Multidimensional competition and corporate
disclosure*
Flora Muiño**
Universidade da Coruña
Manuel Núñez-Nickel
Universidad Carlos III de Madrid
*We would like to acknowledge the financial support of the Spanish Ministry of Education through grants
ECO-2010-22105-C03-03 and ECO2013-45864-P, the Comunidad de Madrid through grant 2008-0059003, and the Xunta de Galicia through grant R2014/018. We appreciate comments and suggestions
contributed by an anonymous reviewer and participants at the 35th European Accounting Association
Annual Congress (Ljubljana, Slovenia), the X Workshop on Empirical Research in Financial Accounting
(A Coruña, Spain) and the XVIII Workshop on Accounting and Management Control “Memorial
Raymond Konopka” (Burgos, Spain).
**Corresponding author: Flora Muiño, Facultade de Economía e Empresa, Universidade da Coruña,
Campus de Elviña, s/n, 15071 A Coruña, Spain. E-mail: [email protected].
Multidimensional competition and corporate disclosure
Abstract
In this paper, we argue that the influence product market competition exerts on disclosure is defined
by the combined effect of the incentives and disincentives to disclose raised by the multiple
competition dimensions. We distinguish between firm and industry level competition measures, and
we hypothesize that the former raises agency and proprietary costs, whereas the latter creates
incentives to disclose either to fulfil the owners’ need of information to monitor managers or to
deter the entrance of new competitors in the industry. Our research design allows for non-monotonic
relationships between competition and disclosure as well as for interactions between competition
dimensions.
Using a sample of U.S. manufacturing companies, we gather evidence that is consistent with our
hypotheses. First, we find an inverted U-shape relationship between corporate disclosure and a
firm’s abnormal profitability, which is suggestive of firms being reluctant to disclose when they are
underperforming (outperforming) their rivals because of the fear of unveiling agency conflicts
(raising proprietary costs). Second, we observe a U-shape relationship between corporate disclosure
and industry profitability, although this U design evolves to approximate a rising function as the
protection provided by entry barriers increases.
Keywords: disclosure, product market competition, incentives to disclose, disincentives to disclose,
segment reporting, agency conflicts, proprietary costs.
JEL codes: M41, L10, L60
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Multidimensional competition and corporate disclosure
1. Introduction
This paper seeks to contribute to the literature that examines the association between product market
competition and disclosure by trying to answer two open questions in this literature (e.g., Beyer et
al., 2010; Berger, 2011). First, what is the appropriate measure of competition, given the multiple
dimensions of this construct derived from product market fundamentals (e.g., Karuna 2010)?
Second, how should the proprietary and agency costs linked to disclosure be disentangled (e.g.,
Berger and Hann, 2007)? We decided to investigate both questions concurrently, as the answer to
each cannot be understood without taking into account the other. Specifically, we differentiate
between firm and industry level competition measures (Lang and Sul, 2014) to argue that: i) firmlevel competition dimensions reflect proprietary and agency costs, both of them acting to discourage
disclosure (e.g., Berger and Hann, 2007; Bens et al., 2011); and ii) global industry competition
creates incentives to disclose to deter the entrance of new competitors (e.g., Darrough and
Stoughton, 1990) and to fulfil the owners’ need for information to control managers 1. In both cases,
proprietary and agency conflicts have opposite predictions on disclosure. That is to say, we
hypothesize that, at the firm level, proprietary (agency) costs linked to disclosure are increasing
(decreasing) in the strength of the competitive position of the firm in the industry, whereas at the
industry level, the incentives to disclose to deter new entrants (alleviate potential agency conflicts)
are increasing (decreasing) in the level of industry rivalry. Furthermore, we posit that the same
competition dimension, either at the firm or industry level, can portray both the proprietary and
1
Product market competition acts as a mechanism that disciplines managers and reduces managerial slack (e.g., Baggs
and de Bettignies, 2007). When the level of competition is low, however, agency conflicts are more likely and owners
will need more information to oversee managerial actions. We further elaborate on this issue when developing the
hypotheses.
1
agency effects. In such a case, we would observe a non-monotonic association between the
competition dimension and corporate disclosure (e.g., Karuna, 2013).
Using a sample of U.S. manufacturing firms for the years 2002 and 2007, we gather evidence that is
consistent with our hypotheses. First, we find that the relationship between disclosure and our firmlevel measure of competition (i.e., abnormal profitability) follows an inverted U-shape design,
thereby suggesting that abnormal profitability proxies for both proprietary and agency costs. Our
evidence suggests that firms are reluctant to provide separate information on those business
segments that obtain abnormally low (high) profits because of the fear of unveiling agency conflicts
(raising proprietary costs).
Likewise, we observe a non-monotonic association between disclosure and industry profitability,
although in this case the relationship follows a U-shape curve. This evidence suggests that industrywide competition creates incentives to disclose either to deter the entrance of new competitors when
the industry is experiencing fierce competition or to provide owners with the necessary information
to monitor managers when the level of competition is low. Furthermore, we observe that this
association is modulated by the level of entry barriers, so that the abovementioned U-shape observed
at low levels of entry barriers evolves toward a rising curve when the industry is protected by heavy
entry barriers. From these findings, we infer that the lack of competition in the product market
creates incentives to disclose to meet the owners’ need of information to monitor managers.
The evidence gathered in this paper contributes to the literature by showing the interplay of the
incentives and disincentives to disclose raised by different competition dimensions. Our findings
suggest that, when examining the role played by product market competition in shaping disclosure
decisions, we should: i) observe competition both at the firm and industry level; ii) allow for a non-
2
linear relationship between a competition dimension and disclosure; and ii) allow for interactions
between competition dimensions. Failure to account for any of these elements could lead to an
incomplete picture of the relationship between competition and disclosure.
The following section develops the hypotheses. Section three presents the research design. Section
four contains the results of our analyses. Finally, Section five discusses the results and presents the
conclusions.
2. Hypothesis development.
In this section, we argue that the influence product market competition exerts on corporate
disclosure is defined by the combination of what might be called the proprietary and agency effects.
By the proprietary (agency) effect, we mean the disclosure costs or the incentives to disclose related
to the release of proprietary information (the agency conflicts). This combined effect, however,
varies depending on whether we look at the strength of the competitive position of the firm in the
industry or at the global level of industry rivalry. We analyse each separately.
2.1. Relative strength of the competitive position of the firm in the industry
Companies operating in a given industry differ in the level of competition they confront compared to
their rivals and, consequently, in their motivations to avoid disclosure. Specifically, a firm might be
reluctant to disclose if it fears that rivals are able to use the released information to erode its relative
competitive position (i.e., disclosure raises proprietary costs). This fear is increasing in the level of
abnormal profitability the firm is earning in the industry because those firms that are outperforming
their competitors have more to hide from rivals than poor performers (e.g., Berger and Hann, 2007).
3
Although the possibility of raising proprietary costs is often cited by managers as one of the main
reasons to withhold information (e.g., Ettredge et al., 2002; Graham et al., 2005; Dedman and
Lennox, 2009), managers might also fear that disclosure unravels unsolved agency conflicts,
particularly in the case of segment reporting (e.g., Berger and Hann, 2003, 2007). For instance, it
has been proven that agency problems are responsible for firms maintaining diversification
strategies, even when diversification is value destroying (Denis et al., 1997). This means that private
benefits (e.g., power and prestige associated with managing a large firm or managerial
compensation related to firm size 2) are the reasons to undertake or maintain value-decreasing
investments in certain lines of business. In these cases, managers will be reluctant to provide
separate information for operations in each business segment because disaggregation could unveil
the agency conflicts (e.g., Huang and Zhang, 2012). Rather, they might use the alleged proprietary
cost as a justification for aggregating information from operations in various business segments.
The relation between both types of costs has two interesting characteristics. First, the coexistence of
both costs guarantees the possibility of withholding information (Bens et al., 2011). If there were
only one type of cost, no-news would be interpreted as the worst possible news and, consequently,
full disclosure would follow (e.g., Grossman 1981; Milgrom, 1981). Second, both costs have
opposite influences on the disclosure decision (e.g., Berger and Hann, 2007). Indeed, when a firm
obtains good results, its managers will have incentives to reveal information to show the absence of
agency problems. Simultaneously, managers will be reluctant to disclose the same information
because of the fear or raising proprietary costs because the good results could induce other firms to
copy their strategy. In the case of poor performance, managers would not fear providing valuable
2
Refer to Denis et al. (1997) for a detailed discussion of these private benefits for managers.
4
inputs to competitors but would be reluctant to disclose because of the possibility of unravelling
agency problems. Figure 1 depicts this intuition.
[Insert Figure 1 about here]
Figure 1 portrays two continuous functions: the decreasing curve reflects the agency costs, while the
increasing one represents the proprietary costs. When we combine both functions to calculate the
total cost of disclosure, we obtain the U-shape design shown in Figure 1. At low (high) levels of
abnormal profits, proprietary (agency) costs are close to zero, whereas agency (proprietary) costs
reach their highest values. Hence, the lowest level of total cost is observed when corporate
performance is close to the industry average.
Insofar as rational managers will try to minimize the total costs of disclosure (i.e., disclosure is
decreasing in the level of total costs), the previous U-shape design in Figure 1 is transformed into an
inverted U-shape function when we depict the relationship between abnormal profits and disclosure.
These arguments lead us to state our first hypothesis as follows:
H1: The relationship between abnormal profitability and disclosure is described by an inverted
U-shape curve.
2.2. Global industry rivalry
In relation to competition, disclosure decisions are not only affected by the relative strength of the
competitive position of the firm in relation to its rivals but also by the competitive tension, at a
global level, among all firms with operations in the same industry. Industry profitability and entry
barriers are common measures used to account for the level of industry rivalry. Next, we discuss the
5
potential impact of each on disclosure decisions, as we expect them to differ in the influences they
exert on corporate disclosure.
Industry profitability.
Industry profitability provides us with an indication of the level of product substitutability in the
industry (Karuna, 2007) and can therefore be seen as a measure of the global competitive tension
between all existing industry members; the higher the level of substitutability, the closer the industry
is to perfect competition.
As a measure of industry rivalry, industry profitability might capture incentives to disclose linked to
the interest of incumbent firms in deterring the entrance of new competitors. When the situation
involves a high degree of competition, incumbents, as a group, are interested in communicating this
situation to avoid successive increments in competition (Darrough and Stoughton, 1990). Adopting
a different point of view, if a firm’s choice of an optimal level of production depends on its
estimation of the demand, existing firms in the industry are inclined to disclose evidence of low
demand to discourage overproduction (Clinch and Verrecchia, 1997). Briefly, firms might be willing
to provide separate information referring to their operations in low profitable industries with the aim
of discouraging the entrance of new competitors (e.g., Darrough and Stoughton, 1990; Wagenhofer,
1990; Suijs, 2005). Because these incentives to disclose diminish as industry profitability improves,
we should observe a negative association between industry profitability and corporate disclosure.
Industry profitability might also raise incentives to disclose related to the owners’ necessity of
information to monitor managers. Product market competition is a powerful force to ensure that
managers do not waste the resources of the firm (Chhaochharia et al. 2012), as it acts as a
disciplinary mechanism that mitigates managerial slack (e.g., Hart, 1983; Baggs and de Bettignies,
6
2007). This explains why strong competition is found to substitute for other mechanisms of
corporate control (e.g., Giroud and Mueller, 2010; 2011; Chhaochharia et al., 2012). Based on this
evidence, we argue that there is a greater necessity for information that is required to monitor
management in highly profitable industries than in those experiencing fierce rivalry. Given that
companies benefit from the fulfilment of the owners’ need of information (e.g., Botosan, 1997;
Francis et al., 2008; Blanco et al., 2015), we expect that industry profitability creates incentives to
disclose linked to the potential agency conflicts and the associated need of information to control
managers. Provided that corporate disclosure policies are responsive to owners’ demand for
information, as documented by extant literature (e.g., Frost and Pownall, 1994; Bushee et al., 2003),
this association translates into a positive relationship between corporate disclosure and industry
profitability.
Overall, industry profitability captures two different types of incentives to disclose with opposite
predictions on the decision to disclose. When we combine both types of incentives, we obtain a nonmonotonic (U-shape) relationship between industry profitability and corporate disclosure. That is,
when referring to the incentives to deter potential entrants (incentives to satisfy the owners’ need for
information), a firm’s willingness to disclose will be decreasing (increasing) in the level of industry
profitability. Accordingly, the lowest levels of disclosure are expected to be observed for
intermediate values of industry profitability, although the exact point where the minimum is reached
will depend on the relative strength of each of these forces. Based on the prior arguments, we state
our second hypothesis as follows:
H2: The relationship between industry profitability and corporate disclosure follows a U-shape
design.
7
Entry barriers as a constraint to industry rivalry
Barriers to entry preserve the extant level of competition between incumbent firms by imposing
restrictions on the threat of entry by new competitors (e.g., Karuna, 2013). As reflected by their
name, they do not act themselves as a measure of industry rivalry but as a restriction that regulates
the entry of new rivals; therefore, they might work as a modulator that changes the relative weight
of the incentives to disclose originated by industry profitability.
On the one hand, the incentives to use disclosure as a means to deter potential entrants diminish as
entry barriers increase; strong entry barriers provide protection from the threat of entry of new
competitors, thereby reducing (or even eliminating) the role played by disclosure as a means of
discouraging the entrance of new rivals. On the other hand, entry barriers reduce the pressure over
managers coming from external rivals, and therefore, they attenuate the monitoring role played by
product market competition. Consequently, the incentives to disclose related to the necessity of
information to oversee managerial actions will increase as entry barriers become stronger.
In brief, entry barriers act to enhance the rising segment of the U-shape function relating industry
profitability and corporate disclosure, and simultaneously, they remove the decreasing segment of
this function. Stated in other words, as entry barriers increase, the increasing (decreasing) part of the
U-shape relationship between industry profitability and disclosure becomes steeper (tends to
disappear), and consequently, this function will evolve towards an increasing relationship. These
arguments form the basis of our third hypothesis:
H3: Increases in the level of entry barriers will transform the original U-shape relationship
between industry profitability and corporate disclosure into an increasing function.
8
2.3. Weight of private firms in the industry.
Our study includes a further industry-level measure of competition that represents the distribution of
different types of industry members rather than the level of rivalry in the industry. Specifically, this
measure accounts for the importance of private firms in relation to public firms operating in the
industry.
The relevance of public information in resolving the information asymmetry between managers and
stakeholders differs substantially between public and private firms (e.g., Ball and Shivakumar, 2005,
2008; Szczesny and Valentincic, 2013). Indeed, owners or creditors of private firms are more likely
to have direct access to corporate information, resulting in a lower demand for public disclosure
(e.g., Tang, 2008; Peek et al., 2010; Hope et al., 2013). The resulting opacity of private firms
increases the proprietary costs (or even raises new ones) faced by public companies. If all firms
operating in a given industry disclose the same level of information, all players have a similar
amount of data about their rivals. The situation changes whenever a significant number of firms in
the industry (private firms) are subject to less stringent disclosure obligations. These firms might
take advantage of their position, as they can use the information released by public companies while
remaining silent about their performance. This concern is often cited by public companies when
opposing new standards requiring further disaggregation in segment reporting (e.g., SFAS 131).
Public companies claim that they are required to provide more information than private companies
or foreign competitors and that this erodes their competitive position (e.g., Ettredge et al., 2002).
Hence, whenever private firms account for a significant proportion of the activity in the industry,
public companies bear a higher level of proprietary costs, which, ceteris paribus, will lead to a lower
level of disclosure.
9
In summary, we view the relative weight of private firms in the industry as a source of additional
proprietary costs for public companies. These arguments lead us to state our fourth hypothesis as
follows:
H4: Corporate disclosure of public firms decreases as the weight of private firms in the industry
increases.
3. Research design.
3.1.
Data.
The data for our analyses are obtained from the Standard & Poor’s Compustat (North America)
database, the Bureau van Dijk’s Osiris database, and the Census of Manufactures provided by the
U.S. Census Bureau. To develop our proxy for disclosure, we identify the industries (SICs) in which
the firm has operations. This identification is complicated by our observation that there are
differences between Compustat and Osiris with respect to the SICs assigned to a firm. To obtain the
most complete picture of the industries in which the firm has operations, we use the SICs identified
by either Compustat or Osiris. Compustat provides a list of up to 60 SIC codes for each firm under
the mnemonic SICALL. In a similar way, Osiris makes available a primary SIC code, but also a
multiple-line item containing the secondary SICs deemed to represent each of the non-core activities
performed by the firm. To obtain a comprehensive view of the industries in which the firm has
operations we combine the data gathered from both databases. Albemarle Corporation, for example,
operates in SICs 2819, 2821, 2834, 2890, and 2899 according to Compustat, whereas Osiris assigns
it the following SICs: 2819, 2821, 2834, 2879, and 3861. Following the business description
included by Albemarle in its 10K report, Albemarle Corporation operates in all of the
aforementioned SICs. Therefore, we include all of them and assume that it operates in the following
10
SICs: 2819, 2821, 2834, 2879, 2890, 2899, and 3861. In the same way, for each of the remaining
firms in our sample, we assume that it has operations in all SICs assigned to the firm either by Osiris
or Compustat 3.
To form our sample, we begin with those firms that operate in two or more three-digit SICs 4 (i.e.,
diversified firms). The SICs that are treated as reported business segments 5 by a firm are obtained
from the business segment file of Compustat. The necessary data to obtain the industry-level
measures of competition used in our main analyses are derived from the Census of Manufactures
provided by the U.S. Census Bureau. Accordingly, our sample has to be restricted to manufacturing
business segments. We therefore start by excluding from our sample all non-manufacturing firms
(i.e., firms with a firm SIC code in Compustat that is lower than 2000 or higher than 3999). In
addition, we have to exclude non-manufacturing business segments of manufacturing firms;
otherwise, we would not be able to obtain the industry competition measures corresponding to these
business segments. Hence, we drop from our sample all business segments with a SIC code
(assigned either by Osiris or Compustat) that is lower than 2000 or higher than 3999. Finally, the use
of Census data imposes a further restriction. Because the Census is conducted every five years, our
sample is restricted to Census years. To ensure that disclosure decisions are not motivated by
changes in segment reporting rules, our period of analysis starts in 2002, the first Census year after
SFAS 131 became effective. We use the data from the Census of Manufactures, despite the
3
Neither Compustat nor Osiris offer historical data on these items. So, we use the SICs data downloaded in May 2010
(i.e., when we gathered the data used in our analysis) for the years covered in this study (i.e., 2002 and 2007). We
acknowledge that the configuration of the firm can change over time, so that it does not necessarily operate in the same
industries along the whole period under analysis. However, these changes should go against finding a significant
association between business segment disclosure and competition in the product market.
4
Following prior research (e.g., Harris, 1998), we define industries at the three-digit SIC level throughout this paper.
5
Hereafter, by reported business segment, we mean each of the business segments for which the company provides
separate information (i.e., each of the business segments in the business segment file of Compustat).
11
reduction in the scope of the sample because the Census includes both public and private firms and
both types should be taken into account when measuring industry competition (Ali et al., 2009).
The remaining data required for our analyses are derived from Compustat. We exclude from our
sample those firm-year observations with a negative value for common equity as well as those with
missing values on any of the variables used in our analysis. Finally, to avoid the undue effects of
outliers, continuous variables in our sample are trimmed at the 0.5% top and bottom levels of their
distribution. Our final sample is composed of 5,179 business segment year observations for the
years 2002 and 2007.
3.2. Variables.
3.2.1. Disclosure.
The dependent variable in our analysis is a measure of corporate disclosure. To test our hypotheses,
we focus our attention on information for which (discretionary) disclosure i) is likely to provide
owners with valuable inputs that facilitate the monitoring of management (i.e., it has the potential to
unveil agency conflicts) and ii) can be used by competitors to take detrimental action against the
disclosing firm. The level of disaggregation in segment reporting satisfies these requirements. First,
disaggregated business segment data are highly demanded by investors (e.g., Epstein and Palepu,
1999). Second, business segment data have the potential to reveal hidden agency conflicts as well as
to provide competitors with valuable information. Third, firms have some discretion as to the level
of aggregation of business segments for reporting purposes. Although SFAS 131 imposes stricter
conditions for the identification of reportable segments than its predecessor (SFAS 14), it still allows
managers to conceal competitively harmful information regarding differences in segment
profitability (e.g., Ettredge et al., 2006).
12
The abovementioned reasons lead us to select a dummy that reflects whether an industry in which
the firm has operations is considered by the firm as a reported business segment as a measure of
disclosure. Following prior literature (e.g., Harris 1998; Bens et al. 2011), we define this variable
(Seg_Reported) as a dummy that takes the value of one when a three-digit SIC code in which the
firm has operations matches the primary SIC code of one of the firm’s business segments in the
Compustat business segment file, and takes the value of zero otherwise. Returning to the Albemarle
Corporation example referenced in the prior section, based on the data on Compustat and Osiris, we
assume that Albemarle Corporation operates in the following three-digit SICs: 281, 282, 283, 287,
289, and 386. However, Albemarle only reports three business segments to which Compustat
assigns the following three-digit SICs: fine chemicals – 281, polymer additives – 289, and catalysts
– 289. In this case, the variable Seg_Reported takes the value of one for SICs 281 and 289 and the
value of zero for SICs 282, 283, 287, and 386. Our proxy for disclosure is therefore defined at the
business-segment level, which is of particular interest in this study because our analysis requires the
use of industry-level measures of competition.
3.2.2. Competition.
This study uses four different proxies for competition. One of them is a firm-specific measure that
accounts for the relative strength of the competitive position of the firm in its industry (i.e.,
abnormal profitability). Because it is firm-specific, the calculation of the measure is based on data
provided by Compustat. The other three proxies are measured at the industry-level, and their
calculation is based on data derived from the U.S. Census of Manufactures, although we also
replicate our analysis after computing the same variables with Compustat data. The main advantage
of the Census of Manufactures is that it covers both public and private firms, whereas the majority
of firms in Compustat are public.
13
We then provide the definition of each of the competition proxies employed in this study.
Abnormal profits.
To obtain a measure of the relative strength of the competitive position of the firm in each of the
industries in which it has operations, we require information on the profitability level obtained by
the firm in each of these industries. However, we only have this information for reported business
segments, which forces us to look for a proxy for this variable that is based on firm-level data. We
start by computing a firm price-cost margin as follows:
𝐹𝑖𝑟𝑚_𝑀𝑎𝑟𝑔𝑖𝑛𝑖 =
𝑆𝑎𝑙𝑒𝑠𝑖
(𝑆𝑎𝑙𝑒𝑠𝑖 − 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐼𝑛𝑐𝑜𝑚𝑒𝑖 )
Where Salesi and OperatingIncomei are net sales and operating income after depreciation,
respectively, for firm i. Data are derived from Compustat.
We then standardize the Firm_Margin ratio within each industry-year to characterize the
performance of the firm compared to its rivals. Specifically, we compute Z_FirmMargin by
subtracting from the Firm_Margin its industry-year mean and dividing the difference by the
industry-year standard deviation. The calculation is performed at the firm level; therefore, we are
implicitly assuming that the firm obtains a similar price-cost margin across all industries in which it
develops its activities. We recognise that this assumption is unrealistic and introduces a bias in our
measure; however, this limitation is counterproductive to finding a significant association between
the Z_FirmMargin variable and corporate disclosure.
Industry profitability.
14
The price-cost margin is often used as a measure of product substitutability in an industry (e.g.,
Karuna 2013). Price tends to approximate marginal cost as the industry approaches close to perfect
competition. Therefore, the price-cost margin is decreasing with respect to the intensity of price
competition because of product substitutability. We define the industry price-cost margin as follows:
𝐶𝑒𝑛𝑠𝑢𝑠_𝑀𝑎𝑟𝑔𝑖𝑛𝑗 =
𝑆𝑗
𝐶𝑗
where Sj is the total value of shipments for industry j, and Cj is the operating costs for industry j,
calculated as the sum of the following items: Total payroll + Total cost of materials + Total
depreciation 6. We derive the data from the U.S. Census of Manufactures. Because the U.S. Census
Bureau defines industries at the six-digit NAICS level, we transform shipments and operating costs
for six-digit NAICS into measures for three-digit SICs. We do this by adding the values of
component six-digit NAICS industries of a broader three-digit SIC using NAICS correspondence
tables provided by the U.S. Census Bureau.
Entry barriers.
We use industry average capital expenditures as a proxy for entry barriers because they reflect the
investments potential entrants should make to compete with existing rivals (Li 2010). We define this
variable as follows:
𝐶𝑒𝑛𝑠𝑢𝑠_𝐶𝑎𝑝𝐸𝑥𝑝𝑗 =
𝐶𝑎𝑝𝐸𝑥𝑝𝑗
𝑛𝑗
where nj is the number of establishments operating in industry j, and CapExpj is the total capital
expenditures for all firms operating in industry j. Data (in U.S. $000) are obtained from the Census
6
Similar results are obtained when Cj also includes the item “Total other expenses”.
15
of Manufactures, and we add the values of component six-digit NAICs industries of a broader threedigit SIC using NAICS correspondence tables. Whereas Census_CapExp is highly skewed, its
natural logarithm transformation is not. We therefore use the natural logarithm of the
Census_CapExp variable (Census_LCapExp) in the regression analyses.
Weight of private firms in the industry.
Following Bens et al. (2011), we use the percentage of the industry activity held by private firms as
a proxy for the (increased) proprietary costs that arise because of the opacity of private firms. We
define this variable as follows:
𝑃𝑟𝑖𝑣𝑎𝑡𝑒𝑗 =
𝑆𝑎𝑙𝑒𝑠 𝑏𝑦 𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝐹𝑖𝑟𝑚𝑠𝑗
𝑇𝑜𝑡𝑎𝑙 𝑆𝑎𝑙𝑒𝑠𝑗
Where Total Salesj is the total value of shipments for industry j as provided by the U.S. Census
Bureau, and Sales by Private Firmsj represents the amount of sales of industry j that were obtained
by private firms. Because neither companies nor the U.S. Census Bureau make public the amount of
sales made by private firms, and given that we cannot use the confidential data employed by Bens et
al. (2011), we approximate it from the data provided by Compustat. While the U.S. Census Bureau
restricts their computation to U.S. plants, the sales figure reported by firms and compiled in
Compustat also includes sales generated abroad. Hence, we start by adjusting the Compustat sales
figure to obtain a measure of the domestic sales made by firms in Compustat. We gather the total
foreign sales for each firm and year from the geographic segment file in Compustat and subtract this
amount from the net sales figure to obtain the amount of domestic sales. Within each three-digit SIC
code and year, we add the domestic sales of all public firms in Compustat and use this amount as a
proxy for the sales made by public companies in that industry-year. Then, we subtract this amount
16
from the total industry sales derived from the U.S. Census of Manufactures to obtain a measure of
the sales made by private firms. Prior to this subtraction, we transform Census shipments for sixdigit NAICS into measures for three-digit SICs using the NAICS correspondence tables provided by
the U.S. Census Bureau.
When computing this variable, we observe that the measure of the industry sales made by public
companies derived from Compustat outweighs the total industry sales as provided by the U.S.
Census Bureau for a number of industries. This translates into a negative number for the Private
variable in 12 percent of the observations in our sample. We attribute this bias to the use of two
different databases in the computation of the Private variable (i.e., Census of Manufactures and
Compustat) 7. Although this bias is counterproductive to finding a significant association between
the Private variable and disclosure 8, the sensitivity analysis section presents the results of the reestimation of our models after setting the negative values of the Private variable at zero, as well as
after dropping from our sample those observations with a negative value for the Private variable.
3.2.3. Control variables.
Industry concentration.
Karuna (2010) stresses the importance of controlling for industry concentration when examining the
impact of price competition measures on disclosure. We use the Herfindahl-Hirschman index
7
Several factors can contribute to this bias. First, the possibility exists that the U.S. Census and Compustat do not
always coincide on the definition of the industry where the firm operates; the U.S. Census uses the industry
classification provided by the firm, whereas Compustat assigns industry codes based on the descriptive information
published by the company. Second, to allow for the calculation of the amount of domestic sales the computation of
industry sales obtained by public firms is based on firm-level data. This procedure introduces a further bias in the
Private variable, as not all firm’s sales come from a single industry.
8
The presence of measurement error in the explanatory variable, as it is the case with the Private variable, leads to
downwardly biased estimated coefficients (Wooldridge, 2010).
17
provided by the U.S. Census Bureau as a control for industry concentration. This index is defined as
follows:
𝑛
𝑆𝑖𝑗
𝐻𝐻𝐼𝑗 = � � ∗ 100�
𝑆𝑗
2
𝑖=1
where Sij is the net sales of company i operating in industry j, and n is 50 or the number of
companies operating in industry j, whichever is lower.
Following Ali et al. (2009), we transform the values of the index for the six-digit NAICS provided
by the U.S. Census Bureau into Herfindahl values for the three-digit SICs by weighting the index
values of the component six-digit NAICS industries by the square of their share of the broader threedigit SIC industry.
The Herfindahl index ranges from zero to 10,000. To avoid a low value for the coefficients of this
variable in the regression models, we divide the index by 10,000 and obtain an indicator
(Census_Concentration) that ranges from zero to one. This is the Census-based measure of industry
concentration that is used in our analyses.
Diversification.
The number of reported segments identified by the firm is expected to depend on the degree of
diversification of its operations (e.g., Harris 1998). We use two variables to control for
diversification: NumberSICS and SICsDiversity. NumberSICS is defined as the number of three-digit
SICs in which the firm operates, and SICsDiversity is the ratio of the number of unique two-digit
SICs in which the firm has operations to the total number of three-digit SICs in which the firm
operates.
18
Market dependence for financing.
A corporate decision to release information is likely to be affected by the level of firm dependence
on the capital market for financing. Following prior literature (e.g., Li 2010), to control for this
dependence, we add a measure of corporate size (LAssets) and a measure of future external
financing (Issue_Equity_Debt). LAssets is the natural logarithm of total assets, and
Issue_Equity_Debt is the sum of the dollar amount of the equity and debt issued over a subsequent
two-year period scaled by total assets.
Institutional ownership.
Because of the disciplinary role played by institutional investors (e.g., Bushee, 1998; Chen et al.,
2007), a lower level of demand for information is expected for those firms with a higher percentage
of shares held by institutions (e.g., Bushee et al., 2003). We measure Institutional_Ownership as the
proportion of a firm’s shares held by institutional shareholders, as provided by Compustat.
4. Results.
4.1. Descriptive statistics.
Table 1 presents the descriptive statistics of the variables used in our analyses. It can be observed
that the sample comprises diversified firms that operate in various industries. The median (mean)
number of industries (defined at the three-digit-SIC code level) in which the firm has operations is 4
(4.9). However, firms do not offer separate information on all of their business segments. The
descriptive statistics show that 45 per cent of segments are not treated as reported business
segments. Rather than offering separate information on their operations in these industries, firms
aggregate them for segment reporting purposes.
19
[Insert Table 1 about here]
Despite the fact that all business segments in our sample are from the manufacturing sector,
descriptive statistics demonstrate that industries vary in the level of price-cost margin, the average
capital expenditures required to operate in the industry, or the relative weight of private firms in the
activity of the industry. Differences are also observed in firms’ attributes, such as size, ownership
structure, and the degree of diversification of a firm’s operations.
[Insert Table 2 about here]
Table 2 presents the correlation coefficients between the disclosure variable, all competition proxies,
and the control variables. The Spearman (Pearson) correlation coefficients are presented below
(above) the diagonal. The measure of disclosure (Seg_Reported) is significantly related to all
proxies for competition except for abnormal profitability. Moreover, the relatively low correlation
coefficients between the different competition measures suggest that each of them is capturing a
different dimension of competition. Regarding correlations between the treatment variables and the
control variables, Table 2 shows that Private is strongly negatively correlated with
Census_Concentration. When presenting the results of the multivariate analysis, we discuss the
findings obtained in the sensitivity analysis carried out to check whether results are affected by this
correlation.
4.2. Multivariate analyses.
We initiate the multivariate analysis by estimating a model where corporate disclosure is regressed
for each of the competition dimensions that are analysed in this study and for a number of factors
20
that prior literature has found to be significantly associated with disclosure. Specifically, we
estimate the following logit model:
Seg_Reportedit = 1/[1+EXP[-(αi + β1Competitionit + β2Census_Concentrationit + β3NumberSICSit
+ β4SICsDiversityit + β5LAssetsit + β6Issue_Equity_Debtit + β7Institutional_Ownershipit + Time
effects)]]
where
Competition
(1)
represents
abnormal
profitability
(Z_FirmMargin),
industry
margin
(Census_Margin), industry margin plus entry barriers (Census_LCapExp) and their interaction, or
the weight of private firms in the industry (Private) when estimating Models 1(a-d) in Table 3,
respectively. The models include a quadratic term for the abnormal profitability variable
(Z_FirmMargin) and the industry profitability variable (Census_Margin) because based on our
theoretical development, we expect a non-monotonic relationship between each of these variables
and corporate disclosure. Our sample includes multiple observations for the same firm, so we cluster
standard errors at the firm level. All of the variables are defined in Section 3.2, and the results of the
estimations are reported in Table 3 under the heading of Model 1(a-d). Table 3 also reports the
results of the estimation of a base model where disclosure is regressed on the control variables.
These results are presented under the heading of Model 0. For each of the models estimated, Table 3
reports coefficients, z statistics, and the economic magnitude of the coefficients 9. For the sake of
brevity, the constant and the year dummy are untabulated.
[Insert Table 3 about here]
9
The economic magnitude reflects the change in the probability that an industry in which the firm has operations is
considered by the firm to be a reported business segment, given a standard deviation change in the independent variables
(xi). It is calculated as the product of the marginal effect and the standard deviation of xi; marginal effects are obtained at
means of the independent variables.
21
By examining Table 3, it can be observed that corporate disclosure is significantly related to each of
the four competition measures investigated in this study. First, we find an inverted U-shape
relationship between a firm’s abnormal profitability and disclosure. These results indicate that firms
are reluctant to disclose separate information on their operations in business segments when they are
obtaining either abnormally high or abnormally low margins at the aggregate firm level.
Unwillingness to disclose when the firm is outperforming its competitors might be due to the fear of
raising proprietary costs, whereas the reluctance to provide information on poor performing business
segments might signal agency conflicts. This evidence suggests that abnormal profitability proxies
for both proprietary costs and agency costs, a result that is consistent with our first hypothesis. The
economic magnitude of the coefficient indicates that a one standard deviation increase of the
abnormal profitability from its mean leads to a 0.3 per cent decrease in the probability that an
industry in which the firm has operations is considered by the firm to be a reported business
segment.
Figure 2 depicts the predicted probability for a number of values of the Z_FirmMargin variable that
cover its range while holding the rest of the independent variables at their means. By examining this
graph, we observe that when we analyse the change in the predicted probability for a one standard
deviation increase of the Z_FirmMargin over its mean, we are examining the decreasing segment of
the curve. This explains the negative sign for the economic magnitude of the abnormal profitability
coefficient shown in Table 3. The relatively low statistical and economic significance of the
coefficient of the Z_FirmMargin variable might be at least partially explained by the limitations of
our abnormal profitability variable, already discussed when defining it (Section 3.2.2).
[Insert Figure 2 about here]
22
In spite of the limitation of the Z_FirmMargin variable, our findings are in line with the evidence
provided by Berger and Hann (2007) and Bens et al. (2011), as we document a positive association
between corporate disclosure and abnormal profitability for those firms that are clearly
underperforming their competitors. This association is suggestive of the existence of agency costs
that lead firms to withhold information that has the potential to unveil their underperformance. In
addition, the inclusion of a quadratic term in our estimation allows us to document a negative
association between corporate disclosure and abnormal profitability for firms whose performance is
higher than the industry average. This evidence indicates that good performers in the industry are
likely to bear proprietary costs that prevent them from providing separate information on their
operations in industries where they are outperforming their competitors.
Second, we observe a U-shape relationship between industry price-cost margin and corporate
disclosure, thereby indicating that a firm’s willingness to disclose decreases as industry profitability
increases up to a certain point beyond which further increases in industry profitability lead to
increases in disclosure. This finding is consistent with our second hypothesis. We interpret this
result as evidence that industry profitability captures two different forces exerting opposite effects
on the decision to disclose. The first is the use of disclosure to deter the entrance of new competitors
and the second is the necessity of information to oversee managerial actions. Hence, the decision to
release information will depend upon the relative weight of each of these forces. The interest in
deterring potential entrants (the necessity of information to oversee the managerial actions) seems to
be the main driver of disclosure at low (high) levels of industry profitability.
The coefficient of the industry margin variable is also economically significant; a one standard
deviation increase of industry margin from its mean value of 1.6 increases the probability of
business segment disclosure by 1.6 per cent.
23
Third, we observe that industry profitability and entry barriers interact with each other in shaping
disclosure decisions. The coefficient on the interaction between industry margin (Census_Margin)
and industry capital expenditures (Census_LCapExp) is positive and significantly different from
zero. To interpret this coefficient, we follow the recommendation by Greene (2010) and construct a
graph in which we plot the values of the interacted variables against the probabilities predicted by
the model. Similar to De Jong et al. (2012), we plot five lines. Each line shows the relationship
between the 10th, 25th, 50th, 75th, and 90th percentile values of the capital expenditures variable and a
number of values of the industry margin variable covering the range of this variable.
[Insert Figure 3 about here]
Figure 3 shows that the relationship between industry margin and corporate disclosure varies
depending on the level of industry average capital expenditures. At low levels of entry barriers, the
relationship between industry profitability and corporate disclosure follows a U-shape curve, which
is consistent with our second hypothesis. Nonetheless, as the level of entry barriers rises, the original
U-shape function evolves towards an increasing function. Stated in other words, the increasing part
of the U curve becomes steeper, whereas the decreasing segment tends to disappear. In fact, the
curve becomes a rising function for the highest levels of entry barriers. This evidence is consistent
with our third hypothesis and suggests that entry barriers, by protecting extant firms from potential
entrants, act to reduce the market pressure over managers, and at the same time, they reduce the
necessity of using disclosure as a means to deter potential entrants. This explains why the U curve
tends to be a rising line as entry barriers increase.
24
Overall, the results speak of the importance of looking at the interaction between entry barriers and
industry margin to gain a clear picture of the association between industry margin and corporate
disclosure.
The economic magnitude of coefficients indicates that a one standard deviation increase in the
industry margin (average capital expenditures) is associated with a 1.6 (1.9) per cent increase in the
probability of business segment disclosure.
Finally, consistent with our fourth hypothesis and with the evidence provided by Bens et al. (2011),
we find that corporate disclosure decreases as the proportion of industry sales made by private firms
increases. This evidence suggests that the proportion of the industry activity held by private firms
acts as a proxy for the (increased) proprietary costs that arise when a significant proportion of
industry incumbents are not required to release business-segment information. The economic
magnitude of the coefficient is also significant; a one standard deviation increase in the proportion
of industry sales held by private firms is associated with a 2.6 per cent decrease in the probability of
business segment disclosure.
For the control variables, we find that all of them are significantly related to corporate disclosure.
We find a positive association between industry concentration and corporate disclosure, a result that
is in line with the evidence provided by Verrecchia and Weber (2006) and Heitzman et al. (2010).
The coefficient is significantly different from zero in all models except for Model 1d. As explained
when describing the correlation coefficients (Table 2), industry concentration is highly correlated
with the proportion of industry activity held by private firms (ρ = -0.603). This might explain why
the coefficient for industry concentration becomes insignificant when the model includes the Private
variable. The results suggest that industry dispersion captures the proprietary costs linked to
25
disclosure that arise whenever a significant proportion of the industry activity is held by private
firms.
Consistent with prior research, we observe that corporate disclosure is negatively related to
diversification when it is proxied by the number of industries in which the firm has operations. The
likelihood of treating each of these industries as a reported business segment decreases as the
number of SICs in which the firm operates increases. The separate business segment reporting is
also less likely when the industries in which the firm has operations are proximal.
Additionally, we find a positive association between disclosure and corporate size. This association
suggests that large firms are compelled to disclose more information because they are more
dependent on the market for their financing. Disclosure might facilitate their access to the capital
market or even reduce their cost of capital. Therefore, large firms are expected to benefit the most
from transparency policies.
The negative association between institutional ownership and corporate disclosure is also consistent
with prior research and suggests that the presence of institutional shareholders acts as a disciplinary
mechanism that reduces the necessity of public disclosure to oversee managerial actions.
Finally, a positive association is observed between corporate disclosure and future external
financing, though the coefficient is not statistically different from zero in Models 1b and 1c. The
relative stability of the reported business segments over time might explain the weak association
between disclosure and the issuance of debt or equity over the two subsequent periods.
Our analysis proceeds by estimating a model that includes all four proxies for competition
concurrently. The joint use of the four measures allows us to examine the individual effect of each
26
one while controlling for the other three. Karuna (2007) explains that this is an advantageous
approach because each proxy is likely to capture a different dimension of competition. We estimate
the following model:
Seg_Reportedit = 1/[1+EXP[-(αi + β1Z_FirmMarginit + β2(Z_FirmMarginit)^2+ β3Census_Marginit
+ β4(Census_Marginit)^2 + β5Census_LCapExpit + β6Census_Marginit*Census_LCapExpit +
β7Privateit +β8Census_Concentrationit + β9NumberSICSit + β10SICsDiversityit + β11LAssetsit +
β12Issue_Equity_Debtit + β13Institutional_Ownershipit + Time effects)]]
(2)
Standard errors are clustered at the firm level, and all the variables are defined in Section 3.2. Table
3 presents the results of this estimation under the heading Model 2.
The results show that coefficients for the treatment and control variables mirror those obtained when
estimating Equation (1), except for the coefficient of the Private variable, which becomes
insignificantly different from zero. As explained above, when discussing the results of the
estimation of Model 1, the high correlation between the Census_Concentration and Private
variables might explain this finding. As a robustness check, we re-estimate Equation (2) after
dropping the industry concentration variable, and the results (untabulated) show a highly significant
(p<0.001) negative association between disclosure and the proportion of industry sales made by
private firms (i.e., Private). The coefficients for the remaining treatment and control variables
remain qualitatively unchanged. Again, we find evidence consistent with industry dispersion
proxying for the (increased) proprietary costs public firms bear whenever a significant proportion of
the industry activity is performed by private firms.
The economic magnitude of coefficients, although generally lower, is in line with that observed
when estimating Model 1. Hence, although the four competition measures used in this study might
27
share a portion of the information they convey, the evidence gathered when estimating Model 2
suggests that each of the competition proxies captures a different dimension of product market
rivalry 10.
4.3. Sensitivity analyses
4.3.1. Measurement error in the Private variable.
When defining the Private variable (Section 3.2.2) we observed that the total amount of domestic
sales for firms in Compustat is higher than the total value of shipments reported by the U.S. Census
Bureau for a number of industries, which is indicative of the existence of measurement error in this
variable. To assess whether our results are affected by this measurement problem, we re-estimate
Models 1d and 2 after winsorising the Private variable at zero (i.e., setting the negative values of
Private at zero. We present the results of these estimations in Table 4, where it can be observed that
the tenor of our findings remains qualitatively unchanged.
[Insert Table 4 about here]
As a further robustness check, we re-estimate all our models after truncating Private at zero (i.e., we
delete from our sample those observations for which the original Private variable takes a negative
value). Table 5 presents the results of these estimations.
[Insert Table 5 about here]
10
We obtain similar results when we re-estimate our models using Compustat-based instead of Census-based measures
(i.e., Census_Margin, Census_LCapExp, and Census_Concentration are substituted with their counterparts computed by
using Compustat data). For the sake of brevity, results are not tabulated, but they are available from the authors. The
results for the treatment variables are consistent with those reported in Table 3, although the level of significance of the
coefficients for the entry barrier variable and the interaction between industry margin and entry barriers is lower than
when using Census data. That almost all firms in Compustat are public might explain this result.
28
Results shown in Table 5 are in line with those reported in Tables 3 and 4, although we observe
changes in the level of significance of the treatment variables. The coefficient of the Private variable
becomes highly significant, whereas the level of significance of coefficients for the industry
competition variables generally diminishes and the coefficient of the abnormal profitability variable
(Z_FirmMargin) becomes statistically insignificant at conventional levels.
Briefly, when we truncate the sample to eliminate the observations for which the Private variable
takes a negative value, the statistical and economic effect of the Private variable (rest of the
competition variables) increases (decreases). From these results we could infer that competition
proxies share a portion of the information they convey and, once we truncate the Private variable, it
gains relevance at the expense of the rest of the competition proxies.
Nonetheless, Table 5 also shows a reduction in the level of significance of the coefficients of the
competition proxies in those models that do not include the Private variable (i.e., Models 1a-c). We
attribute this change to the substantial reduction in the sample that results from the truncation of the
Private variable (4,563 observations in the truncated sample versus 5,179 in the full sample).
Moreover, as the Private variable is measured at the industry level, when we delete observations
with a negative value for Private, we are eliminating industries from a sample containing an already
limited number of industries. As explained in Section 3.1, our sample is restricted to manufactures
because of the use of Census data in computing the industry competition variables. This might
explain why the level of significance of the competition measures diminishes when estimating
Models (1a-1c) for the truncated sample.
In spite of the problems caused by the truncation of the Private variable, results reported in Table 5
provide support for all our hypotheses except for hypothesis one. Once again we should refer to the
29
limitations of our abnormal profitability proxy to explain the weakness of the evidence supporting
our first hypothesis.
4.3.2. Firm-level analysis.
Prior analyses are based on a segment-level proxy for corporate disclosure that indicates whether a
business segment in which the firm has operations is a reported segment in Compustat. The use of
this segment-level measure allows us to examine whether industry-specific competition measures
influence disclosure decisions. However, this proxy is not free of criticism. In constructing the
disclosure measure, we assume that the financial data for each of the business segments in the
Compustat business segment file refers exclusively to a firm’s operations in the SIC assigned to this
segment by Compustat. This is not true in most cases because firms aggregate activity data for
different business segments for reporting purposes. Using the example of Albemarle Corporation
that was used in Section 3.1 and that was based on the data from Compustat and Osiris, the
corporation operates in the following SICs: 2819, 2821, 2834, 2879, 2890, 2899, and 3861.
However, Albemarle reports only three business segments: fine chemicals, polymer additives, and
catalysts, to which Compustat assigns the SICs 2819, 2899, and 2899, respectively, so that
operations in the remainder of the industries are aggregated with the operations in SIC 2819 or SIC
2899.
A finer proxy for corporate disclosure is obtained by defining it at the firm level. We calculate the
ratio of the number of reported business segments to the total number of SICs in which the firm has
operations. The disadvantage of using this proxy for disclosure is that it requires that the rest of the
variables in the model also be defined at the firm level. All the explanatory factors in our models
satisfy this requirement, except for industry competition measures, which are defined at the industry
30
level. We therefore transform the competition measures into firm-level measures by calculating the
average level of industry rivalry faced by a firm across all manufacturing SICs in which it has
operations. We compute an unweighted average because we do not have information on the volume
of a firm’s operations in each industry (e.g., revenue). We acknowledge that the resulting
competition measures are crude proxies for the level of product market rivalry faced by firms,
particularly when there are large differences between industries in the level of rivalry, and/or a
firm’s operations are not evenly distributed across industries. However, we decided to estimate our
models using firm-level data to verify whether the results are sensitive to the definition of the
disclosure variable.
Using the abovementioned firm-level data, we re-estimate Equations (1-2). However, because the
dependent variable is now a continuous variable, we estimate a linear model instead of the logit
model, and we cluster standard errors at the firm level. Table 6 presents the results of these
estimations.
[Insert Table 6 about here]
Despite the change in the definition of the disclosure variable and the limitations of the firm-level
industry competition measures, the findings are largely consistent with those obtained when using
business-segment level data (Table 3). Overall, the evidence reported in Table 6 suggests that our
findings are not sensitive to the definition of the disclosure variable.
5. Discussion and conclusions.
This paper proposes a new focus in the investigation of the association between product market
competition and corporate disclosure. Taking the multidimensionality of competition as a basis, we
31
distinguish between firm and industry level competition measures and argue that, while the former
raises disincentives to disclose (i.e., proprietary and agency costs), the latter creates incentives to
release information either to fulfil the owners’ need for information to monitor managers or to deter
the entrance of new competitors. Using an empirical design that allows for both non-monotonic
relationships between disclosure and competition measures, as well as interactions between
competition dimensions, we gather evidence that is consistent with our hypotheses.
Specifically, we observe an inverted-U-shape relationship between abnormal profitability and
corporate disclosure, which is suggestive of firms being reluctant to disclose at both extremes of the
range of the abnormal profitability variable. We interpret this finding as evidence that abnormal
profitability proxies for both proprietary and agency costs linked to disclosure. At high levels of
abnormal profitability, firms might fear that disclosure provides valuable inputs to their rivals,
whereas poor performing firms might withhold information in an attempt to avoid unravelling
agency conflicts.
We also find a U-shape relationship between industry profitability and corporate disclosure, which
is suggestive of industry profitability proxying for the use of disclosure as a means to deter potential
entrants and proxying for the necessity of information to monitor managers. As industry profitability
increases, the potential agency conflicts also rise, and thus, owners need more information to
oversee managers. Our findings suggest that the deterrence of new competitors is important for
companies operating in low profitable industries, whereas at high levels of industry profitability this
concern is clearly outweighed by the incentives to fulfil the investor’s need for information to
oversee the managerial team. Furthermore, we find that the balance between the deterrence effect
and the necessity of information to oversee managers depends on the level of entry barriers. By
protecting incumbent firms in the industry from potential entrants, barriers to entry act to
32
simultaneously lower the interest in using disclosure to deter potential entrants and to increase the
incentives to disclose because of the reduction in the product market pressure over managers.
Consequently, a U-shape relationship between industry profitability and disclosure is observed at
low levels of entry barriers, but it evolves towards an increasing function when the industry is
protected by heavier entry barriers.
Finally, the negative association we observe between the proportion of industry activity held by
private firms and corporate disclosure suggests that, due to the less-demanding disclosure
requirements for unlisted firms, additional proprietary costs arise when a significant proportion of
the industry activity is performed by private firms. Therefore, the results suggest that the relative
weight of private firms in the industry proxies for proprietary costs that add to those already
captured by the abnormal profitability measure.
Taken together, the evidence gathered in this paper helps us understand certain divergences in prior
studies’ findings. First, our results indicate that not all proxies for competition capture the same
dimensions of this construct. This means that study results based on varying measures of
competition are not necessarily comparable. Furthermore, our findings suggest that certain
competition measures interact with each other in shaping disclosure decisions (e.g., industry
profitability and entry barriers). In such a case, to obtain a clear picture of the relationship between
corporate disclosure and the competition measures, we should look at the interaction between these
measures.
Second, although this is not the first study to document a non-monotonic association between
product market competition and corporate disclosure (e.g., Karuna, 2013), we provide evidence
33
consistent with the existence of non-linearities when competition is observed either at the firm or
industry level.
Finally, the evidence gathered in this paper is consistent with industry competition proxying not
only for the interest in using disclosure as a means to deter the entrance of new competitors but also
for the incentives to disclose that arise whenever the product market pressure on managers is low
(i.e., potential agency conflicts are high). In these cases, corporate disclosure serves the role of
alleviating the potential agency conflicts.
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37
Figure 1. Disclosure costs
38
Figure 2. Relationship between abnormal profitability and disclosure - Model 1a.
1
0.9
0.8
0.7
0.6
Predicted
probability
0.5
0.4
0.3
0.2
0.1
0
-4
-3
-2
-1
0
1
2
3
Z_FirmMargin
39
Figure 3. Interaction between industry margin and industry average capital expenditures –
Model 1c
1
0.9
0.8
0.7
0.6
P10 LCapExp
Predicted
0.5
probability
P25 LCapExp
P50 LCapExp
0.4
P75 LCapExp
0.3
P90 LCapExp
0.2
0.1
0
1
1.5
2
2.5
3
Census_Margin
40
Table 1. Descriptive statistics
Mean
Seg_Reported
Firm_Margin
Z_FirmMargin
Census_Margin
Census_CapExp
Census_LCapExp
Private
Census_Concentration
Number_SICs
SICsDiversity
Assets
LAssets
Issue_Equity_Debt
Inst_Ownership
0.553
1.033
0.104
1.590
17,998
8.609
0.453
0.022
4.907
0.750
4,154
6.265
0.298
63.159
Dev.
Min.
Max.
P25
P50
P75
0.497
0.236
0.764
0.365
49,332
1.441
0.548
0.023
2.792
0.215
9,913
2.266
0.565
33.182
0.000
0.000
-3.589
1.199
124
4.822
-2.385
0.001
2.000
0.250
1.469
0.385
0.000
0.001
1.000
2.048
2.527
2.876
375,133
12.835
0.999
0.173
15.000
1.000
84,828
11.348
4.782
125.687
0.000
1.019
-0.212
1.405
2,064
7.633
0.190
0.006
3.000
0.600
99
4.590
0.025
34.614
1.000
1.081
0.190
1.483
5,118
8.541
0.638
0.016
4.000
0.750
537
6.286
0.102
73.132
1.000
1.139
0.523
1.611
12,750
9.453
0.827
0.028
6.000
1.000
2,733
7.913
0.295
88.148
The sample is composed of 5,179 business segment-year observations for the years 2002 and 2007. Seg_Reported is a
dummy variable that takes the value of one when a three-digit SIC in which the firm has operations matches the primary
or secondary SIC code of one of the firm’s business segments in Compustat and the value of zero otherwise;
Z_FirmMargin is Firm_Margin minus its industry-year mean and divided by the industry-year standard deviation;
Firm_Margin is the price-cost margin for the firm; Census_Margin is the industry price-cost margin (data obtained from
the Economic Census); Census_CapExp (Census_LCapExp) is (the natural logarithm of) industry average capital
expenditures (data –in U.S. $000- taken from the Economic Census); Private is the percentage of industry sales obtained
by private firms; Census_Concentration is the Herfindahl index of industry concentration provided by the U.S. Census
Bureau; NumberSICS is the number of three-digit SICs in which the firm has operations; SICsDiversity is the ratio of the
number of unique two-digit SICs in which the firm has operations to the total number of three-digit SICs in which the
firm operates; Assets (LAssets) is (the natural logarithm of) the book value of a firm’s assets; Issue_Equity_Debt is the
sum of the dollar amount of the equity and debt issued by the firm over a subsequent two-year period scaled by total
assets; Institutional_Ownership is the proportion of a firm’s shares held by institutional shareholders, as provided by
Compustat.
41
Table 2. Correlation matrix: Spearman (Pearson) correlation coefficients below (above) diagonal.
SegRep.
Seg_Reported
Z_FirmMargin
Census_ Margin
Census_ LCapExp
Private
Census_ Concent
NumberSICS
SICsDiversity
LAssets
Issue_EquityDebt
Inst_Ownership.
ZFMargn
-0.017
(0.223)
-0.022
(0.111)
0.132
(0.000)
0.087
(0.000)
-0.164
(0.000)
0.098
(0.000)
-0.233
(0.000)
0.208
(0.000)
-0.036
(0.009)
0.019
(0.181)
-0.036
(0.009)
0.036
(0.009)
-0.023
(0.100)
-0.016
(0.261)
-0.008
(0.554)
0.126
(0.000)
-0.056
(0.000)
0.310
(0.000)
-0.080
(0.000)
0.250
(0.000)
Margin
0.201
(0.000)
0.004
(0.785)
0.005
(0.715)
-0.319
(0.000)
0.058
(0.000)
-0.258
(0.000)
0.191
(0.000)
-0.194
(0.000)
-0.009
(0.520)
-0.129
(0.000)
LCapExp
Private
Concent
0.076
(0.000)
0.004
(0.771)
0.156
(0.000)
-0.111
(0.000)
0.002
(0.915)
-0.178
(0.000)
-0.011
(0.449)
0.046
(0.001)
-0.036
(0.009)
-0.006
(0.676)
-0.064
(0.000)
-0.451
(0.000)
-0.075
(0.000)
0.050
(0.000)
-0.093
(0.000)
0.056
(0.000)
0.038
(0.007)
0.043
(0.002)
-0.001
(0.952)
-0.603
(0.000)
0.231
(0.000)
-0.224
(0.000)
0.110
(0.000)
0.023
(0.098)
0.088
(0.000)
-0.086
(0.000)
0.051
(0.000)
0.011
(0.420)
0.011
(0.441)
-0.031
(0.028)
NSICS
-0.201
(0.000)
0.165
(0.000)
-0.256
(0.000)
-0.060
(0.000)
0.166
(0.000)
0.013
(0.362)
-0.463
(0.000)
0.436
(0.000)
0.026
(0.057)
0.181
(0.000)
SICsDiv
0.207
(0.000)
-0.074
(0.000)
0.255
(0.000)
0.031
(0.026)
-0.205
(0.000)
0.008
(0.581)
-0.419
(0.000)
-0.267
(0.000)
-0.017
(0.218)
-0.099
(0.000)
LAssets
-0.034
(0.014)
0.340
(0.000)
-0.202
(0.000)
0.053
(0.000)
0.093
(0.000)
0.055
(0.000)
0.451
(0.000)
-0.266
(0.000)
-0.035
(0.012)
0.467
(0.000)
Issue
0.019
(0.178)
-0.173
(0.000)
0.096
(0.000)
0.055
(0.000)
0.016
(0.266)
-0.006
(0.694)
-0.024
(0.084)
0.037
(0.008)
-0.187
(0.000)
InstOwn
-0.039
(0.005)
0.292
(0.000)
-0.140
(0.000)
0.011
(0.411)
0.079
(0.000)
0.006
(0.657)
0.200
(0.000)
-0.117
(0.000)
0.499
(0.000)
-0.088
(0.000)
0.043
(0.002)
42
Table 2 (continued)
The sample is composed of 5,179 business segment-year observations for the years 2002 and 2007. Seg_Reported is a dummy variable that takes the value of one
when a three-digit SIC in which the firm has operations matches the primary or secondary SIC code of one of the firm’s business segments in Compustat, and the
value of zero otherwise; Z_FirmMargin is Firm_Margin minus its industry-year mean and divided by the industry-year standard deviation; Firm_Margin is the
price-cost margin for the firm; Census_Margin is the industry price-cost margin; Census_LCapExp is the natural logarithm of industry average capital
expenditures; Private is the percentage of industry sales obtained by private firms; Census_Concentration is the Herfindahl index of industry concentration
provided by the U.S. Census Bureau; NumberSICS is the number of three-digit SICs in which the firm has operations; SICsDiversity is the ratio of the number of
unique two-digit SICs in which the firm has operations to the total number of three-digit SICs in which the firm operates; LAssets is the natural logarithm of the
book value of a firm’s assets; Issue_Equity_Debt is the sum of the dollar amount of the equity and debt issued by the firm over a subsequent two-year period
scaled by total assets; Institutional_Ownership is the proportion of a firm’s shares held by institutional shareholders, as provided by Compustat. P-values are
shown in parentheses.
43
Table 3. Regression of corporate disclosure on competition. Industry competition measures are computed using Census data.
Model 0
Coeff.
(z-stat)
Econ.
Mag.
Z_Firm_Margin
Z_Firm_Margin^2
Model 1a
Coeff.
(z-stat)
-0.008
(-0.199)
-0.050**
(-2.134)
Econ.
Mag.
-0.003
Census_ Margin
Model 1b
Coeff.
(z-stat)
-3.035***
(-2.900)
1.056***
(3.869)
Census_ Margin^2
Econ.
Mag.
0.016
Census_ LCapExp
Census_Margin*
Census_LCapExp
Private
Census_ Concent.
NumberSICS
SICsDiversity
LAssets
Issue_Equity_Debt
Inst_Ownership
Observations
Pseudo R-Sq. (%)
Log likelihood
3.879**
(2.499)
-0.134***
(-9.268)
1.531***
(11.136)
0.101***
(6.063)
0.104**
(2.158)
-0.002**
(-2.445)
5,179
5.10
-3,379
0.021
-0.086
0.076
0.053
0.014
-0.018
3.923**
(2.522)
-0.135***
(-9.240)
1.540***
(11.221)
0.100***
(5.833)
0.114**
(2.291)
-0.003**
(-2.568)
5,179
5.15
-3,377
0.021
-0.087
0.076
0.052
0.015
-0.020
3.970***
(2.646)
-0.119***
(-8.796)
1.279***
(9.355)
0.108***
(6.539)
0.050
(1.162)
-0.002**
(-1.984)
5,179
7.15
-3,306
0.021
-0.075
0.062
0.055
0.006
-0.015
Model 1c
Coeff.
(z-stat)
-4.786***
(-3.598)
0.647**
(2.503)
-0.478***
(-2.631)
0.351***
(2.920)
4.340***
(2.910)
-0.117***
(-8.711)
1.290***
(9.474)
0.105***
(6.371)
0.038
(0.891)
-0.002**
(-1.991)
5,179
7.31
-3,300
Econ.
Mag.
Model 1d
Coeff.
(z-stat)
Econ.
Mag.
0.016
0.019
0.023
-0.073
0.062
0.053
0.005
-0.015
-0.210***
(-2.698)
1.646
(0.961)
-0.130***
(-9.114)
1.454***
(10.469)
0.102***
(6.099)
0.104**
(2.155)
-0.002**
(-2.322)
5,179
5.25
-3,373
-0.026
0.009
-0.084
0.072
0.053
0.014
-0.017
Model 2
Coeff.
(z-stat)
-0.065
(-1.594)
-0.064***
(-2.726)
-4.702***
(-3.526)
0.667**
(2.560)
-0.456**
(-2.489)
0.334***
(2.759)
-0.095
(-1.291)
3.283**
(1.962)
-0.115***
(-8.584)
1.271***
(9.282)
0.108***
(6.357)
0.045
(1.038)
-0.002*
(-1.919)
Econ.
Mag.
-0.013
0.016
0.018
-0.012
0.017
-0.072
0.061
0.055
0.006
-0.014
5,179
7.44
-3,295
44
Table 3 (continued)
The sample is composed of 5,179 business segment-year observations for the years 2002 and 2007. The dependent variable is Seg_Reported, a dummy variable
that takes the value of one when a three-digit SIC in which the firm has operations matches the primary or secondary SIC code of one of the firm’s business
segments in Compustat and the value of zero otherwise; Z_FirmMargin is Firm_Margin minus its industry-year mean and divided by the industry-year standard
deviation; Firm_Margin is the price-cost margin for the firm; Census_Margin is the industry price-cost margin; Census_LCapExp is the natural logarithm of
industry average capital expenditures; Private is the percentage of industry sales obtained by private firms; Census_Concentration is the Herfindahl index of
industry concentration provided by the U.S. Census Bureau; NumberSICS is the number of three-digit SICs in which the firm has operations; SICsDiversity is the
ratio of the number of unique two-digit SICs in which the firm has operations to the total number of three-digit SICs in which the firm operates; LAssets is the
natural logarithm of the book value of a firm’s assets; Issue_Equity_Debt is the sum of the dollar amount of the equity and debt issued by the firm over a
subsequent two-year period scaled by total assets; Institutional_Ownership is the proportion of a firm’s shares held by institutional shareholders, as provided by
Compustat. Logit models are estimated and the standard errors are clustered at the firm level. Z-statistics are shown in parentheses. Constant and year dummy are
omitted from the table. Pseudo R-Squared is defined as (LLr-LL)/LLr, where LLr (LL) is the log likelihood of the restricted (unrestricted). The Econ. Mag.
(economic magnitude) column reflects the change in the probability that an industry in which the firm has operations is considered by the firm as a reported
business segment, given a standard deviation change in the independent variables (xi). It is calculated as the product of the marginal effect and the standard
deviation of xi. The marginal effects are obtained at means of the independent variables.
***, **, * = statistically significant at the 1%, 5%, and 10% levels, respectively.
45
Table 4. Regression of corporate disclosure on competition. Industry competition measures
are computed using Census data and the Private variable is winsorized at zero.
Model 1d
Coeff.
(z-stat)
Econ. Mag.
Z_Firm_Margin
Z_Firm_Margin^2
Census_ Margin
Census_ Margin^2
Census_ LCapExp
Census_Margin*
Census_LCapExp
Private
Census_ Concent.
NumberSICS
SICsDiversity
LAssets
Issue_Equity_Debt
Inst_Ownership
Observations
Pseudo R-Sq. (%)
Log likelihood
-0.751***
(-5.744)
-1.638
(-0.934)
-0.123***
(-8.697)
1.359***
(9.833)
0.103***
(6.126)
0.099**
(2.093)
-0.002**
(-2.098)
5,179
5.79
-3,354
-0.057
-0.009
-0.079
0.066
0.053
0.013
-0.016
Model 2
Coeff.
(z-stat)
-0.067
(-1.643)
-0.063***
(-2.676)
-4.628***
(-3.463)
0.643**
(2.478)
-0.450**
(-2.436)
0.329***
(2.686)
-0.317**
(-2.307)
1.949
(1.097)
-0.114***
(-8.445)
1.252***
(9.183)
0.108***
(6.340)
0.048
(1.085)
-0.002*
(-1.848)
Econ. Mag.
-0.014
0.012
0.017
-0.024
0.010
-0.071
0.060
0.055
0.006
-0.014
5,179
7.52
-3,293
46
Table 4 (continued)
The sample is composed of 5,179 business segment-year observations for the years 2002 and 2007. The dependent
variable is Seg_Reported, a dummy variable that takes the value of one when a three-digit SIC in which the firm has
operations matches the primary or secondary SIC code of one of the firm’s business segments in Compustat and the
value of zero otherwise; Z_FirmMargin is Firm_Margin minus its industry-year mean and divided by the industry-year
standard deviation; Firm_Margin is the price-cost margin for the firm; Census_Margin is the industry price-cost margin;
Census_LCapExp is the natural logarithm of industry average capital expenditures; Private is the percentage of industry
sales obtained by private firms; negative values of the Private variable are set at zero; Census_Concentration is the
Herfindahl index of industry concentration provided by the U.S. Census Bureau; NumberSICS is the number of threedigit SICs in which the firm has operations; SICsDiversity is the ratio of the number of unique two-digit SICs in which
the firm has operations to the total number of three-digit SICs in which the firm operates; LAssets is the natural
logarithm of the book value of a firm’s assets; Issue_Equity_Debt is the sum of the dollar amount of the equity and debt
issued by the firm over a subsequent two-year period scaled by total assets; Institutional_Ownership is the proportion of
a firm’s shares held by institutional shareholders, as provided by Compustat. Logit models are estimated and the
standard errors are clustered at the firm level. Z-statistics are shown in parentheses. Constant and year dummy are
omitted from the table. Pseudo R-Squared is defined as (LLr-LL)/LLr, where LLr (LL) is the log likelihood of the
restricted (unrestricted). The Econ. Mag. (economic magnitude) column reflects the change in the probability that an
industry in which the firm has operations is considered by the firm as a reported business segment, given a standard
deviation change in the independent variables (xi). It is calculated as the product of the marginal effect and the standard
deviation of xi. The marginal effects are obtained at means of the independent variables.
***, **, * = statistically significant at the 1%, 5%, and 10% levels, respectively.
47
Table 5. Regression of corporate disclosure on competition. Industry competition measures are computed using Census data
and the Private variable is truncated at zero.
Model 0
Coeff.
(z-stat)
Econ.
Mag.
Z_Firm_Margin
Z_Firm_Margin^2
Model 1a
Coeff.
(z-stat)
0.015
(0.348)
-0.028
(-1.076)
Econ.
Mag.
0.002
Census_ Margin
Model 1b
Coeff.
(z-stat)
-2.440**
(-2.262)
0.906***
(3.237)
Census_ Margin^2
Econ.
Mag.
0.029
Census_ LCapExp
Census_Margin*
Census_LCapExp
Private
Census_ Concent.
NumberSICS
SICsDiversity
LAssets
Issue_Equity_Debt
Inst_Ownership
Observations
Pseudo R-Sq. (%)
Log likelihood
11.178***
(4.789)
-0.129***
(-8.048)
1.595***
(10.508)
0.083***
(4.334)
0.074
(1.492)
-0.002**
(-2.207)
4,563
5.64
-2,960
0.045
-0.083
0.079
0.043
0.010
-0.018
11.229***
(4.811)
-0.129***
(-8.064)
1.598***
(10.536)
0.081***
(4.131)
0.081
(1.618)
-0.003**
(-2.327)
4,563
5.66
-2,959
0.046
-0.084
0.079
0.042
0.011
-0.020
9.954***
(4.504)
-0.111***
(-7.473)
1.290***
(8.533)
0.092***
(4.816)
0.017
(0.380)
-0.002*
(-1.718)
4,563
7.80
-2,892
0.039
-0.070
0.062
0.046
0.002
-0.014
Model 1c
Coeff.
(z-stat)
-3.740***
(-2.683)
0.607**
(2.301)
-0.353**
(-1.980)
0.259**
(2.193)
9.923***
(4.520)
-0.110***
(-7.414)
1.304***
(8.660)
0.090***
(4.712)
0.008
(0.171)
-0.002*
(-1.729)
4,563
7.90
-2,889
Econ.
Mag.
Model 1d
Coeff.
(z-stat)
Econ.
Mag.
0.029
0.014
0.039
-0.069
0.063
0.045
0.001
-0.014
-1.189***
(-7.514)
2.366
(1.018)
-0.114***
(-7.337)
1.392***
(9.203)
0.090***
(4.660)
0.059
(1.245)
-0.002*
(-1.868)
4,563
6.96
-2,918
-0.077
0.009
-0.072
0.067
0.045
0.008
-0.015
Model 2
Coeff.
(z-stat)
-0.052
(-1.207)
-0.041
(-1.592)
-3.690***
(-2.640)
0.606**
(2.301)
-0.319*
(-1.746)
0.233*
(1.924)
-0.573***
(-3.123)
5.796**
(2.405)
-0.106***
(-7.155)
1.273***
(8.489)
0.093***
(4.769)
0.014
(0.306)
-0.002
(-1.599)
Econ.
Mag.
-0.010
0.014
0.013
-0.036
0.023
-0.067
0.061
0.047
0.002
-0.013
4,563
8.17
-2,880
48
Table 5 (continued)
The sample is composed of 4,563 business segment-year observations for the years 2002 and 2007. The dependent variable is Seg_Reported, a dummy variable
that takes the value of one when a three-digit SIC in which the firm has operations matches the primary or secondary SIC code of one of the firm’s business
segments in Compustat and the value of zero otherwise; Z_FirmMargin is Firm_Margin minus its industry-year mean and divided by the industry-year standard
deviation; Firm_Margin is the price-cost margin for the firm; Census_Margin is the industry price-cost margin; Census_LCapExp is the natural logarithm of
industry average capital expenditures; Private is the percentage of industry sales obtained by private firms and it is truncated at zero; Census_Concentration is the
Herfindahl index of industry concentration provided by the U.S. Census Bureau; NumberSICS is the number of three-digit SICs in which the firm has operations;
SICsDiversity is the ratio of the number of unique two-digit SICs in which the firm has operations to the total number of three-digit SICs in which the firm
operates; LAssets is the natural logarithm of the book value of a firm’s assets; Issue_Equity_Debt is the sum of the dollar amount of the equity and debt issued by
the firm over a subsequent two-year period scaled by total assets; Institutional_Ownership is the proportion of a firm’s shares held by institutional shareholders,
as provided by Compustat. Logit models are estimated and the standard errors are clustered at the firm level. Z-statistics are shown in parentheses. Constant and
year dummy are omitted from the table. Pseudo R-Squared is defined as (LLr-LL)/LLr, where LLr (LL) is the log likelihood of the restricted (unrestricted). The
Econ. Mag. (economic magnitude) column reflects the change in the probability that an industry in which the firm has operations is considered by the firm as a
reported business segment, given a standard deviation change in the independent variables (xi). It is calculated as the product of the marginal effect and the
standard deviation of xi. The marginal effects are obtained at means of the independent variables.
***, **, * = statistically significant at the 1%, 5%, and 10% levels, respectively.
49
Table 6. Regression of corporate disclosure on industry competition. Disclosure is measured at
the firm level, and industry competition measures are based on Census data.
Model 1a
Z_Firm_Margin
Z_Firm_Margin^2
Model 1b
-0.430**
(-2.577)
0.144***
(3.710)
Avrg_Census_ Margin^2
Avrg_Census_ LCapExp
Avrg_Census_ Margin *
Avrg_Census_ LCapExp
Avrg_Private
NumberSICS
SICsDiversity
LAssets
Issue_Equity_Debt
Inst_Ownership
Observations
R-squared (%)
Model 1d
Model 2
-0.020**
(-2.184)
-0.018***
(-3.135)
-0.983***
(-3.376)
0.116***
(3.112)
-0.121***
(-3.556)
0.071***
(3.487)
-0.027
(-1.419)
-0.484
(-0.910)
-0.033***
(-9.069)
0.441***
(11.547)
0.021***
(5.251)
0.019**
(2.132)
-0.000
(-1.071)
2,212
33.4
-0.013
(-1.338)
-0.014**
(-2.323)
Avrg_Census_ Margin
Avrg_Census_ Concentration
Model 1c
-1.088***
(-3.797)
0.131***
(3.538)
-0.127***
(-3.767)
0.075***
(3.742)
-0.224
(-0.445)
-0.039***
(-10.371)
0.530***
(14.254)
0.017***
(4.136)
0.042***
(4.484)
-0.000
(-1.337)
0.065
(0.132)
-0.034***
(-9.404)
0.447***
(11.822)
0.018***
(4.648)
0.022**
(2.572)
-0.000
(-0.927)
-0.051
(-0.104)
-0.033***
(-9.210)
0.452***
(11.965)
0.019***
(4.884)
0.019**
(2.202)
-0.000
(-1.118)
-0.051***
(-2.669)
-0.855
(-1.550)
-0.038***
(-9.925)
0.504***
(13.227)
0.016***
(4.068)
0.041***
(4.376)
-0.000
(-1.223)
2,212
27.1
2,212
32.1
2,212
32.7
2,212
27.3
The sample is composed of 2,212 firm-year observations for the years 2002 and 2007. Firm_Disclosure is the ratio of
the number of manufacturing reported business segments to the total number of manufacturing SICs in which the firm
has operations; Z_FirmMargin is Firm_Margin minus its industry-year mean and divided by the industry-year standard
deviation; Firm_Margin is the price-cost margin for the firm. The prefix Avrg in industry competition measures stands
for the averaging of industry competition measures across all three-digit SICs in which the firm has operations.
Census_Margin is the industry price-cost margin; Census_LCapExp is the natural logarithm of industry average capital
expenditures; Private is the percentage of industry sales obtained by private firms¸ Census_Concentration is the
Herfindahl index of industry concentration provided by the U.S. Census Bureau; NumberSICS is the number of threedigit SICs in which the firm has operations; SICsDiversity is the ratio of the number of unique two-digit SICs in which
the firm has operations to the total number of three-digit SICs in which the firm operates; LAssets is the natural
logarithm of the book value of a firm’s assets; Issue_Equity_Debt is the sum of the dollar amount of the equity and debt
issued by the firm over a subsequent two-year period scaled by total assets; Institutional_Ownership is the proportion of
a firm’s shares held by institutional shareholders, as provided by Compustat.. Standard errors are clustered at the firm
level and t-statistics are shown in parentheses. Constant and year dummy are omitted from the table.
***, **, * = statistically significant at the 1%, 5%, and 10% levels, respectively.
50