Download Does location matter for disclosure? Evidence from geographical

Does location matter for disclosure? Evidence from geographical segments
Edith Leung1
Erasmus University Rotterdam - Erasmus School of Economics
Arnt Verriest
EDHEC Business School
September 2014
Abstract: This paper uses a novel approach to examine motives for geographic segment
disclosure. It is generally assumed that firms prefer not to disclose disaggregated segment
information for proprietary reasons, although there is mixed support for this assertion in
empirical work. We develop an approach that uses the location characteristics of geographic
segments to empirically identify reasons for withholding or disclosing segments. Using handcollected segment data around a switch in reporting standards that forced firms to reveal more
disaggregated segment information, we find that proprietary costs are the main determinant
of geographic segment disclosure. We find that segments in wealthier areas and ranked better
for business tend to be hidden, while higher entry barriers to a segment’s location are
positively related to the likelihood of a segment being disclosed. We also find that among
previously unrevealed segments, proprietary costs also explain the non-disclosure of segment
earnings and other information. In contrast, the attractiveness of a segment and entry barriers
do not explain the amount of disclosed information for segments that were already disclosed
prior to the switch in reporting standards. The findings suggest that proprietary, rather than
agency costs, are a more important determinant of geographic segment disclosure.
1
Corresponding author: [email protected]. We are grateful to David Veenman and seminar participants at the
2014 EAA annual meeting, CUHK, Humboldt University and WHU for helpful comments.
1. Introduction
This paper investigates whether the characteristics of the location of a firm’s segment
determine disclosure. Theoretical models of information disclosure typically assume the
existence of disclosure costs to explain why firms do not fully disclose all information in
their possession. The cost that is most commonly put forward as an example are proprietary
costs (Verrecchia 2001), which are the costs of disclosing strategic information to potential
competitors that can harm the disclosing firm (Darrough and Stoughton, 1990). However,
empirically verifying this assertion proves to be difficult for several reasons. First, many
papers have relied on measures of industry concentration or industry profitability (e.g.,
Harris, 1998; Botosan and Stanford, 2005) as proxies for proprietary costs. However, the
relationship between such measures and competition is not clear-cut: for instance, cut-throat
competition can exist in highly concentrated industries such as duopolies. In addition, it is
unclear whether competition is expected to lead to more or less disclosure: Li (2010) finds
that competition from potential entrants leads to more disclosure while competition from
existing rivals has the opposite effect on disclosure. Moreover, concentration is often
measured inaccurately since most researchers ignore the market share of private firms (Ali,
Klasa and Yeung, 2009) and industries are difficult to define (Bhojraj, Lee and Oler 2003).
Dedman and Lennox (2009) also find that traditional competition measures are not or at best
weakly related to managers’ perception of competition.
Second, Berger and Hann (2007) state that many studies ignore that disclosure may be
costly for other reasons. For instance, agency costs can lead to non-disclosure of poor
performance, whereas proprietary costs more likely lead to non-disclosure of high
performance, which is not always taken into account. Berger and Hann (2007) and Bens,
Berger and Monahan (2011) show that agency costs are indeed a major determinant of
disclosure.
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The purpose of this paper is twofold. First, we examine the importance of both agency
costs and proprietary costs to disclosure of geographic segments. Our choice for this setting is
related to our second purpose, namely to develop new proxies for proprietary costs that do
not rely on industry concentration or competition. Instead, we look at the characteristics of a
segment’s location, including the region’s economic growth, wealth, attractiveness for
business and entry barriers, as proxies for agency costs and proprietary costs of disclosure. If
agency cost considerations dominate the decision to disclose information about a segment,
we expect firms to show off “good” segments, which we define as segments in wealthier
(high GDP) areas, segments with higher economic growth or segments with better business
conditions. In contrast, when proprietary cost considerations are prevalent, we predict firms
to hide such segments or reveal less information about these segments. We also examine
whether entry barriers, measured as the cost and time necessary to start a new firm in a
particular region, affect disclosure of a particular segment. Our prediction is that when it is
easier for other firms to enter a particular country or region, revealing information about
segments in such areas can be more costly, since it gives away strategic information that
makes it easier for competitors to enter those markets.
To determine whether firms conceal or disclose segments, we follow the approach in
Berger and Hann (2007) and examine a switch in segment reporting standards that forces
firms to disaggregate geographic segments.2 We use the switch from IAS 14 to IFRS 8 in
2009 as our setting, as prior literature has shown that this change in reporting standards
resulted in more disaggregated segments (e.g., Crawford, Extance, Helliar and Power, 2012;
Nichols, Street and Cereola, 2012; Leung and Verriest, 2014). Under IFRS 8, firms are
required to restate their segments for the year prior to adoption, to allow users of financial
statements to compare performance across time. This is an ideal setting for us, since it allows
2
By disaggregation, we are referring to whether segments are reported at the country, multi-country or continent
level.
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us to identify which segments are newly disclosed under IFRS 8 for the pre-adoption year,
since we also have the original segment disclosures under IAS 14. We interpret newly
disclosed segments as segments that management wanted to hide, and segments that are
revealed under both standards as those that management wanted to disclose.
We find that, on average, new segments tend to be located in wealthier countries or
regions, which is consistent with the existence of proprietary costs that prevent disclosure of
“good” segments. Managers also tend to hide segments in regions that are ranked as better for
business by Forbes. We also find that when it is harder for other firms to enter a particular
region, segments in those areas are more likely to be disclosed, which also supports the
proprietary cost explanation for non-disclosure. These results are robust to controlling for
other factors that could determine disclosure, such as tax avoidance incentives and materiality
of the segment.
In addition to this, we examine how much information is disclosed about new segments. If
these newly revealed segments were indeed intentionally hidden by managers, we would
expect managers to disclose less information about these segments once they have to disclose
them under IFRS 8. We investigate for these segments whether earnings are less likely to be
disclosed and whether fewer line items are reported when proprietary cost concerns are
greater. We find that this is the case: the likelihood that segment earnings are disclosed is
greater when the segment’s location is less attractive and when the cost of starting a business
is higher. Results are similar for the number of segment line items: fewer items are reported
when segments are located in higher growth, more attractive regions and when the cost of
starting business is greater. Collectively, these results suggest that proprietary costs are an
important motivation for non-disclosure: segments that are potentially attractive to
competitors are not disclosed until IFRS 8 requires them to be. When adopting IFRS 8,
managers limit the amount of information they disclose about these new segments.
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Finally, we examine whether results are similar for segments that were already disclosed
(old segments). For the proprietary cost hypothesis to hold, we do not expect this to be the
case. Indeed, we find only weak evidence for proprietary costs being a determinant of
disclosure in this setting. Rather, agency costs appear to play a more important role. We find
that for already disclosed segments, GDP growth has a significantly positive effect on the
likelihood of earnings being disclosed and the number of disclosed line items, consistent with
managers revealing more about “good” segments. Entry barriers are no longer related to
disclosure for old segments. This reinforces our earlier conclusion that non-disclosure of
segment information is primarily due to proprietary cost concerns.
This study makes two main contributions. First, we contribute to the literature on
disclosure incentives, and segment disclosure in particular, by showing that proprietary costs
play an important role in a manager’s decision to disclose or withhold information about
geographic segments. Our results complement those of Berger and Hann (2007) and Bens et
al. (2011), who find that both agency and proprietary costs are important drivers of segment
disclosure. In contrast to these studies though, we find that proprietary cost motives, rather
than agency cost motives determine disclosure of geographic segments.
Second, our study demonstrates that the location of a segment and economic
characteristics of location are important determinants of segment disclosure. Many crosscountry studies in the accounting literature have examined the effect of country institutions
and legal regimes on reporting quality or cost of capital (e.g., Ball, Kothari and Robin, 2000;
Ball, Robin and Wu, 2003; Burgstahler, Hail and Leuz, 2006; Hail and Leuz, 2006) but to our
knowledge, we are the first to examine the effect of economic country and region
characteristics on disclosure and to disaggregate these characteristics to the segment level.
Our approach provides a novel measure of proprietary costs that does not suffer from the
aforementioned measurement issues of concentration or other competition measures.
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The paper proceeds as follows. Section 2 provides theoretical background on the
relationship between disclosure and agency or proprietary costs. Section 3 presents our
methodology; Section 4 discusses the sample and Section 5 presents the empirical results.
Section 6 provides concluding remarks.
2. Theoretical Background
Theoretical models in the accounting literature propose that the presence of costs to
disclosure result in equilibria where information can be withheld (e.g., Verrecchia, 1983).
Among the variety of reasons that can explain non-disclosure (such as agency costs or
uncertainty about the information signal received), proprietary costs, i.e., the cost of revealing
sensitive information that can be used by competitors to gain a strategic advantage, are the
most compelling explanation for not observing full disclosure equilibria (Verrecchia, 2001).
Indeed, managers and prior literature commonly propose proprietary costs as a reason for
concealing segment information. Most empirical studies on segment reporting use a proxy
for competition, such as industry concentration or the speed of profit adjustment over time, as
a measure of the extent of proprietary costs firms face and assess whether there are
systematic differences in disclosure behavior across competition levels. This approach has
two major limitations. First, it is not obvious that higher industry concentration indicates
lower levels of competition, nor is it clear whether greater competition leads to more or less
disclosure. Theoretically, there can be fierce competition in highly concentrated industries
(e.g., as Bertrand noted in 1883, there can be cut-throat price competition in duopolies),
different types of competition (from potential entrants versus existing rivals) affect disclosure
behavior differently (see e.g., Li, 2010), and the effect of competition on disclosure also
depends on whether it concerns good or bad news (e.g., Verrecchia, 2001). Indeed, empirical
research produces conflicting results: Harris (1998) and Botosan and Stanford (2005) find
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that there is more segment disclosure in less concentrated industries, whereas Botosan and
Harris (2000) find no evidence that increases in competition lead managers to start disclosing
more segment information. Moreover, Ali et al. (2009) criticize the use of concentration
measures as proxies for competition, as most studies calculate concentration using only
public firms. They show that including private firms in the calculation can reverse the results
obtained in prior literature. Dedman and Lennox (2009) also show that traditional
competition measures are weakly or not correlated with managers’ perception of competition,
which casts further doubt on the validity of these proxies.
Second, Berger and Hann (2007) and Bens et al. (2011) argue that agency costs are a
plausible alternative explanation to many of the results found in prior studies. For instance,
Berger and Hann (2007) argue that non-disclosure can also be a result of managers not
wanting to report detailed information about segments with poor performance to investors,
particularly when agency cost considerations dominate. Both of these studies show strong
support for the agency cost explanation of non-disclosure, with weaker support for the
proprietary cost explanation.
In this study, we address both of these issues explicitly. We introduce alternative
measures of proprietary costs that do not depend on traditional measures of competition and
also distinguish between both agency and proprietary cost explanations for segment
disclosure. This is also the reason we look at geographic segment disclosures. With the
exception of a few studies (e.g. Callen, Hope and Segal, 2006; Hope and Thomas, 2008),
most segment reporting studies focus on business segments. By instead focusing on
geographic segments, we can use the economic characteristics of the location of geographic
segments to infer whether proprietary or agency costs dominate the decision to disclose. We
argue that when agency cost considerations dominate, managers have incentives to disclose
or “show off” segments in regions with higher current and expected GDP growth, higher
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GDP (our measures of economic growth and economic wealth) and those rated better for
business in popular press. In contrast, when proprietary costs are more important to a firm,
we expect managers to hide segments in more attractive areas. The intuition behind this
approach is similar to that in Berger and Hann (2007), who distinguish between segments
based on segment profitability, whereas we distinguish based on the inferred profitability of
segments based on their location.
H1: When agency costs dominate, firms are more likely to disclose (information about)
segments in more attractive regions (i.e., higher economic wealth, growth or ranked
better for business).
H2: When proprietary costs dominate, firms are more likely to hide (information about)
segments with higher economic wealth or growth. (i.e., higher economic wealth, growth
or ranked better for business).
Another location characteristic that is an intuitive measure of proprietary costs is the ease
of starting a business in a particular country or region. There is substantial variation in the
time, cost and effort to start a new business across countries (Djankov, La Porta, Lopez-deSilanes and Shleifer, 2002). Intuitively, the proprietary costs of disclosing information about
a certain geographic segment are likely lower when it is more difficult for competitors to act
upon this information, i.e., when it is harder for them to start or do business in a particular
region. In our setting, this means that we would observe more disclosure when entry barriers
are high (ease of doing business is low). We therefore predict that:
H3: When a segment’s location has high entry barriers, firms are more likely to disclose
(information about) this segment.
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In the next section, we explain how we determine which segments are hidden or disclosed
and the importance of proprietary or agency costs.
3. Methodology
To test our hypotheses, it is necessary to determine two things: whether segments are
hidden or disclosed and how we measure agency and proprietary costs. We explain below
how we test this, as well as how we measure location characteristics for each segment.
Further details on variable measurement are provided in the Appendix.
3.1 Determining Hidden Segments
With respect to the first issue, we use the switch from IAS 14 to IFRS 8 to assess which
geographic segments managers wanted to keep hidden. IFRS 8 requires firms to report
operating segments, which can be line-of-business, product line or geographic segments.
Reported segments have to be consistent with how they are defined internally for decision
making purposes. This approach, also called the “management approach”, is intended to
increase the usefulness of segment reports by allowing investors to view operations through
the eyes of management. An initial concern that accompanied the introduction of IFRS 8 was
that it may lead to a loss of geographic segment reporting. Under IAS 14, if a firm chose
business segments as its “primary” segments, geographic segments were automatically
designated as “secondary” segments. This meant a firm would have to report revenues, assets
and capital expenditures for these secondary segments. In contrast, if a firm reports business
segments as its operating segments under IFRS 8, there is no secondary reporting format for
geographic segments. This caused concern that IFRS 8 would reduce the amount of
geographic segment information available to investors and led to opposition to the
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introduction of this standard (Véron 2007).
However, to counter this concern, IFRS 8
includes disclosure requirements for geographical areas and even disclosures by individual
foreign country, which went further than IAS 14. Indeed, several prior papers show that IFRS
8 resulted in greater disaggregation of geographic segments (see e.g., Crawford et al., 2012;
Nichols et al., 2012; Leung and Verriest, 2014) and thus forced disclosure of hidden
geographic segments.
As IFRS 8 also requires firms to restate their segment disclosures for the year prior to
adoption for comparison purposes, we can compare these restated disclosures to the original
disclosures and infer which segments are newly disclosed and which are old. If we find that a
segment in the restated segment footnote does not exist in the original pre-adoption year
segment footnote, we view these as new or previously hidden segments. If a segment appears
in both the original and restated segment footnote, we view these as old. Segment names can
differ slightly from year to year, which is why do not match on segment name alone. We first
match the segments from restated reports to those in the original report on segment name and
check whether the reported amount for sales and assets are the same for both reports to
determine whether they are old or new. For the remaining unmatched segments, we manually
review the original report to see whether these segments were disclosed in the old report. For
example, if a segment is defined as “Europe” in the original report and as “European region”
in the restated report, but either the segment sales or segment assets are the same, we do not
view these segments as new. Only when there is no clear match on segment name and
segment assets or sales, do we code a segment as new. We code a dummy variable D(New
Segment) equal to 1 for newly disclosed segments and 0 otherwise, which is the dependent
variable in our main test.
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3.2 Model
Under H1, we expect there to be a negative relationship between GDP (growth) or the
Forbes ranking and D(New Segment), whereas under H2 we expect this relationship to be
positive. We run the following logistic regression model to test the hypotheses:
, (1)
where i represents segment, j represents firm, and Segment Location Characteristic can be
current GDP growth, expected GDP growth, current GDP per capita, the Forbes Best
Countries for Business ranking (H1 and H2) or a measure of the ease of starting a business
(H3). We use the GDP and Forbes variables to capture the attractiveness of a segment’s
location. As mentioned, we predict that when agency costs dominate, bad segments will be
hidden (i.e., there should be a negative relationship between these measures and D(New
Segment). When proprietary costs explain disclosure, we expect to see segments in more
attractive locations to be hidden from competitors (i..e, we expect to see a positive
relationship between these variables and the likelihood of a segment being newly revealed).
We expect the ease of starting a business, which is our measure of entry barriers, to be
negatively related to disclosure (i.e., the coefficient on these variables is expected to be
negative in these regressions). We obtain data on GDP growth and GDP per capita from the
World Bank and the Forbes Best Countries for Business ranking from the Forbes website. We
use the Doing Business database developed by the World Bank and International Finance
Corporation to obtain measures on the ease of starting a new business (our measure of entry
barriers). This database measures the number of procedures, time and cost to comply with all
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procedures, and minimum capital required to start a new firm in 177 countries. These
measures are constructed based on official legislation, surveys and interviews with experts.3
We control for a number of factors that can influence whether a segment is hidden or not,
such as firm size, profitability, growth opportunities, industry concentration, segment size,
the corporate tax rate and labor market regulations in the segment’s region. We include a
measure of the segment’s size, measured as the segment’s sales relative to firm sales, to
alleviate concerns that disclosure is purely driven by whether a segment is material and thus
has to be disclosed. We include the segment location’s corporate tax rate to control for taxrelated incentives to hide a segment, as tax avoidance and non-disclosure of segmental
information are related (e.g., Hope et al., 2013). We cluster standard errors at the firm-level,
although results are unchanged if we cluster at the country- or industry-level. Further details
on all variables are provided in the Appendix.
4. Sample
We hand-collect segment reports for the years around the switch to IFRS 8 for the largest
non-financial publicly listed firms in the following European countries: Austria, Belgium,
Czech Republic, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg,
the Netherlands, Norway, Poland, Portugal, Spain, Sweden, Switzerland and the UK. As
mentioned in the previous section, we use this change in regulation to identify hidden
segments. We retrieve the pre-adoption and adoption year annual reports and collect the
original and restated segment data for the pre-adoption year. We collect each geographic
segment’s name and record whether earnings or other information is provided in the segment
footnote. We are able to find geographic segment data in the annual reports for 1,711 stocklisted firms.
3
Detailed
description
on
the
measurement
of
http://www.doingbusiness.org/methodology/starting-a-business.
http://www.doingbusiness.org/custom-query.
11
these
We
variables
obtained
is
the
available
at:
data
from:
For our tests, we have to assign measures of location characteristics such as GDP and
GDP growth to each segment. Since these data are provided at the country-level and not all
geographic segments are reported at the country-level, we employ the following method. If a
segment consists of a single separate country, we assign the corresponding GDP and GDP
growth as reported by the World Bank in the year of disclosure. If firms aggregate two or
more countries into a single segment and report the names of the aggregated countries, we
assign the average GDP and GDP growth to those segments. If firms label a segment as a
sub-continent or continent without reporting the specific countries, we assign the average
across all countries in a sub-continent or continent. Since we cannot assign GDP or GDP
growth to segments labeled “Other”, “Rest of the World”, “Headquarters” etc., we remove
these from our sample. We employ the same procedure for assigning the Forbes ranking and
the entry barrier measures. After matching this to the necessary data for the control variables
in the regression analyses, our sample consists of 1,345 firms with 5,317 geographic
segments, of which 1,319 are new segments. Table 1 provides details on sample selection and
distribution across countries. As expected, a significant proportion of our sample consists of
firms from the UK, followed by Germany and France.4
We provide descriptive statistics on our sample in Table 2. We find that 25% of all
geographic segments in our sample are newly disclosed, which is consistent with prior studies
that show greater disaggregation under IFRS 8. Since the disclosure of geographic segment
earnings is not mandatory, this is only reported for 27% of all segments. We find that there is
considerable variation in the economic characteristics of segment locations. For instance,
current GDP growth ranges from -8.23% to 9.22% while GDP per capita ranges from USD
2,083.71 to USD 76,542.54. On average, a segment is located in the 41st most attractive
4
The results are similar when we include country-fixed effects. Excluding each country from our sample also
does not affect our main results; we even find that the coefficients on the GDP growth variables become
significantly positive when Austria or Denmark is excluded from the sample. This result is in line with the
positive coefficients on GDP per capita and the Forbes Best for Business variable.
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region for business and location attractiveness varies considerably across segments. The ease
of doing business also varies greatly across segments: for some regions, only 2 procedures
are necessary before starting a firm, while it can take up to 15 procedures for other regions.
5. Results
5.1 New Segments and Location Characteristics
We first analyze whether our samples of old and new segments are significantly different.
In Table 3, we compare the means of firm and segment characteristics across the two
subsamples. We find that firms with new segments are smaller on average, both in terms of
assets and sales, but have higher market-to-book ratios, which is consistent with growth firms
having greater incentive to hide potentially proprietary information. We also find that firms
with new segments have a higher proportion of foreign sales relative to total sales. This is
consistent with Tsakumis et al. (2006), who find that firms reveal less geographic segment
information for proprietary reasons when they depend more on foreign activities. We also
find that the corporate tax rate of new segments is higher, which would be consistent with
firms trying to conceal information about activities in highly taxed areas.
Turning to segment location characteristics, we fail to find a significant difference in the
GDP growth of new versus old segments, although GDP per capita and the Forbes ranking is
significantly higher for newly revealed segments, which is consistent with H2. We also find
that it is easier to start a new firm in the locations of newly revealed segments. For instance,
on average it takes almost 3 days less to start a business in the locations of hidden segments,
and the cost of complying with all procedures to start a business is 8.4% lower in hidden
segments. These results are consistent with H3.
We present our main results in Table 4. In columns (1) and (2) of Table 4, we find that
current and expected GDP growth of a segment’s location do not influence the decision to
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disclose or conceal this segment, consistent with Table 3. However, we do find evidence that
supports H2 and H3 in columns (3) to (7). Column (3) shows that new segments tend to be
located in wealthier regions, suggesting that firms hide segments in areas with higher
standards of living and thus are likely to be more profitable. We find that a one standard
deviation increase in GDP per capita (an increase of 17,370 USD) results in a 3.15% decrease
in the likelihood of disclosure in absolute terms. For the regression analyses, we reversecoded the Forbes-variable such that a higher score now indicates that a segment is in a region
more attractive for business. Consistent with column (3), we find that new segments tend to
be located in areas that are better for business. For instance, if a segment location’s
attractiveness increases by 10 places in the ranking, the likelihood of disclosure decreases by
1.5%. Both these results are consistent with H2, namely that proprietary costs result in less
disclosure of more attractive segments.
We also find that after controlling for other variables that could affect disclosure, entry
barriers are still negatively related to D(New Segment), which suggests that firms are more
likely to disclose a segment when it is harder for other firms to enter that particular area. For
instance, a one standard deviation increase in the number of procedures required to start a
business (i.e., approximately 2.5 procedures) is associated with a 1.46% higher likelihood of
that segment being disclosed in absolute terms. The likelihood of reporting a segment is also
increasing in the time it costs to start a business (column (6)), and the cost of complying with
all procedures (column (7)), which is consistent with H3. A one standard deviation increase
in the cost of complying with all procedures has the strongest impact on the likelihood of
segment disclosure, namely 5.91% in absolute terms. These results strongly support the idea
that proprietary costs are important determinant of geographic segment disclosure.
With regards to the control variables, we find that firms with higher R&D expenditures are
more likely to disclose new segments. This is consistent with Ellis, Fee and Thomas (2012),
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who show that proprietary costs of disclosure are greater for firms that engage in R&D. We
also find that firms are less likely to hide segments when they are in more concentrated
industries. This is consistent with Harris (1998) and Botosan and Stanford (2005) who find
that lower competition is related to more disclosure. However, as we explain earlier, given
the ambiguous interpretation of industry concentration as a measure of proprietary costs, we
are cautious in interpreting this result.5 We continue to find that the corporate tax rate of a
segment location is positively related to non-disclosure, consistent with tax avoidance driving
disclosure of segment information. As expected, larger segments are more likely to be
already disclosed, since materiality of the segment is an important determinant of segment
disaggregation.
In sum, our findings confirm that proprietary costs are an important determinant of nondisclosure in general and that economic characteristics of a segment’s location matter for
disclosure. Interestingly, we don’t seem to find that agency costs determine disclosure in our
setting, perhaps indicating that proprietary costs are the primary determinant of geographic
segment disclosure.
5.2 How Much Information is Disclosed about New Segments?
In Table 4, we find that segments that were only disclosed after the introduction of IFRS 8
tend to be located in more attractive regions with lower entry barriers. Since IFRS 8
apparently prevents managers from hiding the existence of these segments, we next examine
whether the amount of information disclosed about these segments varies with location
characteristics as well. Even though a segment has to be disclosed under this new rule,
companies still have flexibility in the amount of information that is disclosed per segment.
5
Note that we do measure concentration using both public and private firms. We use the Amadeus database to
obtain sales data for private firms.
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We focus on the disclosure of geographic segment earnings as well as the number of line
items disclosed per segment (e.g., assets, sales, non-capital expenditures, etc.) in these tests.
We first examine the characteristics of firms that do and do not disclose segment earnings
for newly revealed segments. As shown in Table 3, earnings are reported significantly less
frequently for new segments (18% versus 30%), which can be a sign that managers are
attempting to withhold information about these particular segments. Among the 1,319
segments that are newly disclosed under IFRS 8, earnings are disclosed for 236 segments.
We first look at the firm and segment characteristics of segments that do and do not report
earnings in Table 5. We find that more profitable firms and those followed by more analysts
tend to withhold segment earnings. Similar to the findings in Tables 3 and 4, segments tend to
be larger and have lower tax rates when earnings are disclosed. We also find that segment
earnings are not disclosed when segments are located in more attractive areas (higher GDP
and ranked higher by Forbes) and that entry barriers are higher when geographic earnings are
disclosed. These results complement our earlier findings: even when segments have to be
revealed under IFRS 8, firms disclose less information about segments when proprietary cost
concerns are greater.
In Table 6, we examine whether these results hold after controlling for the same firm and
segment characteristics as in Table 4. We find that the likelihood of reporting earnings
decreases with the segment location’s economic wealth and Forbes ranking and increases
with the cost of starting a business in those regions. A one-standard deviation increase of
GDP per capita (USD 18,450) is associated with a 2.4% decrease in the likelihood of
disclosure, while a one-standard deviation increase in the Forbes ranking of a segment
location would be associated with a 2.5% decrease in the likelihood of reporting geographic
earnings. Similarly, a one-standard deviation increase in the cost of starting a new business
results in a 4.8% higher likelihood of earnings being disclosed. The results in Panel B are
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similar to those in Panel A. For the number of reported items, we also find that the economic
growth of a region matters: higher GDP growth is associated with fewer items being
disclosed, although this is not an economically large effect. The control variables are related
to the likelihood of disclosure in a similar way as before: e.g., higher corporate tax rates lead
to less information being disclosed and more is reported for larger segments presumably due
to materiality of these segments.
In sum, our results in Tables 5 and 6 support our conclusions from Table 4. The
characteristics of a segment’s location are significantly related to segment disclosure and the
direction of these associations is consistent with a proprietary costs explanation for nondisclosure.
5.3 Disclosure of Information in Old Segments
As a final test, we examine whether the relationships found in Table 6 also hold for
segments that were already disclosed under IAS 14. We expect proprietary costs to play a less
important role for these segments, since these were already revealed prior to IFRS 8. Hence,
we expect that the likelihood of reporting earnings and the number of reported items will not
be negatively related to the attractiveness of a segment’s location or positively related to
entry barriers for these segments. Table 7 shows our findings for the sample of old segments,
with Panel A showing the results for the likelihood of reporting earnings and Panel B
showing the effect of location characteristics on the number of reported items. Consistent
with our expectations, we find that none of the entry barrier measures are significantly related
to the amount of information that is disclosed. We do still find that there are some proprietary
cost concerns, since less information is disclosed for segments in regions with higher GDP
(columns (3) in both Panels). However, we also find that for old segments GDP growth is
positively related to the likelihood of earnings being disclosed as well as the number of
reported line items. This suggests that for segments in low GDP, or less attractive, regions,
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managers prefer to disclose less information, which is consistent with the agency costs
explanation of disclosure. Overall, these results complement our earlier findings in sections
5.1 and 5.2.
6. Conclusion
In this paper, we investigate whether location characteristics of geographic segments can
explain the likelihood of their disclosure. Theory suggests that a variety of costs determine
(non-)disclosure of information, in particular proprietary and agency costs, although there are
difficulties in accurately measuring these costs. We advance this literature by proposing
measures of proprietary and agency costs that do not rely on typical competition measures
that have been criticized in the literature. Instead, we examine the economic conditions of a
segment’s location, such as its economic growth, wealth, and ease of entry for a new firm.
We predict that if agency costs dominate, firms are more likely to show off segments in
regions that are more attractive for business, whereas if proprietary costs are more important,
such segments are more likely to be hidden. We also predict that if entry barriers are high, a
segment will more likely be disclosed.
Our findings support the proprietary cost explanation for disclosure. We find that more
attractive segments, i.e., those with higher GDP and ranked better for business, are more
likely to be hidden. We also find that entry barriers are significantly negatively related to
disclosure: when it is easier for other competitors to start a business in a particular region, a
segment is less likely to be disclosed. Furthermore, we find that within these newly disclosed
segments, less information is provided. We interpret this as evidence that even though IFRS 8
forces firms to reveal segments that they wish to conceal for proprietary reasons, they try to
reveal as little as possible by reducing the amount of information reported about each
segment. In contrast, proprietary costs do not determine the amount of reported information
18
for segments that were already revealed (i.e., old segments). Rather, managers reveal more
information (disclose earnings and more line items) about already disclosed segments in high
growth regions. Overall, these results are consistent with proprietary concerns being the main
driver of geographic segment disclosure.
Our study contributes to the literature on disclosure incentives and extends the literature on
the measurement of proprietary costs and the effect of country-level factors on disclosure. We
find strong evidence that proprietary costs matter for geographic segment disclosure and that
agency costs play a less important role in this setting.
19
Appendix: Variable Definitions
D(New Segment): Dummy variable equal to 1 if a geographic segment is only disclosed in
the restated IFRS 8 segment report, 0 if a segment is disclosed in both the restated IFRS 8
and original IAS 14 segment report. (Source: hand-collected from IFRS 8 (pre-)adoption
year annual report.)
D(Report Earnings): Dummy variable equal to 1 if an earnings measure is reported for a
geographic segment, 0 otherwise. (Source: hand-collected from IFRS 8 (pre-)adoption year
annual report.)
Number of Segment Items: Number of line items reported per segment. (Source: handcollected from IFRS 8 (pre-)adoption year annual report.)
Assets (USD): Firm-level total assets in USD. (Source: Datastream.)
Log(Assets (USD)): Log of Assets (USD). (Source: Datastream.)
Sales (USD): Firm-level sales in USD. (Source: Datastream.)
Log(Sales (USD)): Log of Sales (USD). (Source: Datastream.)
Return on Sales: Firm-level net income divided by sales. (Source: Datastream.)
Herfindahl (SIC3): ∑(firm sales/total industry sales)2, measured using all public and private
firms in the Amadeus database. (Source: Amadeus.)
D(Loss): Dummy variable equal to 1 if firm has negative earnings, 0 otherwise.
MTB: Market value of equity divided by book value of equity. (Source: Datastream.)
Analyst Following: Number of analysts following a firm. (Source: I/B/E/S.)
Foreign Sales %: Foreign sales divided by total sales. (Source: Datastream.)
Segment Size %: Segment sales divided by total sales. (Source: hand-collected from annual
report.)
Corporate Tax Rate: Country-level Corporate Tax Rates from the KPMG corporate tax rates
table. If the tax rate is progressive, the highest tax rate is used in this measure. (Source:
20
http://www.kpmg.com/global/en/services/tax/tax-tools-and-resources/pages/corporate-taxrates-table.aspx.)
Labor Market Regulation Index: Component of the Economic Freedom of the World Index
developed by the Fraser Institute, which measures the strength of labor market regulations.
This measure captures hiring regulations and minimum wage, hiring and firing regulations,
centralized collective bargaining, hours regulation, mandated cost of worker dismissal and
conscription. Higher values of this index represent less labor market regulation. (Source:
Fraser Institute - http://www.freetheworld.com/reports.html.)
GDP measures
Current GDP Growth (%): yearly GDP growth in year of IFRS 8 adoption for the location of
a segment. If a segment consists of a single separate country, we assign the corresponding
GDP and GDP growth as reported by the World Bank in the year of disclosure. If firms
aggregate two or more countries into a single segment and report the names of the
aggregated countries, we assign the average GDP and GDP growth to those segments. If
firms label a segment as a sub-continent or continent without reporting the specific
countries, we assign the average across all countries in a sub-continent or continent.
(Source: World Bank.)
Expected GDP Growth (%): average expected yearly GDP growth for 5 years following IFRS
8 adoption for the location of a segment. See also Current GDP Growth (%). (Source:
World Bank.)
GDP per capita (USD): GDP per capita in USD for the location of a segment. See also
Current GDP Growth (%). (Source: World Bank).
Forbes Best for Business index: 2009 Forbes Best Countries for Business Ranking, which
rates countries based on business conditions such as economic growth, unemployment,
personal freedom, regulation, corruption and taxation in 2008. Index can range from 1 (best
for business = Denmark) to 127 (worst for business = Zimbabwe). (Source: Forbes Best
Countries for Business Ranking - http://www.forbes.com/lists/2009/6/bizcountries09-bestcountries-for-business_Best-Countries-for-Business_Rank.html.)
(For the regressions, the Forbes index is reverse-coded such that higher values indicate
segments in countries that are better for business. This is done to be consistent with the
21
GDP measures, which also measure the attractiveness of a doing business in a particular
region.)
Entry Barrier measures (see also: http://www.doingbusiness.org/methodology/starting-abusiness)
Start a Business – Number of Procedures: Start-up procedures required to start a limited
liability company, including interactions to obtain necessary permits and licenses and to
complete all inscriptions, verifications, and notifications to start operations. Data are for
businesses with specific characteristics of ownership, size, and type of production (Source:
Doing Business database.)
Start a Business – Time in Days: Median duration indicated by incorporation lawyers of
completing all start-up procedures with minimum follow-up with government agencies and
no extra payment. (Source: Doing Business database.)
Start a Business – Cost (% Income per Capita): All official fees and fees for legal or
professional services required by law to start a business. Expressed as a percentage of the
economy’s income per capita. (Source: Doing Business database.)
Start a Business – Minimum Capital Required (% Income per Capita): Amount needed as a
deposit in a bank or with a notary before registration and up to 3 months following
incorporation. Expressed as a percentage of the economy’s income per capita. (Source:
Doing Business database.)
22
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24
Table 1 Sample
Panel A: Sample Selection
Step
Largest public non-financial European firms
(selected in Datastream)
Less: firms without segment footnote/restated
segment data
Firms with segment data
Less: firms without data for regression analyses
Missing GDP/GDP growth
Missing control variables
Number of firms
4,531
Sample
Panel B: Sample Distribution across Countries
Country
Firms
Austria
37
Belgium
37
Czech Republic
2
Denmark
36
Finland
64
France
119
Germany
169
Greece
36
Ireland
21
Italy
71
Luxembourg
12
The Netherlands
56
Norway
58
Poland
14
Portugal
13
Spain
40
Sweden
86
Switzerland
87
United Kingdom
387
Number of segments
- 2,820
1,711
6,709
- 300
- 66
- 1,153
- 239
1,345
5,317
Segments
138
148
17
137
306
469
580
103
66
294
49
281
248
47
57
159
476
389
1,353
Total
1, 345
5, 317
Table 1 details our sample selection process (Panel A) and presents the distribution of our firms
and segments across countries (Panel B). All variables are winsorized at the 1st and 99th percentile
and are defined in the Appendix.
25
Table 2 Descriptive Statistics
Variable
Mean
Std Dev
P1
P25
P50
P75
P99
D(New Segment)
0.25
0.43
0.00
0.00
0.00
0.00
1.00
D(Report Earnings)
0.27
0.44
0.00
0.00
0.00
1.00
1.00
Assets (1,000 USD)
7,384,718.78 19,014,322.54 6,822.22 280,644.04 972,156.38 4,489,239.60 134,173,368.00
Log(Assets (USD))
13.91
2.12
8.67
12.54
13.79
15.32
18.71
Sales (1,000 USD)
5,474,390.77 13,414,773.88 1,661.60 262,073.05 982,984.38 3,745,767.58 94,015,781.92
Log(Sales (USD))
13.72
2.17
7.42
12.48
13.80
15.14
18.36
Return on Sales
-0.05
0.50
-3.19
-0.01
0.03
0.08
0.34
D(Loss)
0.29
0.45
0.00
0.00
0.00
1.00
1.00
MTB
2.08
2.02
0.00
0.91
1.49
2.53
13.24
Analyst Following
7.99
8.09
0.00
1.00
5.00
13.00
32.00
Foreign Sales %
66.68
26.53
0.00
52.70
70.75
87.81
100.00
Herfindahl (SIC3)
0.07
0.16
0.00
0.01
0.02
0.06
1.00
Segment Size (%)
0.23
0.23
0.00
0.06
0.15
0.33
0.97
Corporate Tax Rate
25.53
5.96
12.50
21.49
25.00
29.36
40.00
Labor regulation index
6.73
1.11
4.44
6.08
6.58
7.26
9.10
Current GDP Growth (%)
2.53
203.72
-8.23
-4.15
-1.02
0.48
9.22
Expected GDP Growth (%)
7.35
203.68
0.19
1.98
4.07
4.31
9.56
GDP per capita (USD)
25,530.55
17,370.12
2,083.71 11,544.53
18,880.86
40,446.70
76,542.54
Forbes Best for Business
40.74
27.51
1.00
15.00
50.09
61.16
103.00
Start a Business – Number of Procedures
7.53
2.44
2.00
6.00
7.28
9.00
15.00
Start a Business – Time in Days
24.63
17.46
4.00
13.00
22.60
34.91
76.00
Start a Business – Cost (% Income per
28.55
29.95
0.00
2.10
18.80
45.18
136.28
Capita)
Start a Business – Minimum Capital
87.65
109.91
0.00
7.40
44.20
165.48
670.06
Required (% Income per Capita)
Table 2 presents descriptive statistics for the entire sample. *, ** and *** denote significance at the 10%, 5% and 1% level respectively. All variables
are winsorized at the 1st and 99th percentile and are defined in the Appendix.
26
Table 3 Old versus New Segments
Variable
Mean Old Segments Mean New Segments
New-Old
T-statistic
(equal variance)
-8.64***
-3.33***
-2.63***
-3.06***
-2.06**
0.18
-1.89*
2.96***
-1.08
5.06***
-3.42***
-9.61***
6.83***
2.76***
0.76
0.80
7.23***
-7.87***
-3.31***
-4.65***
T-statistic
(unequal variance)
-9.45***
-3.72***
-2.72***
-3.37***
-2.12**
0.18
-1.91*
3.01***
-1.10
4.79***
-3.89***
-10.09***
6.39***
2.59***
0.58
0.62
6.92***
-7.81***
-2.99***
-4.26***
D(Report Earnings)
0.30
0.18
-0.12
Assets (1,000 USD)
7,883,024.23
5,874,313.02
-2,008,711.21
Log(Assets (USD))
13.95
13.77
-0.18
Sales (1,000 USD)
5,797,217.14
4,495,876.85
-1,301,340.29
Log(Sales (USD))
13.76
13.62
-0.14
Return on Sales
-0.05
-0.04
0.01
D(Loss)
0.29
0.27
-0.02
MTB
2.03
2.22
0.19
Analyst Following
8.06
7.78
-0.28
Foreign Sales %
65.63
69.88
4.25
Herfindahl (SIC3)
0.08
0.06
-0.02
Segment Size
0.25
0.18
-0.07
Corporate Tax Rate
25.21
26.49
1.28
Labor regulation index
6.71
6.81
0.10
Current GDP Growth (%)
1.32
6.21
4.89
Expected GDP Growth (%)
6.07
11.25
5.18
GDP per capita (USD)
24,545.76
28,515.52
3,969.76
Forbes Best for Business
42.44
35.61
-6.83
Start a Business – Number of Procedures
7.59
7.34
-0.25
Start a Business – Time in Days
25.26
22.69
-2.57
Start a Business – Cost (% Income per
30.63
22.23
-8.40
-8.90***
-9.19***
Capita)
Start a Business – Minimum Capital
Required
90.62
78.67
-11.95
-3.43***
-3.28***
(% Income per Capita)
Table 3 compares the mean of firm and segment characteristics across old and new segments. *, ** and *** denote significance at the 10%, 5% and
1% level respectively. All variables are winsorized at the 1st and 99th percentile and are defined in the Appendix.
27
Table 4 Segment Disclosure and Location Characteristics
(1)
(2)
0.000
Current GDP Growth (%)
(1.083)
0.000
Expected GDP Growth (%)
(1.107)
(3)
(4)
(5)
(6)
(7)
0.010***
(3.913)
GDP per capita (in 1,000 USD)
0.008***
(4.426)
Forbes Best For Business
-0.033*
(-1.937)
Starting a Business- N Procedures
-0.008***
(-2.837)
Starting a Business - Time
-0.009***
(-5.037)
Starting a Business – Cost
Starting a Business – Min. Capital
D(R&D)
Return on Sales
MTB
Size
Herfindahl
Corporate Tax Rate
(8)
0.283**
(2.198)
0.088
(0.807)
0.038
(1.291)
-0.065**
(-2.169)
-0.707*
(-1.675)
0.036***
(5.109)
0.283**
(2.198)
0.088
(0.807)
0.038
(1.290)
-0.065**
(-2.168)
-0.707*
(-1.675)
0.036***
(5.109)
0.306**
(2.365)
0.084
(0.764)
0.040
(1.377)
-0.060**
(-2.008)
-0.692*
(-1.650)
0.023***
(3.074)
0.315**
(2.431)
0.078
(0.707)
0.042
(1.442)
-0.058*
(-1.915)
-0.683
(-1.638)
0.018**
(2.441)
Continued on next page
28
0.292**
(2.267)
0.081
(0.739)
0.039
(1.345)
-0.063**
(-2.115)
-0.703*
(-1.671)
0.034***
(4.936)
0.297**
(2.309)
0.082
(0.748)
0.041
(1.383)
-0.061**
(-2.029)
-0.707*
(-1.686)
0.031***
(4.126)
0.320**
(2.466)
0.082
(0.757)
0.041
(1.425)
-0.058*
(-1.941)
-0.687
(-1.642)
0.018***
(2.627)
0.000
(0.582)
0.281**
(2.189)
0.082
(0.747)
0.039
(1.314)
-0.065**
(-2.169)
-0.711*
(-1.682)
0.037***
(5.163)
Labor Market Regulation index
Segment Size
Intercept
0.029
(0.957)
0.029
(0.959)
0.010
(0.343)
-0.023
(-0.744)
-0.000
(-0.013)
-0.018
(-0.550)
0.008
(0.294)
-1.676***
-1.676***
-1.689***
-1.719***
-1.686***
-1.738***
-1.650***
(-7.488)
-1.159**
(-2.111)
(-7.488)
-1.160**
(-2.113)
(-7.518)
-1.045*
(-1.895)
(-7.588)
-0.159
(-0.266)
(-7.498)
-0.713
(-1.205)
(-7.670)
-0.577
(-0.981)
(-7.485)
-0.469
(-0.836)
0.027
(0.913)
1.685***
(-7.507)
-1.182**
(-2.134)
Pseudo R2
0.0344
0.0344
0.0382
0.0400
0.0352
0.0368
0.0421
0.0344
2
88.31***
88.36***
95.52***
96.82***
88.85***
94.37***
97.23***
86.64***

Table 4 presents logistic regression analyses with D(New Segment) as the dependent variable. The Forbes Best for Business is reverse-coded for this
regression to ease interpretation (higher values indicate a segment located in a more attractive country or region). A negative coefficient on the GDP
measures or Forbes index indicates that more attractive are more likely to be disclosed (less likely to be hidden), consistent with H1. Alternatively, a
positive coefficient on the GDP measures or Forbes index indicates that more attractive segments are less likely to be disclosed (more likely to be
hidden), consistent with H2. Z-statistics (in parentheses) are presented below the coefficients and are based on heteroscedasticity-robust standard errors
clustered by firm. *, ** and *** denote significance at the 10%, 5% and 1% level respectively. All variables are winsorized at the 1st and 99th
percentile and are defined in the Appendix.
29
Table 5 Characteristics of New Segments (Not) Disclosing Earnings
Mean Segments Not
Mean Segments
T-statistic
T-statistic
Variable
Difference
Disclosing Earnings
Disclosing Earnings
(equal variance) (unequal variance)
Assets (1,000 USD)
5,952,960.38
5,513,401.62
-439,558.76
-0.38
-0.45
Log(Assets (USD))
13.82
13.59
-0.23
-1.59
-1.52
Sales (1,000 USD)
4,545,049.73
4,270,223.34
-274,826.39
-0.33
-0.34
Log(Sales (USD))
13.69
13.28
-0.41
-2.72***
-2.51**
Return on Sales
-0.03
-0.09
-0.06
-1.75*
-1.26
D(Loss)
0.25
0.34
0.09
2.83***
2.67***
MTB
2.25
2.11
-0.14
1.00
0.98
Analyst Following
8.20
5.87
-2.33
-4.12***
-4.31***
Foreign Sales %
72.74
56.78
-15.96
-7.93***
-8.06***
Herfindahl (SIC3)
0.06
0.05
-0.02
-1.73*
-1.70*
Segment Size
0.16
0.24
0.08
5.49***
4.86***
Corporate Tax Rate
27.06
23.91
-3.15
-6.82***
-7.60***
Labor regulation index
6.83
6.69
-0.14
-1.63
-1.91*
Current GDP Growth (%)
-1.83
43.11
44.94
2.17**
1.01
Expected GDP Growth (%)
3.26
47.96
44.70
2.15**
1.00
GDP per capita (USD)
29,628.19
23,409.47
-6,218.72
-4.73***
-4.86***
Forbes Best for Business
33.90
43.42
9.51
4.83***
5.07***
Start a Business – Number
7.32
7.43
0.11
0.56
0.63
of Procedures
Start a Business – Time in
22.28
24.56
2.28
1.61
1.86*
Days
Start a Business – Cost (%
20.00
32.46
12.46
6.22***
5.46***
Income per Capita)
Start a Business – Minimum
Capital Required (% Income
71.29
112.53
41.24
4.94***
3.86***
per Capita)
Table 5 compares the mean of firm and segment characteristics across segments that do and do not disclose earnings for newly reported segments. *,
** and *** denote significance at the 10%, 5% and 1% level respectively. All variables are winsorized at the 1st and 99th percentile and defined in the
Appendix.
30
Table 6 Amount of Disclosed Information for New Segments
Panel A: Dependent Variable = D(Report Earnings)
(1)
(2)
0.005
Current GDP Growth (%)
(0.189)
0.002
Expected GDP Growth (%)
(0.046)
(3)
(4)
(5)
(6)
(7)
-0.010*
(-1.745)
GDP per capita (in 1,000 USD)
-0.007**
(-2.041)
Forbes Best For Business
-0.018
(-0.553)
Starting a Business- N Procedures
0.002
(0.561)
Starting a Business - Time
0.008***
(2.626)
Starting a Business – Cost
Starting a Business – Min. Capital
D(R&D)
Return on Sales
MTB
Size
Herfindahl
Corporate Tax Rate
(8)
-0.731***
(-2.709)
-0.227
(-0.899)
-0.011
(-0.173)
0.018
(0.240)
-1.743
(-1.069)
-0.087***
(-5.050)
-0.730***
(-2.701)
-0.227
(-0.897)
-0.011
(-0.169)
0.018
(0.244)
-1.740
(-1.071)
-0.088***
(-4.967)
-0.748***
(-2.791)
-0.221
(-0.871)
-0.009
(-0.142)
0.013
(0.175)
-1.843
(-1.074)
-0.077***
(-4.383)
-0.748***
(-2.796)
-0.225
(-0.902)
-0.006
(-0.094)
0.010
(0.138)
-1.824
(-1.064)
-0.073***
(-4.188)
Continued on next page
31
-0.720***
(-2.685)
-0.226
(-0.888)
-0.006
(-0.093)
0.019
(0.257)
-1.739
(-1.079)
-0.090***
(-5.359)
-0.731***
(-2.723)
-0.226
(-0.894)
-0.006
(-0.099)
0.017
(0.229)
-1.762
(-1.076)
-0.088***
(-5.235)
-0.771***
(-2.866)
-0.221
(-0.887)
-0.006
(-0.094)
0.012
(0.165)
-1.741
(-1.044)
-0.075***
(-4.329)
0.000
(0.808)
-0.727***
(-2.697)
-0.229
(-0.912)
-0.008
(-0.119)
0.018
(0.240)
-1.741
(-1.067)
-0.087***
(-5.284)
Labor Market Regulation index
Segment Size
Intercept
Pseudo R2
2
-0.080
(-1.115)
1.516***
(4.964)
1.183
(1.019)
-0.080
(-1.098)
1.519***
(4.956)
1.175
(0.946)
-0.063
(-0.824)
1.515***
(4.928)
1.158
(0.997)
-0.040
(-0.519)
1.530***
(4.963)
0.358
(0.288)
-0.106
(-1.363)
1.532***
(4.979)
1.525
(1.214)
-0.075
(-0.971)
1.524***
(4.963)
1.122
(0.937)
-0.067
(-0.889)
1.463***
(4.825)
0.651
(0.549)
-0.088
(-1.235)
1.506***
(4.907)
1.204
(1.037)
0.0916
65.76***
0.0916
65.75***
0.0943
67.17***
0.0916
67.74***
0.0907
66.06***
0.0907
65.82***
0.0990
69.97***
0.0909
66.29***
(3)
(4)
(5)
(6)
(7)
(8)
Panel B: Dependent Variable = Number of Items
(1)
(2)
-0.000***
Current GDP Growth (%)
(-2.967)
-0.000***
Expected GDP Growth (%)
(-2.959)
-0.005
(-1.435)
GDP per capita (in 1,000 USD)
-0.005**
(-1.968)
Forbes Best For Business
-0.003
(-0.195)
Starting a Business- N Procedures
0.002
(0.993)
Starting a Business - Time
0.007**
(2.274)
Starting a Business – Cost
Starting a Business – Min. Capital
D(R&D)
-0.436**
(-2.214)
-0.436**
(-2.214)
-0.446**
(-2.266)
-0.437**
(-2.246)
Continued on next page
32
-0.436**
(-2.216)
-0.441**
(-2.242)
-0.460**
(-2.348)
0.000
(0.175)
-0.437**
(-2.218)
Return on Sales
MTB
Size
Herfindahl
Corporate Tax Rate
Labor Market Regulation index
Segment Size
Intercept
-0.436**
(-2.214)
-0.121
(-0.547)
-0.033
(-0.867)
0.075
(1.509)
-0.968**
(-1.991)
-0.043***
(-4.189)
-0.019
(-0.556)
1.254***
(4.399)
-0.436**
(-2.214)
-0.121
(-0.547)
-0.033
(-0.867)
0.075
(1.509)
-0.968**
(-1.991)
-0.043***
(-4.189)
-0.019
(-0.557)
1.254***
(4.399)
-0.446**
(-2.266)
-0.118
(-0.537)
-0.036
(-0.945)
0.072
(1.461)
-0.985**
(-2.018)
-0.037***
(-3.618)
-0.008
(-0.220)
1.245***
(4.373)
-0.437**
(-2.246)
-0.126
(-0.579)
-0.034
(-0.909)
0.070
(1.425)
-0.965**
(-1.994)
-0.030***
(-3.199)
0.007
(0.213)
1.270***
(4.437)
-0.436**
(-2.216)
-0.121
(-0.550)
-0.034
(-0.904)
0.075
(1.508)
-0.963**
(-1.980)
-0.042***
(-4.213)
-0.020
(-0.557)
1.257***
(4.403)
-0.441**
(-2.242)
-0.121
(-0.547)
-0.035
(-0.924)
0.074
(1.487)
-0.966**
(-1.995)
-0.041***
(-4.133)
-0.002
(-0.062)
1.260***
(4.419)
-0.460**
(-2.348)
-0.118
(-0.539)
-0.035
(-0.927)
0.071
(1.439)
-0.947**
(-1.992)
-0.031***
(-2.907)
-0.008
(-0.251)
1.194***
(4.217)
-0.437**
(-2.218)
-0.122
(-0.552)
-0.035
(-0.911)
0.075
(1.506)
-0.962**
(-1.982)
-0.042***
(-4.115)
-0.017
(-0.519)
1.251***
(4.352)
Adj. R2
0.064
0.064
0.066
0.065
0.064
0.065
0.074
0.064
Table 6 Panel A presents logistic regression analyses with D(Report Earnings) as the dependent variable; Panel B presents OLS regressions with
Number of Segment Items as the dependent variable for the sample of new segments. The Forbes Best for Business is reverse-coded for this regression
to ease interpretation (higher values indicate a segment located in a more attractive country or region). Z-statistics and T-statistics (in parentheses) are
presented below the coefficients and are based on heteroscedasticity-robust standard errors clustered by firm. *, ** and *** denote significance at the
10%, 5% and 1% level respectively. All variables are winsorized at the 1st and 99th percentile and are defined in the Appendix.
33
Table 7 Amount of Disclosed Information for Old Segments
Panel A: Dependent Variable = D(Report Earnings)
(1)
(2)
0.001***
Current GDP Growth (%)
(3.692)
0.001
Expected GDP Growth (%)
(1.432)
(3)
(4)
(5)
(6)
(7)
-0.006*
(-1.845)
GDP per capita (in 1,000 USD)
-0.001
(-0.674)
Forbes Best For Business
-0.010
(-0.482)
Starting a Business- N Procedures
0.002
(0.854)
Starting a Business - Time
0.000
(0.026)
Starting a Business – Cost
Starting a Business – Min. Capital
D(R&D)
Return on Sales
MTB
Size
Herfindahl
Corporate Tax Rate
(8)
-1.046***
(-7.005)
-0.079
(-0.700)
0.060*
(1.854)
-0.027
(-0.716)
-0.330
(-0.573)
-0.004
(-0.593)
-1.046***
(-7.005)
-0.079
(-0.701)
0.060*
(1.854)
-0.027
(-0.715)
-0.330
(-0.573)
-0.004
(-0.596)
-1.061***
(-7.079)
-0.074
(-0.660)
0.060*
(1.870)
-0.030
(-0.784)
-0.336
(-0.579)
0.003
(0.370)
Continued on next page
34
-1.047***
(-6.995)
-0.068
(-0.613)
0.059*
(1.809)
-0.028
(-0.735)
-0.340
(-0.588)
-0.001
(-0.179)
-1.041***
(-6.966)
-0.068
(-0.612)
0.060*
(1.855)
-0.028
(-0.736)
-0.333
(-0.578)
-0.005
(-0.706)
-1.048***
(-7.013)
-0.066
(-0.595)
0.060*
(1.848)
-0.029
(-0.770)
-0.334
(-0.579)
-0.004
(-0.487)
-1.044***
(-6.998)
-0.068
(-0.606)
0.060*
(1.853)
-0.028
(-0.745)
-0.335
(-0.581)
-0.005
(-0.655)
-0.000
(-0.077)
-1.043***
(-6.992)
-0.068
(-0.607)
0.060*
(1.853)
-0.028
(-0.743)
-0.334
(-0.580)
-0.005
(-0.657)
Labor Market Regulation index
Segment Size
Intercept
Pseudo R2
2
-0.028
(-0.800)
0.446***
(2.777)
0.110
(0.177)
-0.028
(-0.799)
0.446***
(2.777)
0.106
(0.171)
-0.020
(-0.539)
0.467***
(2.884)
0.050
(0.080)
-0.025
(-0.650)
0.465***
(2.807)
-0.041
(-0.060)
-0.039
(-0.992)
0.451***
(2.784)
0.280
(0.404)
-0.018
(-0.461)
0.471***
(2.870)
-0.016
(-0.024)
-0.030
(-0.815)
0.454***
(2.784)
0.143
(0.220)
-0.030
(-0.852)
0.455***
(2.853)
0.152
(0.242)
0.0526
83.30***
0.0526
72.90***
0.0533
74.55***
0.0518
72.65***
0.0521
73.40***
0.0522
73.20***
0.0520
73.21***
0.0520
72.89***
(5)
(6)
(7)
(8)
Panel B: Dependent Variable = Number of Items
(1)
(2)
0.000***
Current GDP Growth (%)
(12.807)
0.000***
Expected GDP Growth (%)
(14.195)
(3)
(4)
-0.002
(-0.577)
GDP per capita (in 1,000 USD)
0.001
(0.648)
Forbes Best For Business
-0.015
(-0.742)
Starting a Business- N Procedures
-0.002
(-0.937)
Starting a Business - Time
-0.002
(-1.517)
Starting a Business – Cost
Starting a Business – Min. Capital
D(R&D)
-0.728***
(-5.314)
-0.728***
(-5.314)
-0.731***
(-5.325)
-0.716***
(-5.228)
Continued on next page
35
-0.721***
(-5.283)
-0.722***
(-5.291)
-0.715***
(-5.242)
-0.000
(-0.610)
-0.724***
(-5.288)
Return on Sales
MTB
Size
Herfindahl
Corporate Tax Rate
Labor Market Regulation index
Segment Size
Intercept
-0.048
(-0.342)
0.039
(1.211)
0.055*
(1.649)
-0.571
(-1.636)
-0.010
(-1.442)
-0.030
(-0.943)
0.563***
(3.649)
3.072***
(5.735)
-0.048
(-0.342)
0.039
(1.210)
0.055*
(1.650)
-0.571
(-1.636)
-0.010
(-1.445)
-0.030
(-0.942)
0.563***
(3.648)
3.071***
(5.733)
-0.047
(-0.332)
0.039
(1.221)
0.054
(1.624)
-0.572
(-1.639)
-0.008
(-1.071)
-0.029
(-0.876)
0.568***
(3.654)
3.069***
(5.678)
-0.041
(-0.290)
0.039
(1.220)
0.056*
(1.697)
-0.579*
(-1.664)
-0.013*
(-1.753)
-0.046
(-1.294)
0.559***
(3.512)
3.281***
(5.350)
-0.037
(-0.264)
0.039
(1.214)
0.054
(1.641)
-0.573
(-1.642)
-0.011
(-1.588)
-0.044
(-1.218)
0.564***
(3.617)
3.302***
(5.389)
-0.037
(-0.267)
0.039
(1.217)
0.055*
(1.651)
-0.576*
(-1.651)
-0.011*
(-1.703)
-0.045
(-1.233)
0.551***
(3.448)
3.271***
(5.697)
-0.037
(-0.264)
0.039
(1.230)
0.055*
(1.663)
-0.568
(-1.630)
-0.015**
(-2.160)
-0.039
(-1.179)
0.561***
(3.581)
3.326***
(5.839)
-0.036
(-0.258)
0.038
(1.205)
0.054
(1.630)
-0.571
(-1.636)
-0.012
(-1.595)
-0.031
(-0.963)
0.579***
(3.785)
3.150***
(5.761)
Adj. R2
0.038
0.038
0.037
0.038
0.038
0.038
0.038
0.037
Table 7 Panel A presents logistic regression analyses with D(Report Earnings) as the dependent variable; Panel B presents OLS regressions with
Number of Segment Items as the dependent variable for old segments.The Forbes Best for Business is reverse-coded for this regression to ease
interpretation (higher values indicate a segment located in a more attractive country or region). Z-statistics and T-statistics (in parentheses) are
presented below the coefficients and are based on heteroscedasticity-robust standard errors clustered by firm. *, ** and *** denote significance at the
10%, 5% and 1% level respectively. All variables are winsorized at the 1st and 99th percentile and are defined in the Appendix.
36