Faculty of Economics & Business
Voluntary IFRS adoption and Earnings Quality
Research among private companies
Luc Schoemaker Bsc
Master Thesis Accountancy 2013
Msc Accountancy & Control
Date: 24 June 2013
Studentnumber: 10440429
Supervisor: dhr. prof. dr. V.R. O’Connell
Second reader: dhr. drs. S.W. Bissessur
ABSTRACT – This research is concerned with the effects a voluntary adoption of IFRS by
private firms has on earnings quality. In contrast to public firms, private firms are not
mandated to adopt IFRS. Research has shown that adopting IFRS has a positive effect on
public firms’ earnings quality. However, the role financial reporting fulfills is different for
private companies. This raises the question whether a voluntary adoption of IFRS is also
beneficial for a private firms’ earnings quality. Empirical evidence from this research
indicates a small positive relationship between adoption of IFRS and earnings quality, but
with low significance. It is also tested whether the relationship between adoption of IFRS
and earnings quality is moderated by different countries and firm characteristics, but no
empirically evidence is found.
.
2
Content
Section 1: Introduction
4
1.1 Background
4
1.2 Research question
5
1.3 Contribution
5
1.4 Structure
6
Section 2: Literature review and hypotheses development
6
2.1 Public and private companies
6
2.2 Private companies and financial reporting
7
2.3 Private companies and IFRS
10
2.4 Hypotheses development
12
Section 3: Method
14
3.1 Earnings quality
14
3.2 Model specification
16
3.3 Data
17
Section 4: Results
18
4.1 Descriptive statistics
18
4.2 Main results
21
4.3 Additional tests
22
Section 5: Concluding comments
23
5.1 Summary and conclusion
23
5.2 Limitations and further research
24
5.3 Implications
25
Section 6: Bibliography
27-30
Section 7: Tables
31-38
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Section 1: Introduction
1.1 Background
The world is globalizing rapidly. To catch up with this globalisation, the International
Accounting Standards Board (IASB) introduced the International Financial Reporting
Standards (IFRS) in 2002. The introduction of the IFRS for listed companies in many
countries around the world is one of the most significantly regulatory changes in accounting
history (Daske, Hail, Leuz, & Verdi, 2008). Objective of the introduction of IFRS is to develop
a single set of high quality, understandable, enforceable and globally accepted reporting
principles (Foundation, 2004). The European Union made IFRS mandatory for listed
companies in 2005. As expected benefits the European Union identified: eliminate barriers
to cross-border trading and ensuring that company accounts are more reliable, transparent
and more easily compared (Brown, 2011). After the introduction of IFRS in Europe, a lot of
research has been done on the effects of the mandatory transition to IFRS. Consensus has
been reach that, in general, the mandatory transition to IFRS had a positive effect on
earnings quality.
Although not mandatory, private companies have the choice to adopt IFRS. Francis
et al (2008) argue that private firms which are actively engaged in external contracting could
also have incentives to adopt IFRS. Bassimir (2012) argues that private firms are more likely
to switch to increase earnings quality if they have more growth opportunities, are more
leveraged, are younger, are externally rated, seek to raise external capital by issuing public
bonds or equity, are registered as a stock corporation, are characterized by private equity
involvement, have more international sales and operations, belong to high-tech industries
and have a Big Four auditor. André et al (2012) on the other hand define international
orientated, leverage, firm size and auditor reputation as most important factors to
determine private firm’s choice to increase earnings quality. Based on these studies, it can
be concluded that some firms face incentives that lead them to voluntary adopt IFRS, while
other firms do not. These studies have in common that they discuss the ex ante decisionmaking to voluntary adopt or not.
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1.2 Research question
Much less research has been done on the ex post effects of a voluntary adoption of IFRS. For
public companies, the transition has been mandatory and one of the effects has been an
increase in earnings quality. For private companies, an incentive to adopt is expected
benefits from higher quality reporting. But that expectation is based on the fact that
adoption has led to higher earnings quality by public companies. So far, empirical evidence
that adoption of IFRS also leads to higher earnings quality for voluntary adopters is lacking.
IFRS
Incentives to adopt:
Effects of adoption:
Public companies
Mandatory
Increased earnings quality
Private companies
Expected benefits
Unknown
Therefore, the research question is: what is the effect of voluntary IFRS adoption among
private companies on the quality of earnings? In other words: has voluntary adoption of
IFRS effect on the earnings quality of a private company.
So far, little research has been done on the topic. Results have shown that a
voluntary adoption of IFRS for private companies in Italy did not lead to lower earnings
management (Cameran, Campa, & Pettinicchio, 2008). In line with their findings,
Tamosiunas (2012) concludes that for UK private companies, voluntary adoption of IFRS has
not led to lower earnings management. These studies have in common that they only focus
on one single country, whereas this research also takes cross-country differences into
account. It is tested whether the relationship between IFRS and earnings quality differs per
country. Finally, it is also tested whether different firm characteristics affect the relationship.
1.3 Contribution
For listed companies, the adoption of IFRS was mandatory and the effects of this transition
have been well covered by literature. For private companies, the adoption of IFRS is
voluntary and a lot of research has been done on the incentives for private companies to
switch to IFRS. One area that has not been well covered by research is the effects of
voluntary adoption of IFRS on private companies. This research seeks to fill that gap within
existing literature by focusing on the relationship between IFRS and earnings quality.
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Adoption of IFRS is seen as one means of improving quality of financial reporting
(Francis J. R., Khurana, Martin, & Pereira, 2008). However, this assertion is only for public
companies grounded with empirical evidence. For private companies that seek for increased
quality of financial reporting there is no evidence that IFRS adoption would realise that.
Since a voluntary adoption of IFRS is costy and complex (Christensen, 2012), the relation
between adoption and the quality of financial reporting is an important one to consider in
the cost/benefit trade-off. There is great consensus that firms will only voluntarily improve
their financial reporting quality when the net benefits of doing so are positive (La Porta,
Lopez-de-Silanes, Shleifer, & Vishny, 1998; Leuz, Nanda, & Wysocki, 2003; Francis, Khurana,
& Pereira, 2003; Francis J. R., Khurana, Martin, & Pereira, 2008)
1.4 Structure
The remainder is structured as follows: section 2 reviews the literature and hypotheses are
developed. Section 3 contains the methodology. Descriptive statistics, main results and
additional tests are provided in section 4 and section 5 concludes.
Section 2: Literature review and hypotheses development
2.1 Public and private companies
If a company is a separate legal entity it distincts from a natural person in that it can only
acquire or be subject to a very much more restricted range of rights and liabilities than
natural persons. Besides that, a company has the same legal responsibilities as a natural
person (Pickering, 1968 ). Between separate legal entities one can define two mainstreams,
namely public limited companies and private limited companies. Although both private and
public companies have limited liability, separate legal entity and perpetual succession, they
have some clear distinctions (Icab Tutorial, 2012). The most important distinction between
private and public companies relates to their ability to raise funds from the general public. A
public company has an unrestricted right to offer shares or debentures to the public,
whereas this is prohibited in the case of a private company. Since only public companies can
issue shares to the general public, only they are eligible to be listed (Ball & Shivakumar,
2005). Because public companies are listed, they can transfer its shares easily. In all West6
European legal systems, the trading of private company shares on a public exchange is
prohibited (Kalss, 2005).
By offering shares to the public, a company can provide liquidity. However, offering
shares to the public stipulates corporate governance that imposes generic exogenous
controls, so the manager may not attain the desired trade-off between autonomy and the
cost of capital (Boot, Gopalan, & Thakor, 2006). In other words, if the decision is made to
offer shares to the public, the company raises liquidity at the cost of control. While it is well
known that liquidity considerations affect the type of ownership chosen, there is also
evidence that managers/entrepreneurs give control issues considerable weight (Boot,
Gopalan, & Thakor, 2006). The idea that going public is simply a stage in the growth of a
company is outdated. Cross-sectional and cross-country differences indicate that going
public is not a stage that all companies eventually reach, but is a choice (Pagano, Panetta, &
Zingales, 1998). The question of what motivates companies to go public goes beyond the
objective of this research and is therefore left unanswered.
2.2 Private companies and financial reporting
Given the fact that there are public limited companies and private limited companies, and
given the differences between them, it is interesting to look at how their financial reporting
differs. The main focus in this research lies with the role financial reporting fulfills within
both public and private companies. Much literature has been written about the functions of
financial reporting in public companies, which first will be summarized. After that, we
explain to what extent these functions of financial reporting also apply to private companies.
Core is to get a clear picture of the role of financial reporting within private companies.
Accounting information plays two important roles in market-based economies. First,
it allows capital providers to evaluate the return potential from investment opportunities.
Second, accounting information allows capital providers to monitor the use of their capital
once committed (Beyer, Cohen, Lys, & Walther, 2010). The reporting of information that is
useful to rational investors is called the decision usefulness approach (Scott, 2011). The
assistance financial reporting provides to capital providers to monitor, is called the
stewardship approach (Murphy, O'Connell, & hÓgartaigh, 2013).
The decision usefulness approach deals with the problem of adverse selection.
Adverse selection occurs because some person, such as firm managers and other insiders,
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will know more about the current condition and future prospects of the firm than outside
investors (Scott, 2011). This information asymmetry makes it difficult for outside capital
providers to assess the profitability of the firm’s investment opportunities. This problem is
exacerbated because insiders have incentives to exaggerate their firms’ projected
profitability to attract capital (Beyer, Cohen, Lys, & Walther, 2010). The decision usefulness
approach to financial reporting tries to overcome the problem of information asymmetry.
Within decision usefulness, there are two perspectives. The so-called measurement
perspective is the more traditional view. The fundamental notion underlying the
measurement perspective is that accounting should directly measure and report the basic
information required by investors, which is the value of the firm (Hitz, 2007). The
information perspective on the other hand takes the view that the form of disclosure does
not matter (Scott, 2011). Thus, for the information perspective, in contrast to the
measurement perspective, specific accounting representations such as balance sheets,
captions and categories such as assets and liabilities are irrelevant (Hitz, 2007). Rational
investors are regarded as sufficiently sophisticated on average that they can digest
implications of public information from any source (Scott, 2011).
The stewardship approach to financial reporting deals with the problem of moral
hazard. Moral hazard occurs because of the separation of ownership and control that
characterizes most large business entities (Scott, 2011). On the one hand are there investors
who own the company and on the other hand managers who exercise control over the
company. Because people are generally opportunistic and self-interested, a manager tends
not to act in the best interest of the owner (Williamson, 1981). Investors delegate decision
making to managers. There may be a demand for information about the action that are
taken by the managers, for the purpose of controlling those managers (Gjesdal, 1981).
Investors value such information ex-post and require a lower rate of return ex-ante when
they can rely on such information (Beyer, Cohen, Lys, & Walther, 2010).
Theory implies financial reporting deals with the problem of information asymmetry,
which can be separated between adverse selection and moral hazard (Gjesdal, 1981; Beyer,
Cohen, Lys, & Walther, 2010; Scott, 2011) However, these are based on public limited
companies. However, more than 99 percent of the companies in Europe and in the US are
private limited companies and unlisted (Bassimir, 2012). Because private companies are
unlisted, there is no separation of ownership and control. The absence of this separation
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indicates a different role for financial reporting for private companies.
When looking at decision usefulness the question arises who are the users of
financial statements of private companies and what decisions have to be made (Scott, 2011).
These users could be members, contributors, creditors and other users making rational
investment, credit and similar decisions (Statement of Financial Accounting Concepts no.1,
1978). Raising capital in public markets has a systematic influence on its incentives to report
earnings that reflect economic performance. These incentives to report shape the way in
which information asymmetries between firms and key financing parties are resolved
(Burgstahler, Hail, & Leuz, 2004). If a firm reports its earnings, a potential creditor does not
have to make estimations on its financial positions. This, in turn, decreases the likelihood of
an estimation error. The lower the chance of an estimation error, the lower the risk
premium the potential creditor charges the firm. In order to raise capital at a lower rate, the
firm is required to report financial information properly. The same story could apply to
potential customers and suppliers that have to decide whether to do business with the
reporting firm.
From a stewardship perspective it is questionable if financial reporting plays a role
for private companies. As mentioned above, stewardship deals with the problem of moral
hazard, which is derived from the separation of ownership and control. For a private
company, a separation of ownership and control does not exist. However, in a more broader
sense, financial reporting plays an important role because it allows outsiders to monitor firm
performance and contractual commitments (Khanna, Palepu, & Srinivasan, 2004). This
suggests a stewardship approach that is not only orientated towards investors, but also
towards other outsiders such as creditors, suppliers and customers.
Everything that is discussed above related to market forces that drive financial
reporting. They all mention incentives for firms to report their financial information.
However, there are also institutional factors that are of importance. Examples of
institutional factors are tax or accounting regimes. Such factors motivate a company to
report earnings that reflect economic performance (Burgstahler, Hail, & Leuz, 2004).
Information asymmetry, which creates the demand for information production by firms,
also creates a demand for regulation of that information production (Scott, 2011). That
same regulation of information production also creates more demand for information
production.
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2.3 IFRS and private companies
International Financial Reporting Standards (IFRS) are accounting rules issued by the
International Accounting Standards Board (IASB) with the objective of setting rules that
ideally would apply equally to financial reporting by public companies worldwide (Ball R. ,
2006). With the decision to mandate IFRS for public companies, the European Union aimed
to establish a single set of high quality financial reporting standards well as contribute to the
efficient and cost-effective functioning of the capital market (Capkun, Casavan-Jeny,
Jeanjean, & Weiss, 2008). The expected benefits for European companies were listed in an
EU statement in Brussels, which said that IFRS would help eliminate barriers to cross-border
trading by ensuring that company accounts are more reliable, more transparent and more
easily compared, which will in turn increase market efficiency and reduce the cost of raising
capital for companies, ultimately improving competitiveness and helping to boost growth
(Brown, 2011).
Logically, the expected benefits could differ from what actually happened. There has
been research done on what effects this transition to IFRS had. A lot of support has been
found that the transition to IFRS increases quality of earnings for public firms through
limitation of managerial discretion, more disclosure, greater transparency, less earnings
management, more timely loss recognition, more value relevant accounting numbers and
smaller analyst forecast errors (Ashbaugh, 2001; Ashbaugh & Pincus, 2001; Leuz C. , 2003;
Barth, Kaufmann, & Stone, 2005). Overall, there has been much research that indicates that
the mandatory transition to IFRS has been beneficial to both preparers and users of financial
statement.
In contrast to public firms, private firms can voluntary adopt IFRS. Private firms have
the choice to either adopt IFRS or stick to their local general accepted accounting principles
(GAAP). In the previous section we explained that there are market forces and institutional
factors that drive financial reporting for private firms. Since the adoption of IFRS is voluntary,
the institutional factors can be excluded from determinants that influence the choice to
adopt IFRS. However, there are market forces that could drive a company to voluntary
switch from local GAAP to IFRS. We give an overview on the research that has been done on
the incentives for private firms to voluntary adopt.
Because of the absence of institutional factors, firms are making rational decisions
on the choice of financial reporting standards through weighting benefits and costs (André,
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Walton, & Yang, 2012). Therefore, it is reasonable to assume that managers of voluntary
adopters view IFRS as their preferred reporting strategy while managers of others do not
view it as such (Drake, Myers, & Yao, 2010). The question arises as to why one manager
would see adoption of IFRS as beneficial and another would not. While private firms do not
have a separation of ownership and management, they still engage in external contracts. As
mentoined above, a private company could engage with creditors, suppliers, customers etc.
Firms which are actively engaged in external contracting also have incentives to improve the
quality of their financial reports in order to reduce information asymmetry and increase
decision usefulness, which in turn leads to reduced cost of capital through lower risk
premium. An adoption of IFRS is seen as one means of improving quality of financial
reporting (Francis J. R., Khurana, Martin, & Pereira, 2008).
Bassimir (2012) argues that private firms are more likely to increase earnings quality
if they have more growth opportunities, are more leveraged, are younger, are externally
rated, seek to raise external capital by issuing public bonds or equity, are registered as a
stock corporation, are characterized by private equity involvement, have more international
sales and operations, belong to high-tech industries and have a Big Four auditor. André et al
(2012) on the other hand define international orientated, leverage, firm size and auditor
reputation as most important factors to determine private firm’s choice to increase earnings
quality.
Altogether, one can conclude that private companies have several incentives to
increase earnings quality. Given the fact IFRS is widely recognized as being of higher quality
than the domestic standards in most countries (Ashbaugh, 2001), a company must decide
whether the benefits of these higher quality compensate for the cost IFRS brings along.
Since a voluntary adoption of IFRS is costy and complex (Christensen, 2012), the relation
between adoption and the quality of financial reporting is an important one to consider in
the cost/benefit trade-off. There is great consensus that firms will only voluntarily improve
their financial reporting quality when the net benefits of doing so are positive (La Porta,
Lopez-de-Silanes, Shleifer, & Vishny, 1998; Leuz, Nanda, & Wysocki, 2003; Francis, Khurana,
& Pereira, 2003; Francis J. R., Khurana, Martin, & Pereira, 2008)
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2.4 Hypotheses development
The mandatory transition to IFRS for European public firms has been discussed. Research
suggests that this transition has had some important benefits, such as that information
comparability has increased (Yip & Young, 2012). In general, researchers have concluded
that IFRS adoption has led to increased earnings quality for public firms (Soderstrom & Sun,
2007; Capkun, Casavan-Jeny, Jeanjean, & Weiss, 2008; Daske, Hail, Leuz, & Verdi, 2008).
As mentioned above, there are private companies that could benefit from higher
earnings quality. However, the assumption that IFRS leads to higher earnings quality is
based on research at public companies. As also discussed above, there are some major
differences between financial reporting for public firms and financial reporting for private
firms. Therefore, the question arises whether IFRS has the same effect on private companies
as it has for public companies. So far, there has been limited research on the topic. Results
from Italy show that IFRS do not contribute to the improvement of earnings quality.
(Cameran, Campa, & Pettinicchio, 2008). In the United Kingdom, research shows that
adopting of IFRS is not associated with lower earnings management (Tamosiunas, 2012).
Hypothesis one therefore reads:
H1: Voluntary adoption of International Financial Reporting Standard by private companies
leads to higher earnings quality.
Private companies have the possibility to voluntarily adopt IFRS. If they choose not to, they
have to comply with accounting principles set by their local government. These accounting
principles differ per country and therefore the amount of change a firm has to make to
comply with IFRS differs. The impact is likely to be smaller in countries that already have
high reporting quality or where local GAAP and IFRS are fairly close (Daske, Hail, Leuz, &
Verdi, 2008). Based on this fact, ‘Country’ is added as a moderating variable to the former
hypothesis.
H2: the effect of voluntary adoption of International Financial Reporting Standards by
private companies on earnings quality differs per country.
Besides that the effect of voluntary adoption could differ per country it could also differ
based on firm characteristics. As we noticed, there has been research done on the
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incentives for private companies to voluntary adopt IFRS. One of the most extensive studies
has been conducted by André et al (2012). Their research contains both a literature review
and as empirical investigation on firm characteristics that determine the choice of voluntary
adoption. Of the ten factors investigated, four of them seem to be a significant determinant
for IFRS adoption: Internationally oriented, Capital Structure/Leverage, Size and Auditors’
reputation. One can expect that if a firm characteristic is of significant influence on the
decision whether to adopt IFRS or not, the decision is strongly related with the firm
characteristic. In other words: since the firm characteristic effects the choice of whether a
company voluntarily adopts IFRS, the firm characteristic have to be taken into account when
we investigate the effects of voluntary adoption. We follow André et al (2012) by taking
three determinants that found to have significant influence on the decision to voluntary
adopt IFRS. Subsequently, we examine whether these determinants effect the relationship
between adoption of IFRS and earnings quality. We chose Internationally orientated,
leverage and size as determinants and discuss them hereafter.
Generally, firms operating internationally are more likely 1) to have a much more
heterogeneous group of stakeholders; 2) to need to report to various international
constituents; 3) to need to improve international customer recognition; and 4) to prefer
reducing restatement costs and increasing reporting transparency (André, Walton, & Yang,
2012). Following this, it is assumed that firms operating internationally have more benefits
from adopting IFRS and therefore adoption would result in higher earnings quality for them.
H3: for more internationally orientated private firms, a voluntary adoption of International
Financial Reporting Standards results in higher earnings quality then for less internationally
orientated firms.
In order to grow, firms often need external capital. If a firm already has a high level of debts,
the current lender may use this information advantage when negotiating the refinancing
terms (Rajan, 1992). Private companies used to have closely located banks and used to
communicate with them in a personal way. However, to decrease their dependency on one
bank, private firms may want to use multiple, more distant and impersonal banks. (Petersen
& Rajan, 2002). In order to overcome information asymmetry with those banks, firms have
the incentive to increase their public information (Bassimir, 2012). Banks can better assess
13
borrower credit quality because they have to make less estimations. In turn, this will
decrease estimation errors and thus banks will charge a lower borrowing rate. (Kim, Tsui, &
Yi, 2011). More leveraged firms are therefore more inclined to adopt IFRS voluntarily. Based
on that fact, it is assumed that more leveraged firms are more affected by voluntarily IFRS
adoption, with regard to their earnings quality, and the (directional) hypothesis is:
H4: for more leveraged orientated private firms, a voluntary adoption of International
Financial Reporting Standards results in higher earnings quality then for less leveraged firms.
Literature generally agrees on the fact that bigger firms have more benefits from adopting
IFRS then smaller firms. Some arguments are that larger firms are more likely to depend on
long-term financing and therefore large firms are more likely to voluntarily adopt better
governance structures (Beck, Demirguc, & Maksimovic, 2005). It is also stated that higher
level of information disclosure is less costly for larger firms since these are expected to
produce this information for internal purposes (Dumontier & Raffournier, 1998). As a result
of this, the (directional) hypothesis is:
H5: for larger private firms, a voluntary adoption of International Financial Reporting
Standards results in higher earnings quality then for smaller private companies.
Section 3: Method
3.1 Earnings quality.
The dependent variable used in this research is earnings quality, which can be measured in
various ways. Indications of earnings quality could be persistence, abnormal accruals,
smoothness, timeliness, loss avoidance, investor responsiveness, and external indicators
such as restatements and SEC enforcements releases (Dechow, Ge, & Schrand, 2010). For
this research, we chose accruals to be most appropriate. The major benefit of accruals is to
reduce timing and mismatching problems in the underlying cash flows. However, accruals
accomplish this benefit at the cost of making assumptions and estimations about future
cash flows, which implies that accruals include errors of estimation or noise (Dechow &
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Dichev, 2001). The normal accruals are meant to capture adjustments that reflect
fundamental performance, while the abnormal accruals are meant to capture distortions
induced by application of the accounting rules or earnings management. The general
interpretation is that if the “normal” component of accruals is modelled properly, then the
abnormal component represents a distortion that is of lower quality (Dechow, Ge, &
Schrand, 2010). The quality of accruals and earnings is decreasing in the magnitude of the
accrual estimation error. Therefore, the higher the abnormal accruals, the lower the
earnings quality (Dechow & Dichev, 2002).
Within the field of measurements for abnormal accruals, the Modified Jones model
is chosen to be most appropriate for this research, in particular, the version developed by
Dechow, Sloan & Sweeney (1995). It elaborates on the Jones Model, which attempts to
control for the effect of changes in a firm’s economic circumstances (Jones, 1991). The
modification is designed to eliminate the conjectured tendency of the Jones Model to
measure discretionary accruals with error when discretion is exercised over revenues
(Dechow, Sloan, & Sweeney, 1995). In the Modified Jones model, accruals are defined as:
ACC 𝑡-1 = α + β₁ (ΔREV 𝑡 - ΔREC 𝑡) + β₂ PPE 𝑡 + ε 𝑡
Where:
ACC 𝑡-1 = Total Accruals in year 𝑡
α = Constant
ΔREV 𝑡 = Revenues in year 𝑡 less revenues in year 𝑡-1 scaled by total assets
ΔREC 𝑡 = Net receivables in year 𝑡 less net receivables in year 𝑡-1 scaled by total assets
PPE 𝑡 = Gross property plant and equipment in year 𝑡 scaled by total assets
ε 𝑡 = Residual
Total accruals are a function of the change in revenue, adjusted for change in accounts
receivable, and the level of property, plant and equipment. The portion of total accruals
unexplained by normal operating activities are discretionary/abnormal accruals (Jones,
1991). By computing the portion of total accruals that is nondiscretionary, the residuals (ε in
15
the equation) are the discretionary accruals. First, total accruals is computed for every firm
year observation using the balance sheet approach (Jones, 1991):
Where
TA 𝑡 = ΔCA 𝑡 - ΔCash 𝑡 - ΔCL 𝑡 + ΔDPL 𝑡 - DEP 𝑡
∆CA 𝑡 = is the change in current assets in year 𝑡
∆Cash 𝑡 = is the change in cash and cash equivalents in year 𝑡
∆CL 𝑡 = is the change in current liabilities in year 𝑡
∆DCL 𝑡 = is the change in debt included in current liabilities in year 𝑡
DEP 𝑡 = is depreciation and amortization expense in year 𝑡
After computing total accruals, for every firm year the constant, change in revenues, change
in net receivables and gross property, plant and equipment is computed. Subsequently, by
using a least-squares regression with all above mentioned variables, the residuals for every
firm year observation are found. These residuals are used as the proxy for earnings quality.
3.2 Model specification
As described above, the measurement for earnings quality is computed as the portion of
total accruals that are not non-discretionary accruals. The first hypothesis developed is the
effect of voluntary adoption of IFRS on earnings quality. In order for the results to be as
accurate as possible, only the last year of non-adoption is compared to the first year of
adoption. The variable IFRS takes the value of 1 for the first year the annual report is
prepared under IFRS and takes the value of 0 for the year preceding the IFRS adoption; the
last year of local GAAP reporting. Besides that, the results are controlled for number of
subsidiaries, leverage, size, country, and industry. Taken together, the following multiple
regression model is computed for hypothesis 1:
Hypothesis 1: Earnings Quality 𝑖,𝑡= α+ β₁FIRST 𝑖,𝑡 + β₂IO 𝑖,𝑡 + β₃LEV 𝑖,𝑡 + β₄SIZE 𝑖,𝑡 +
β₅COUNTRY 𝑖,𝑡 + β₆IND 𝑖,𝑡 + ε 𝑖,𝑡
The next hypothesis is concerned with the same question, but investigates whether country
has a moderating effect on the relationship between the adoption of IFRS and earnings
16
quality. The categorical variable Country can take the value of 1 for Germany, 2 for France
and 3 for the United Kingdom. Whereas the variable Country is tested for the moderating
effect, the other variables remain as controls, which results in the following model:
Hypothesis 2: Earnings Quality 𝑖,𝑡= α+ β₁FIRST 𝑖,𝑡 + β₂COUNTRY 𝑖,𝑡 +
β₃IFRS*COUNTRY 𝑖,𝑡 + β₄IO 𝑖,𝑡 + β₅LEV 𝑖,𝑡 + β₆SIZE 𝑖,𝑡 + β₇IND 𝑖,𝑡 + ε 𝑖,𝑡
Hypothesis 3 is also concerned with the relationship between the adoption of IFRS and
earnings quality but then looks at any possible moderating effects of firm characteristics on
the relationship. Hypothesis 3 takes into account whether a company is internationally
orientated, which is measured by the number of subsidiaries a company has. Hypothesis 4
looks at potential differences between high and low leveraged firms. Leverage is measured
as total debts divided by total assets. Finally, the effect of firm size on the relationship
between IFRS adoption and earnings quality is tested in hypothesis 5. Firm size is measured
as number of employees. The preceding results in the following multiple regression models:
Hypothesis 3: Earnings quality 𝑖,𝑡= α+ β₁FIRST𝑖,𝑡 + β₂IO 𝑖,𝑡 + β₃IFRS*IO 𝑖,𝑡 + β₄LEV 𝑖,𝑡
+ β₅SIZE 𝑖,𝑡 + β₆COUNTRY 𝑖,𝑡 + β₇IND 𝑖,𝑡 + ε 𝑖,𝑡
Hypothesis 4: Earnings quality 𝑖,𝑡= α+ β₁FIRST 𝑖,𝑡 + β₂LEV 𝑖,𝑡 + β₃IFRS*LEV 𝑖,𝑡 + β₄IO
𝑖,𝑡 + β₅SIZE 𝑖,𝑡 + β₆COUNTRY 𝑖,𝑡 + β₇IND 𝑖,𝑡 + ε 𝑖,𝑡
Hypothesis 5: Earnings quality 𝑖,𝑡= α+ β₁FIRST 𝑖,𝑡 + β₂SIZE 𝑖,𝑡 + β₃IFRS*SIZE𝑖,𝑡 + β₄IO
𝑖,𝑡 + β₅LEV 𝑖,𝑡 + β₆COUNTRY 𝑖,𝑡 + β₇IND 𝑖,𝑡 + ε 𝑖,𝑡
Detailed description of the variables can be found in Table 1.
3.3 Data
The empirical data that is used for this research is retrieved from Amadeus, Bureau van Dijk.
Bureau van Dijk has its expertise in Europe, mainly focussing on private firms. Because of
this, Bureau van Dijk was most suitable and provided all necessary information. The data is
extracted with a few upfront conditions. First of all, because this research is only concerned
with German, French and English private companies, logically only data from those countries
is extracted. In order to compare the same kind of companies throughout those three
17
different countries, more legally complex structured companies are excluded. Only simple
limited private companies are used: In Germany the “Gesellschaft mit beschränkter Haftung”
(GmbH), in France the limited Société Anonyme (S.A.) and in the United Kingdom the Private
Limited (Ltd). Secondly, because of the differences in corporate structure, financial and
insurance companies are excluded from the data sample. Finally, holdings, annuity holdings
and other firms that are not used for operational business are excluded. Besides that, in
order to limit our dataset, too small firms are also desired to be excluded. To achieve the
above mentioned goals, only firms with a minimum of 100 employees are taken into the
dataset.
Firm information is extracted from Amadeus, Bureau van Dijk for the period 20042011. This period is forcedly chosen, because Bureau van Dijk only provides complete
information over this period. Given the above conditions, over 16,000 unique firms are
covered. However, after removing all the firms that 1) had already adopted IFRS in 2004 and
2) have not yet adopted IFRS up until 2011, in total 820 unique firms and 6560 firm-year
observations are availabe.
Section 4. Results
4.1 Descriptive Statistics
The sample contains 820 unique firms that have made a total of 843 transitions to IFRS
somewhere between 2004 and 2011. 23 firms made two transitions to IFRS: they adopted
IFRS, changed back to local GAAP and adopted IFRS again between 2004 and 2011. In this
study, the last year of local GAAP is compared with the first year IFRS. Therefore, there is no
need to exclude these 23 second transitions from the sample. Table 2.1 reports how these
transitions are distributed between 2004 and 2011. A majority of the transitions took place
in the years 2009 (22,78%) and 2010 (40,81%). The question arises why 2009 and 2010 have
a relatively high amount of transitions, but that is left for further research. Another
substantial part of the transitions took place in 2005, which could relate to the mandating of
IFRS for public firms in that year.
Within the data sample, which contains 6560 firm-year observations, firms from
several industries are represented. Because this research is focused on Europe, the NACE
18
Rev 2 Statistical classification of economic activities in the European Community is used
(Eurostat European Commission, 2008). An overview can be found in Table 2.2. Professional,
scientific and technical activities are well represented in the sample (23,17%). This
classification includes legal, accounting, consulting, architectural, engineering and marketing
activities. Subsequently, manufacturing (17,56%)and administrative and support service
activities (12,68) also have substantial share of the sample. Twelve of the 21 categories only
represent between 0 and 2,5% of the total sample.
Table 2.3 shows the sample distribution between countries. Only a minority of the
firm year observations originate from Germany (4,15%). France is better represented
(19,27%) and the absolute majority of the firm year observations stems from the United
Kingdom (76,59%).
Table 3.1 reports on the summarize statistics of the dependent variable, which is earnings
quality. Besides, the Table also focuses on three more control variables, namely: number of
recorded subsidiaries, number of employees and leverage. With regard to the dependent
variable, earnings quality, it can be concluded that it is centered around zero, because it
averages 0,01 and first and third quartile are respectively -0,05 and 0,08. However, a
standard deviation of 0,30 and a minimum and maximum of respectively -10,54 and 2,29
confirm some extreme outliers.
The first control variable ‘number of recorded subsidiaries’ ranges from 0 to 741. For
our second control variable ‘number of employees’ applies a range from 2 to 391148. These
numbers tell something about the nature and diversity of the data sample. The third control
variable, the calculated variable ‘leverage’ has a maximum value of 73. This means a firm
has 73 times more debt than it has assets, which also implies an extreme outlier.
In regression analysis, the presence of outliers in the data set can strongly distort the
classical least squares estimator and lead to unreliable results (Verardi & Croux, 2009). As
mentioned above, there are indications that our variables contain such outliers. The box
plot (figure 1) shows us the presence of outliers in the dependent variable Earnings Quality.
A solution to prevent that these outliers distort the results must be found.
19
-10
0
-5
5
EarningsQuality
Figure 1: Box plot distribution of earnings quality
One possibility is to employ Winsorization, where the lower and upper extreme values are
replaced by their nearest neighbours (Barnett & Lewis, 1978). Extreme values increase
skewness and kurtosis. Winsorization is a simple procedure for reducing skewness and
kurtosis (Shete, et al., 2004). Skewness has to do with the symmetry of the distribution,
while kurtosis has to do with the peakedness of a distribution. (Tabachnick & Fidell, 1996).
When a distribution is perfectly normal, the values of skewness and kurtosis are zero
(Tabachnick & Fidell, 1996).
The continuous variables Earnings Quality, Number of recorded subsidiaries,
Leverage and Size are winsorized. A P(0,01) level is used, which indicates that for each
variable, the upper and lower 1% is replaced by its neighbouring value. This has a major
impact on skewness and kurtosis. For example, the kurtosis of Leverage decreased from
3422,10 to 7,19. Table 3.2 provides an overview of the effect winsorizing has on the
continuous variables.
Table 3.3, which describes the Pearson’s correlation matrix, shows the correlation
coefficients between the (winsorized) variables are low. Only the positive association
between number of recorded subsidiaries and number of employees is striking (β = .65; p
< .01). It seems logic that a company having a greater number of subsidiaries also has a
higher number of employees. Leverage also correlates significantly with Earnings quality (β
20
= -.05; p < .01), Number of subsidiaries (β = -.07; p < .01) and Size (β = -0,02; p < .10), but
coefficients are rather low.
4.2 Main results
In this chapter, we test hypotheses 1 to 5 using least squares regression. The least squares
method produces a straight line drawn through the points so that the sum of squared
deviations between the points and the line is minimized (Keller, 2008). The models
developed in chapter 3, based on the hypotheses explained in chapter 2, are tested using
least squares regression. Hereafter, results are discussed.
Hypothesis 1 is solely concerned with the effect of a transition to IFRS on earnings
quality. Therefore, variables FIRST and earnings quality are regressed using all other
variables as controls, following the model for hypothesis 1. Results of testing hypothesis 1
can be found in Table 4.1. The transition to IFRS has a very small positive effect on earnings
quality. However, using a 0,10 significance level, the effect is not significant (β = .01; p =
0,615). The low Adjusted R-square value (-0,020) indicates a low explanatory power for the
model. Using a 0,10 significance level, none of the control variables has a significant
influence on earnings quality. Hypothesis 1 asserted that a transition to IFRS has a positive
effect on earnings quality. These results indicate no significant relationship and therefore
Hypothesis 1 is rejected.
Hypothesis 2 investigates the moderating effect of a particular country on the
relationship between transition to IFRS on earnings quality. Table 4.2 shows the results of
this regression. It shows that compared to Germany, for France (β = -0,033; p = 0.592) and
the United Kingdom (β = -0,076; p = 0.197) the relationship between adoption of IFRS and
earnings quality is weaker. However, results indicate that using a 0,10 significance level, no
significant differences are evident. In contrast to what hypothesis 2 predicted, no significant
difference between countries is found, thus hypothesis 2 must be rejected.
This research also investigates the moderating effect of firm characteristics, namely
number of subsidiaries, number of employees and leverage, on the relationship between
transition to IFRS on earnings quality. Hypothesis 3 examines the moderating effect of
number of subsidiaries. It stated that firms having a higher amount of subsidiaries, benefit
more from a transition in terms of earnings quality. However, results show there is no
interaction effect (β = 0,00; p = 0.99). Therefore, hypothesis 3 must be rejected. Complete
21
results can be found in Table 4.3.
Hypothesis 4 assumed that firms with a higher leverage benefit more from a
transition in terms of earnings quality. When looking at the results of hypothesis 4, which
can be found in Table 4.4, this cannot be confirmed. Results indicates that firms with higher
leverage, experience a weaker relationship between transition to IFRS and earnings quality
(β = -0,01; p = 0.745). This weaker relationship is not significant. Anyway, the assertion
from hypothesis 4 that firms with higher leverage experience a stronger relationship is
rejected.
Finally, hypothesis 5 states that bigger firms benefit more from transition. The
number of employees is regressed as a moderating variable between transition to IFRS and
earnings quality. Results can be found in Table 4.5 The moderating coefficient equals zero,
which indicates no interaction effect of firm size on the relationship between a transition to
IFRS and earnings quality (β = 0,00; p = 0.465). Therefore, hypothesis 5 must also be
rejected.
4.3 Additional tests
As mentioned above, outliers can strongly distort outcomes and lead to unreliable results.
However, there are no indications that these outliers are the result of data entry errors. This
means these outliers cannot be excluded without a compelling reason. A good strategy to
deal with this problem is the use of robust regression. The idea of robust regression is to
weigh the observations differently based on how well behaved these observations are.
Roughly speaking, it is a form of weighted and reweighted least squares regression.
(Institute for digital research and education, 2006). Robust regression can be used in any
situation one would use a least squares regression. In short, the most influential points are
dropped, and then cases with large absolute residuals are down-weighted (Institute for
digital research and education, 2006).
Because of the outliers in earnings quality, the relationship between a transition to
IFRS and earnings quality is again regressed using the robust regression method. Table 5
reports the results. Compared to least-squares regression, the relationship between a
transition to earnings quality is stronger and, using a 0,10 significance level, significant (β =
0,01; p = 0.07). This means that, using the robust regression method, the transition to IFRS
22
has a significant positive effect on earnings quality. In turn, this means, following the robust
regression method, that hypothesis 1 can be accepted.
For the main model, the effect of a voluntary transition to IFRS on earnings quality, it could
be interesting to use the t-test for independent samples. One independent sample group is
the 843 firm year observations that are in their last year of reporting under local GAAP
(group 0). The other independent sample group is the 843 firm year observations that
report under IFRS for the first year (group 1). A t-test is performed and results are reported
in Table 6.1. Group 0 averages -0,001 while group 1 averages 0,005, a mean difference of 0,006. Based on hypothesis 1, it is assumed that the group that adopted IFRS (group 1)
averages higher. However, the independent t-test indicates that the alternative hypothesis,
where group 1 averages higher than group 0, is not significant (p = 0,286). This suggests
there is no significance difference between the sample group that reports under local GAAP
and the sample group that reports under IFRS for the first year. The absence of significant
difference is in line with the results from the regression, but is in contrast to hypothesis 1.
Based on these results, hypothesis 1 is rejected.
On the contrary, if an independent t-test is done with the original data, so before
winsorization, results differ. Without adjusting for outliers, group 0 averages -0,031 and
group 1 averages 0.003, a mean difference of -0.034. An independent t-test indicates that
the alternative hypothesis, group 1 averages higher than group 0, is significant (p = 0,07)
using a 0,10 significance level. Table 6.2 reports the results. This means, using the original
data, there is significant difference between the sample groups and hypothesis 1 can be
accepted.
Section 5. Concluding comments
5.1 Summary & conclusion
At the heart of this study was the question whether a voluntary transition to IFRS by private
companies has an effect on earnings quality. It has been extensively studied that the
mandatory adoption of IFRS by public companies has led to higher earnings quality. The
same effect is assumed for private companies. However, we explained that there are some
23
important differences in financial reporting for public and private companies. the main
motivation of this research is the absence of extensive research in this area. It contributes
by studying the effects a voluntary transition to IFRS has on the earnings quality for private
companies. To achieve this goal, we compared for a private company its last year of
reporting under local GAAP with its first year of reporting under IFRS. A total of 843
transitions to IFRS within three countries were examined for their effect on earnings quality.
From the empirical results this research provided, we can conclude that a voluntary
adoption of IFRS by a private company has only little significant effect on that private firms’
earnings quality.
We used least squares regressions to determine the relationship between a
transition to IFRS and earnings quality. Earnings quality was determined by using the
modified Jones model (Dechow, Sloan, & Sweeney, 1995). Furthermore, we investigate
whether the relationship between transitions to IFRS differs per country and for different
firm characteristics. Based on the least squares regressions performed with these variables,
we did not find significant results. Besides that, we performed additional analysis. Using a
robust regression method, the relationship between a transition to IFRS and earnings quality
can be confirmed, however with low significance. In line with these results, an independent
t-test with our original data also suggests a transition to IFRS has some effect on earnings
quality, again with low significance. Altogether, it can be concluded that, based on these
results, a voluntary adoption of IFRS only has very little effect on a private companies
earnings quality. We provide some indications that a positive relationship between adopting
IFRS and earnings quality could be possible, but these indications have low significance.
5.2 Limitations & further research
There are some concerns related to the results that should be taken into account. First of all,
this research only used one measurement for earnings quality. Among earnings quality
measurements are persistence, abnormal accruals, smoothness, timeliness, loss avoidance
and investor responsiveness (Dechow, Ge, & Schrand, 2010), while this study only used
abnormal accruals. A more appropriate conclusion could be that this study did not find
much significant evidence for a relationship between a transition to IFRS and abnormal
accruals, instead of earnings quality. It is interesting for further research to look on the
24
effects a transition to IFRS has on the other measurements of earnings quality.
Secondly, also the dataset has some limitations. The dataset is dominated by the
United Kingdom (77%), while an evenly distributed sample would be more informative. The
question why UK firms have adopted IFRS disproportionately is also an interesting topic for
further research. For this research, the three largest European economies were chosen, but
it could be interesting to also include data from other European countries.
Thirdly, this study uses time series data. A time series is a collection of observations
of well-defined data items obtained through repeated measurements over time (Australian
Bureau of Statistics, 2013). In contrast, a cross-sectional analysis could be performed which
would compare companies that have adopted with companies that have not adopted at one
point in time and therefore exclude the time factor.
Finally, in order to examine the effects of a transition to IFRS, we compared the last
year a company reported under local GAAP with the first year a company reports under IFRS.
However, research indicates that earnings quality is affected in the transition period due to
earnings management (Capkun, Casavan-Jeny, Jeanjean, & Weiss, 2008). The effect of a
transition to IFRS on earnings quality might be better measured by looking at the second
year after adoption (Pagano, Panetta, & Zingales, 1998).
5.3 Implications
As we mentioned before, research indicates that the mandatory adoption of IFRS by public
companies has led to increased earnings quality for them. Other research shows that private
companies could also benefit from increased earnings quality. However current studies
ignore the fact that financial reporting plays a different role for private companies than it
does for public companies. Therefore, there is no empirical evidence that, in line with public
firms, a voluntary adoption to IFRS also leads to an increase in earnings quality. This
research contributes to the existing literature by investigating effects of a voluntary
transition to IFRS on earnings quality. Based on our results, the relationship between a
transition to IFRS and earnings quality cannot be confirmed. These results can have two
different implications for existing literature. On the one hand, one can argue that the
differences between public and private companies cause the results for mandatory and
voluntary adoption to differ. On the other hand, the different results for mandatory and
voluntary adoption could underline the differences between public and private companies.
25
For practitioners, the results could be of potential interest when managers have to
make a decision whether to adopt IFRS or not. We explained that managers will do so when
the expected net benefits of doing so are positive (Francis J. R., Khurana, Martin, & Pereira,
2008). Studies to public companies conclude that one of the expected benefits is an increase
in earnings quality. Results from this study indicate that the increase in earnings quality is
not a matter of course.
26
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30
Section 7: Tables
Table 1: Variables
Table 1: List of variables
Variable name:
Explanation:
Earnings quality 𝑖,𝑡
Measured as residuals (ε 𝑡) of Dechow’s
Modified Jones accrual model:
ACC 𝑡-1 = α + β1 (ΔREV 𝑡- ΔREC 𝑡)
FIRST 𝑖,𝑡
+ β2 PPE 𝑡 + ε 𝑡
Voluntary adoption of IFRS whereas 1=first
year of adoption 0=last year of local GAAP
IO 𝑖,𝑡
Internationally orientated measured as
LEV 𝑖,𝑡
Capital structure measured as total debts
SIZE 𝑖,𝑡
Firm size measured by total amount of
COUNTRY 𝑖,𝑡
Country in Europe whereas 1=Germany
IND 𝑖,𝑡
Industry classified by NACE rev 2
number of subsidiaries
divided by total assets
employees
2=France and 3=United Kingdom
31
Table 2: Descriptive Statistics: sample distribution
Table 2.1: Year of Transition
2004
2005
2006
2007
2008
2009
2010
2011
Total:
Last year local GAAP
167
22
23
17
192
344
78
0
843
First year IFRS
0
167
22
23
17
192
344
78
843
19,8
2,61
2,72
2,02
22,78
40,81
9,26
100%
First year IFRS %
Table 2.2: Industry Classifications (of total firm year observations)
A
B
C
D
E
F
G
H
I
J
K L
M
N
O
P
Q
R
S
T
U
Total:
Amount
56
112
1152
104
88
280
624
304
296
696
0
88
1520
832
32
16
88
128
136
0 8
6560
%
0.85 1.71 17.56
10.61
0
1.34 23.17
12.68
0.49 0.24 1.34 1.95
2.07
0 0.12 100%
1.59 1.34 4.27
9.51 4.63 4.51
A: Agriculture, forestry and fishing; B: Mining and quarrying; C: Manufacturing; D: Electricity, gas, steam and air conditioning supply; E: Water supply, sewerage, waste
management and remediation activities; F: Construction; G: Wholesale and retail trade; repair of motor vehicles and motorcycles; H: Transportation and storage; I:
Accommodation and food service activities; J: Information and communication; K: Financial and insurance activities; L: Real Estate activities; M: Professional, scientific and
technical activities; N: Administrative and support service activities; O: Public administration and defence; compulsory social security; P: Education; Q: Human healt and
social work activities; R: Arts, entertainment and recreation; S: Other service activities; T: Activities of households as employers; undifferentiated goods- and servicesproducing activities of households; U: Activities of extraterritorial organizations and bodies
Table 2.3 Country (of total firm year observations)
Germany
France
United Kingdom
Total:
Amount
272
1264
5024
6560
%
4.15
19.27
76.59
100%
32
Table 3: Descriptive statistics: summary statistics
Table 3.1: Descriptive statistics
Variable
Obs
Earnings Quality
4488
No Recorded subsid.
6560
Employees
5817
Leverage
5913
Mean
0,01
18,25
6506
0.76
St.dev
0,300
59,53
25396
1.09
Q1
-0,05
1
408
0.53
Med
0,01
3
928
0.71
Q3
0,08
12
2799
0.88
Min
-10,54
0
2
-0.48
Max
2,29
741
391148
73.67
Table 3.2: Winsorization
Variable:
Earnings Quality
No recorded subsid.
Employees
Leverage
Before winsorization
Skewness
-13,10
7,50
8,22
52,32
Table 3.3: Pearson’s Correlations Matrix
Variable
Earnings Quality
Earnings Quality
1,00
No recorded subsid.
0,01
Employees
0,01
Leverage
-0,05***
Kurtosis
400,39
72,81
86,06
3422,10
After winsorization
Skewness
-0,65
4,61
5,59
1,26
Kurtosis
7,36
25,80
36,87
7,19
No recorded subsid.
Employees
Leverage
1,00
0,65***
-0,07***
1,00
-.0,02*
1,00
***p < .01 ;**p < .05 ;* p < .10
33
Table 4 Regression output
Table 4.1 Regression output hypothesis 1
Source
SS
df
MS
Model
Residual
.142105104
54.9163392
6
1311
.023684184
.041888893
Total
55.0584443
1317
.041805956
EarningsQua~x
Coef.
FIRST
Country1
Subsidiariesx
Leveragex
Employeesx
IndustryM
_cons
.0056839
-.0031648
.000021
-.0089117
2.79e-08
-.0019685
.0310743
Std. Err.
Number of obs
F( 6, 1311)
Prob > F
R-squared
Adj R-squared
Root MSE
t
.0112846
.0112082
.0001765
.0198954
4.08e-07
.0012776
.0344393
0.50
-0.28
0.12
-0.45
0.07
-1.54
0.90
P>|t|
0.615
0.778
0.905
0.654
0.946
0.124
0.367
=
1318
=
0.57
= 0.7581
= 0.0026
= -0.0020
= .20467
[95% Conf. Interval]
-.016454
-.0251528
-.0003252
-.0479421
-7.72e-07
-.004475
-.0364878
.0278218
.0188231
.0003671
.0301187
8.28e-07
.0005379
.0986364
Table 4.2 Regression output hypothesis 2
Source
SS
df
MS
Model
Residual
.35391564
54.7045286
9
1308
.03932396
.041823034
Total
55.0584443
1317
.041805956
Number of obs
F( 9, 1308)
Prob > F
R-squared
Adj R-squared
Root MSE
=
1318
=
0.94
= 0.4888
= 0.0064
= -0.0004
= .20451
EarningsQual~x
Coef.
1.FIRST
.0689186
.057903
1.19
0.234
-.0446744
.1825116
Country1
2
3
.0457334
.0541732
.0450951
.0428913
1.01
1.26
0.311
0.207
-.0427332
-.0299701
.1341999
.1383165
FIRST#Country1
1 2
1 3
-.0332946
-.076775
.0621917
.0594162
-0.54
-1.29
0.592
0.197
-.155301
-.1933365
.0887118
.0397865
Subsidiariesx
Leveragex
Employeesx
IndustryM
_cons
-.0000268
-.0051501
4.81e-08
-.001944
-.0295453
.0001828
.0200662
4.08e-07
.0012773
.0452935
-0.15
-0.26
0.12
-1.52
-0.65
0.883
0.797
0.906
0.128
0.514
-.0003855
-.0445156
-7.53e-07
-.0044497
-.1184012
.0003319
.0342154
8.49e-07
.0005617
.0593107
Std. Err.
t
P>|t|
[95% Conf. Interval]
34
Table 4.3 Regression output hypothesis 3
Source
SS
df
MS
Model
Residual
.142117173
54.9163271
7
1310
.020302453
.04192086
Total
55.0584443
1317
.041805956
Number of obs
F( 7, 1310)
Prob > F
R-squared
Adj R-squared
Root MSE
Std. Err.
t
P>|t|
=
1318
=
0.48
= 0.8465
= 0.0026
= -0.0027
= .20475
EarningsQualityx
Coef.
[95% Conf. Interval]
1.FIRST
Subsidiariesx
.0056035
.0000188
.012242
.0002168
0.46
0.09
0.647
0.931
-.0184126
-.0004064
.0296197
.0004441
FIRST#c.Subsidiariesx
1
4.40e-06
.0002594
0.02
0.986
-.0005045
.0005133
Country1
Leveragex
Employeesx
IndustryM
_cons
-.0031636
-.0089071
2.78e-08
-.0019684
.0311067
.0112127
.0199049
4.08e-07
.0012781
.0345052
-0.28
-0.45
0.07
-1.54
0.90
0.778
0.655
0.946
0.124
0.367
-.0251604
-.047956
-7.72e-07
-.0044758
-.0365848
.0188333
.0301418
8.28e-07
.000539
.0987981
Table 4.4 Regression output hypothesis 4
Source
SS
df
MS
Model
Residual
.146549457
54.9118948
7
1310
.020935637
.041917477
Total
55.0584443
1317
.041805956
Std. Err.
Number of obs
F( 7, 1310)
Prob > F
R-squared
Adj R-squared
Root MSE
t
P>|t|
=
1318
=
0.50
= 0.8354
= 0.0027
= -0.0027
= .20474
EarningsQualityx
Coef.
[95% Conf. Interval]
1.FIRST
Leveragex
.0148724
-.002089
.0303928
.0288986
0.49
-0.07
0.625
0.942
-.0447515
-.0587815
.0744962
.0546035
FIRST#c.Leveragex
1
-.0125904
.0386663
-0.33
0.745
-.088445
.0632642
Country1
Subsidiariesx
Employeesx
IndustryM
_cons
-.0031312
.0000211
2.65e-08
-.0019724
.0260605
.0112125
.0001765
4.08e-07
.0012781
.0377355
-0.28
0.12
0.06
-1.54
0.69
0.780
0.905
0.948
0.123
0.490
-.0251276
-.0003252
-7.74e-07
-.0044798
-.0479681
.0188652
.0003674
8.27e-07
.000535
.1000891
35
Table 4.5 Regression output hypothesis 5
Source
SS
df
MS
Model
Residual
.164485207
54.893959
7
1310
.023497887
.041903786
Total
55.0584443
1317
.041805956
Std. Err.
Number of obs
F( 7, 1310)
Prob > F
R-squared
Adj R-squared
Root MSE
t
P>|t|
=
1318
=
0.56
= 0.7882
= 0.0030
= -0.0023
=
.2047
EarningsQualityx
Coef.
[95% Conf. Interval]
1.FIRST
Employeesx
.0083627
2.73e-07
.0118669
5.28e-07
0.70
0.52
0.481
0.605
-.0149176
-7.63e-07
.0316429
1.31e-06
FIRST#c.Employeesx
1
-4.58e-07
6.27e-07
-0.73
0.465
-1.69e-06
7.72e-07
Country1
Subsidiariesx
Leveragex
IndustryM
_cons
-.0032199
.000016
-.0091371
-.0019672
.0300179
.0112104
.0001766
.0199014
.0012779
.0344757
-0.29
0.09
-0.46
-1.54
0.87
0.774
0.928
0.646
0.124
0.384
-.0252123
-.0003305
-.0481791
-.004474
-.0376157
.0187725
.0003625
.0299049
.0005397
.0976515
36
Table 5: Robustness test
Table 5: Robust Regression test
Huber
Huber
Huber
Biweight
Biweight
Biweight
iteration
iteration
iteration
iteration
iteration
iteration
1:
2:
3:
4:
5:
6:
maximum
maximum
maximum
maximum
maximum
maximum
difference
difference
difference
differe nce
difference
difference
in
in
in
in
in
in
weights
weights
weights
weights
weights
weights
=
=
=
=
=
=
Robust regression
.79956935
.05786964
.00949149
.29382238
.01429939
.00322772
Number of obs =
F( 1, 1327) =
Prob > F
=
EarningsQu~x
Coef.
FIRST
_cons
.0133652
.0052692
Std. Err.
.0074231
.0053487
t
1.80
0.99
P>|t|
0.072
0.325
1329
3.24
0.0720
[95% Conf. Interval]
-.001197
-.0052235
.0279274
.015762
37
Table 6: T-test for independent samples
Table 6: T-test for Earnings quality (after winsorization)
Two-sample t test with equal variances
Group
Obs
Mean
0
1
639
690
combined
1329
diff
Std. Err.
Std. Dev.
-.001684
.0046356
.0083693
.0074821
.2115638
.1965397
-.0181187
-.0100549
.0147508
.0193261
.0015971
.0055917
.203849
-.0093725
.0125667
-.0063196
.0111946
-.0282806
.0156414
diff = mean(0) - mean(1)
Ho: diff = 0
Ha: diff < 0
Pr(T < t) = 0.2862
[95% Conf. Interval]
t =
degrees of freedom =
Ha: diff != 0
Pr(|T| > |t|) = 0.5725
-0.5645
1327
Ha: diff > 0
Pr(T > t) = 0.7138
Table 6: T-test for Earnings quality (before winsorization)
Two-sample t test with equal variances
Group
Obs
Mean
0
1
639
690
combined
1329
diff
Std. Err.
Std. Dev.
-.0313499
.0026791
.02203
.0096342
.5568853
.2530695
-.07461
-.0162368
.0119103
.021595
-.0136824
.0117186
.4272072
-.0366715
.0093066
-.034029
.0234447
-.0800218
.0119638
diff = mean(0) - mean(1)
Ho: diff = 0
Ha: diff < 0
Pr(T < t) = 0.0734
[95% Conf. Interval]
t =
degrees of freedom =
Ha: diff != 0
Pr(|T| > |t|) = 0.1469
-1.4515
1327
Ha: diff > 0
Pr(T > t) = 0.9266
38