-IFRS period (2000-2004) and the post-IFRS period

(2005-2008) in certain EU member states. Within the sample of 31 countries there are 14

EU member states.


9 EMPIRICAL STUDY: INFLUENCE OF INVESTOR PROTECTION ON INCOME SMOOTHING

9.1 INTRODUCTION

The first part of the empirical study, the influence of investor protection on earnings

management, is performed in this chapter. Several sub questions discussed in chapter 1

will be answered. First the hypotheses researched in this chapter will be shortly reviewed

again. The parameters for the 31 countries will also be presented. Afterwards the results

and conclusions regarding hypotheses 1 to 3 will be presented.

9.2 HYPOTHESES

The first three hypotheses that were formulated in the previous chapter will be tested in

this chapter. As noted earlier, earnings management can present itself in the form of real

smoothing as well as artificial smoothing. The standard used to measure the former is a

country’s median ratio of the firm-level standard deviation of operating earnings divided

by the firm-level standard deviation of cash flow from operations. The first hypothesis will

test whether investor protection affects real smoothing. This leads to the first hypothesis to

be tested.

H1: Real income smoothing is more pervasive in countries with weak investor

protection than in countries with strong investor protection

Besides real smoothing I will also measure the impact of investor protection on artificial

smoothing. The measure used is the contemporaneous Spearman correlation between

changes in accounting accruals and changes in operating cash flows. The data is not

normally distributed; therefore it is necessary to use the Spearman correlation, which deals

with non-parametric data. The following hypothesis is formulated.

H2: Artificial income smoothing is more pervasive in weak investor protection

countries

The first two measures can be ranked in such a way that a higher score implies a higher

level of income smoothing. These rankings of the aforementioned components of income


smoothing will then be used to determine the aggregate measure for income smoothing.

This leads to the third hypothesis to be tested in this chapter.

H3: Aggregate income smoothing is negatively related to private enforcement and

public enforcement

9.3 SAMPLE

From the Thomson One Banker database I retrieved data for 21.492 companies across 31

countries. Not all companies are included in the final sample however, because in order to

be included in the sample each firm must have financial data available for at least three

consecutive years. Due to a lack of information on total current assets, cash and

investments, total current liabilities, short term debt, income taxes and depreciation many

were excluded. Also, financial institutions were excluded. The final sample consists of

20.367 firms.

9.4 DESCRIPTIVE STATISTICS

Table 2 Descriptive statistics

Country # Firms IFRS adopter Median firm sales in $

Australia 1.346 Yes 336.432

Sweden 282 Yes 360.521

Finland 108 Yes 401.123

Canada 893 No7 350.632

United States 4.573 No8 4.597.532

Norway 110 Yes 190.812

Denmark 101 Yes 210.567

United Kingdom 1.041 Yes 223.365

Netherlands 121 Yes 430.865

France 645 Yes 260.932

7 Changes of the variables above are calculated by year t – year t-1

8 The use of IFRS will be required for Canadian publicly accountable profit-oriented enterprises for financial periods beginning on or after 1of January 2011


Switzerland 164 No9 470.485

Singapore 507 No10 193.451

Germany 671 Yes 424.641

South Africa 223 Yes11 483.573

Hong Kong 749 Yes12 267.311

Belgium 94 Yes 362.115

Austria 64 Yes 287.912

Japan 3.354 No13 552.187

Pakistan 99 No14 30.423

Thailand 374 No15 59.603

Philippines 136 Yes16 65.841

Taiwan 576 No17 272.692

Ireland 48 Yes 178.513

Indonesia 267 No18 82.167

Korea 997 No19 548.948

Malaysia 792 No20 101.567

Italy 216 Yes 411.316

Greece 247 Yes 43.561

Spain 94 Yes 392.873

Portugal 52 Yes 190.654

India 1.432 No21 176.469

Mean 657.29 - 278.720

Median 267 - 270.00

Standard deviation 978.57 - 152.06

Min 48 - 30.42

Max 4573 - 552.19

9 The SEC released its proposed written roadmap in November 2008 and reaffirmed its commitment to one global set of accounting standards in a statement released in February 2010.10 Registrants at the main board of the SIX (Swiss Stock Exchange) are required to use either IFRS or US GAAP.11 SFRS (Singapore Financial Reporting Standards) will fully converge with IFRS by 2012.12 As of January 1st 2005 all listed companies are required to report according to IFRS.13 As of 2005, Hong Kong Financial Reporting Standards (HKFRS) are identical to International Financial Reporting Standards.14 The Accounting Standards Board of Japan has agreed to resolve all inconsistencies between the current JP-GAAP and IFRS wholly by 2011.15 Not all IFRS/IAS are obligatory for listed companies.16 IFRS will be converged with local GAAP; some standards will be adopted as TAS (Thai GAAP) in 2011 and some in 2013. Full convergence is expected by January 1, 2013.17 IFRS is required for consolidated and standalone/separate financial statements18 Starting 2013 all listed companies will have to report according to IFRS.19 Indonesia is planning to converge with IFRS by 2012.20 Early adoption of IFRS, with exception of financial institutions, is permitted from 2009. Adoption of IFRS is required for all listed companies and certain financial institutions from 2011.21 The Malaysian Accounting Standards Board (MASB) announced on August 1, 2008, plans to bring Malaysia to full convergence with IFRS by January 1, 2012.


http://en.wikipedia.org/wiki/Hong_Kong_Financial_Reporting_Standards

9.5 INCOME SMOOTHING MEASURES

This subsection will take a closer look at the real and artificial income smoothing

measures for each of the 31 countries. Table 3 shows the values of both incomes

smoothing measures as well as the aggregate income smoothing measure.

Income smoothing measure 1 (IS 1) should be interpreted as follows: low values indicate

that controlling insiders smooth income. From the table can be concluded that India is the

country with the lowest value of this measure, which means that insiders use their

accounting discretion to manage the firm’s income. IS 1 is computed by dividing the

median ratio of the firm-level standard deviation of operating earnings by the firm-level

standard deviation of cash flow from operations. Dividing it by the cash flow from

operations takes care of the problem with differences in variability of economic

performance across firms. IS 1 values for most EU countries lie around the mean (0.579).

The values for the PIGS22 and Austria however are well below the mean and show strong

evidence of income smoothing.

Income smoothing measure 2 (IS 2) measures the correlation between changes in

accounting accruals and cash flows from operations. Controlling insiders are in the

position to use accounting discretion to misrepresent the cash flow from operations. They

can delay the recognition of current profit to create reserves for the future in case the

future performance will not be as good as the current performance. Large negative

correlations are an indication for income smoothing. For example, income smoothing is

more pervasive in Greece and Korea, than in the US and Canada. The table below shows

that India is the country with the largest IS 2 value. Once again, the PIGS have values that

show strong evidence of income smoothing.

The aggregate income smoothing measure is computed by adding up the rankings of IS 1

and IS 2 and dividing the total by two. The first two measures are ranked in such a way

that a higher score indicates a higher level of earnings management. India had for both

IS 1 and IS 2 respectively the lowest and highest values, so it is only logical that India will

have the highest aggregate income smoothing score.

22 Financial statements must be prepared according to Indian GAAP. The Institute of Chartered Accountants of India (ICAI) has announced that IFRS will be mandatory in India for financial statements for the periods beginning on or after 1 April 2011.


http://en.wikipedia.org/wiki/Financial_statements

http://en.wikipedia.org/wiki/Institute_of_Chartered_Accountants_of_India

Table 3 Income smoothing measures

Country IS 123 IS 224 Aggregate IS score

India 0.328 -0.901 31

Portugal 0.398 -0.898 29.5

Indonesia 0.518 -0.834 24

Spain 0.430 -0.83 28

Korea 0.507 -0.819 24.5

Italy 0.489 -0.812 25.5

Greece 0.365 -0.805 27.5

Malaysia 0.486 -0.804 25

Taiwan 0.508 -0.802 22

Ireland 0.500 -0.790 23

Pakistan 0.529 -0.747 19.5

Thailand 0.515 -0.740 20

Philippines 0.501 -0.738 21

Japan 0.559 -0.731 16.5

South Africa 0.636 -0.699 13

Switzerland 0.677 -0.691 11.5

Singapore 0.655 -0.673 11.5

Belgium 0.557 -0.657 15

Germany 0.608 -0.656 12

Hong Kong 0.552 -0.654 14.5

UK 0.704 -0.644 8.5

USA 0.847 -0.637 6

Netherlands 0.599 -0.610 11

France 0.574 -0.605 11

Canada 0.835 -0.594 5.5

Austria 0.437 -0.571 16.5

Norway 0.625 -0.553 7.5

Denmark 0.602 -0.512 8

Sweden 0.844 -0.472 3

Finland 0.719 -0.432 3.5

Australia 0.851 -0.421 1

Mean 0.579 -0.688 16

Median 0.557 -0.691 15

Standard deviation 0.138 0.129 8.57

Min 0.328 -0.901 1

23 PIGS: Portugal, Italy, Spain and Greece24 IS 1 = σ(Operating earnings)/σ(Operating cash flows)


Max 0.851 -0.421 31

9.6 CLUSTER ANALYSIS

In order to provide evidence on the patterns of income smoothing within a group of

countries with similar institutional characteristics, it is necessary to perform a k-means

cluster analysis. By dividing the countries in three clusters based on the level of investor

protection in each country, I will be able to determine whether income smoothing is more

pervasive in high or low investor protection countries. The k-means cluster analysis is

based on three investor protection variables from Djankov et al. (2008) which are: ex ante

private control of self-dealing, ex post private control of self dealing and the public

enforcement index. In order to get a good grasp of these variables I will elaborate more on

them.

Table 4 Investor protection variablesThe anti-self dealing index is the average of ex ante and ex post private control of self-dealing

Country Ex-ante private

control of self-

dealing

Ex-post private

control of self

dealing

Anti-self-

dealing index

Public

enforcement

Austria 0.00 0.42 0.21 1.00

Greece 0.08 0.37 0.23 0.50

Korea 0.25 0.67 0.46 0.50

Portugal 0.22 0.75 0.49 1.00

Italy 0.08 0.69 0.39 0.25

Taiwan 0.42 0.70 0.56 0.00

Switzerland 0.08 0.45 0.27 0.50

Singapore 1.00 1.00 1.00 1.00

Germany 0.14 0.42 0.28 1.00

Japan 0.22 0.74 0.48 0.00

Belgium 0.39 0.69 0.54 0.50

Hong Kong 1.00 0.93 0.96 0.00

India 0.33 0.76 0.55 0.50

Spain 0.22 0.52 0.37 0.75

Indonesia 0.81 0.56 0.68 0.00

Thailand 1.00 0.70 0.85 0.00


Pakistan 0.17 0.65 0.41 0.75

Netherlands 0.06 0.36 0.21 0.00

Denmark 0.25 0.68 0.47 0.75

Malaysia 1.00 0.90 0.95 1.00

France 0.08 0.68 0.38 0.50

Finland 0.14 0.78 0.46 0.00

Philippines 0.06 0.42 0.24 0.00

United Kingdom 1.00 0.85 0.93 0.00

Sweden 0.17 0.51 0.34 1.00

Norway 0.42 0.45 0.44 1.00

South Africa 1.00 0.63 0.81 0.00

Canada 0.33 0.97 0.65 1.00

Ireland 0.78 0.80 0.79 0.00

Australia 0.89 0.69 0.79 0.50

United States 0.33 0.97 0.65 0.00

Self-dealing is often referred to as investor expropriation; the individuals who control a

corporation (managers, controlling shareholders etc.) are in the position to use their power

to benefit themselves instead of sharing corporate wealth with other investors. Such

diversion of firm resources to their controllers is referred to in literature as ‘private

benefits of control’. The law plays a crucial role in controlling corporate self-dealing.

There are various forms of self-dealing: excessive compensation, executive perquisites,

transfer pricing, and appropriation of corporate opportunities and plain theft of corporate

assets (Shleifer and Vishny, 1997). There are different ways to deal with corporate self-

dealing. One extreme solution is to leave the problem to the market forces and hope that

the problem will be sorted out. The other extreme solution is to prohibit all conflicted

transactions. Both of these are not likely to be implemented in societies, because of their

extreme character. In between these two extremes there are several options. One of them is

to facilitate private enforcement of good behavior which basically focuses on extensive

disclosure, approval procedures for transactions and facilitation of private litigation when

self-dealing is suspected. Society can also use the approach in which it relies on public

enforcement which discourages wrongdoing through sanctions such as fines and prison

terms for the controlling shareholder(s). (Djankov et al. 2008). The anti-self-dealing index

(average of the ex ante and ex post private control of self-dealing indexes) measures the

obstacles that the controlling shareholder must overcome in order to increase his own


benefits. A higher anti-self-dealing index means that more obstacles need to be overcome,

so it is more difficult for controlling shareholders or managers to serve their own benefits.

Transactions involving conflicts of interest can be regulated by the law in such a way that

the terms and conditions are similar to the ones that would exist in an arm’s length

transaction. The law can also facilitate expropriated minority shareholders who are

seeking remedy through the courts or via fines and criminal sanctions on the ones who

expropriated them.

After having performed the cluster analysis based on the three investor protection

variables 3 clusters appeared. Table 5.1 shows these three clusters. The first cluster is

defined by a high ex ante private control of self-dealing index. This means that several

disclosures have to be made by all parties in a transaction and that the transaction must be

approved by disinterested shareholders. The ex post private control of self-dealing index is

also fairly high for this cluster, which shows that the countries in cluster 1 empower

shareholders to sue the parties in a transaction for damages. Shareholders are also

facilitated in proving wrongdoing. Countries in cluster 1 score high on public

enforcement; shareholders can seek justice through fines and jail sentences against

expropriators. So, the countries in cluster 1 can be referred to as high investor protection

countries. The second cluster is characterized by rather low indices of ex ante private

control of self-dealing and medium to high indices of ex post private control of self-

dealing. The economies in cluster 2 can be identified as medium investor protection

countries. The third cluster consists of countries with low to medium indices of both ex

ante private control of self-dealing and ex post private control of self-dealing. The public

enforcement index however, is very low for these countries. Therefore this cluster consists

of the low investor protection countries. It seems that cluster 2 lies in between the high

and low investor protection countries respectively cluster 1 and cluster 3. But when

looking at the Euclidean distances it becomes clear that cluster 1 and 2 are slightly closer

to each other than cluster 2 and 3.

Table 5.1 shows the membership of the three clusters and table 5.2 shows the mean values

of investor protection variables per cluster.

Table 5.1 Cluster membership

Countries are sorted by aggregate income smoothing score.


Cluster 1 Cluster 2 Cluster 3Country Australia Sweden Finland

Singapore Canada United States Malaysia Norway United Kingdom

Denmark Netherlands France South Africa Switzerland Hong Kong Germany JapanBelgium ThailandAustria PhilippinesPakistan TaiwanKorea IrelandGreece IndonesiaSpain ItalyPortugalIndia

Table 5.2 Mean values of investor protection variables by cluster

Cluster 1 Cluster 2 Cluster 3Ex ante private control of self-dealing

0.96 0.21 0.53

Ex post private control of self-dealing

0.86 0.60 0.70

Public enforcement index 0.83 0.75 0.02

9.7 STATISTICAL ANALYSIS

At the beginning of this chapter the three hypotheses applicable to this chapter were

stated. This section will start off by looking at the correlations and multiple regression in

order to test the first and second hypothesis. Afterwards I will discuss the results of the

multiple regression used to test the third hypothesis.

9.7.1 REAL INCOME SMOOTHING AND ARTIFICIAL INCOME SMOOTHING

The multiple regressions includes three variables which are ex ante private control of self-

dealing, ex post private control of self-dealing and the public enforcement index. The first

two variables are both entered into the model first, because previous research has led me

to believe that these are important factors (predictors) in predicting income smoothing.

The third variable, public enforcement is entered last.


The dependent variable is the real income smoothing measure (IS1). The correlation

matrix can be found in appendix F. From this matrix I learned that there are no significant

correlations between the aggregate income smoothing score and any one of the three

predictors. Leaving this important aspect aside, the correlations are all positive, which is

the exact opposite of my expectations. A positive relationship would mean that whenever

investor protection increases income smoothing increases as well.

The model summary, which can also be found in appendix F, presents the values of the

multiple correlation coefficients between the predictors and real income smoothing. The

first two predictors predict a measly 6.9% of the variation in income smoothing. When the

third predictor is entered 7% is accounted for. So, these three predictors do not explain

much of the variation in real income smoothing.

In order to test whether the level of investor protection in a country influences the

occurrence of artificial income smoothing a hierarchical multiple regression is executed.

The SPSS output is presented in appendix G. The correlation matrix produces Pearson

correlation coefficients between every pair of variables and reveals that there is no

significant relationship between any of the variables. This means that in contrast to the

correlations from the real income smoothing analysis, the correlations between artificial

smoothing and two of the investor protection variables are negative. But I cannot draw any

accurate conclusions from these correlations, because of the fact that they are not

significant.

The model summary shows that the three variables hardly explain 2% of the variation in

artificial income smoothing. So, the three predictors used in the model are not good at

predicting the variation in the dependent variable.

9.7.2 AGGREGATE INCOME SMOOTHING

This section will discuss the statistical analysis for the aggregate income smoothing

measure and its outcome in detail by looking at the results of the correlation matrix and

the regression analysis.

9.7.2.1 CORRELATION MATRIX


We will first take a look at the correlation between the aggregate income smoothing score

and investor protection variables. The correlation matrix in table 6 shows that there are no

significant correlations between the aggregate income smoothing score and any one of the

investor protection variables. Usually, social scientists accept any probability value below

0.05 as being statistically meaningful and so any probability value below 0.05 is regarded

as indicative of genuine effect (Field, 2005).

Table 6 Correlations

Aggregate IS

Ex ante private

control of self-

dealing

Ex post private

control of self-

dealing

Public

enforcement

Pearson Correlation Aggregate IS 1.000 -.288 -.274 .275

Ex ante private control of

self-dealing

-.288 1.000 .540 -.224

Ex post private control of

self-dealing

-.274 .540 1.000 -.095

Public enforcement .275 -.224 -.095 1.000

Sig. (1-tailed) Aggregate IS . .058 .068 .067


self-dealing

.058 . .001 .113


self-dealing

.068 .001 . .306

Public enforcement .067 .113 .306 .

N Aggregate IS 31 31 31 31


self-dealing

31 31 31 31


self-dealing

31 31 31 31

Public enforcement 31 31 31 31

When we disregard (for a moment) the fact that the correlations are not significant, and

only look at the predictors, it becomes clear that the coefficients are rather small (R=0.224

and R=0.95). The only correlation that is fairly high is the correlation between ex post

private control of self-dealing and ex ante private control of self-dealing(R=0.540). It

appears that, if the correlations were significant, the predictors are measuring different

things. The first predictor, ex ante private control of self-dealing, comes close to being

significant (p=0.058) and has the highest correlation with the aggregate income smoothing


score. If it were significant it is this predictor that would predict income smoothing the

best.

Furthermore, this matrix shows that there is no multicollinearity; there are no correlations

higher than 0.9.

9.7.2.2 REGRESSION ANALYSIS

The model used in my research is a hierarchical regression, see section 8.3.3. As

mentioned earlier the ex ante and ex post private control of self-dealing indexes are

entered first. The model summary represents this method. Model 1 refers to the first stage

in which only the first two predictors are included in the model; this gives a correlation of

0.320. When all three predictors are included in the model (Model 2) the correlation

increases to 0.389.

From the model summary we can conclude that the ex ante and ex post private control of

self-dealing indexes only account for 10.3% of the variability in the outcome. When the

third predictor is added, 15.1% of the variability in the outcome is accounted for by the

three predictors. Including the last predictor, public enforcement, did not give an outcome

which explains a relatively large amount of the variation in income smoothing.

Table 7 Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson

1 .320a .103 .039 8.39986

2 .389b .151 .057 8.31817 1.684

a. Predictors: (Constant), Ex post private control of self-dealing, Ex ante private control of self-dealing

b. Predictors: (Constant), Ex post private control of self-dealing, Ex ante private control of self-dealing,

Public enforcement

c. Dependent Variable: Aggregate IS

I already discussed whether or not the model has improved the ability to predict the

outcome variable. As we’ve seen this is not the case with this model, only 15.1% of the

income smoothing in countries is explained by the three predictor variables. Now I will


move on to address the parameters of the model. The beta values are presented in table 8

and give information about the relationship between income smoothing and each investor

protection variable.

The values for ex ante private control of self dealing and ex post private control of self-

dealing are both negative which represents a negative relationship between these

predictors and income smoothing. The correlation between income smoothing and public

enforcement however, has a positive sign. This is in clear contrast to what I expected.

Table 8 Coefficients

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) 23.131 5.922 3.906 .001


self-dealing

-4.687 5.051 -.197 -.928 .361


self-dealing

-7.751 9.852 -.167 -.787 .438

2 (Constant) 20.732 6.172 3.359 .002


self-dealing

-3.377 5.111 -.142 -.661 .514


self-dealing

-8.138 9.761 -.176 -.834 .412

Public enforcement 4.676 3.752 .227 1.246 .223

a. Dependent Variable: Aggregate IS

9. 8 MODEL ASSUMPTIONS

In order to draw conclusions about a population based on a regression analysis done on a

sample, several assumptions have to be met according to Field (2005). It is necessary to

know whether these assumptions are broken, because if they are broken one cannot draw


accurate conclusions about reality. If the assumptions are met the coefficients and

parameters of the multiple regression are unbiased and the regression model can be

accurately applied to the population. I will elaborate more on these 9 assumptions and

discuss whether they are true or not for this research.

Type of variables

It is required that the variables are either quantitative or categorical and the outcome

variable must be quantitative, continuous and unbounded. Assumption is met.

Non-zero variance

There should be some variation in the value of the predictors. Assumption is met.

No perfect multicollinearity

Two or more predictor variables should not have a perfect linear relationship. The

predictors should not correlate too highly. Multicollinearity only forms a threat for

multiple regression ( not for a linear regression) because there are more than one

predictors. If the collinearity between predictors is perfect it will be impossible to obtain

unique estimates of the coefficients, because there are many combinations of coefficients

that would give the same estimates.

A good way to identify multicollinearity is the variance inflation factor (VIF) which gives

an indication whether a predictor has a strong linear relationship with the other predictors.

Field (2005) states that according to Myers a value of 10 as a VIF value is the point at

which you should start worrying.

The VIF values of the predictors in the multiple regression I used are all below 2 and

greater than 1. This indicates that there are no perfect linear relationships between the

predictors, so there is no reason for concern.

There is another statistic that can be used to discover whether predictors are dependent or

not, the tolerance statistic. For this statistic values below 0.1 are problematic (Field,

2005). The values for this statistic that I found confirm the findings mentioned above,

there is no perfect linear relationship between the predictors. The tolerance statistic values

are all greater than 0.1. See appendix E for SPSS output. Assumption is met.


Predictors are uncorrelated with ‘external variables’

External variables refer to the variables that have not been included in the regression

model, but which influence the outcome variable. The external variables should not

correlate with the variables included. If they do, the conclusions drawn from the model are

unreliable, because other variables could have predicted the outcome just as well. The

previous chapters have shed light on the variables that influence income smoothing. I have

taken these variables into account and therefore this assumption is met.

Homoscedasticity

The variance of the residual terms should be constant at each level of the predictor

variables. If this is not the case, there is said to be heteroscedasticity. No pattern can be

detected (see appendix D), thus this assumption is met.

Independent errors

This means that for any two observations the residual terms should be uncorrelated. The

Durbin-Watson test can be used to test this assumption. The Durbin-Watson test looks for

serial correlations between errors. The outcome of this test can vary between 0 and 4

whereas a value of 2 indicates that the residuals are uncorrelated. Values below 1 or

greater than 3 are causes for concern according to Field (2005). The Durbin-Watson value

for my research is 1.684, so there is no reason for concern. Assumption is met.

Normally distributed errors

This assumption assumes that the residuals in the model are random, normally distributed

variables with a mean of 0, which basically means that the differences between the model

and the observed data are zero or very close to zero. Differences that are greater than 3 are

not common and happen occasionally. The mean of the predicted variable score is close to

0. The SPSS output in appendix E shows the necessary information; the standard

predictive value of the mean is 0.000. The assumption therefore is met.

Independence

This assumption simply means that all values of the dependent variable are independent.

The independence assumption is also met, because each value in my research comes from

a separate entity. Assumption is met.


Linearity

It is assumed that the relationship modeled is a linear one. This means that the mean

values of the outcome variable for each increment of the predictors lie along a straight

line. Appendix D provides a scatter plot from which we can conclude that there is a linear

relationship; there is no curve or whatsoever in this plot. This assumption is met.

I mentioned earlier that if the aforementioned assumptions are met, which is the case in

my research, one can draw accurate conclusions about reality. This however, does not

mean that the regression model used is the exact same as the model that I would have

obtained had I tested the entire population (Field, 2005). What the regression model used

actually tells me is that on average the regression model from the sample is the same as

the population model. Once again there is a but: It is possible that the sample may not be

the same as the population model, but the likelihood of them being the same is increased.

9.9 RESULTS AND DISCUSSION

The literature which I discussed in the first seven chapters of this thesis argued that there

is a relationship between the occurrence of income smoothing and the level of investor

protection in a particular country. Previous studies have provided evidence that a higher

level of investor protection will discourage controlling insiders from smoothing income,

so legal systems that protect outside investors reduce the need for insiders to conceal their

activities. This basically means that high investor protection countries have a low level of

income smoothing and low investor protection have a high level of income smoothing.

The correlation matrix showed that real income smoothing has a positive correlation with

all three of the investor protection variables, which is the complete opposite of what I

expected to find and what previous studies proved. The three variables only accounted for

7% of the variation in real income smoothing, which is extremely low. On top of it all,

none of the correlations are statistically significant. The first hypothesis cannot be

confirmed.

There is a negative correlation between artificial income smoothing and the ex ante private

control of self-dealing index as well as the ex post private control of self-dealing index.

Not one of these correlations is significant however, which restrains me from telling


whether the relationship is genuine or not. The three variables hardly account for 2% of

the variation in artificial income smoothing, which means that they are not good in

predicting the level of income smoothing. Thus, the second hypothesis cannot be

confirmed.

The results from the statistical analysis shown in the previous sections of this chapter

show that there is indeed a negative correlation between income smoothing and two out of

three investor protection variables. Despite this I cannot confirm any of the hypotheses

because these correlations are not significant and therefore I cannot confirm the

expectations I had. From the statistical analysis it appears that there is a positive

relationship between income smoothing and the level of public enforcement. This is the

exact opposite from that which I argued in the literature review of this thesis. This

correlation however is not significant, so I cannot say that this relationship is genuine.

Apart from having taken a look at the relationships between the occurrence of income

smoothing and the level of investor protection, I also tested what influence investor

protection has on income smoothing by deploying a hierarchical multiple regression. The

result of this regression is shown in table 7. This table shows that the first two predictors

account for 10.3% of the variability in the data. When the third predictor is entered in the

model this percentage increases to 15.1%. So, the third predictor accounts for 4.8% of the

variability. The third hypothesis cannot be confirmed either.

From the statistical analysis it became clear that there is no significant relationship

between investor protection and income smoothing. So, I will take a closer look at the

income smoothing measures presented in table 3 and compare them to the income

smoothing measures obtained by Leuz et al. (2003). Table 3 shows that the IS 2 measure

varies between -0.421 and -0.901 while the IS 2 measure obtained by Leuz et al. (2003)

varies between -0.722 and -0.928 (appendix J). Because the IS 2 measures in table 3 have

a broader range, the aggregate income smoothing score will also differ from the aggregate

earnings management score from Leuz et al. (2003). Appendix J presents the earnings

management measures for the study done by Leuz et al. (2003). When comparing this

table with table 3 it becomes clear that the aggregate scores for several countries indeed

differ significantly between this study and the previously mentioned study. Ireland for

instance, has an aggregate income smoothing score of 23 in this research, while in the

previous study it had a score of 5.1. The aggregate score in the previous study can also be


averaged out due to the fact that Luez et al. (2003) used four measures. Nonetheless, it is

remarkable that countries like Austria, Ireland, Switzerland, Singapore, India, Spain,

Denmark and Malaysia have such large differences in the aggregate scores. I believe that

these large differences can be explained by the large variance in the IS 2 measure.

Throughout this thesis it must have become apparent that I expected to find a significant

negative correlation between the three investor protection variables and real income

smoothing. I also expected a significant negative correlation between the three predictors

and artificial income smoothing. Automatically, I assumed that if there would be a

significant negative correlation between the income smoothing measures and three

predictors separately, there would also be a significant negative correlation between the

aggregate income smoothing score and investor protection variables. The statistical

analysis however, clearly exhibits that none of these three expectations is met. Since there

is no significant relationship between income smoothing and the level of investor

protection I am not at liberty to say whether the occurrence of income smoothing is lower

in higher investor protection countries.

Nothing else rests me to do but try to explain why there are no significant correlations and

why this differs from previous research. Explanations can be sought in two corners: one,

there occurred a problem with the income smoothing measures and therefore also with the

aggregate income smoothing score or two, there is something wrong with the investor

protection indexes. Since the indexes are derived from Djankov et al. (2007) the latter

option is highly unlikely to be the case. In their research Djankov et al. (2007) state that

these indexes are better grounded in theory than the previous index, the anti-director rights

index constructed by La Porta et al (1998). The latter index was used in the work of Leuz

et al. (2003) and resulted in a significant negative correlation between the investor

protection variable and earnings management. So, it was only obvious to assume that a

new and improved index would lead to a significant negative correlation as well. Thus, the

second possible explanation, a problem with the investor protection variables, can be

rejected.

This means that the problem lies within the income smoothing measures. In order to

determine the income smoothing measures several formulas have been used, which I have


executed very accurately. I also took into consideration possible outliers, which are

atypical, infrequent observations. Because of the way in which the regression line is

determined outliers have a profound influence on the slope of the regression line and

consequently on the value of the correlation coefficient. A single outlier is capable of

considerably changing the slope of the regression line and, consequently, the value of the

correlation. Therefore, one should never base important conclusions on the value of the

correlation coefficient alone. For this reason I examined the scatter plots for the income

smoothing measures for each country.

The data used to calculate these measures have been obtained from a very reliable source,

Thomson One Banker. The significance of a correlation coefficient of a particular

magnitude will change depending on the size of the sample from which it was computed.

After comparing my research sample to that of Leuz et al. (2003) I noticed that there was a

relatively large difference in the sample population, their sample included 8.616 firms

whereas mine consisted of 21.492 firms. This large difference can be explained by small

and middle-sized companies (SMEs) that made their financial data available during the

sample period I have chosen, even though they are not always obliged to do so.

Controlling insiders of SMEs often have no incentives to smooth income, because these

firms often do not have to answer to shareholders in the same way large public firms need

to answer to shareholders. The principal-agent problem is not present in the same way it is

present in public firms. By including the SMEs in the sample there occurs a distortion in

the income smoothing scores. These distortions could have contributed to the results I

obtained.

In the text above I attempted to give a reasonable explanation for the results obtained. I do

have to admit that the results came as a surprise to me. I definitely was not prepared to

find that there is no significant negative correlation between income smoothing and the

level of investor protection. This made if quite difficult to find an explanation for the

findings of this research.


10 EMPIRICAL STUDY: INCOME SMOOTHING IN THE PRE-IFRS AND POST-IFRS PERIOD

10.1 INTRODUCTION

This chapter will discuss the second leg of this research, the influence of investor

protection on income smoothing in the pre-IFRS period and the post-IFRS period. The

goal of this second leg of research is to determine whether the introduction of IFRS has

had a significant influence on the occurrence of income smoothing. It became clear from

the first leg of this research that there is no significant relationship between income

smoothing and any of the investor protection variables. That however, will not withhold

me from continuing the remainder of the research. The two remaining hypotheses will be

tested in this chapter:

H4: Real income smoothing in the post-IFRS period is low in countries with weak

investor protection

H5: Artificial income smoothing in the post-IFRS period is more pervasive in weak

investor protection countries

The sample consists of 3.707 firms across 14 European Union countries. The original

sample included 3.860 firms, but a lack of data required a relatively small number of firms

to be excluded. Firms included in this sample are required to have data available for at

least three consecutive years.

Proponents of IFRS have argued that limiting accounting alternatives can increase

accounting quality and decrease the occurrence of earnings management. Barth et al.

(2008) hypothesized that applying IAS would lead to higher quality financial data and

therefore less earnings management, their empirical research provided evidence that firms

applying IAS generally indeed evidence less earnings management. The IFRS countries

that are investigated are the 14 EU countries included in the original sample. These

countries, together with the income smoothing measures, are shown in table 9. The income


smoothing and investor protection measures are the same ones used in the previous part of

the research. Table 9 shows that the values of the income smoothing measures in the pre-

IFRS period are relatively lower for some countries than the values of these measures in

the post-IFRS while some countries have higher values in the post-IFRS period. For

example, Germany and the Netherlands both show a lower IS1 value in the post-IFRS

period, while Portugal and Spain both have higher IS1 values in the post-IFRS period.

Table 9 Income smoothing measures pre-IFRS and post-IFRS

Country IS 1 IS 2

2000 - 2004 2005 - 2008 2000 - 2004 2005 - 2008Portugal 0.367 0.376 -0.874 -0.879Spain 0.345 0.452 -0.794 -0.765Greece 0.284 0.375 -0.767 -0.942Italy 0.447 0.474 -0.758 -0.755Germany 0.562 0.514 -0.707 -0.621France 0.499 0.495 -0.668 -0.640Netherlands 0.574 0.503 -0.625 -0.654Sweden 0.795 0.712 -0.572 -0.438Belgium 0.543 0.556 -0.566 -0.636United Kingdom 0.616 0.627 -0.563 -0.598Austria 0.445 0.453 -0.548 -0.618Finland 0.669 0.568 -0.544 -0.641Denmark 0.580 0.567 -0.496 -0.362Ireland 0.488 0.586 -0.355 -0.662

Mean 0.515 0.519 -0.631 -0.658Median 0.521 0.509 -0.599 -0.641Standard deviation 0.135 0.093 0.138 0.150Min 0.284 0.375 -0.874 -0.942Max 0.795 0.712 -0.355 -0.362

10.2 INCOME SMOOTHING IN THE PRE-IFRS PERIOD

First, the pre-IFRS period will be looked at. Table 10 presents the income smoothing

measures 1 and 2 for the pre-IFRS period (2000-2004) as well as the aggregate income

smoothing score. Previous research has been performed on this subject, but the results

have not been unanimous. Some researchers found evidence that indicated that firms

evidence more income smoothing in the post-IFRS period than in the pre-IFRS period,


while other researchers found that firms are less prone to engage in income smoothing in

the post-IFRS period than in the pre-IFRS period.

Panaanen and Lin (2009) investigated whether the quality of accounting numbers of

German companies under IAS differed under IFRS. Their results indicated that accounting

quality has decreased in the post-IFRS period. In the pre-IFRS period the researchers

looked at companies reporting under IAS, while companies in the post-IFRS period

reported under IFRS. Earnings reported by German companies in the pre-IFRS period

were more value relevant than in the post-IFRS period.

As noted earlier, Barth et al. (2008) did research on the influence of IAS on accounting

quality. Their sample consisted of 21 countries including Germany. They found that firms

switching to IAS/IFRS showed a higher quality financial reporting. The results indicate

that IAS/IFRS adopters are less inclined to engage in income smoothing, which is the

exact opposite of Paananen and Lin’s (2009) findings.

From table 10 it becomes clear that Portugal, Spain and Greece (all PIGS member

countries) have the highest aggregate score, which means that income smoothing was

strongly present in the pre-IFRS period in these countries.

Table 10 Aggregate income smoothing score pre-IFRS period

Country IS 1 IS 2 Aggregate IS

2000 - 2004 2000 - 2004 2000-2004Portugal 0.367 -0.874 13Spain 0.345 -0.794 13Greece 0.284 -0.767 13Italy 0.447 -0.758 10.5Germany 0.562 -0.707 8France 0.499 -0.668 8.5Netherlands 0.574 -0.625 6.5Sweden 0.795 -0.572 4Belgium 0.543 -0.566 6.5United Kingdom 0.616 -0.563 4Austria 0.445 -0.548 7.5Finland 0.669 -0.544 2.5Denmark 0.580 -0.496 3Ireland 0.488 -0.355 5

Mean 0.515 -0.631 7.5Median 0.521 -0.599 7.0


Standard deviation 0.135 0.138 3.7Min 0.284 -0.874 2.5Max 0.795 -0.355 13.0

10.3 INCOME SMOOTHING IN THE POST-IFRS PERIOD

Table 11 contains the income smoothing variables for the post-IFRS period. The variables

are calculated by using the same formulas used in the first leg of this research. In the post-

IFRS period Greece is the country with the highest aggregate income smoothing score,

followed by Portugal and Spain. Low IS1 values indicate that income is smoothed. From

table 11 can be derived that in the post-IFRS period income is more likely to be smoothed

in e.g. Portugal than Sweden. Large negative correlations between changes in firms’

accruals and cash flows (IS2) imply that income smoothing is more pervasive in e.g.

Greece and Portugal than in Denmark and Sweden.

Table 11 Aggregate income smoothing score post-IFRS period

Country IS 1 IS 2 Aggregate IS

2005 - 2008 2005 - 2008 2005 - 2008Portugal 0.376 -0.879 13Spain 0.452 -0.765 12Greece 0.375 -0.942 14Italy 0.474 -0.755 10.5Germany 0.514 -0.621 6France 0.495 -0.640 8Netherlands 0.503 -0.654 8.5Sweden 0.712 -0.438 1.5Belgium 0.556 -0.636 6United Kingdom 0.627 -0.598 2.5Austria 0.453 -0.618 7.5Finland 0.568 -0.641 6Denmark 0.567 -0.362 3Ireland 0.586 -0.662 6.5

Mean 0.519 -0.658 7.5Median 0.509 -0.641 7.0Standard deviation 0.093 0.150 3.8Min 0.375 -0.942 1.5Max 0.712 -0.362 14.0


10.4 STATISTICAL ANALYSIS

This subsection will focus on the statistical analysis. Similar to the multiple regression used

in the first leg of this research, the dependent variable is the aggregate income smoothing

score and the independent variables are the three investor protection measures. After having

studied the results of the first part of the research, it became clear that there was no

significant relationship between income smoothing and the investor protection variables. So,

it is only rational to assume that there will be no significant relationship between the

dependent and independent variables in this part of the research. Nonetheless, I will leave

my expectations and assumptions for what they are and proceed with the statistical analysis.

I will start off by looking at the correlation matrixes for both the pre- and post-IFRS period.

10.4.1 CORRELATION MATRIX PRE-IFRS PERIOD

The correlation matrix in appendix H presents the Pearson correlations between every pair

of variables as well as the one-tailed significance levels in the pre-IFRS period. It shows that

there is a negative correlation between the aggregate income smoothing score and the ex

ante private control of self-dealing index as well as with the ex post private control of self-

dealing. These correlations however are not significant and neither is the correlation

between public enforcement and the aggregate income smoothing score. So, I cannot say

that these variables will predict the level of income smoothing in a country. The table also

shows that there is no multicollinearity; there are no correlations higher than 0.9

10.4.2 REGRESSION ANALYSIS PRE-IFRS PERIOD

In addition to the relationship discussed earlier a regression analysis is performed to

establish whether or not income smoothing in the pre-IFRS period is influenced by the

level of investor protection in a country. Once again, I used a hierarchical multiple

regression, whereby the ex ante and ex post private control of self-dealing have been

entered first. The model summary in appendix H shows values of the multiple correlation


coefficient between aggregate income smoothing and the ex ante and ex post private

control of self-dealing indexes of 0.390, which is neither high or low. The R2 value

indicates that only 15.2% of the variability in income smoothing is accounted for by these

two predictors. When the third investor protection variable is entered into the model the R2

value increases to 18.3%. This means that 81.7% is not explained by these three variables.

Thus there are other variables that have an influence on income smoothing occurrence.

10.4.3 CORRELATION MATRIX POST-IFRS PERIOD

Appendix I shows the correlation matrix for the post-IFRS period. As you can see there is

a negative correlation between aggregate income smoothing score and the ex ante private

control of self-dealing index. There is also a negative correlation between the aggregate

income smoothing score and the ex post private control of self-dealing index. These

negative correlations are in accordance with my expectations; a higher level of investor

protection causes a decrease in income smoothing. There is a problem however, the

correlations are not significant. Thus, the predictors do not predict the dependent variable.

10.4.4 REGRESSION ANALYSIS POST-IFRS PERIOD

The statistical analysis will continue with a hierarchical regression analysis. The model

summary of this multiple regression is presented in appendix I and shows that 16.7% of

the variation in income smoothing can be explained by the ex ante and ex post private

control of self-dealing indexes. When the third variable is entered into the model this

percentage increases to 18.3%, so the public enforcement index accounts for an additional

1.6% of the variation in income smoothing. The three predictors account for a relatively

low percentage of the variability in income smoothing. This means that there are other

variables that have an influence of the occurrence of income smoothing.

10.5 RESULTS AND CONCLUSIONS

Earlier on it was mentioned that several researchers have investigated the implementation

of IFRS on the occurrence of income smoothing. The expected outcome of IFRS adoption


was a decrease in information asymmetry as well as a decrease in earnings management

e.g. income smoothing. These expectations were formed because of the fact that IFRS are

more precise and they admit a limited number of options; principles-based standards were

implemented and the number of accounting alternatives was limited. However, the

researchers did not have one unanimous result. Paananen and Lin (2008) and Barth et al.

(2008) for example both had different results. Therefore it seemed interesting to me to

once again examine the result of IFRS implementation in the pre- and post-IFRS period.

But after having performed the first leg of this research it became clear that the predictors

were not able to predict the occurrence of income smoothing. So I suspected that the

second leg of this research would not have significant results. Unfortunately, this was

indeed the case.

The correlation matrix in appendix H showed that in the pre-IFRS period there is a

negative relationship between the aggregate income smoothing score and both the indexes

for ex ante and ex post private control of self-dealing. This was in accordance with my

expectations. The third variable, public enforcement, however, has an positive correlation

with income smoothing, which implies that as public enforcement increases the level of

income smoothing increases as well. All three correlations are invalid, since none of them

are significant. The fourth hypothesis cannot be confirmed.

In order to test the fifth and final hypothesis the last hierarchical regression is performed.

The SPSS output can be found in appendix I. The correlation matrix presents the

relationships between the aggregate income smoothing score and the three investor

protection variables as well as the relationships between the predictors themselves. Once

again the correlation between income smoothing and both the ex ante and ex post private

control of self-dealing indexes are negative, but not significant. The correlation between

income smoothing and public enforcement is not alone positive but very low and not

significant as well. The fifth hypothesis cannot be confirmed.

The statistical analysis regarding the pre- and post-IFRS period confirmed the suspicions I

had after performing the first leg of this research. The predictors are not capable of

predicting the dependent variable, income smoothing. Thus, I am not at liberty to say

whether income smoothing has decreased or increased in the post-IFRS period. The

empirical evidence also showed that the predictors do not account for much of the


variation in income smoothing. This means that there probably are other predictors that

can better predict income smoothing.

11.1 LIMITATIONS

The goal of this thesis was to investigate whether or not the level of investor protection

has an influence on the occurrence of income smoothing and if so, in what way. It has to

be acknowledged that there are certain limitations to this research, which will be discussed

here.

Capturing income smoothing is not an easy task, therefore I have chosen to measure

income smoothing by using the measures developed by Leuz et al. (2003). These measures

have proven to capture income smoothing in a sufficient way in their research as well as in

research done by Lee et al. (2008). The findings of this thesis rely on the ability of the

measures to capture income smoothing. There are other models available to measure

income smoothing. There is a chance that if another measure was used, the results would

have differed from the current findings.

Earlier on it was mentioned that financial institutions have been excluded from the sample.

Mainly because the two income smoothing measures used require variables that are not

applicable to financial institutions. For instance, financial institutions do not rely as much

on assets as for example a textile production company. Since the level of income

smoothing of financial institutions is not measured, it is possible that banks, insurance

companies etc. in high investor protection countries engage in income smoothing on a

relatively large scale. If the empirical research had proven that there is a significant

negative correlation between income smoothing and investor protection these findings

could not be generalized to all firms in the respective countries. As I mentioned earlier the

sample consists of both large and small and middle-sized firms. Controlling insiders of

SMEs often have no incentives to smooth income, because these firms often do not have

to answer to shareholders in the same way large public firms need to answer to

shareholders. Table 2 shows that the mean firm size for this sample is 278.720, which is

clearly lower than 316.657, the mean firm size for the sample used by Leuz et al. (2003).


By including the SMEs in the sample there occurs a distortion in the income smoothing,

which could have caused the correlations to be not significant.

Furthermore it is necessary to mention that there are other factors that are complementary

to insiders managing income. It is not an easy task to fully control for the potential impact

of all factors affecting investor protection. Because of this, an endogeneity bias is possible.

Throughout this thesis I have consistently argued that strong investor protection

discourages controlling insiders from cooking the books. The empirical analysis however,

has not provided any evidence to corroborate this. As stated earlier controlling insiders can

act in their own interest and benefit themselves at the stakeholders’ cost. These private

control benefits are not greatly appreciated by stakeholders and when detected,

stakeholders will take action. One can argue that strong investor protection can encourage

controlling insiders to smooth income in order to cover up their private control benefits. In

this scenario, controlling insiders will engage in income smoothing to cover up their

control benefits in an attempt to escape higher penalties. The empirical evidence from this

research cannot confirm nor deny either one of the scenarios.

11.2 SUGGESTIONS FOR FURTHER RESEARCH

This research included 31 countries, which means that countries with very different

institutional characters are examined. As you have read the results from the statistical

analysis indicate that there is no relationship between income smoothing and investor

protection. Even though the three investor protection variable incorporate aspects of the

legal tradition and legal origin of countries, it might be useful to include these two

variables separately in the multiple regression. By doing this different results could be

obtained which could actually confirm that there exists a significant negative correlation

between the occurrence of income smoothing and the level of investor protection.

In chapter 9.9 I argued that the not significant results obtained can be caused by the

differentiation in the research sample. The current sample consists of all companies in

Thomson One Banker that reported their financial data for at least three consecutive years,

financial institutions excluded. The result of this was that small and medium sized firms

were also included. Their inclusion might have caused a distortion and resulted in the not


significant correlations. Therefore I would suggest that future research on this subject

would be done with a sample consisting of large public firms.

12 THESIS CONCLUSION

Earnings management can best be defined as a strategy of generating accounting earnings,

which is accomplished though managerial discretion over accounting choices and

operating cash flows (Phillips et al. 2003) as well as production and investment decisions

that reduce the variability of earnings. It comes in different forms one of which is income

smoothing. Earnings management is a widespread phenomenon. Several studies suggest

that earnings management can be limited by well-designed corporate governance

structures. Numerous researchers have examined the phenomenon that is earnings

management. Over the years several models have been developed to capture earnings

management some of which have been discussed in the literature review.

The goal of this thesis however, was to examine the relationship between the occurrence

of income smoothing and the level of investor protection in 31 countries. Previous studies

have shown that there is a significant relationship between these two variables. Leuz et al.

(2003) found evidence that earnings management is more pervasive in low investor

protection countries than in high investor protection countries. In high investor protection

countries there are strong and well-enforced outsider rights in place which limit the private

control benefits of controlling insiders. There are, broadly taken, two reasons why

controlling insiders engage in income smoothing. One, the goal is to gain private benefits

at the expense of shareholders or two; the goal is to communicate private information

about future earnings to outsiders. Whatever the reason is for engaging in income

smoothing, the reported financial data is not a true representation of the firms

performance.


Over the years regulators have attempted to harmonize the global reporting practices. The

International Financial Reporting Standards, which were formerly known as International

Accounting Standards (IAS), were chosen as the common language for financial reporting

by Europe and several other countries. IFRS are said to be more precise than local

standards and they admit a limited number of reporting options. Therefore it is argued that

the implementation of IFRS will lead to less income smoothing. With regard to these

expectations, two hypotheses were tested.

In order to determine whether income smoothing is more pervasive in low investor

protection countries it is necessary to divide the sample countries in clusters. This is done

by means of a hierarchical k-means cluster analysis. The investor protection variables used

to predict the dependent variable in the multiple regression are derived from Djankov et al.

(2007). The income smoothing measures used are derived from Leuz et al. (2003). As you

may know by now, income smoothing can appear in two forms: real income smoothing

and artificial income smoothing. Therefore, a measure for each of these forms is used.

From these two individual measures the aggregate income smoothing measures is created

which is the dependent variable in the multiple regression. The statistical analysis showed

that there is no significant negative correlation between the dependent veriable, income

smoothing, and the predictors, ex ante and ex post private control of self-dealing indices

and the public enforcement index. None of the hypotheses could be confirmed. The

research results lead to believe that there is no relationship between income smoothing and

the level of investor protection and thus I am not able to say whether the occurrence of

income smoothing is lower in high investor protection countries than in low investor

protection countries. The second leg of this research looked at the occurrence of income

smoothing in the pre- and post-IFRS period. Because of the fact that there is no significant

relationship between the predictors and income smoothing, I cannot say that income

smoothing is less pervasive in the post-IFRS period.


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APPENDIX A SCHEMATIC OVERVIEW OF PREVIOUS STUDIES

Author Object of Study

Sample Methodology Outcome

Cahan, Liu and Sun

(2008)

Investor protection, Income smoothing and Earnings Informativeness

44 countries,

55,357 firm year observations,

Period:

1993 - 2002

Earnings Informativeness: Model of Collins et al. (1994)

Income smoothing: Contemporaneous Spearman correlation

Investor protection:

Index from La Porta et al. (1998)

Earnings informativeness is more positively associated with income smoothing in countries with strong investor protection than it is in countries with weak investor protection

Nabar and Boonlert-

Earnings Management,

30 countries, Earnings Earnings management is


U-Thai

(2007)

Investor Protection, and National Culture

70,208 firm-year observations,

Period:

1990 - 1999

Management:

Aggregate earnings management score by Leuz et al. (2003)

Investor Protection:

Indices from La Porta et al. (1998)

National Culture:

Hofstede scores (1980)

influenced by both investor protection and culture. Also, earnings management is relatively low in countries with high outside investor protection, high in high uncertainty-avoidance countries and low in English speaking-countries.

Wright, Shaw and Guan

(2006)

Corporate Governance and Investor Protection

2 countries,

92 U.K. firms,

Period:

1997 - 2002

63 U.S. firms,

Period:

1981 - 1988

The Modified Jones model (1995)

Earnings management in the U.S. and the U.K. is not similar and infrequent.

Leuz, Nanda and Wysocki

Earnings management and investor protection

31 countries, Earnings management: Four different country-level measures of

Firms in countries with strong investor protection engage less in


(2003) 8,000 firms,

Period:

1990-1999

earnings management.

Aggregate earnings measurement score.

Investor protection:

La Porta index (1998)

earnings management than firms in countries with weak investor protection.

APPENDIX B VARIABLES DOWNLOADED FROM WORLDSCOPE

The variables used in my research are collected from the Worldschope database. Because

the names from this database differ from the names used throughout my thesis, I include

this list.

For instance, “Operating Income” can be found in the database under the name

“OperatingIncomeAfterDepr”

Cash = CashAndSTInvestment

Operating Income = OperatingIncomeAfterDepr

Current Assets = TotalCurrentAssets

Current Liabilities = TotalCurrentLiabilities

Short term debt = STDebtAndCurPortLTDebt

Depreciation and Amortization expense = DepreciationDeplAmortExpense


Taxes Payable = IncomeTaxesPayable

Total assets = TotalAssets

APPENDIX C HIERARCHICAL K-MEANS CLUSTER ANALYSIS

Table .1 Distances between Final Cluster Centers

Cluster 1 2 3

dimension

0

1 .804 .936

2 .804 .805

3 .936 .805

Table .2

Number of Cases in each Cluster

Cluster 1 3.000

2 15.000

3 13.000

Valid 31.000

Missing 1.000


Table .3

ANOVA

Cluster Error

F Sig.Mean Square df Mean Square df

ex-ante control of self dealing

.857 2 .078 28 10.968 .000

ex-post control of self dealing

.100 2 .029 28 3.401 .048

public enforcement index 2.102 2 .035 28 60.391 .000

APPENDIX D SCATTER PLOT


APPENDIX E RESIDUALS STATISTICS


Residuals Statistics

Minimum Maximum Mean Std. Deviation N

Predicted Value 27.9956 127.2022 80.4516 29.57064 31

Residual -107.53355 185.11377 .00000 86.32516 31

Std. Predicted Value -1,774 1,581 .000 1,000 31

Std. Residual -1,182 2,034 .000 ,949 31

a. Dependent Variable: aggregate IS

APPENDIX F CORRELATION MATRIX AND MODEL SUMMARY REAL SMOOTHING


Correlations

IS1

Ex ante private control of self-

dealing

Ex post private control of self-

dealingPublic

enforcement

Pearson Correlation IS1 1.000 .150 .263 .001

Ex ante private control of self-dealing

.150 1.000 .540 -.224


.263 .540 1.000 -.095


Sig. (1-tailed) IS1 . .211 .077 .498


.211 . .001 .113


.077 .001 . .306


N IS1 31 31 31 31


31 31 31 31


31 31 31 31


Model Summary

a. Predictors: (Constant), ex post private control of self-dealing, ex ante private control of self-dealing

b. Predictors: (Constant), ex post private control of self-dealing, ex ante private control of self dealing, public enforcement

c. Dependent Variable: IS1

APPENDIX G CORRELATION MATRIX AND MODEL SUMMARY ARTIFICIAL SMOOTHING

Correlations


Model

RR

SquareAdjusted R Square

Std. Error of the Estimate Durbin-Watson

dimensi

1 .263a .069 .003 .13819

2 .265b .070 -.033 .14067 1.887

IS2


dealing


dealingPublic

enforcement

Pearson Correlation IS2 1.000 -.047 -.032 .120


-.047 1.000 .540 -.224


-.032 .540 1.000 -.095


Sig. (1-tailed) IS2 . .401 .432 .260


.401 . .001 .113


.432 .001 . .306


N IS2 31 31 31 31


31 31 31 31


31 31 31 31


Model Summary

a. Predictors: (Constant), ex post private control of self-dealing, ex ante private control of self-dealingb. Predictors: (Constant), ex post private control of self-dealing, ex ante private control of self-dealing, public enforcementc. Dependent Variable: IS2

APPENDIX H CORRELATION MATRIX AND MODEL SUMMARY PRE-IFRS PERIOD

Correlations


Model

RR

SquareAdjusted R

SquareStd. Error of the Estimate Durbin-Watson

dimension0

1 .048a .002 -.069 .133582

2 .123b .015 -.094 .135163 1.063

Aggregate IS


dealing


dealingPublic

enforcement



-.365 1.000 .646 -.451


-.340 .646 1.000 -.408




.100 . .006 .053


.117 .006 . .074




14 14 14 14


14 14 14 14


Model

RR

SquareAdjusted R Square


dimens

1 .390a .152 -.002 3.71500

2 .428b .183 -.062 3.82337 .962

APPENDIX I CORRELATION MATRIX AND MODEL SUMMARY POST-IFRS PERIOD

Correlations


Aggregate IS


dealing


dealingPublic

enforcement



-.408 1.000 .646 -.451


-.279 .646 1.000 -.408




.074 . .006 .053


.167 .006 . .074




14 14 14 14


14 14 14 14


Model

R R SquareAdjusted R Square


dimens

1 .408a .167 .015 3.81400

2 .428b .183 -.062 3.95993 .975

APPENDIX J EARNINGS MANAGEMENT MEASURES FROM LEUZ ET AL. (2003)

EM 1, EM 2 and the Aggregate EM score are calculated using the same formulas discussed in chapter 8.


Country EM EM 2 Aggregate EM score

Austria 0.345 -0.921 28.3Greece 0.415 -0.928 28.3Korea 0.399 -0.922 26.8Portugal 0.402 -0.911 25.1Italy 0.488 -0.912 24.8Taiwan 0.431 -0.898 22.5Switzerland 0.473 -0.873 22.0Singapore 0.455 -0.882 21.6Germany 0.510 -0.867 21.5Japan 0.560 -0.905 20.5Belgium 0.526 -0.831 19.5Hong Kong 0.451 -0.850 19.5India 0.523 -0.867 19.1Spain 0.539 -0.865 18.6Indonesia 0.481 -0.825 18.3Thailand 0.602 -0.868 18.3Pakistan 0.508 -0.913 17.8Netherlands 0.491 -0.861 16.5Denmark 0.559 -0.875 16.0Malaysia 0.569 -0.857 14.8France 0.561 -0.845 13.5Finland 0.555 -0.818 12.0Philippines 0.722 -0.804 8.8United Kingdom 0.574 -0.807 7.0Sweden 0.621 -0.764 6.8Norway 0.713 -0.722 5.8South Africa 0.643 -0.840 5.6Canada 0.649 -0.759 5.3Ireland 0.607 -0.788 5.1Australia 0.625 -0.790 4.8USA 0.765 -0.740 2.0

Mean 0.541 -0.849Median 0.539 -0.861Standard Deviation

0.100 0.056

Min 0.345 -0.928Max 0.765 -0.722


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