What Determines Goodwill Impairment?
Arnt Verriest
Ann Gaeremynck
Abstract:
This study investigates determinants of goodwill impairment decisions and their disclosure
quality. Under IAS36 goodwill is subject to an annual impairment test in which the carrying
amount of goodwill is not allowed to exceed the recoverable amount. However, valuing this
recoverable amount is subject to substantial managerial discretion. Therefore, we predict that
ownership concentration, corporate governance quality and firm performance provide
incentives for managers to engage in goodwill impairment or not, and thus determine
financial reporting quality. We construct a sample of firms that should engage in goodwill
impairment. Our results convey that better performing firms and firms with stronger corporate
governance mechanisms are more likely to impair. Further, ownership structure and
governance have a weak impact on the degree of impairment disclosure.
1
1. Introduction
The introduction of International Financial Reporting Standards (IFRS) in 2005 entails
substantial changes for financial reporting across Europe. A remarkable feature about IFRS is
that its standards are principle-basedrather than rules-based. This implies that managers are
provided with significant discretion with respect to the application of the standards. Fair value
is an important example of such a principle (Schipper, 2005; Hamberg, Novak, Paananen,
2006).
An important application of fair value accounting can be found in the IAS36 standard. This
standard deals with the impairment of assets. The objective of this standard is the reflection of
the true value of a firm’s assets on its balance sheet. More specifically, IAS36 is designed to
ensure that assets are carried at no more than their recoverable amount and to define how
recoverable amount is to be determined. The standard applies to most of a firm’s long term
assets such as land, buildings, machinery and equipment, intangible assets and goodwill.
In this paper we focus on goodwill impairment decisions. The introduction of IFRS implies a
significant change in the way goodwill is to be valued. Under most previous national
accounting standards firms wrote off goodwill on an annual base. Under IFRS 3 firms are
required to engage in an annual impairment test. Specifically firms need to assess whether the
carrying amount of goodwill (i.e. the value on the balance sheet) does not exceed the true or
real value. Therefore, this particular standard is one of the main reasons why net income
under IFRS is in most cases higher than under local standards, as linear depreciation
disappears. More importantly, the standard provides managers with considerable discretion
about how to assess the true value of the firm’s goodwill.
A firm’s decision to impair its goodwill or not, can be considered as an important indicator of
its financial reporting quality. Further, the quality and amount of disclosure concerning this
decision are also important indicators of the informativeness of the accounting numbers in the
2
balance sheet. If the carrying value of goodwill exceeds the recoverable amount, impairment
is necessary. However, in their annual impairment test managers are able to exercise their
discretion over the calculation of this recoverable amount (Harris and Caplan, 2002).
Consequently, managers that want to conceal relevant information from investors have an
incentive to declare that the book value of goodwill is not higher than its fair value when the
opposite is true.
In our study we select a sample of firms that have a high likelihood of having to impair their
goodwill. For these firms we handcollect and investigate firm-specific determinants of two
impairment decisions: (i) the choice to impair goodwill or not and (ii) the amount and quality
of the information provided in the annual report in terms of their goodwill impairment tests
and/or decisions. Relying on previous literature, we predict a significant impact of governance
quality and firm performance on these two impairment decisions. First, we hypothesize that a
lower level of concentrated ownership, a higher number of independent board members and
the separation of CEO and chairman of the board will ceteris paribus lead to a larger
probability of impairment and a higher quality disclosure. As such, the level of financial
reporting quality increases as well. Second, we predict that firms exhibiting a better
accounting and market performance will be more likely to decide to impair goodwill and to
provide higher quality information regarding their impairment decision.
Our empirical tests provide mixed support for these predictions. Most notably, we find that
the number of independent board members provides important incentives for managers to
engage in an impairment of goodwill. Next, higher firm performance leads to a significantly
higher likelihood of goodwill impairment. Further, we find that firms with higher growth
opportunities are be less likely to impair goodwill. These firms probably have fewer reasons
to impair goodwill, as they have high growth prospects. Conversely, larger firms are more
likely to impair goodwill. Additionally, stronger outsider investor rights also have a positive
3
impact on the chance that a firm impairs its goodwill when it has to do so. Governance
indicators do seem not have a significant impact on the disclosure quality of impairment
decisions, although we do find a positive (resp. negative) association between disclosure
quality and firm performance (resp. leverage).
This study contributes to the literature in two ways. First, our results stress the importance of
corporate governance quality in providing incentives for managers to engage in high quality
financial reporting. Second, we add to the stream of literature documenting the consequences
of the significant amount of discretion managers are provided with when applying IFRS. Our
results contrast with the belief that the adoption of IFRS around the world is likely to entail a
standardization of accounting practice. To obtain high quality financial statements, the
introduction of IFRS on itself is unlikely to be sufficient without the necessary level of
enforcement. Therefore, our results suggest that policy makers need to stress the importance
of high quality corporate governance practices.
The remainder of this paper is structured as follows. Section 2 motivates the research
question, describes prior literature and builds hypotheses. Section 3 describes the design of
our regression models and defines variables. Section 4 provides details on our sample. In
Section 5 we report descriptive, univariate and multivariate results. Section 6 concludes.
2. Motivation and Hypotheses
2.1 Background of IAS 36 “Impairment of Assets”
Before focusing on the potential determinants of goodwill decisions, we first explain briefly
what IAS 36, Impairment of Assets, exactly entails. IAS 36 applies to most long term assets,
and thus goodwill amongst others. The standard encompasses the requirement to make all
assets subject to an annual impairment test. In this situation, assets are no longer to be
depreciated annually. IAS 36 proposes specific procedures which are designed to prevent an
4
asset’s carrying value to exceed its recoverable amount. This recoverable amount is the higher
value of the value in use and the exit value of the asset. Value in use is defined as “the present
value of estimated future cash flows expected to arise from the continuing use of an asset and
from its disposal at the end of its useful life”. Exit value is defined as “the amount obtainable
from the sale of an asset in an arm’s length transaction between knowledgeable, willing
parties, which is also referred to as the fair value, less the costs of disposal.” Thus, two
numbers are to be calculated in order to obtain the recoverable amount of an asset: the fair
value minus costs of disposal and the value in use. The recoverable amount is calculated for
the smallest cash-generating unit (CGU) to which the asset belongs1. Goodwill is hard to
attribute to a specific CGU. Goodwill acquired in business combinations (IFRS 3) is to be
attributed to each of the CGUs or groups of CGUs of the acquiring firm. Assessing the fair
value of goodwill is extremely difficult and highly subjective. Therefore, in most cases the
recoverable amount is based on the value in use (McDonnel, 2005). The value in use of
goodwill is the net present value for each CGU for which goodwill is attributed to separately.
If the carrying amount of goodwill (i.e. the book value on the balance sheet) is lower than this
recoverable amount, the firm must engage in an impairment. In case of an impairment loss, a
firm is required to disclose the causes of the loss, the amount of the loss and where in the
firm’s assets the loss is situated. Firms must also explain how the future expected cash flows
are assessed. Also, the discount rate applied to these cash flows must be disclosed.
Clearly, managers can exercise their discretion over the calculation of the value in use by
exaggerating the expected future cash flows and/or understating the discount rate. Goodwill
and intangible assets in general are sensitive to managerial manipulation (Wilson, 1996). For
outside shareholders it is very hard to judge whether these estimations are conservative rather
than aggressive.
1
A cash-generating unit is the smallest identifiable group of assets that generates cash inflows from continuing
use that are largely independent of the cash inflows from other assets or groups of assets .
5
In related literature, Hamberg et al. (2006) investigate the impact of IFRS adoption on
goodwill and other intangible assets. They find evidence that goodwill value becomes more
persistent when a yearly impairment test is executed. Consequently, the losses in goodwill
value are lower than the previous goodwill depreciation. The question whether companies
impair when they should do so remains unanswered.
The argument the FASB and IASB put forward to prefer a yearly impairment test is that
writing of goodwill annually does not represent the economic reality. There is not yet a full
agreement between the FASB and the IASB in terms of how goodwill should be valued
exactly. However, both standard setting bodies agree upon the replacement of yearly
depreciation through an annual impairment test (Whittington, 2005). Because SFAS 142 was
implemented earlier than IFRS in Europe, most research is available on US data. Most
prominently, Beatty and Weber (2005) investigate determinants of goodwill impairment
decisions under SFAS 142. They document that both stock markets as well as debt contracting
agreements provide managers with significant incentives in their decision to impair goodwill
or not. Firms that engage in less goodwill impairment are also providing fewer financial
information in general to investors. Further, they find that firms reporting a large impairment
loss at one point in time are impairing less in future years. They conclude that SFAS 142 does
not contribute to a larger objectivity with respect to the treatment of goodwill, as managers
can exercise their discretion over these decisions.
Similarly, Francis, Hanna and Vincent (1997) find that discretionary impairment decisions or
announcement of goodwill depreciation are significantly more prominent for firms with a
change in management. Further, they find that expected goodwill depreciation decreases for
bad performing firms and for firms with an exceptionally good performance. These latter
results are in contrast to the big bath and earnings smoothing arguments. More recently, Hayn
6
and Hughes (2006) find that take-over characteristics are significant determinants of goodwill
write-off decisions, whereas performance variables are not or only marginally significant.
Similar to our study, Astami, Hartadi and Tower (2008) investigate the impact of firmspecific variables on goodwill impairment decisions for companies from Australia, Hong
Kong, Indonesia, Malaysia and Singapore. They find evidence that managers tend to make
income increasing decisions in valuing goodwill when firm performance is low and when the
firm is highly levered. This result is consistent with the notion that managers use the
discretion available in the goodwill impairment rules in order to prevent covenant violations
in debt contracting. Further, the authors document that larger firms use fewer income
increasing accounting techniques in terms of goodwill impairment. Finally, Astami et al.
(2008) find that country-level characteristics critically determine goodwill impairment
decisions. In countries with a more stringent legal enforcement, discretionary income
increasing decisions are less likely. This result is consistent with Ball, Robin and Wu (2003)
who argue that the introduction of international standards will only entail significant increases
in financial reporting quality in environment with sufficient legal enforcement and investor
protection.
We contribute to this literature in at least three ways. First, we document on the prevalence
and magnitude of goodwill impairment under IFRS for European firms. Secondly, we are the
first to explicitly investigate to which extent corporate governance practices determine
goodwill impairment decisions. Thirdly, we are among the first to investigate disclosure
quality of impairment decisions and its potential determinants.
2.2 Determinants of Goodwill Impairment
In this study we hypothesize that managers will use the discretion available in the IFRS regulation
to their own advantage. The extent to which they will engage in these activities depends upon the
7
firm’s reporting incentives provided by its governance environment. Some firms may adopt IFRS
without making any material changes to their reporting quality (Daske et al., 2008). They may
rather simply adopt IFRS because they are required to do so. Other firms may consider the
adoption of IFRS as an opportunity to provide higher quality financial information to the market,
and to raise the company to a higher level of transparency.
The theoretical underpinnings of our hypotheses are a number of recent studies that stress the
importance of reporting incentives, rather than accounting standards itself, as key
determinants of accounting practice.2 This stream of literature claims that the adoption of
IFRS on itself is not sufficient to increase financial reporting quality. Part of the explanation
is that IFRS, along with many other accounting standards, allows managers to exercise their
discretion when applying the standards. The way in which firms use this discretion depends
on their reporting incentives. The latter are determined by many factors, including countries’
institutional environment (Ball et al., 2000 and 2003, Leuz et al., 2003) and firm
characteristics (Wang, 2006 and Watts and Zimmermann, 1978).
This prior literature allows us to predict significant differences in the way firms make
impairment decisions under the new IFRS regulation. As argued above, IAS36 leaves
considerable discretion possible for managers in valuing goodwill. Therefore, we argue that a
firm’s decision to impair goodwill when it should do so and the quality of its impairment
disclosure both depend upon a number of firm-specific characteristics. In this paper, we test
the impact of governance and performance related factors on impairment decisions.
Specifically, we hypothesize better corporate governance and better firm performance to
positively influence our two dependent variables, i.e. the decision to impair goodwill or not
and the quality of disclosure about goodwill impairment decisions.
2
The literature on the role of reporting incentives provided by the legal and financial environment versus
standards is rapidly growing. Examples are Ball et al. (2000, 2003), Leuz (2003), Ball and Shivakumar (2005),
Lang et al. (2006), Burgstahler et al. (2006), Skinner (2008) and Verriest et al., (2008).
8
First, we predict a negative association between the level of ownership concentration and the
likelihood to engage in an impairment decision. Governance literature describes a so-called
entrenchement effect. Thereby, there is possibly greater information asymmetry between large
(majority) shareholders of a company and other shareholders. Fan and Wong (2002) state that
concentrated ownership limits accounting information flows to outside investors. Further,
Francis, Schipper, and Vincent (2005) argue that information asymmetry lowers the
transparency of accounting disclosures. Therefore, majority shareholders (or possibly
founding family members that have large stakes in the firm) have the incentive to manipulate
accounting earnings for their own benefit. Thus, the entrenchment effect predicts that
ownership concentration is negatively associated with earnings and financial reporting
quality. As a result, we expect these managers to be more reluctant to engage in an
impairment decision and to disclose lower quality information about goodwill impairment:
Hypothesis 1a: Firms with a higher amount of free float and a less concentrated
ownership structure are more likely to engage in a goodwill impairment decision.
Hypothesis 1b: Firms with a higher amount of free float and a less concentrated
ownership structure disclose more information on their goodwill impairment decision.
Next, we focus on corporate governance quality as a potential driver of goodwill impairment
decisions. The impact of country-level governance quality has been documented in literature
(e.g. Leuz et al., 2003 and Daske et al., 2008). Firm-specific governance has been far less the
subject of investigation but it is equally important. Corporate governance mechanisms and
structures are important determinants of financial reporting quality (e.g. Klein, 2002; Larcker
et al., 2007) and IFRS adoption quality (Gaeremynck and Verriest, 2009). Therefore, in this
study we focus on the impact of corporate governance on impairment decisions.
9
Corporate governance is typically defined as “a set of structures that monitor a firm’s
operations (Shleifer and Vishny, 1998; Larcker et al., 2007) and limits agency problems,
originating from the separation of ownership and control within a firm”. Agency problems
between insiders and outsiders can be limited by providing more transparent information.
Prior literature has documented that effective governance practices are positively related with
disclosure quality (e.g. Karamanou and Vafeas, 2005; Marques, 2006) and financial reporting
quality. However, results are rather weak and inconsistent in most cases (Larcker et al., 2007).
Klein (2002) and Chtourou et al. (2001) find a negative association between earnings
management and corporate governance measures such as board independence and functioning
of the audit committee. Fan and Wong (2002) find significant negative relations between
earnings quality and a firm’s degree of ownership concentration. DeFond and Jiambalvo
(1991) argue that the overstatement of earnings is less likely among firms with audit
committees. In sum, we add to literature by investigating the impact of corporate governance
on the decision to impair goodwill and on the disclosure quality involved in this decision.
This results in the following hypotheses:
Hypothesis 2a: Firms with higher board independence and separation of CEO and
board chairman are more likely to engage in a goodwill impairment decision.
Hypothesis 2b: Firms with higher board independence and separation of CEO and
board chairman disclose more information on their goodwill impairment decision.
Finally, we predict that goodwill impairment decisions will also be influenced by firm
performance. Francis, Hanna and Vincent (1996) argue that better performing firms are more
likely to engage in goodwill impairment. The signal they send out to investors of a lower
profitability will be weaker and of a lower importance, provided that these firms are
10
financially healthy. Conversely, financially distressed firms will have more incentives to
avoid impairments, as it will lower their earnings even further.
Similar to Francis, Hanna and Vincent (1996), we use a firm’s return on assets as an
(accounting) performance measure. Next, we also consider market returns as a performance
measure. We expect a positive relation between these variables and the likelihood of
impairment. Further, we expect these firms to engage in a higher disclosure quality of the
impairment process (Raffournier, 1995). Hence, we come to the following predictions:
Hypothesis 3a: Better performing firms are more likely to engage in a goodwill
impairment decision.
Hypothesis 3b: Better performing firms disclose more information on their goodwill
impairment decision.
Next to these test variables, we include control variables in our regression models. As a large
body of literature describes the significant impact of institutional factors on capital markets
(La Porta et al., 1998) and on financial reporting quality (Leuz et al., 2003), we include a
measure for the level of outside investor protection. We expect a positive impact on both of
our dependent variables. A detailed description of our two models and variable definitions is
provided in the next section.
3. Model Design and Variable Definition
In order to test our research hypotheses 1a, 2a and 3a, we develop an impairment decision
model. Whether a firm decides to impair its goodwill or not is predicted to be dependent upon
the factors described in the previous section. The dependent variable IMPAIR takes the value
of 1 if the company is impairing its goodwill and 0 otherwise. Since IMPAIR is a binary
11
variable, we estimate the regression as a logit model. Recall that we only consider those firms
for which we expect a decline in goodwill value. The way in which we determine this
subsample of firms is explained in the next section.
Our test variables include measures for ownership structure, corporate governance quality and
firm performance. First, we include FREE_FLOAT in the regression. This variable is
measured as the percentage of shares that are not owned by majority shareholders or family
owners. As stated in hypothesis 1a, we expect a positive sign on this variable. A second
measure of ownership structure is defined by AVG_PERC_MAJOR_SH. This is the average
percentage of shares held by the three biggest shareholders of the firm. For firms for which
ownership data were not available on Datastream, we collected the data from the annual
reports. Following hypothesis 1a, we expect a negative sign on this variable.
Second, we include two corporate governance variables to test our second hypothesis. First,
we use IND_TOT which captures the percentage of independent members of the board of
directors and is a good indicator of how boards are able to make decisions independent from
the company’s management. Next, we use a dummy variable CHAIRMAN_CEO that takes
the value of 1 if the director of the board and the chief executive officer are one and the same
person, and 0 otherwise. According to hypothesis 2a, we expect a positive sign on IND_TOT
and a negative one on CHAIRMAN_CEO. Again, we handcollect data on these variables
from the annual reports.
Third, we measure firm performance by means of two variables. Regarding accounting
performance (ACC_PERF), we calculate the firm’s earnings before interests and taxes (EBIT)
before the impairment of goodwill. Thus, ACC_PERF equals the return on assets ratio. To
measure market performance (MARKET_PERF), we calculate the growth in market
capitalization measured as the difference between market value at the end of the current fiscal
12
year minus market value at the end of the previous year, scaled by lagged market value. As
stated in hypothesis 3a, we expect positive signs on each of these two performance measures.
In our empirical analyses, we stress the importance of a number of control variables. To
measure the degree of outside investor protection (OIP), we include the anti-director rights
index created by La Porta et al. (1998). It is an aggregate measure of minority shareholder
rights and ranges from zero to five. Better institutional quality is expected to lead to a higher
likelihood of impairment decisions and to more disclosure. Further, we include the number of
CGUs over which goodwill is to be allocated. If not available, we use the number of segments
reported under IAS14. LISTINGS counts the number of stock exchanges on which the firm is
listed on. SIZE is measured as the natural logarithm of market capitalization. The expected
sign on CGU, LISTINGS and SIZE is positive, as larger firms have been documented to
provide higher quality information to investors. Additionally, we include a firm’s market-tobook value (MTBV) in order to control for differences in growth across the sample firms3.
Finally, LEVERAGE enters the model, measured as total debt on total assets. As a result, our
impairment decision model looks as follows:
IMPAIRi
=
ß0
+
ß1
FREE_FLOATi
+
ß2
AVG_PERC_MAJOR_SHi
+
ß3
CHAIRMAN_CEOi + ß4 IND/TOTALi + ß5 ACC_PERFi + ß6 MARKET_PERFi + ß7 OIPi
+ ß8 CGUi + ß9 LISTINGSi + ß10 SIZEi + ß11 MTBVi + ß12 LEVERAGEi + ε
(1).
To be able to test hypotheses 1b, 2b and 3b, we develop a disclosure model. The disclosure
quality of goodwill impairment decisions is expected to depend upon governance and
performance indicators, as noted above. Our dependent variable in these models is
SCORE_DISC. We measure this variable based on a check box of five disclosure items which
3
We are unable to make a prediction about the sign of MTBV. On the one hand, we might consider firms with a
high MTBV are in no need to impair goodwill. On the other hand, since these firms are generally well
performing (in terms of stock value), we might expect them to engage in impairment decisions.
13
all provide relevant information to investors in terms of goodwill valuation. Specifically, we
gather these disclosure data manually from the notes in the annual reports of our sample firms
and code whether the firm provides information on these items, in which case the firm gets a
1 for that item, or not, in which case a 0 is provided. These five items include: (1) whether
goodwill is mentioned separately in the notes or not; (2) whether the CGUs are mentioned
over which goodwill is allocated or not; (3) whether the effective amount of goodwill that is
allocated to the CGUs is disclosed or not; (4) whether the discount rate used to calculate value
in use is disclosed or not; and (5) whether the growth rates of the expected future cash flows
used to calculate the value in use is disclosed or not. Since SCORE_DISC is an ordinal
dependent variable, we use an ordered logit specification to test the disclosure model.
All test variables in the disclosure model are equal to those used in the impairment decision
model, so are their predicted signs. Control variables are the same as well. Consequently, the
disclosure model we test looks as follows:
SCORE_DISCi = ß0 + ß1 FREE_FLOATi + ß2 AVG_PERC_MAJOR_SHi + ß3
CHAIRMAN_CEOi + ß4 IND/TOTALi + ß5 ACC_PERFi + ß6 MARKET_PERFi + ß7 OIPi
+ ß8 CGUi + ß9 LISTINGSi + ß10 SIZEi + ß11 MTBVi + ß12 LEVERAGEi + ε
(2).
4. Sample Composition
To answer our research questions, we rely on a sample of FTSE 300 companies for the years
2005 and 2006. This sample contains the 300 largest European listed firms in terms of market
capitalization. One specific feature about our study is that we only consider firms in our
sample that are expected to engage in goodwill impairment. We operationalize this by
investigating whether a firm’s book value of equity minus its market value is smaller than the
amount of goodwill reported on the balance sheet. Clearly, a decrease in goodwill value is
14
very likely to be necessary for these firms in order to provide high quality financial
information to the market. This approach is similar to Beatty and Weber (2005). This
procedure limits our sample to 62 firms in 2005 and 2006. Firms without any goodwill on the
balance sheet are left out since our research question is not applicable for these firms. Further,
we exclude firms with a fiscal year-end before 31/12/2005. These firms were not yet required
to apply IFRS, as part of their annual statements go back to 2004. Finally, we exclude any
financial firms from our sample. This provides us with a sample of 47 companies (Table 1). A
potential concern with this small dataset is that our statistical tests might turn out not to be
significant (or marginally significant) because of a lack of statistical power.
5. Results
5.1 Descriptive results
In Table 2 we document descriptive statistics. The most relevant number in this table is the
relatively low amount of firms that engage in goodwill impairment. The percentage is only
53%. Recall that all firms in the sample are expected to engage in some form of goodwill
impairment. This low number itself already indicates that the interpretation of IAS36 is likely
to differ across firms. The average amount of goodwill depreciation amounts to 1,93% of
goodwill value, which is a relatively low number. Disclosure quality on impairment decisions
reaches 3,3 on a maximum of 5 for the average firm. Only two firms provide no information
at all (SCORE_DISC = 0).
The average firm has a free float of 66%, which is considerably high. This is not surprising as
our sample only consists of large firms. Similarly, the average percentage of major
shareholders in the firm is pretty low, attaining a value of 23,3%. For only 21% of the sample
companies, the CEO and chairman of the board are the same person. Both the mean and
median percentage of independent board members is above 50%. These figures indicate a
15
high level of corporate governance quality for our sample firms. Again, this is not really
surprising as governance quality and firm size are positively correlated (see for example
Renders and Gaeremynck, 2008).
Firm performance proves to be relatively stable across the sample, showing a low variation.
On average, the return on assets (ACC_PERF) reaches a value of 5,9%. Market returns
(MARKET_PERF) are positive for over 75% of the sample (Q1 = 2,7%). This result seems
reasonable given that the years 2005 and 2006 have entailed highly positive stock returns in
general. Further, we notice that the median firm in our sample consists of 5 CGUs, is listed on
two stock exchanges, has a market-to-book value of 1,45 and a debt-to-assets ratio of 66%.
5.2 Univariate results
Table 3 provides Pearson and rank (Spearman) correlations between all variables of interest.
Most of the independent variables do not show significant correlations but there are a few
exceptions. Multicollinearity is not expected to be a relevant issue. However, we do notice a
significantly positive correlation between SIZE on the one hand and CGU and LISTINGS on
the other. In general, there are no big differences between rank and ordinary correlations,
which makes us conclude that there is no significant outlier effect in our sample.
More importantly, we notice a significantly positive correlation between IMPAIR and
IND/TOTAL (p=0,01). This confirms hypothesis 2a. Contrary to hypothesis 1a, we do not
see a significant correlation between the impairment dummy and either of the ownership
variables. Nevertheless, IMPAIR is positively related to FREE_FLOAT (p=0,17). IMPAIR is
not signficantly related to MARKET_PERF or ACC_PERF, although the coefficient with the
latter one is positive (p=0,29). Next, we find that IMPAIR is significantly correlated with
SIZE (p=0,03) and CGU (p=0,14).
16
We find weak evidence for hypotheses 1b, as the correlation between SCORE_DISC and
AVG_PERC_MAJOR_SH is marginally signficant in the hypothesized direction (p=0,12).
Also, firms with a separation of CEO and board chairman disclose significantly more
impairment information (p=0,04). This is in line with hypothesis 2b. We find mixed evidence
again for hypothesis 3, as SCORE_DISC is positively related to ACC_PERF (p=0,23) but
negatively associated with MARKET_PERF (p=0,15). We do find a significant (rank)
correlation between OIR and SCORE_DISC (p=0,06). This indicates that disclosure quality is
higher in high quality institutional settings.
In table 4, we execute t-tests and Wilcoxon-rangsum tests to test for significant differences
between firms that impair and those that do not. The former test for mean differences,
whereas the latter test for median differences. We find significant evidence that firms with
more independent boards are more likely to engage in an impairment decision when they
should do so. Further, impairing firms seem to have a larger free float as well. They also tend
to have a higher return on assets (ACC_PERF). Finally, impairing firms appear to be larger,
have more CGUs and operate in countries with high investor protection. Overall, we conclude
from our univariate analyses to find mixed evidence on the association between ownership
structure and firm performance on the one hand and impairment decisions on the other. We
find strong evidence for the notion that better corporate governance leads to a higher
likelihood of impairment, and thus to a higher financial reporting quality.
5.3 Multivariate results
A. Impairment Decision Model
Table 5 shows regression results from our impairment decision model, which is tested as a
logit specification. The (McFadden) R² is 51% in our full model (column 4), which is
significantly higher than the R² of 20% in our control model (column 1). This provides
17
evidence for the notion that ownership, governance and performance variables have
significant incremental explanatory power in explaining IMPAIR.
Our control model conveys that larger firms and firms operating in countries with a higher
level of shareholder protection are more likely to impair goodwill. Firms with higher marketto-book values appear to be less likely to impair. Probably they have less of a reason to do so
since their market value is high.
In line with our first hypothesis, we find a positive coefficient on FREE_FLOAT (p=0,22) in
column 2. However, the alternative ownership indicator has not the expected negative sign.
When considering our governance variables, we find a significantly positive coefficient on
IND_TOTAL.
This
confirms
hypothesis
2a
(p=0,02).
The
dummy
variable
CHAIRMAN_CEO is indistinguishable from zero. In column 3, we find evidence consistent
with hypothesis 3a. In column 3, the coefficients on both ACC_PERF and MARKET_PERF
are positive and significant (p= 0,12 and 0,03 resp.). In column 4, we combine all test
variables and conclude that the decision to impair is significantly positively related to
IND_TOTAL, ACC_PERF and MARKET_PERF. This is consistent with our predictions.
Further, we notice that the control variables SIZE and OIP continue to carry significantly
positive signs throughout the four models tested in Table 5.
B. Impairment Disclosure Model
When executing the control specification of the disclosure model, we find significantly
positive variables on LISTING and CGU. However, the coefficient on SIZE is negative. This
is counterintuitive and not consistent with univariate correlations4. Therefore, we decide to
4
The reason is because these variables are significantly correlated.
18
run the disclosure model without LISTINGS5. Results of this ordered logit model are shown
in Table 6.
In the control specification, LEVERAGE and MTBV appear to significantly drive the
disclosure score on impairment decisions. In column 2, we detect negative signs on the
coefficients for AVG_PER_MAJOR_SH and CHAIRMAN_CEO. Although the variables are
only marginally significant (p=0,26 and 0,20 resp.), this result is in line with hypotheses 1b
and 2b. The coefficients on IND_TOTAL and FREE_FLOAT are indistinguishable from zero.
In other words, the significant impact of board independence on the decision to impair does
not hold for the disclosure quality of the impairment. When entering our performance
variables in the model (column 3), MARKET_PERF carries a significantly negative
coefficient, whereas ACC_PERF is not significant. These results goes against hypothesis 3b.
In the combined model (column 4) we only find significant coefficients with control
variables. MARKET_PERF still carries the opposite sign as we expect. IND_TOTAL and
CHAIRMAN_CEO carry coefficients with the predicted sign but statistical significance is
weak (p=0,32 and 0,19 resp.). Although this lack of significance could be due to a statistical
power issue, our test variables do not seem to be as accurate in explaining variation in
disclosure quality on impairment decisions as they are in explaining the decision to impair or
not.
In further (unreported) analyses, we distinguish between the physical disclosure aspect of
goodwill impairment and the calculations aspect. The former is a score on 2 measuring the
presence of goodwill in general and the number of CGUs in the firms while the latter is a
score on 3 measuring whether the effective amounts allocated to the CGUs, the discount rates
and growth rates of future expected cash flows are reported. We expect most firms to disclose
significanlty upon the first one. Conversely, we expect more variation across firms for the
5
Replacing LISTINGS through SIZE, or leaving both variables in the regression does not alter the coefficients
of our test variables in a significant manner.
19
second disclosure part. Consistent with these prediction, we find that the coefficients on
AVG_PERC_MAJOR_SH, INT_TOT and CHAIRMAN_CEO carry the expected sign and
are marginally significant in explaining the calculations aspect of SCORE_DISC. They are
not significant in explaining the other part of goodwill disclosure. In this case we find a low
variation across our sample firms. We interpret this result as consistent with our predictions
and prior findings.
6. Conclusion
In this study we investigate to what extent ownership structure, corporate governance quality
and firm performance influence both the decision to impair goodwill and the disclosure
quality of these impairment decisions. To test our research question, we select a sample of
firms that should impair their goodwill. This procedure limits our sample size considerably
but enables us to eliminate firms that are not expected to impair their goodwill.
IFRS introduced fair value principles in Europe. In light of this, the standard dealing with the
impairment of assets (IAS36) has the objective to reflect the true value of a firm’s assets on its
balance sheet. More specifically, IAS36 is designed to ensure that the carrying amount of
goodwill does not exceed its recoverable amount. However, the standard provides managers
with considerable discretion about how to assess the true value of goodwill. Managers are
able to exercise their discretion over the calculation of what is supposed to be the true value of
goodwill. Thereby, they can conceal relevant information from outside investors. We consider
management decision not to impair goodwill when they should do so as a lack of financial
reporting quality. Therefore, we predict that ownership structure, governance quality and firm
performance are relevant determinants of impairment decisions.
Our univariate results show that the decision to impair goodwill is significantly and positively
influenced by a firm’s corporate governance quality, measured by the amount of independent
20
members on the board. This is our single most important result and it is confirmed in our
regression analyses. Further, our multiple regressions convey that better performing firms are
more likely to engage in goodwill impairment when they need to do so. Further, we find that
outside investor rights are positively associated with the likelihood of a firm to impair its
goodwill. Next, we find mixed evidence for the notion that a less concentrated ownership
structure and better governance quality are associated with better impairment disclosure.
Finally, we document that firm size and the number of cash-generating units in a firm are
positively related to the decision to impair.
Our results confirm prior findings that international standards will only entail the desired
outcome when managers have incentives to apply the new standards as a commitment towards
more transparency. Therefore, our results highlight the importance of corporate governance
quality in obtaining high quality financial statements.
21
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[05/12/2007]
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http://www.fasb.org/ [05/12/2007)
24
Table 1: Sample Selection
Starting sample: FTSE 300 firms with fiscal year-ends
in 2005 and 2006
minus number of
Firm years in which market value of equity minus book
value of equity exceeds the carrying amount of goodwill
Firm years with zero goodwill value
Firms with a fiscal year-end in 2005 other than
31/12/2005
Financial firms
Final Sample:
600
538
3
7
5
47
25
Table 2: Descriptive Statistics
This table shows results for the mean, median, minimum, maximum, 25 th percentile, 75th percentile and standard
deviation of all relevant variables in our study. The sample contains 47 firms, derived in Table 1. IMPAIR is a
binary variable taking the value 1 if the firm engaged in a goodwill impairment in 2005 and/or 2006.
SCORE_DISC is an indicator variable on 5 based on the presence of information in the annual report on (1)
goodwill value itself; (2) the CGUs over which goodwill is allocated; (3) the effective amount of goodwill
allocated to the CGUs; (4) the discount rate and (5) growth rates of the expected future cash flows both used to
calculate the value in use. ACC_PERF is net income before tax plus interest expenses scaled by total assets.
MARKET_PERF is the difference between current market value (at fiscal year-end) minus lagged market value
scaled by lagged market value. FREE_FLOAT is the percentage of shares not in control of majority shareholders
or controlling owners. AVG_PERC_MAJOR_SH is the average percentage of shares held by the three biggest
shareholders of the firm. CHAIRMAN_CEO is a binary variable that equals 1 if the board director and the chief
executive officer are the same person. IND_TOT is the percentage of independent members of the board of
directors. OIP is the anti-director rights index created by La Porta et al. (1998). It is an aggregate measure of
minority shareholder rights and ranges from zero to five. LISTINGS is the number of stock exchange the firm is
listed on. CGU is the number of cash genereting units reported by the firm. SIZE is the natural logarithm of
market capitalization at fiscal year-end. MTBV is the market capitalization at fiscal year-end divided by book
value of equity. LEVERAGE is total assets minus book value of equity scaled by total assets.
Variable
IMPAIR
SCORE_DISC
ACC_PERF
MARKET_PERF
FREE_FLOAT
AVG_PERC_MAJOR_SH
CHAIRMAN_CEO
IND/TOTAL
OIP
LISTINGS
CGU
SIZE
MTBV
LEVERAGE
No Obs
MEAN
MEDIAN
MIN
MAX
Q1
Q3
ST DEV
47
47
47
47
47
47
47
47
47
47
47
47
47
47
0,532
3,277
0,059
0,218
65,9
0,233
0,213
0,564
2,617
2,957
5,426
16,674
1,490
0,656
1,000
3,000
0,056
0,208
67,0
0,178
0,000
0,579
3,000
2,000
5,000
16,420
1,455
0,657
0,000
0,000
-0,002
-0,177
14,0
0,018
0,000
0,167
0,000
1,000
2,000
15,325
0,666
0,325
1,000
5,000
0,110
0,953
100,0
0,662
1,000
0,889
5,000
8,000
16,000
18,652
2,956
0,935
0,000
2,000
0,040
0,027
53,0
0,076
0,000
0,467
1,000
1,000
4,000
16,080
1,167
0,567
1,000
5,000
0,086
0,346
83,0
0,338
0,000
0,654
3,000
4,000
6,000
17,229
1,697
0,742
0,504
1,528
0,028
0,248
21,4
0,185
0,414
0,166
1,423
1,956
2,611
0,821
0,502
0,123
26
Table 3: Correlations
This table show Pearson correlations with p-values below in the upper right triangle. Spearman (rank) correlations with p-values below are provided in the lower left triangle.
IMPAIR is a binary variable taking the value 1 if the firm engaged in a goodwill impairment in 2005 and/or 2006. SCORE_DISC is an indicator variable on 5 based on the
presence of information in the annual report on (1) goodwill value itself; (2) the CGUs over which goodwill is allocated; (3) the effective amount of goodwill allocated to the
CGUs; (4) the discount rate and (5) growth rates of the expected future cash flows both used to calculate the value in use. ACC_PERF is net income before tax plus interest
expenses scaled by total assets. MARKET_PERF is the difference between current market value (at fiscal year-end) minus lagged market value scaled by lagged market
value. FREE_FLOAT is the percentage of shares not in control of majority shareholders or controlling owners. AVG_PERC_MAJOR_SH is the average percentage of shares
held by the three biggest shareholders of the firm. CHAIRMAN_CEO is a binary variable that equals 1 if the board director and the chief executive officer are the same
person. IND_TOT is the percentage of independent members of the board of directors. OIP is the anti-director rights index created by La Porta et al. (1998). It is an aggregate
measure of minority shareholder rights and ranges from zero to five. LISTINGS is the number of stock exchange the firm is listed on. CGU is the number of cash genereting
units reported by the firm. SIZE is the natural logarithm of market capitalization at fiscal year-end. MTBV is the market capitalization at fiscal year-end divided by book value
of equity. LEVERAGE is total assets minus book value of equity scaled by total assets.
1
1. IMPAIR
2. SCORE_DISC
3. ACC_PERF
4. MARKET_ PERF
5. FREE_ FLOAT
6. AVG_PERC_ MJR_SH
7. CHAIRMAN_ CEO
8. IND/TOTAL
9. OIP
10. LISTINGS
11. CGU
12. SIZE
13. MTBV
14. LEVERAGE
-0,066
0,658
0,179
0,228
-0,053
0,721
0,165
0,267
-0,060
0,690
0,071
0,636
0,380
0,009
0,186
0,211
-0,053
0,723
0,267
0,070
0,327
0,025
-0,066
0,659
0,025
0,867
2
-0,054
0,718
0,189
0,204
-0,205
0,168
0,050
0,741
-0,313
0,032
-0,319
0,029
0,148
0,320
0,278
0,058
0,033
0,827
0,016
0,915
-0,141
0,343
0,274
0,063
-0,340
0,019
3
0,158
0,288
0,177
0,234
0,054
0,718
0,263
0,074
-0,279
0,058
-0,096
0,522
-0,090
0,546
-0,016
0,915
-0,055
0,712
-0,031
0,836
0,311
0,033
0,491
0,000
-0,035
0,816
4
-0,025
0,868
-0,212
0,152
0,091
0,544
0,119
0,424
0,126
0,398
0,138
0,355
0,013
0,932
-0,185
0,213
-0,131
0,379
-0,326
0,025
-0,386
0,007
0,068
0,650
0,004
0,981
5
0,206
0,165
0,005
0,973
0,255
0,084
0,173
0,244
-0,524
0,000
-0,174
0,241
0,187
0,208
0,152
0,309
0,081
0,589
-0,077
0,608
0,239
0,105
0,247
0,095
0,012
0,936
6
-0,042
0,779
-0,232
0,117
-0,259
0,079
0,107
0,474
-0,500
0,000
0,341
0,019
-0,334
0,022
-0,438
0,002
0,023
0,878
0,121
0,419
-0,237
0,109
-0,238
0,107
0,275
0,061
7
0,071
0,636
-0,302
0,039
-0,089
0,553
0,027
0,856
-0,152
0,309
0,364
0,012
-0,065
0,663
-0,060
0,688
-0,184
0,215
0,150
0,315
-0,080
0,591
-0,130
0,383
0,134
0,369
8
0,370
0,010
0,144
0,335
-0,022
0,885
0,039
0,796
0,235
0,111
-0,352
0,015
-0,040
0,791
0,047
0,751
0,067
0,657
0,017
0,909
0,015
0,919
-0,155
0,297
-0,118
0,430
9
0,169
0,256
0,280
0,057
-0,032
0,832
-0,252
0,088
0,198
0,182
-0,314
0,031
-0,080
0,592
0,124
0,407
-0,311
0,033
0,111
0,457
0,012
0,937
0,242
0,102
-0,081
0,587
10
-0,021
0,891
0,004
0,979
-0,050
0,741
-0,008
0,960
0,058
0,701
-0,146
0,329
-0,177
0,235
0,053
0,725
-0,295
0,044
-0,194
0,192
0,219
0,139
-0,124
0,407
0,350
0,016
11
0,221
0,136
0,166
0,265
-0,012
0,937
-0,344
0,018
0,001
0,995
0,099
0,510
0,015
0,920
0,073
0,628
0,215
0,148
-0,077
0,606
0,293
0,046
0,121
0,418
0,114
0,445
12
0,317
0,030
-0,066
0,662
0,290
0,048
-0,413
0,004
0,241
0,102
-0,264
0,073
-0,066
0,660
0,044
0,767
-0,047
0,755
0,317
0,030
0,336
0,021
0,229
0,121
0,150
0,313
13
-0,137
0,360
0,278
0,058
0,443
0,002
0,070
0,638
0,211
0,155
-0,113
0,448
-0,155
0,299
-0,154
0,302
0,231
0,118
-0,096
0,519
0,203
0,170
0,122
0,415
14
-0,032
0,832
-0,348
0,016
-0,067
0,657
0,020
0,896
-0,024
0,873
0,204
0,170
0,149
0,317
-0,140
0,349
-0,103
0,493
0,312
0,033
0,093
0,534
-0,017
0,912
0,296
0,044
0,189
0,203
27
Table 4: Univariate Tests
Panel A displays mean values for non-impairing and impairing firms. Significance levels are shown by means of
two-tailed p-values from T-tests. Panel B displays median values for non-impairing and impairing firms.
Significance levels are shown by means of two-tailed p-values from Wilcoxon rangsum tests. IMPAIR is a
binary variable taking the value 1 if the firm engaged in a goodwill impairment in 2005 and/or 2006.
ACC_PERF is net income before tax plus interest expenses scaled by total assets. MARKET_PERF is the
difference between current market value (at fiscal year-end) minus lagged market value scaled by lagged market
value. FREE_FLOAT is the percentage of shares not in control of majority shareholders or controlling owners.
AVG_PERC_MAJOR_SH is the average percentage of shares held by the three biggest shareholders of the firm.
CHAIRMAN_CEO is a binary variable that equals 1 if the board director and the chief executive officer are the
same person. IND_TOT is the percentage of independent members of the board of directors. OIP is the antidirector rights index created by La Porta et al. (1998). It is an aggregate measure of minority shareholder rights
and ranges from zero to five. LISTINGS is the number of stock exchange the firm is listed on. CGU is the
number of cash genereting units reported by the firm. SIZE is the natural logarithm of market capitalization at
fiscal year-end. MTBV is the market capitalization at fiscal year-end divided by book value of equity.
LEVERAGE is total assets minus book value of equity scaled by total assets.
Panel A: Mean Differences (T-tests)
IMPAIR =0
IMPAIR =1
p-value
0,055
0,224
0,064
0,212
0,288
0,868
FREE_FLOAT
AVG_PERC_MAJOR_SH
61,23
0,241
69,96
0,226
0,165
0,779
CHAIRMAN_CEO
IND/TOTAL
0,182
0,499
0,240
0,621
0,636
0,010
OIP
LISTINGS
2,364
3,000
2,840
2,920
0,256
0,891
CGU
SIZE
4,818
16,400
5,960
16,915
0,136
0,030
MTBV
LEVERAGE
1,563
0,660
1,426
0,653
0,360
0,832
IMPAIR =0
IMPAIR =1
p-value
ACC_PERF
MARKET_PERF
0,052
0,213
0,060
0,159
0,224
0,717
FREE_FLOAT
AVG_PERC_MAJOR_SH
65,50
0,186
67,00
0,160
0,263
0,685
CHAIRMAN_CEO
IND/TOTAL
0,000
0,517
0,000
0,615
0,733
0,010
OIP
LISTINGS
1,500
2,500
3,000
2,000
0,228
0,725
CGU
SIZE
4,500
16,192
5,000
16,635
0,075
0,027
MTBV
LEVERAGE
1,503
0,658
1,433
0,657
0,654
0,865
Variable
ACC_PERF
MARKET_PERF
Panel B:Median Differences (Rank Sum tests)
Variable
29
Table 5: Impairment Decision Model
This table provides coefficients and p-values from model 1, estimated as a logit model. IMPAIR is the dependent
variable in each regression and is a binary variable taking the value 1 if the firm engaged in a goodwill
impairment in 2005 and/or 2006. ACC_PERF is net income before tax plus interest expenses scaled by total
assets. MARKET_PERF is the difference between current market value (at fiscal year-end) minus lagged market
value scaled by lagged market value. FREE_FLOAT is the percentage of shares not in control of majority
shareholders or controlling owners. AVG_PERC_MAJOR_SH is the average percentage of shares held by the
three biggest shareholders of the firm. CHAIRMAN_CEO is a binary variable that equals 1 if the board director
and the chief executive officer are the same person. IND_TOT is the percentage of independent members of the
board of directors. OIP is the anti-director rights index created by La Porta et al. (1998). It is an aggregate
measure of minority shareholder rights and ranges from zero to five. LISTINGS is the number of stock exchange
the firm is listed on. CGU is the number of cash genereting units reported by the firm. SIZE is the natural
logarithm of market capitalization at fiscal year-end. MTBV is the market capitalization at fiscal year-end
divided by book value of equity. LEVERAGE is total assets minus book value of equity scaled by total assets.
Pred. Sign
INTERCEPT
?
FREE_FLOAT
+
AVG_PERC_MAJOR_SH
-
CHAIRMAN_CEO
-
IND/TOTAL
+
ACC_PERF
+
MARKET_PERF
+
OIP
+
LISTINGS
+
CGU
+
SIZE
+
MTBV
?
LEVERAGE
?
McFadden R²
No Observations
1
Coefficient
p-value
2
Coefficient
p-value
3
Coefficient
p-value
4
Coefficient
p-value
-21,804
0,020
-38,353
0,006
0,033
0,217
5,428
0,119
-38,037
0,011
-64,678
0,006
0,056
0,169
8,790
0,066
0,022
0,984
7,972
0,019
0,499
0,107
-0,126
0,588
0,215
0,269
1,371
0,020
-1,988
0,041
0,004
0,999
0,198
47
0,784
0,077
-0,181
0,535
0,147
0,504
1,857
0,017
-2,090
0,072
0,553
0,899
0,355
47
26,291
0,115
4,781
0,034
1,034
0,023
-0,164
0,541
0,438
0,076
2,123
0,015
-3,735
0,006
1,853
0,650
0,331
47
-0,394
0,786
12,238
0,015
54,501
0,037
4,881
0,093
1,747
0,021
-0,218
0,541
0,511
0,155
2,780
0,019
-5,207
0,012
3,813
0,504
0,507
47
30
Table 6: Impairment Disclosure Model
This table provides coefficients and p-values from model 2, estimated as an ordered logit model. SCORE_DISC
is the dependent variable in each regression and is an indicator variable on 5 based on the presence of
information in the annual report on (1) goodwill value itself; (2) the CGUs over which goodwill is allocated; (3)
the effective amount of goodwill allocated to the CGUs; (4) the discount rate and (5) growth rates of the
expected future cash flows both used to calculate the value in use. ACC_PERF is net income before tax plus
interest expenses scaled by total assets. MARKET_PERF is the difference between current market value (at
fiscal year-end) minus lagged market value scaled by lagged market value. FREE_FLOAT is the percentage of
shares not in control of majority shareholders or controlling owners. AVG_PERC_MAJOR_SH is the average
percentage of shares held by the three biggest shareholders of the firm. CHAIRMAN_CEO is a binary variable
that equals 1 if the board director and the chief executive officer are the same person. IND_TOT is the
percentage of independent members of the board of directors. OIP is the anti-director rights index created by La
Porta et al. (1998). It is an aggregate measure of minority shareholder rights and ranges from zero to five.
LISTINGS is the number of stock exchange the firm is listed on. CGU is the number of cash genereting units
reported by the firm. SIZE is the natural logarithm of market capitalization at fiscal year-end. MTBV is the
market capitalization at fiscal year-end divided by book value of equity. LEVERAGE is total assets minus book
value of equity scaled by total assets.
Pred. Sign
FREE_FLOAT
+
AVG_PERC_MAJOR_SH
-
CHAIRMAN_CEO
-
IND/TOTAL
+
ACC_PERF
+
MARKET_PERF
+
OIP
+
CGU
+
SIZE
+
MTBV
?
LEVERAGE
?
Psuedo R²
No Observations
1
Coefficient
p-value
2
Coefficient
p-value
3
Coefficient
p-value
4
Coefficient
p-value
-0,016
0,371
-2,298
0,263
-0,006
0,756
-1,423
0,498
-0,906
0,201
1,216
0,512
-0,959
0,187
1,871
0,319
9,428
0,430
-2,815
0,073
0,105
0,669
0,105
0,440
-0,927
0,041
1,698
0,035
-7,136
0,007
0,176
47
0,250
0,243
0,118
0,311
-0,374
0,304
1,407
0,028
-7,712
0,002
0,116
0,189
0,412
0,172
0,191
-0,550
0,174
1,410
0,039
-6,909
0,006
0,151
8,526
0,463
-2,690
0,060
0,176
0,434
0,071
0,556
-0,753
0,073
1,635
0,025
-7,798
0,003
0,142
47
47
47
31