*Title Page/Author Identifier Page/Abstract
Industry concentration and corporate disclosure policy*
Ashiq Ali
Naveen Jindal School of Management
University of Texas at Dallas
Richardson, TX 75083-0688
972.883.6360
ashiq.ali@utdallas.edu
Sandy Klasa
Eller College of Management
University of Arizona
Tucson, AZ 85721-0108
520.621.8761
sklasa@eller.arizona.edu
Eric Yeung
Johnson Graduate School of Management
Cornell University
Ithaca, NY 14853-6201
607.255.4961
eric.yeung@cornell.edu
October 2013
Abstract:
This study examines the association between industry concentration and the informativeness of corporate
disclosure policy. We argue that because firms in more concentrated industries tend to be more innovative,
they incur higher proprietary costs of disclosure and disclose less. We find that in more concentrated
industries firms’ management earnings forecasts are less frequent and have shorter horizons, their disclosure
ratings by analysts are lower, and they have more opaque information environments, as measured by the
properties of analysts’ earnings forecasts. Also, when these firms raise funds they prefer private placements,
which have minimal SEC-mandated disclosure requirements, over seasoned equity offerings. Likewise, when
these firms engage in takeovers they tend to acquire small private targets, enabling them to avoid disclosing
significant details about their acquisitions. Overall, our findings suggest that firms in more concentrated
industries disclose less and avoid certain financing and investment decisions that have non-trivial disclosure
implications.
*
We gratefully acknowledge the comments of John Core (the editor), an anonymous referee, Aziz Alimov, Steve
Baginski, Linda Bamber, John Campbell, Dan Dhaliwal, Bernhard Ganglmair, David Haushalter, Shane Heitzman, Jean
Helwege, Kathy Kahle, Chris Lamoureux, Bill Maxwell, Jeff Miller, Ram Natarajan, Hernan Ortiz-Molina, Pradyot Sen,
Mike Stegemoller, Tom Stober, Mo Xiao, and seminar participants at Georgetown University, George Washington
University, Hong Kong University of Science and Technology, National University of Singapore, Texas A&M
University, and the Universities of Arizona, Cincinnati, Georgia, and Notre Dame. We also recognize the excellent
research assistance provided by Matthew Serfling and Qin Wang.
*Manuscript
Industry concentration and corporate disclosure policy
Abstract:
This study examines the association between industry concentration and the informativeness of
corporate disclosure policy. We argue that because firms in more concentrated industries tend to be
more innovative, they incur higher proprietary costs of disclosure and disclose less. We find that in
more concentrated industries firms‟ management earnings forecasts are less frequent and have
shorter horizons, their disclosure ratings by analysts are lower, and they have more opaque
information environments, as measured by the properties of analysts‟ earnings forecasts. Also, when
these firms raise funds they prefer private placements, which have minimal SEC-mandated
disclosure requirements, over seasoned equity offerings. Likewise, when these firms engage in
takeovers they tend to acquire small private targets, enabling them to avoid disclosing significant
details about their acquisitions. Overall, our findings suggest that firms in more concentrated
industries disclose less and avoid certain financing and investment decisions that have non-trivial
disclosure implications.
1. Introduction
Verrecchia (1983) predicts that firms with higher proprietary costs of disclosure disclose less than
firms with lower proprietary costs of disclosure. We further argue that firms in more concentrated
industries are likely to be subject to higher proprietary costs of disclosure for the following reasons.
First, in concentrated industries firms tend to more innovative, because innovation is an important
factor in industries becoming and remaining concentrated (Demsetz, 1973; Dasgupta and Stiglitz,
1980; Shaked and Sutton, 1987; Sutton 1991, 1998).1 As a result, a firm in a more concentrated
industry runs a greater risk of revealing company secrets through its disclosures. Such revelations
would adversely affect its competitive advantages over its rivals, because the rivals would likely use
the disclosed information to take actions to enhance their profits at the expense of the firm. Thus,
proprietary costs of disclosures are likely to be higher for firms in more concentrated industries than
for firms in less concentrated industries. Second, more concentrated industries have fewer firms,
therefore, each firm typically accounts for a large fraction of aggregate industry output. Thus, in
these industries firms‟ disclosures are likely to provide more substantive and less noisy information
about future industry demand than similar disclosures by firms in less concentrated industries. Given
that rivals are likely to use more reliable information on industry demand to revise their strategies to
the detriment of the disclosing firm, proprietary costs of disclosure are likely to be higher for firms
in more concentrated industries. It follows from the above arguments that proprietary costs of
disclosure are greater for firms in more concentrated industries and therefore firms in more
concentrated industries disclose less than firms in less concentrated industries. We empirically test
this prediction.
Firms in concentrated industries tend to be more innovative across many dimensions (e.g., Sutton, 1991). In these
industries innovative activities by a firm include creating new or improving existing products and services, improving the
efficiency of its production processes and operating procedures, as well as, novel marketing strategies to enhance
consumers‟ desirability for its products and services.
1
1
Prior studies have empirically examined the association between industry concentration and
corporate disclosures, but, the extant evidence is inconclusive (Beyer et al., 2010; Berger, 2011).
Harris (1998, Table 3) shows that firms in more concentrated industries are less likely to provide
separate business segment disclosures, and Bamber and Cheon (1998, Table 3) report that firms in
more concentrated industries provide less specific management forecasts of their earnings. On the
other hand, Verrecchia and Weber (2006, Table 6) document that firms in more concentrated
industries are less likely to request the SEC to withhold proprietary information from their filings.
Finally, Li (2010, Table 12) shows that in more concentrated industries, firms are more likely to
provide management forecasts. An important concern with these prior empirical studies is that they
all use industry concentration measures constructed from Compustat data, which do not include
data on privately held companies.
Ali et al. (2009) show that Compustat based concentration measures are poor proxies for actual
industry concentration. They also report that the results of a number of accounting and finance
studies that use Compustat based industry concentration measures are sensitive to using the U.S.
Census measure of industry concentration, which is constructed with data for both publicly traded
and privately held companies. Thus, the mixed evidence reported in prior studies on the association
between industry concentration and corporate disclosures could be due to the limitations of industry
concentration measures constructed with Compustat data. The fact that a number of studies have
attempted to examine the association between industry concentration and disclosure underscores the
importance of a proper examination of this relation. We attempt to do so in this study by not only
using the U.S. Census based industry concentration measure, but by also considering several novel
disclosure contexts that have not been previously considered in the literature, thereby enhancing the
generalizability of our findings.
2
We examine the association of industry concentration with certain properties of management
forecasts of earnings and with certain financing and investing choices that have significant disclosure
implications, specifically, equity financing via private placements versus seasoned equity offerings,
and the acquisition of private versus public targets. We choose to examine these varied disclosure
settings because the related disclosures have the potential to provide rival firms with different types
of information. For a more comprehensive examination of whether industry concentration is
associated with corporate disclosure, we investigate whether firms in more concentrated industries
receive lower disclosure ratings from analysts and have poorer information environments, as
measured by properties of analysts‟ forecasts of earnings. An advantage of these tests is that they do
not require us to specify the methods by which firms disclose their private information.
We first investigate the relation between industry concentration and the frequency of voluntary
management earnings forecasts made during a fiscal year for that year‟s earnings. 2 We consider these
forecasts because they tend to be made enough in advance of the earnings announcement date to be
associated with significant proprietary costs. We find that the number of management earnings
forecasts made per year by a firm is negatively associated with industry concentration. Next, we
examine the association between industry concentration and the horizon of management forecasts,
defined as the number of days between the earnings forecast date and the end date of the fiscal year
for which the forecast is made. We document a negative relation between management forecast
horizon and industry concentration. These results suggest that firms in more concentrated industries
disclose less, and when they disclose they are less prompt.
Some of the prior studies on corporate disclosures assume that firms incur proprietary costs from providing earnings
forecasts (see e.g., Verrecchia, 1983; Bamber and Cheon, 1998; Li, 2010). Accordingly, we consider management
earnings forecasts in our analyses. This assumption may not be valid, however, if earnings data are too aggregate to
provide any strategic information to rivals.
2
3
Subsequently, we examine the association between industry concentration and certain financing
and investing decisions that have significant disclosure implications. When firms raise funds via sales
of new common stock, there are notably fewer Securities and Exchange Commission (SEC)mandated public disclosure requirements for private placements than for seasoned equity offerings
(SEOs) and in most cases firms do not disclose any information about a private placement until
several days after the deal takes place. Thus, if firms in more concentrated industries prefer to
disclose less, they would use private placements over SEOs, because raising funds through a private
placement shortens the amount of time that rivals have to react to a stock sale. We find that when
firms sell new common stock, the likelihood of selling shares via a private placement rather than an
SEO is positively associated with industry concentration.
We also investigate if firms in more concentrated industries tend to acquire private rather than
public targets when they make acquisitions. When acquiring public targets, bidders are required to
disclose details of a planned acquisition as soon as there is a tentative acquisition agreement, whereas
in acquisitions of private targets mandated disclosures concerning an acquisition are not required
until after it is completed. Also, in most acquisitions of private firms by public firms, the ratio of the
size of the target to the bidder is less than the twenty percent threshold, above which a public bidder
would have to disclose the financial statements of a private target subsequent to the acquisition
(Rodrigues and Stegemoller, 2007). Thus, by acquiring private targets, firms in more concentrated
industries can effectively delay and limit their rivals‟ scrutiny of their takeovers.
We show that the likelihood of a bidder acquiring a private rather than a public target is
positively associated with the bidder‟s industry concentration. Furthermore, when acquiring a private
target, the likelihood of acquiring a target that is less than 20 percent of the size of the bidder is also
positively associated with industry concentration. These positive associations are more pronounced
when a bidder acquires a target belonging to its own industry, consistent with the argument that
4
compared to diversifying acquisitions, related acquisitions are likely to have greater strategic
implications for the bidder‟s rivals, and hence the bidder‟s incentive to limit its rivals‟ information
about related acquisitions is greater than that for unrelated acquisitions.
We use analysts‟ ratings of the quality of a firm‟s disclosures available from the Report of the
Association for Investment Management and Research as another measure of the informativeness of
disclosure practices. We find that firms‟ disclosure ratings are negatively related with industry
concentration, suggesting further that firms in more concentrated industries tend to disclose less.
Also, if a firm discloses less, then analysts should have more difficulty making earnings forecasts for
the firm (Lang and Lundholm, 1996). We show that for firms in more concentrated industries
dispersion in analysts‟ earnings forecasts is higher, analyst earnings forecast errors are larger, and
there is a greater volatility of analysts‟ forecast revisions. These results further suggest that firms in
more concentrated industries disclose less.
In disclosure models, such as in Bhattacharya and Ritter (1983) and Maksimovic and Pichler
(2001), it is often assumed that in younger, less established industries, firms are likely to have more
innovative technologies and marketing strategies. Thus, innovation is potentially a more important
determinant of industry concentration in younger industries and, if so, proprietary costs of
disclosure are likely to be greater for firms that are in younger, concentrated industries than in older,
concentrated industries. Consistent with the expectation, we find that the associations involving
industry concentration that we report in the paper are generally more pronounced for younger
industries than for older industries. These results further support the argument that in concentrated
industries the risk of revealing innovation related company secrets to rivals significantly affects
corporate disclosure policies.
Chevalier (1995a, 1995b) and Phillips (1995) show that when incumbents in a concentrated
industry have greater financial leverage, the intensity of industry rivalry is lower because higher
5
leverage limits firms‟ ability to invest in market share building. Thus, in concentrated industries in
which firms have higher debt levels, the probability that information contained in a firm‟s
disclosures would be used against it by rivals is likely lower. Consistent with this argument, we find
that the associations involving industry concentration that we document in the paper are for the
most part less pronounced in industries with greater financial leverage. These results are consistent
with the notion that a firm in a concentrated industry discloses less because of the concern that its
rivals can use the disclosed information against it.
Our study makes several contributions. First, it addresses the concern raised in Beyer et al.
(2010) and Berger (2011) that prior accounting studies report mixed evidence on the association
between industry concentration and corporate disclosure. A likely reason for the mixed evidence is
that these studies use Compustat-based industry concentration measures, which are poor proxies of
industry concentration, rather than U.S. Census-based measures of industry concentration. We
document a consistent negative relation between U.S. Census-based industry concentration and
different measures of corporate disclosure. When we repeat our analyses with the Compustat
measures of industry concentration in place of the Census measures of industry concentration, we
find that in almost all cases industry concentration does not exhibit a significantly negative
association with our corporate disclosure measures.
Second, prior studies examining the effect of proprietary costs of disclosure on the
informativeness of corporate disclosure policy consider decisions that are primarily related to
information disclosure, e.g., separate business segment disclosures in financial statements (Harris,
1998; Botosan and Stanford, 2005; Bens et al., 2011), management forecasts of earnings (Bamber
and Cheon, 1998; Li, 2010), a firm requesting the SEC to withhold proprietary information from its
filings (Verrecchia and Weber, 2006), and information about customers (Ellis et al., 2012). Our
evidence on the association of industry concentration with a firm‟s decision to sell new equity shares
6
through private placements versus SEOs and with the decision to acquire private versus public
targets suggest that proprietary costs of disclosure not only affect disclosure decisions, but also
affect other business decisions that have non-trivial disclosure implications.
Finally, our paper also contributes to the growing empirical literature on the relation between
industry concentration and different types of corporate decisions, such as those related to financing
and investing (e.g., Kovenock and Phillips, 1997; Mackay and Phillips, 2005; Haushalter et al., 2007).
We show that industry concentration is associated with corporate disclosure decisions, presumably
due to proprietary cost concerns.
The remainder of the paper is organized as follows. Section 2 discusses related prior work, our
hypotheses, and empirical predictions. Section 3 discusses data sources and variable definitions.
Section 4 presents our empirical results and Section 5 concludes.
2. Hypotheses development and empirical predictions
2.1. Proprietary costs of disclosure
A large body of theoretical work examines managers‟ incentives to disclose information to
outside parties. Grossman (1981) and Milgrom (1981) argue that given adverse selection problems,
managers should disclose to capital market participants all value-relevant information. Verrecchia
(1983) allows for the existence of proprietary costs of disclosure in his model of discretionary
disclosure and arrives at an equilibrium in which some firms do not disclose all value-relevant
information. Specifically, he shows that capital market participants will provide firms that have
higher proprietary costs of disclosure more discretion in their disclosure practices and that these
firms consequently disclose less than firms with lower proprietary costs of disclosure.
7
2.2. Industry Concentration and the Proprietary Costs of Disclosure
Proprietary costs of a firm‟s disclosures are higher the more potentially useful is the disclosed
information to the firm‟s product market rivals and the greater is the extent to which these rivals
take advantage of the information at the expense of the disclosing firm. Below, we argue that the
proprietary costs of disclosure are likely to be greater for firms in more concentrated industries.
2.2.1. Industry concentration and the significance of corporate disclosure for rivals
We argue that firms in more concentrated industries tend to more innovative than firms in less
concentrated industries, and therefore a firm in a more concentrated industry runs a greater risk of
revealing company secrets though its public disclosures. Such revelations can adversely affect some
of the competitive advantages the firm has over its product market rivals given that these rivals can
use the disclosed information to take actions that would enhance their profits at the expense of the
disclosing firm. As a result, the proprietary costs of disclosure are likely to be higher for firms in
more concentrated industries.3
The notion that innovation, which brings about superiority in producing and marketing products,
is related to and affects industry concentration is argued in Demsetz (1973):
“Under the pressure of competitive rivalry, and in the apparent absence of effective barriers to
entry, it would seem that the concentration of an industry‟s output in a few firms could only
derive from their superiority in producing and marketing products or in the superiority of a
structure of industry in which there are only a few firms. In a world in which information and
resource mobility can be secured only at a cost, an industry will become more concentrated
under competitive conditions only if a differential advantage in expanding output develops in
some firms.”
Bamber and Cheon (1998) and Harris (1998) also argue that proprietary costs of disclosure are higher in more
concentrated industries. Bamber and Cheon note that “markets become concentrated when some firms acquire a
competitive strategic advantage …” Their argument is similar to ours. Harris provides a somewhat different reason. She
argues that more concentrated industries are less competitive and firms in these industries disclose less to protect their
abnormal profits.
3
8
The relation between innovation and industry concentration is more formally and rigorously
developed by subsequent studies in industrial economics (e.g., Dasgupta and Stiglitz, 1980; Shaked
and Sutton, 1987; Sutton 1991, 1998). These studies argue that through innovation a firm can create
product differentiation to enhance the demand for its products, and thereby increase the fixed costs
for potential entrants to the industry. The new entrants would not only have to incur setup costs,
but also other fixed outlays, such as research and development and advertising, for developing and
establishing a product line. Sutton (1991) argues that in innovative industries a lower bound of
concentration exists under equilibrium, no matter how large the market becomes.4 Shaked and
Sutton (1987, p 141) summarize the intuition behind the relation between innovation and industry
concentration as follows: “(E)ntry in certain industries is limited to a small number of firm, not
because fixed costs are so high relative to the size of the market, but rather because the possibility
exists, primarily through additional fixed costs, of shifting the technological frontier constantly
forward towards more sophisticated products.”
The above prediction applies to all types of outlays that help differentiate the firm‟s goods and
services and thereby raise consumers‟ willingness to pay for them as opposed to rivals‟ products and
services. “All that matters to the analysis is that the enhancement of consumers‟ willingness-to-pay
involves an increase in fixed outlays by firms – possibly accompanied by a limited increase in unit
variable costs” (Sutton 1991, p. 45).
Examples of such fixed outlays are costs incurred to create
new or improve existing products, costs incurred to improve efficiency of production and other
operations, as well as, costs incurred to enhance the desirability for consumers of the firm‟s products
and services through novel marketing strategies. Consistent with Sutton‟s predictions, Ellickson
(2007) documents that innovation in variety-enhancing distribution systems has yielded a highly
4
The level of the lower bound of concentration for an industry depends on the degree of demand responsiveness to
increases in innovation related outlays by the firms in the industry. “The higher the degree of responsiveness, the higher
will be the lower bound of equilibrium concentration levels in the industry” (Sutton 1991, p. 11).
9
concentrated retail industry.5 Also, for a cross-section of industries, Robinson and Chiang (1996)
empirically document a positive association between the level of innovative activities and industry
concentration.6
The above discussion suggests that corporate disclosures, such as those required when firms sell
new equity in an SEO or when they acquire a public company, as well as, those disclosed voluntarily
in SEC filings and to analysts (which we capture through the AIMR disclosure ratings by analysts)
are likely to reveal greater information about innovative activities for firms in more concentrated
industries than in less concentrated industries. Rivals can use such information to undercut the
firm‟s competitive advantage by profiting at the expense of the disclosing firm. Thus, firms in more
concentrated industries would disclose less.
We also argue that corporate disclosures by firms in more concentrated industries are likely to
provide more substantive and less noisy information about future demand of products and services
in their industry. In more concentrated industries, there are fewer firms, and each one typically
accounts for a fairly substantial share of aggregate industry output. In less concentrated industries,
on the other hand, there are many firms, and each one usually accounts for a relatively small share of
aggregate industry output. Thus, corporate disclosures by firms in more concentrated industries are
likely to provide more substantive information about future industry demand. For instance,
corporate disclosures such as earnings forecasts and the ones required for SEOs and for the
acquisition of public companies are likely to contain more substantive and less noisy information
While R&D costs are positively related to industry concentration (Ali et al., 2009), R&D costs are unlikely to
comprehensively capture the level of innovative activities in a firm. Innovations could be related to marketing or
operating strategies, and the costs of such activities are unlikely to be reported as R&D costs. For example, costs related
to innovations in variety-enhancing distribution systems in the retail industry are not included in R&D costs (Ellickson,
2007; Bardhan et al., 2013).
6 In his review of the literature, Sutton (2007) discusses several studies that empirically examine the association between
technological innovation and the evolution of the concentration of particular industries, for example, the computer,
television broadcasting, pharmaceuticals, color film, movie, and aircraft industries. Similarly, Sutton (1991) discusses the
association between marketing innovations and the evolution of the concentration of other industries, such as the frozen
food, chocolate confectionary, prepared soup, and soft drink industries.
5
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about future industry demand, if the disclosing firm belongs to a more concentrated industry rather
than a less concentrated industry. Rivals in more concentrated industries are therefore more likely to
react to this information by adjusting their own investment plans, with the goal of increasing their
market share at the expense of the disclosing firm. The above discussion provides another reason
for why proprietary costs of disclosure would be higher in more concentrated industries.
2.2.2. Industry concentration and competition
Several prior empirical studies on corporate disclosure assume that product market
competition is less intense in more concentrated industries than in less concentrated industries (see
e.g., Harris, 1998; Botosan and Stanford, 2005; Verrecchia and Weber, 2006; Li, 2010; Tang, 2012).
This assumption has been questioned by recent studies in industrial economics and accounting
(Raith, 2003; Karuna, 2007; Dedman and Lennox, 2009). For example Raith (2003, p. 1430) notes:
“Concentration indices alone are poor measures of competition unless it is clear what causes
their variation: when markets vary in size or entry costs, less concentrated markets will tend to
be more competitive. In contrast, if markets vary in product substitutability or other
dimensions of the toughness of competition, high levels of concentration are indicative of
intense competition, not a lack of it, cf. Sutton (1991) and George Symeonidis (2000a) for
evidence.”
Also, Verrecchia and Weber (2006) note that the theoretical literature offers conflicting
arguments on how the intensity of competition affects a firm‟s decision to disclose proprietary
information. Verrecchia (1990) and Clinch and Verrecchia (1997) argue that greater competition
among incumbents leads to less disclosure. Alternatively, Darrough and Stoughton (1990) predict
that greater competition from potential entrants leads to more disclosure by incumbent firms, as a
device to discourage entry into their product market (see also, Wagenhofer, 1990; Feltham and Xie
1992). Given the opposite predictions in the theoretical literature on the relation between the
intensity of competition and disclosure and given the ambiguity about whether industry
concentration represents low or high intensity of competition, we do not predict a relation between
11
industry concentration and discretionary disclosure, based on the assumption that competition
differs for firms in high versus low concentration industries.
2.3. Hypotheses and empirical predictions
The prior discussion suggests that firms with higher proprietary costs of disclosure are expected
to disclose less than firms with lower proprietary costs of disclosure and that proprietary costs of
disclosure are likely to be higher for firms in more concentrated industries. We therefore propose
the following hypothesis:
Hypothesis 1: Firms in more concentrated industries have less informative disclosure policies.
This hypothesis leads to the following empirical predictions: First, industry concentration is
negatively associated with the frequency and horizon of management earnings forecasts, measures of
the informativeness of disclosure policies. Second, in more concentrated industries, firms are more
likely to sell new equity through private placements, rather than SEOs, and when making
acquisitions are more likely to acquire a private, rather than a public target. As discussed earlier,
these financing and investment decisions have significant disclosure implications. Third, firms in
more concentrated industries are expected to have lower analysts‟ disclosure ratings, larger
dispersion in analysts‟ forecasts, larger forecast errors, and greater volatility in forecast revisions; the
reason being that analysts would have greater difficulty in making earnings forecasts when firms
have less informative disclosure policies (Lang and Lundholm, 1996).
In disclosure models, it is often assumed that younger industries tend to be more innovative
than older industries (e.g., Bhattacharya and Ritter, 1983; Maksimovic and Pichler, 2001). Thus,
innovation is potentially a more important reason for concentration in younger industries. In older
industries, other factors, such as larger setup costs could be a more important reason for industry
12
concentration. If so, among the firms in concentrated industries, those that are in younger industries
are more likely to run the risk of revealing company secrets through their disclosures and are
therefore likely to experience greater proprietary costs of disclosures than those that are in older
industries. We therefore propose the following sub-hypothesis.
Hypothesis 1a. The association between industry concentration and the informativeness of disclosure policies is
more pronounced for firms in younger industries.
This sub-hypothesis implies that all the empirical predictions suggested by Hypothesis 1, are
expected to be more pronounced in younger industries. Evidence consistent with this hypothesis
would further support the argument that the risk of revealing innovation related trade secrets to
rivals significantly affects a firm‟s discretionary disclosure decisions.
Prior work argues and shows that in concentrated industries the intensity of industry rivalry is
reduced when incumbents have higher financial leverage (Chevalier, 1995a; 1995b; Phillips, 1995).
Higher debt potentially limits rivals‟ abilities to take advantage of proprietary information contained
in a firm‟s disclosures. For example, if a rival seeks to protect or increase its market share by making
new investments in response to new information contained in another firm‟s disclosures, high debt
levels could prevent the rival from raising the needed funds. Thus, a firm in a concentrated industry
would be less concerned about the proprietary costs of its disclosures if its industry is more
leveraged than if it is less leveraged. Accordingly, we propose the following sub-hypothesis:
Hypothesis 1b. The association between industry concentration and the informativeness of disclosure policies is less
pronounced for firms in more leveraged industries.
This sub-hypothesis implies that all of the empirical predictions suggested by Hypothesis 1 are
expected to be less pronounced in industries with higher financial leverage. Evidence consistent with
this sub-hypothesis would support the argument that a firm‟s concerns about the adverse effects
13
from its rivals‟ responses to its disclosed information, is a significant reason for why firms in more
concentrated industries disclose less.
It is not obvious, however, that the proposed negative relation between industry concentration
and discretionary disclosure would be empirically observed. Below we discuss a few scenarios where
this relation may not be observed. The likelihood of finding the negative relation at the aggregate
level in the data depends on the pervasiveness of these scenarios.7
First, as discussed by Vives (2008) in his review of the information sharing literature, unilaterally
revealing information can be the dominant strategy in certain circumstances. For example, Vives
(1984) predicts that when firms compete on price and the goods are substitutes, firms would share
information about industry demand. Also, Gal-Or (1986) predicts that when firms compete on
quantity and the goods are substitutes, that firms would share information about costs. 8 Vives (2008)
argues that the existence of trade associations is evidence that firms do at times voluntarily precommit to share information. If firms commit ex-ante to fully disclose most of their private
information to competitors, the proprietary costs of ex-post discretionary disclosures due to product
market rivalry becomes unimportant.9 Hence, firms can no longer justify withholding information
from the capital markets, and we would not observe any association between industry concentration
and discretionary disclosure.
Second, if it is possible for firms to collude, the likelihood that firms enter into information
sharing agreements will increase and we would not observe any association between industry
concentration and discretionary disclosure. However, Kuhn and Vives (1995) and Vives (2006) note
7
We thank the referee for suggesting this point and these scenarios.
studies assume truth-telling, that is, they ignore firms‟ incentives to disclose misleading information to
competitors. See Ziv (1993) for an example of a theoretical analysis on the effect of relaxing the truth-telling assumption
on firms‟ incentives to share information.
9 Verrecchia (2001, p. 146) describes the difference between an ex ante commitment to disclose and ex post discretionary
disclosure as follows: “By a discretionary disclosure arrangement, I mean a situation in which managers or firms exercise
discretion with respect to the disclosure of information about which they may have knowledge (i.e., ex post).
Alternatively, by a pre-commitment arrangement or mechanism, I mean a situation in which managers or firms establish
a preferred disclosure policy in the absence of any prior knowledge of the information (i.e., ex ante).”
8These
14
that hurdles, such as the threat of being sued by government agencies, often prevents such
agreements.10
Finally, if investors hold well-diversified portfolios and thereby invest in all the firms in an
industry, they may be less concerned about proprietary costs of disclosure. Any transfers of wealth
between the firms due to information disclosure would not reduce the aggregate profits of the
portfolio firms. In fact, sharing of information could have a favorable effect on aggregate profits by
reducing cost and demand uncertainty. The reduced uncertainty would enable firms to better tailor
output and pricing decisions to actual market conditions. However, this scenario may not be valid,
because investors could be averse to such information sharing. Information sharing could make it
more difficult for the firms to capture rents from innovation, reducing their incentives to innovate,
thereby lowering the aggregate profits.
3. Methodology
3.1. Sample and measurement of industry concentration
We study Compustat manufacturing firms (6-digit North American Industry Classification
System (NAICS) codes between 311111-339999) over the years 1995 to 2004. We restrict our
sample period to the years 1995-2004 because data on management forecasts, which we use in some
of our tests, are only widely available starting in 1995 and data on the Herfindahl-Hirschman index,
our measure for industry concentration, are collected from Census of Manufactures publications for the
1997 and 2002 U.S. Census years.11 The Herfindahl-Hirschman index is calculated for a 6-digit
NAICS industry within the manufacturing sector by summing the squares of the individual company
Another hurdle is that in most cases tacit collusion agreements are expected to last for only a short period of time
because it is difficult for firms to detect whether a competitor has violated the agreement (e.g., Stigler, 1964; Green and
Porter, 1984).
11 The study‟s results are very similar if we measure industry concentration with four-firm ratios available from Census of
Manufactures publications. Also, we use industry concentration data from only the 1997 and 2002 Census of Manufactures
publications, because prior to 1997 the Census of Manufactures reported industry concentration measures for 4-digit
Standard Industrial Classification (SIC) industries rather than for 6-digit NAICS industries.
10
15
market shares based on total sales of the 50 largest public and private companies in the industry or
all the public and private companies in the industry, whichever is lower. Following prior studies (see
e.g., Haushalter at al., 2007; Ali et al., 2009), we assume that the values of the 1997 and 2002 index
are valid for the five-year windows centered on 1997 and 2002, namely, the 1995-1999 and 20002004 periods, respectively.12
Panel A in Table 1 presents descriptive statistics of the Herfindahl-Hirschman index. We use the
Herfindahl-Hirschman index calculated at the 6-digit NAICS level for 356 industries in our
multivariate tests. However, given this large number of industries, we provide statistics for broader
industry groups in this table. For each 6-digit NAICS industry, we first calculate the mean values of
the Herfindahl-Hirschman index across the years 1997 and 2002. Next, for each 3-digit NAICS
industry, we report the median value of the Herfindahl-Hirschman index for the 6-digit NAICS
industries within the particular 3-digit NAICS industry. This allows us to provide statistics for 21
broader 3-digit NAICS industries.
In Panel A of Table 1, the industries are listed in ascending order of the Herfindahl-Hirschman
index, and the results show that there is significant variation for industry concentration across our
sample. For instance, the printing and related support, wood products, and the fabricated metal
products industries are the least concentrated with median 6-digit NAICS industry HerfindahlHirschman index values of 112, 206, and 230. In contrast, the chemicals, leather and allied products,
and transportation equipment industries are the most concentrated with median 6-digit NAICS
industry Herfindahl-Hirschman index values of 944, 989, and 1054. Within each of the 3-digit
NAICS industry groups, there can also be significant variation in the values of the HerfindahlAs in most work in the product markets area, we assume that there is a one-to-one relationship between the industry
classification code assigned to a firm and the product market in which the firm competes. We address the sensitivity of
our results to this issue in Section 4.6. Also, like other studies that use industry concentration measures provided by the
U.S. Census (see, e.g., Kovenock and Phillips, 1997; Mackay and Phillips, 2005; Haushalter et al., 2007), we use industry
concentration measures which are calculated with data from only U.S. firms. Industry concentration measures calculated
at the global level are not available.
12
16
Hirschman index across different six-digit NAICS industries. For example, in the transportation
equipment industry group (3-digit NAICS code of 336), the automobile manufacturing industry (6digit NAICS code of 336111) has a Herfindahl-Hirschman index value of 2754.0. Similarly, in the
fabricated metal products industry group (3-digit NAICS code of 332), the sheet metal work
manufacturing industry (6-digit NAICS code of 332322) has a Herfindahl-Hirschman index value of
only 15.8.
Panel B in Table 1 reports some of the distinguishing characteristics of firms belonging to more
versus less concentrated industries. For each of the quintiles sorted by the U.S. Census HerfindahlHirschman index reported for 6-digit NAICS industries, this panel reports median values of the total
number of firms (public plus private) per industry and median values of firm size for the sample
period 1995 to 2004. This table shows that industries with higher U.S. Herfindahl-Hirschman index
values are populated by fewer and larger firms. Specifically, the median value of the number of firms
in a 6-digit NAICS industry is 1077 for the first quintile, which contains the least concentrated
industries, and the corresponding value is only 135 for the fifth quintile. Panel B also shows that
average firm size as measured by net sales, market capitalization, or book assets, (obtained from the
Compustat database) is monotonically increasing from the first to the fifth industry concentration
quintiles. Median firm size is about three times larger for firms in industries in the highest quintile of
industry concentration as compared to firms in the lowest quintile. Specifically, median net sales
(market capitalization, book assets) for firms in the lowest and highest quintiles are $282m ($218m,
$266m) and $863m ($635m, $858m), respectively.
3.2. Management and analyst earnings forecast data
Data on management earnings forecasts are obtained from the Company Issued Guidelines
(CIG) database maintained by First Call. Data on analysts‟ earnings forecasts are obtained from the
Institutional Brokers‟ Estimate System (IBES).
17
3.3. Corporate disclosure ratings data
Analysts‟ overall ratings of a firm‟s disclosure policy are hand-collected from the Report of the
Association for Investment Management and Research for the years 1995 and 1996, the last two years for
which these data are available. These ratings, which range from a score of zero to 100, are
determined by analyst subcommittees, organized by industry. They consider the overall quality of a
firm‟s disclosures over a particular year (Lang and Lundholm, 1996; Healy et al., 1999). Specifically,
these ratings represent the quality of a firm‟s disclosures along three dimensions: (1) annual
published information, such as annual reports, (2) quarterly and other published information, such
as quarterly reports, press releases, and proxy statements, and (3) investor relations and related
aspects, such as how responsive companies are to analyst questions, the accessibility of management
and their candor in discussing corporate developments, and the frequency and content of
presentations to analysts. The industry subcommittees attempt to provide disclosure ratings for the
„leading‟ firms in an industry, resulting in only large firms in an industry being selected for
evaluation. Thus, the number of firms with disclosure ratings is relatively small.
3.4. Private placements and seasoned equity offerings
Data on common stock sales inside the United States through private placements are handcollected by searching through news stories on the Lexis-Nexis database. Data for SEOs of new
common stock that take place in the United States are obtained from Securities Data Corporation‟s
(SDC) Global New Issues database.
3.5. Takeover data
We identify domestic takeovers in the United States from SDC‟s U.S. Mergers and Acquisitions
database. We collect data for only completed takeovers in which public firms acquire all of either a
public or private firm.
18
3.6. Sample selection bias
The U.S. Census data on industry concentration are available only for the manufacturing sector
and therefore we conduct our tests using a sample of manufacturing firms. Over our sample period,
approximately 42 percent of non-financial Compustat firms are in the manufacturing sector. It is
possible, however, that the results we obtain in our study may not apply to firms in nonmanufacturing industries. Moreover, the U.S. Census industry concentration data are only available
every five years, hence we use the Herfindahl-Hirschman index of a particular U.S. Census year as a
proxy for industry concentration for a five-year window surrounding the U.S. Census year. The
resulting measurement error could introduce a bias in our results.
Samples of firms that issue management forecasts of earnings, receive analysts‟ disclosure
ratings, sell new equity shares, or make acquisitions are not random samples, which could also
introduce a bias in our results. Further, loss of observations due to data requirements for our
explanatory variables may also lead to a bias in our results. Table A1 in the appendix reports how
data requirements affect the sample sizes for our different analyses. Furthermore, Table A2 in the
appendix compares the mean and median values of the variables for the sample used for a particular
analysis with the corresponding mean and median values of the variables for (i) all manufacturing
firms on Compustat and (ii) all non-financial firms on Compustat. This table shows that for some of
our samples, the values of the Herfindahl-Hirschman index are slightly higher than those for the
sample of Compustat manufacturing firms. Also, firm size tends to be slightly larger for our samples.
The larger firm size of our sample firms is the likely reason why the average values of certain
variables are different between our sample firms and both the manufacturing firms on Compustat
and all non-financial firms on Compustat.
19
4. Empirical findings
4.1. Industry concentration and the frequency and horizon of management earnings forecasts
To test the validity of our hypothesis that firms in more concentrated industries disclose less, we
first examine the association between industry concentration and the frequency of voluntary
management earnings forecasts and report the results in Table 2. The dependent variable of the
regression models in this table is the number of management forecasts made by a firm during a fiscal
year for the earnings of that year. We consider management forecasts of annual earnings because
these forecasts tend to be made enough in advance of the earnings announcement date to be
associated with significant proprietary costs. In approximately half of our sample firm-years,
managers do not make earnings forecasts, so we use one-sided Tobit models.13
We follow prior studies and include several control variables in the Table 2 models. Bens et al.
(2011) argue that public firms competing in an industry with a higher proportion of private firms
may disclose less, given that private firms are likely to follow non-disclosure policies. We control for
this factor by including the ratio of the number of public to private firms in the firm‟s 6-digit
NAICS industry. Greater innovation may make managers more uncertain about their firms‟ future
prospects and they may therefore disclose less (Dye, 1985; Jung and Kwon, 1988). To control for
this factor, we include two variables in our models: stock return volatility over the prior year and the
current year absolute change in earnings per share (Waymire, 1985; Baginski et al., 2004). To control
for management‟s greater desire to disclose good news than bad news (Miller, 2002; Kothari et al.,
2009), we include the current period‟s market-adjusted stock return in our models. Analysts are likely
to have greater difficulty forecasting earnings of higher R&D firms (Barth et al., 2001) and the
managers of these firms may therefore provide more earnings guidance. On the other hand,
managers of higher R&D firms may provide fewer forecasts because of a greater concern about their
13
Our results are very similar if we use Poisson regression models rather than one-sided Tobit models.
20
accuracy. We control for these two factors by including R&D expenses in the Table 2 models.
Finally, we include the following previously identified determinants of the frequency of management
forecasts: firm size (Kasznik and Lev, 1995), analyst coverage (e.g., Abarbanell et al., 1995;
Karamanou and Vafeas, 2005), institutional ownership (Ajinkya et al., 2005), and an indicator
variable for the post-Regulation Fair Disclosure period (Heflin et al., 2003).
The first column of Table 2 shows that the coefficient on Herfindahl-Hirschman index is
negative, but not significant. The second model in Table 2 includes the 6-digit NAICS industry
mean of the number of years since a firm‟s initial public offering (IPO), a proxy for industry age, and
its interaction with the firm‟s industry Herfindahl-Hirschman index as additional variables. The
coefficient on the Herfindahl-Hirschman index is negative and significant and the coefficient on the
interaction variable is positive and significant. 14 These results are consistent with Hypothesis 1 and
1a, that there is a negative association between industry concentration and disclosure and it is more
pronounced for firms in younger industries, presumably because a concentrated industry is likely to
be more innovative if it is younger rather than older. Thus, firms in younger, concentrated industries
face a greater risk of revealing company secrets through their disclosures.
The third model of Table 2 is the same as the first model, except that it includes as additional
variables the 6-digit NAICS asset-weighted industry average of the net-debt-to-assets ratio,
calculated for firms on Compustat, and its interaction with the Herfindahl-Hirschman index. In
defining this ratio, we subtract cash holdings from debt because corporate cash reserves provide
financial flexibility. We use an asset-weighted industry average because large companies with low
leverage are likely to respond more aggressively than small companies with low leverage in exploiting
the information disclosed by a firm in the industry. The coefficient on the Herfindahl-Hirschman
index is negative and significant and the coefficient on the interaction variable is positive and
14
Where applicable, we use standard errors clustered by industry (see table legends). Our results are robust to using
standard errors clustered by firm instead of industry in these cases.
21
significant. These results supports Hypotheses 1 and 1b that there is a negative association between
industry concentration and disclosure and it is less pronounced in industries with higher leverage.
This result suggests that a firm in a more concentrated industry discloses less if its industry has lower
leverage than if it has higher leverage, because rivals‟ responses to the disclosed information is likely
to be stronger in the less leveraged industry, as they are likely to be less constrained in their ability to
invest in building market share. 15
Next, we examine the association between industry concentration and the horizon of
management earnings forecasts, defined as the number of days between the date of a management
forecast of annual earnings and the firm‟s fiscal year-end date. For this analysis firm-years with no
management earnings forecasts are dropped. The models are the same as those in Table 2, except
that the dependent variable is the average horizon of management earnings forecasts. Also, for this
analysis we use the OLS procedure. The results for the first model in Table 3 show that the
coefficient on industry concentration is negative and significant, indicating that firms in more
concentrated industries make forecasts with shorter horizons. The second and third models include
in addition the industry age and industry leverage variables, respectively, along with their interactions
with the Herfindahl-Hirschman index. The coefficients on both of the interaction variables are
positive and significant. These results support our hypotheses that firms in more concentrated
Using industry concentration measures constructed with Compustat data, Li (2010) estimates a model of the
percentage of firms providing management forecasts in an industry and obtains a positive coefficient on industry
concentration. We estimate her model using our sample and are able to replicate her finding. However, when we
estimate her model after replacing the Compustat-based industry concentration measure with the U.S. Census measure
as well as its interaction with industry age and leverage, we obtain insignificant coefficients on industry concentration. A
question that arises, however, is why the coefficient on the U.S. Census measure is not negative and significant, as it is in
our Table 2 model of the frequency of management forecasts. We think there are two reasons for this. First, the
dependent variable in Li‟s (2010) model is an industry-level variable, the percentage of firms providing management
forecasts in an industry, while the dependent variable in our Table 2 model is a firm-level variable, the number of
forecasts made by a firm. Our dependent variable is more meaningful, because the decision to make management
forecasts is a firm-level decision (see e.g., Baginski et al., 2004). Second, prior work shows that a firm‟s decision to make
forecasts is correlated with a number of firm-level factors, and in our Table 2 model, we control for these factors. On
the other hand, Li (2010) does not include firm-level control variables in her model.
15
22
industries disclose less and this association is more pronounced within younger and less leveraged
industries.
4.2. Industry concentration and the private placement versus seasoned equity offering decision
As another test of Hypothesis 1, we examine the association between industry concentration and
a financing decision, that has important disclosure implications. Specifically, we consider the choice
of selling new shares via a private placement or an SEO. There are significantly fewer SECmandated public disclosure requirements for private placements of new common stock than for
SEOs. For instance, firms are required to disclose in their SEO prospectus the proposed use of the
funds, but for private placements there is no such requirement. Also, in private placement
transactions, firms are typically not required to disclose that a transaction has taken place and what
was the amount of funds raised in the transaction until four business days after closing the deal. We
predict that firms in more concentrated industries prefer private placements over SEOs when they
sell new equity, because selling new shares via a private placement reduces the amount and the
timeliness of stock-sale related disclosures.
We hand-collect data on common stock sales through private placements from news stories on
the Lexis-Nexis database. Our sample period for this analysis consists of the years 1997 and 2002,
the two years for which we obtain from the Census of Manufactures data on industry concentration.16
We obtain data on SEOs of common stock for these two years from SDC‟s Global New Issues
database. Our sample consists of 64 percent SEOs and 36 percent private placements. This
distribution is comparable to that reported in prior studies (see e.g., Wu, 2004; Gomes and Phillips,
2007).
We hand-collected the data on private placements for only two years because of the significant costs related to
collecting these data.
16
23
Table 4 reports estimated marginal effects from probit models in which the dependent variable
equals one for a private placement and zero for an SEO. As in our other models, we include the
ratio of the number of public to private firms in the firm‟s 6-digit NAICS industry as a control
variable. This variable is expected to capture the issue that a public firm may disclose less if more of
its competitors are private with minimal disclosure requirements. Wu (2004) and Gomes and Phillips
(2007) show that firms with greater information asymmetry are more likely to sell new shares via a
private placement rather than through a seasoned equity offering. To control for information
asymmetry, we follow Wu (2004) and include in our models the natural logarithm of book assets,
analyst coverage, and the number of years since a firm‟s IPO; and to control for investors‟ revisions
of firm growth potential, we include industry-adjusted sales growth during the year prior to the sale
of new shares and the change in the industry-adjusted market-to-book ratio during the year of the
stock issue.
Following Gomes and Phillips (2007), we include in the Table 4 models operating cash flow
scaled by assets, cash flow volatility, and Altman-Z bankruptcy risk score, as controls for
profitability, earning uncertainty, and distress risk, respectively. To control for the possibility that
managers of firms in more concentrated industries may disclose less because they are less informed
about their firms‟ future prospects, we include stock return volatility during the year prior to the
equity sale. We also include in our models the prior year‟s market-adjusted stock return to capture
firms‟ tendencies to have an SEO following good stock performance. Finally, we include an
indicator variable for whether an SEO takes place within one week of an earnings announcement,
because Korajczyk et al. (1991) show that SEOs often take place soon after an earnings
announcement that conveys unusually good news about a firm.
The results of the first model in Table 4 show a positive coefficient on the HerfindahlHirschman index variable, suggesting that firms in more concentrated industries prefer private
24
placements over SEOs. Consistent with prior work, the results also show that larger and more
profitable firms prefer to sell new shares in an SEO. The second and third models in Table 4 also
include the industry age and industry leverage variables, respectively, along with their interactions
with the Herfindahl-Hirschman index. The coefficients on both the interaction variables are negative
and significant.17 These results suggest that, as predicted, firms in more concentrated industries
prefer private placements over SEOs when they sell new shares, and this association is more
pronounced in younger and less leveraged industries.
Our finding that industry concentration is associated with the choice of a private placement
versus an SEO may be subject to sample selection bias because the sample used for this analysis
consists only of firms that sell new equity shares. To address this concern, we first estimate a model
of the probability that a firm sells new equity shares, and then include the inverse Mills ratio from
this estimation as a control variable in our models of the choice of a private placement versus an
SEO. To estimate the probability that a firm sells new equity shares in a given year, we use the
DeAngelo et al. (2010) model, which tests the market timing and corporate lifecycle explanations for
these sales. The dependent variable in this model takes the value of one if a firm sells new equity
shares in a given year, and zero otherwise. The explanatory variables are the ones used in DeAngelo
et al. (2010) and we add the firm‟s industry Herfindahl-Hirschman index. The sample used for
estimating this model consists of our sample firms that sell new equity shares via private placements
or SEOs in 1997 or 2002 and the rest of the manufacturing firms in Compustat and CRSP for which
the required data are available for these two years. We find that the correlation between the
Herfindahl-Hirschman index and the dependent variable, whether a firm sells equity in a given year,
We do not use clustered standard errors for the results reported in Table 4 because of inadequate data for the private
placement versus SEO sample to reliably estimate the clustered standard errors. Nevertheless, to get an idea of the
potential overstatement of the significance levels, we calculate standard errors clustered by industry. We find that the
coefficients on the variables of interest are significant for two-tailed tests in three out of the four cases that they are
significant without clustered standard errors. Since our hypotheses are directional, we also consider one-tailed tests, for
which the corresponding numbers are four out of four cases.
17
25
is only -0.002, and the regression results (untabulated) show that the coefficient on the HerfindahlHirschman index is insignificant.
We include the inverse Mills ratio from the first stage model as an additional control variable in
our Table 4 models of the choice of a private placement versus an SEO to sell equity. We find that
the coefficients on the inverse Mills ratio variable are insignificant and that our results related to the
Herfindahl-Hirschman variables remain qualitatively the same. These results suggest that the
association between the Herfindahl-Hirschman index and the choice of a private placement versus
an SEO is not subject to significant sample selection bias.18
Our conclusion that firms in concentrated industries tend to issue equity through private
placements in order to hide information raises the following two concerns. First, firms in more
concentrated industries can also achieve the goal of hiding information from their rivals by holding
larger cash reserves or by paying out smaller dividends, thereby reducing their reliance on external
financing to fund investment. Two prior studies document a positive relation between industry
concentration and corporate cash holdings and a negative relation between industry concentration
and the amount of dividends a firm pays out. Haushalter et al. (2007) show that firms in more
concentrated industries have larger cash reserves. They argue that firms in concentrated industries
hold more cash to reduce the risk of predatory behavior by rival firms attempting to increase their
market share. Grullon and Michaely (2007) report that firms in more concentrated industries pay out
smaller dividends. They contend that this result could imply that corporate payouts are the outcome
of external disciplinary forces. We estimate the models in Haushalter et al. (2007) and Grullon and
18
We also investigate whether the association between the Herfindahl-Hirschman index and the choice of a private
placement versus an SEO is robust to using propensity score matching. We find this to be the case. In addition, the
results of a Rosenbaum (2002) test for whether this association is sensitive to endogeneity, if it exists, indicate that the
odds ratio at which a significance level of 0.10 obtains is 1.498. This result suggests that the association between industry
concentration and the choice of a private placement versus an SEO would be significant (p-value=0.10) if control firms
were actually 1.5 times more (rather than equally) likely to be in an industry with low concentration than treatment firms,
after conditioning on observable firm characteristics using the propensity score matching.
26
Michaely (2007) for our sample period and confirm their results that firms in more concentrated
industries hold more cash and pay out smaller dividends. Although these findings are consistent with
the explanations provided in these papers, they could also in part be driven by firms in concentrated
industries attempting to lower their reliance on external financing to reduce their disclosures.
The second concern with our conclusion that firms in concentrated industries prefer to sell
equity through private placements in order to hide information is that when external financing is
needed, firms in concentrated industries can avoid public disclosures by borrowing from banks. If
that is the case, a firm in a more concentrated industry should have a higher ratio of bank debt to
publicly traded debt. Unfortunately, Compustat does not provide the data needed to calculate this
ratio, so we proxy for it with the firm‟s average debt maturity. Harford et al. (2013) document that
the maturity of bank debt is markedly shorter than the maturity of publicly traded debt, implying
that average debt maturity is shorter for firms with more bank debt. Following Harford et al. (2013),
we measure debt maturity as the fraction of a firm‟s long-term debt due in the next three years. A
higher value for this fraction indicates that the average debt maturity of the firm is shorter. We
estimate their debt maturity model for our sample period after including the industry HerfindahlHirschman index as an additional explanatory variable.
We find that the coefficient on the Herfindahl-Hirschman index variable is positive and
significant, suggesting that firms in more concentrated industries have shorter maturity debt,
presumably due to the greater use of bank debt. However, firms in concentrated industries are
unlikely to use only bank debt for external financing given that high financial leverage would raise
their bankruptcy risk and the risk of predatory actions on the part of rivals (e.g., Chevalier, 1995;
Campello, 2003; 2006). Thus, firms in concentrated industries are likely to rely on sales of new
equity shares as well when they raise external funds. Overall, our results related to equity financing,
corporate cash holdings, dividends, and debt maturity support the proposition that financing
27
decisions of firms in concentrated industries are affected by their unwillingness to disclose
proprietary information.
4.3. Industry concentration and the decision to acquire a public or private target
We also examine the association between industry concentration and an investment decision that
has important disclosure implications. Specifically, we consider the choice of acquiring a private
versus a public target when making an acquisition. When acquiring public targets, public bidders
have to disclose details of a planned acquisition before it is even completed. For instance, in tender
offers and merger offers, acquirers have to disclose details about a potential deal as soon as a
tentative acquisition agreement exists, even though in most cases the agreement is non-binding and
it could take several months before the final acquisition agreement is finalized. In contrast, when a
public bidder acquires a private target, disclosures about the acquisition are not required until after it
is completed. Further, beginning in 1996, the SEC relaxed its regulations with respect to the
disclosure of the financial statements of a private target. Under the new regulations, only when a
private target is at least twenty percent of the size of the public bidder is the disclosure of financial
statements of the private target required at the time of the acquisition (Rodrigues and Stegemoller,
2007). Thus, by acquiring private targets, bidders in concentrated industries can effectively delay and
limit their rivals‟ scrutiny of their acquisitions.
We study domestic takeovers in the Unites States in which a public firm acquires all of either a
public or private firm. The takeovers are announced and completed between the years 1996 and
2004. We study takeovers from 1996 onwards, rather than 1995 onwards, which is the start of the
sample period for our other tests. We do so, because the new SEC disclosure regulation for private
targets came into effect in 1996. Approximately 63 percent of the acquisitions in our sample are for
private targets and the remaining are for publicly traded targets. Also, in 69 percent of the private
target deals, the target‟s size is less than 20 percent of the size of the bidder, indicating that in our
28
sample the bidder is not required to disclose the financial statements of the target for most of the
private target deals.
Table 5 reports estimated marginal effects from probit models with a dependent variable that
equals one if the target is a private firm and zero if it is a publicly traded firm. In addition to industry
concentration, we include several control variables. We control for whether the target is not in the
bidder‟s 6-digit NAICS industry, because in that case, the acquisition is less likely to have strategic
implications for the bidder‟s rivals. We control for the size of the bidder as a smaller bidder could
find it more difficult to acquire a public target given that public targets are typically larger than
private targets. We also control for the ratio of target to bidder firm size because this ratio is
expected to be smaller in the case of private acquisitions and it may be the case that in concentrated
industries bidders would be more likely to acquire targets that are considerably smaller than they are
in order to avoid challenges by the government on antitrust grounds. We further include in the
model the ratio of the number of public to private firms in a bidder‟s 6-digit NAICS industry
because of the possibility that this ratio could be smaller in concentrated industries, and this could
therefore lead to a positive relation between industry concentration and whether a bidder acquires a
private target. This ratio also controls for the possibility that public firms prefer to disclose less
when a greater proportion of their industry rivals are private firms, who have minimal disclosure
requirements.
We also control for whether an acquisition is paid for with stock, because bidders that are unable
to pay for an acquisition with cash and instead offer only stock as the medium of payment could
find it difficult to win tender offer contests for public firms, given that in such contests cash is
typically the method of payment (e.g., Gilson (1986) and Fishman (1989)). This factor could increase
the propensity of such bidders to acquire private targets because there is usually less competition for
29
private targets. To ascertain that our results are not driven by clustering of acquisitions in time (e.g.,
Mitchell and Mulherin, 1996; Harford, 2005), we include year dummies in our models.
The results for the first model in Table 5 show a positive and significant coefficient on the
bidder‟s industry Herfindahl-Hirschman index. This finding suggests that bidders in more
concentrated industries have a preference for private rather than public targets, presumably to avoid
disclosing significant details about their acquisitions. In addition, almost all of the control variables
have significant coefficients with the expected signs.
In the second model in Table 5, we include the interaction of the bidder‟s industry HerfindahlHirschman index with a variable indicating whether the target firm is not in the same 6-digit NAICS
industry as the bidder. Related acquisitions are likely to have greater strategic implications for the
rivals of a bidder, given that such acquisitions allow merging firms to gain advantages over their
rivals through the creation of product differentiation (e.g., Hoberg and Phillips, 2010). Thus, for
bidders in more concentrated industries, the incentive to hide information from rivals would be
greater for related than for unrelated acquisitions. Consistent with this argument, Table 5 shows that
the coefficient on the interaction variable is negative and significant. This result indicates that the
positive association between industry concentration and the likelihood of acquiring a private target is
more pronounced when the bidder and target firms are in the same industry.
The third and the fourth models of Table 5 are the same as the first model, except that they
additionally include the industry age and industry leverage variables, respectively, along with their
interactions with the bidder‟s industry Herfindahl-Hirschman index. The coefficients on the industry
leverage and age interaction variables are negative and significant, suggesting that the preference for
acquiring private rather than public targets by bidders in more concentrated industries is more
pronounced for bidders in less leveraged or younger industries.
30
Within private targets, a bidder in a concentrated industry is likely to have a preference to
acquire a target that is less than twenty percent of the bidder‟s size, because the bidder would not
have to disclose the target‟s financial statements at the time of the acquisition. We investigate this
issue using a multinomial logit model with the dependent variable taking the value of zero if the
target is a public firm, one if the target is a private firm and its size is more than twenty percent of
the size of the bidder, and two if a target is a private firm and its size is less than twenty percent of
the size of the bidder. Table 6 reports estimated marginal effects from the multinomial logit model.
The first column in Table 6 provides results for the decision to acquire a small versus a large private
target, and the second column provides results for the decision to acquire a small private target
versus a public target. The control variables in the Table 6 models are the same as in the Table 5
models. The results show that, as predicted, firms in more concentrated industries are more likely to
acquire a small rather than a large private target and are also more likely to acquire a small private
target than a public target.
In Table 7, we report the results of estimating three multinomial logit models. These models are
the same as the ones in Table 6, except that they include the following additional variables. The first
model includes an indicator variable for whether the bidder and target are not in the same industry
and its interaction with the bidder‟ industry Herfindahl-Hirschman index, the second model includes
industry age and its interaction with the Herfindahl-Hirschman index, and the third model includes
industry leverage and its interaction with the Herfindahl-Hirschman index. The coefficients on the
interactions of the Herfindahl-Hirschman index with the indicator variable for related acquisitions
and with industry leverage are negative and significant. These results suggest that the greater
preference of bidders in more concentrated industries to acquire a small rather than a large private
target is more pronounced when the acquisition is a related acquisition and when the bidder‟s
industry is less leveraged. Likewise, the greater preference of bidders in more concentrated industries
31
to acquire small private rather than public targets is more pronounced when the acquisition is a
related acquisition and when the bidder‟s industry is less leveraged. In sum, the results of Tables 5, 6,
and 7 are consistent with our hypothesis that firms in more concentrated industries disclose less,
specifically through their choice of investment decisions that have non-trivial disclosure
implications.19, 20
4.4. Industry concentration and corporate disclosure ratings
Our tests so far consider specific disclosures. For a more comprehensive test of the hypothesis
that industry concentration is negatively associated with the informativeness of corporate disclosure
policy, we examine whether firms in more concentrated industries receive lower disclosure ratings
from analysts. These ratings are obtained from the Report of the Association for Investment Management
and Research and represent analysts‟ perceptions of the overall quality of a variety of disclosures that a
firm makes (see Lang and Lundholm, 1993).
Table 8 provides OLS estimates of a regression of analysts‟ disclosure ratings on industry
concentration and control variables. As in our other models, we include the ratio of the number of
public to private firms in the firm‟s 6-digit NAICS industry to control for the possibility that in an
industry with a higher concentration of private competitors, public firms may disclose less. The ease
with which analysts can make accurate forecasts of a firm‟s earnings may impact their perception of
As previously noted, after 1995 it is no longer necessary to disclose the financial statements of a private target that is
less than twenty percent of the size of the bidder. It would have been useful to examine if the relation between industry
concentration and the likelihood of acquiring a small private target versus a large private target or a public target changes
from before to after 1995, but we could not examine this issue because of data limitations. The U.S. Census calculates
industry concentration for 4-digit SIC industries prior to 1997 and for 6-digit NAICS after 1997. The concentration data
for 4-digit and 6-digit SIC industries are not directly comparable because 6-digit NAICS industries have fewer firms
resulting in higher industry concentration values.
20 We do not use clustered standard errors for the results reported in Tables 5-7 because of a lack of a proper panel of
data for the private versus public acquisition sample to reliably estimate the clustered standard errors. However, we reestimate the models in these tables using standard errors clustered by industry in order to get a sense of the possible
overstatement of the significance levels. We find that the coefficients on the variables of interest are significant for twotailed tests in 12 out of the 19 cases that are significant without clustered standard errors. Given that our hypotheses are
directional, we also consider one-tailed tests, for which the corresponding numbers are 20 out of 20 cases.
19
32
the quality of the firm‟s disclosures. Thus, we control for three variables that previous research (e.g.,
Lang and Lundholm, 1993) uses to proxy for the ease with which analysts can make accurate
forecasts. These three variables are firm size, historical earnings volatility, for which we require
earnings data for at least three prior years, and the current year absolute change in earnings per
share. Historical earnings volatility and the current year absolute change in earnings per share also
control for the possibility that managers of firms in more concentrated industries disclose less
because they are uncertain about their firms‟ future performance. We also include as controls the
book-to-market ratio, return on assets, and one-year sales growth, because firm performance can
impact analysts‟ perception of the quality of its disclosures (Lang and Lundholm, 1993; Bens and
Monahan, 2004). Following Bens and Monahan (2004), we also include as controls analyst coverage,
analyst forecast dispersion, analyst forecast error, and analyst forecast revision volatility. Finally, we
control for a firm‟s membership in the S&P 500 index as it could positively impact analysts‟
perceptions of the quality of a firm‟s disclosures (Bushee and Noe, 2000).
The first model in Table 8 shows that firms‟ analyst disclosure ratings are negatively related with
their industry Herfindahl-Hirschman index, supporting our hypothesis that firms in more
concentrated industries disclose less. The second and third models in Table 8 are the same as the
first model, except that they also include the industry age and industry leverage variables,
respectively, along with their interactions with the Herfindahl-Hirschman index. The coefficients on
the interaction variables are significantly positive in both models. These results suggest that, as
expected, the preference of firms in more concentrated industries to disclose less is more
pronounced in younger industries and is less pronounced in industries with higher financial
leverage.21
We do not use clustered standard errors for the results reported in Table 8 because of insufficient data on analysts‟
disclosure ratings to reliably estimate the clustered standard errors. Nevertheless, we re-estimate the Table 8 models
21
33
4.5. Industry concentration and analysts’ forecasts of earnings
For another comprehensive test of whether industry concentration is associated with the
informativeness of corporate disclosure policy, we examine the association between industry
concentration and certain properties of analysts‟ forecasts of earnings. If firms in more concentrated
industries exhibit greater dispersion in analysts‟ earnings forecasts, greater forecast error, and a
greater volatility of revisions in forecasts, it would suggest that firms in more concentrated industries
disclose less (see, e.g., Lang and Lundholm, 1996).
We use models of dispersion in analysts‟ forecasts, analyst forecast errors, and the volatility of
analyst forecast revisions that are similar to those in Lang and Lundholm (1996). As in all our other
models, we control for the ratio of the number of public to private firms in the firm‟s 6-digit NAICS
industry. We also include variables that prior work argues are related to the ease with which analysts
can make forecasts. Specifically, we control for the natural logarithm of the market value of equity,
R&D expenses, analyst coverage, and the historical correlation of a firm‟s earnings with its stock
returns (Lang and Lundholm, 1996; Barth et al., 2001). To control for the possibility that managers
of firms in more concentrated industries disclose less because they are uncertain about their firms‟
future performance, we include the following variables: historical earnings volatility, the firm‟s stock
return volatility over the prior year, and the current year absolute change in earnings per share. We
also include the current period‟s market-adjusted stock return to control for contemporaneous
performance and management‟s desire to disclose good news more promptly than bad news (Miller,
2002; Kothari et al., 2009). Finally, to control for the staleness of IBES forecast data, we include the
percentage of forecasts at the end of the month that are newly revised or first-time forecasts.
using standard errors clustered by industry and find that the coefficients on the variables of interest are significant for
two-tailed tests in five out of the five cases that are significant without clustered standard errors.
34
Table 9 shows that industry concentration is positively associated with dispersion in analysts‟
earnings forecasts, analyst forecast errors, as well as the volatility of analyst forecast revisions. These
results suggest that firms in more concentrated industries have inferior information environments,
presumably because they disclose less.
The first two models in Table 10 are models of forecast dispersion, which are the same as the
forecast dispersion model in Table 9, except that they include industry age and industry leverage,
respectively, along with their interactions with the Herfindahl-Hirschman index. The next two
models in Table 10 are equivalent models for forecast error and the last two models in Table 10 are
equivalent models for volatility in forecast revisions. The coefficients on the three industry age
interactions are negative, however none of the coefficients are significant at conventional levels. The
coefficients on the three industry leverage interactions are also negative and two of the three are
significant. This is consistent with the proposition that the tendency of firms in more concentrated
industries to disclose less is mitigated in more leveraged industries, presumably because of weaker
rivalry in these industries. Collectively, these findings provide some further support to the notion
that firms in more concentrated industries have inferior information environments, presumably
because of less informative disclosure policies.
4.6. Robustness checks
A potential concern with our use of the U.S. Census Herfindahl-Hirschman index measure is
that for multi-segment firms on Compustat, the Herfindahl-Hirschman index value we assign to a
given firm is the value that corresponds to its segment with the greatest sales. As a result, we do not
consider the industry concentration of other business segments in which a multi-segment firm
operates. Further, some multi-segment firms whose primary industry is a manufacturing industry
may have other business segments that are not in the manufacturing sector. Panel A of Table A2 in
the appendix shows that for Compustat manufacturing firms over our 1995-2004 sample period, the
35
mean and median values of the fraction of a firm‟s sales that are manufacturing related are 98.2%
and 100%. Likewise, the mean and median values of the fraction of a firm‟s sales that relate to its
primary industry are 93.4% and 100%, while the mean and median values of the fraction of a firm‟s
manufacturing sales that relate to its primary industry are 94.1% and 100%. These results suggest
that the error in our industry concentration measure arising from firms operating in multiple
industries is not likely to be very large. Nevertheless, we use the following approaches to address this
measurement error.
First, in all of our models we include the following variables: the fraction of a firm‟s sales that
relate to manufacturing and the fraction of its manufacturing sales that relate to its primary industry.
For the tests related to Hypothesis 1, in all cases where we found support for this hypothesis the
results continue to support the conclusion that firms in concentrated industries disclose less. With
regards to the tests of Hypotheses 1a and 1b and the tests concerning related acquisitions, of the 17
interaction variables that had significant coefficients with expected signs across all of our models, 14
retain significant coefficients with expected signs. Second, we estimate all of our models after
excluding sample firms with less than 50% of their sales in manufacturing. Here, again in all cases
where we found support for Hypothesis 1 the results continue to support the conclusion that firms
in concentrated industries disclose less. Further, for the tests of Hypotheses 1a and 1b and the tests
concerning related acquisitions, of the 17 interaction variables that had significant coefficients with
expected signs across all of our models, 15 retain significant coefficients with expected signs.
Third, we estimate all our models after replacing a firm‟s industry Herfindahl-Hirschman index
with a sales-weighted measure of the Herfindahl-Hirschman index, which is the fraction of the
firm‟s sales in a business segment multiplied by the segment‟s industry Herfindahl-Hirschman index
added across all the firm‟s segments. A benefit of using this composite measure of industry
concentration is that it considers the concentration of all the industries in which a firm operates.
36
However, a potential concern is that the linear weighting with sales may not be the optimal way to
construct the measure, given that it is not clear how the proprietary costs of disclosure associated
with each of the firm‟s non-primary segments affect the firm‟s overall disclosure practices. On using
the sales-weighted Herfindahl-Hirschman index measure, we find that except for analyst forecast
dispersion, in all cases where we found support for Hypothesis 1 the results continue to support the
conclusion that firms in concentrated industries disclose less. Further, for the tests of Hypotheses 1a
and 1b and the tests concerning related acquisitions, of the 17 interaction variables that had
significant coefficients with expected signs across all of our models, 10 retain significant coefficients
with expected signs.
Finally, for our sample firms, we examine the sensitivity of our results to using the Compustatbased Herfindahl-Hirschman index in place of the U.S. Census-based Herfindahl-Hirschman index.
For our tests of Hypothesis 1, we find that except for management forecast horizon, in none of our
models the coefficient on the Compustat-based industry Herfindahl-Hirschman index is significant
with the predicted sign. In a few instances, the coefficients are significant but with signs that are
opposite to our predictions. Thus, the results obtained using the Compustat-based industry
concentration measure do not provide support to our hypothesis that firms in more concentrated
industries prefer to disclose less. For the tests using interaction variables, of the 17 interaction
variables that had significant coefficients with predicted signs, only 2 retain significant coefficients
and predicted signs when the Compustat-based Herfindahl-Hirschman index is used. Overall, our
results are sensitive to using the Compustat-based industry concentration measure in place of the
U.S. Census-based measure.
5. Conclusion
This study tests the prediction that firms in more concentrated industries have less informative
disclosure practices due to greater proprietary costs of disclosure in these industries. The prediction
37
is based on two factors. First, because firms in more concentrated industries tend to be more
innovative, these firms face a greater risk of revealing company secrets to their rivals through
discretionary disclosures. Such revelations would likely adversely affect a firm‟s competitive
advantages over its rivals, because the disclosed information would likely prompt rivals to take
actions to enhance their profits at the expense of the firm. Second, in concentrated industries each
firm typically accounts for a larger fraction of aggregate industry output. Consequently, in these
industries a firm‟s disclosures are likely to provide more substantive and less noisy information
about future changes in industry demand. Given that the firm‟s rivals are likely to use more reliable
information on industry demand to revise their strategies, this would lead to greater proprietary
costs of disclosure in more concentrated industries. We find that, as expected, firms in concentrated
industries have less informative disclosure practices across a variety of disclosure settings.
First, we show that firms in more concentrated industries provide fewer management earnings
forecasts and the horizons of their forecasts tend to be shorter. Second, we document that when
firms in more concentrated industries sell new shares, they are more likely to do so via private
placement deals rather than SEOs. This result is consistent with the greater incentives of firms in
more concentrated industries to avoid SEC-mandated public disclosure requirements for SEOs.
Third, we show that when firms in more concentrated industries acquire another firm, they are more
likely to acquire small private targets rather than large private targets or public targets. These results
are consistent with bidders in concentrated industries preferring to acquire small private targets to
delay the disclosure of information about their acquisitions and to avoid disclosing the financial
statements of the target. Fourth, we document that firms in more concentrated industries are
characterized by lower analysts‟ disclosure ratings, greater dispersion in analysts‟ earnings forecasts,
greater analyst earnings forecast errors, and a higher volatility of analyst forecast revisions. These
38
findings further suggest that firms in more concentrated industries have less informative disclosure
policies.
Finally, we find that the associations involving industry concentration that we document in the
context of different disclosure settings are generally more pronounced in younger industries and are
less pronounced in more leveraged industries. These results are consistent with the notion that
among firms in concentrated industries, those in younger industries tend to innovate more and these
firms‟ consequently face a greater risk of revealing company secrets through their corporate
disclosures. Also, among firms in concentrated industries, those that are in industries with higher
leverage are expected to face less intense industry rivalry, and therefore lower proprietary costs from
their disclosures. Overall, our study shows that firms in more concentrated industries tend to
disclose less and avoid certain financing and investing decisions that have non-trivial disclosure
implications, and that a likely reason for these associations is the greater proprietary costs of
disclosure in these industries.
39
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45
Table 1: Industry concentration and industry characteristics
In Panel A, for each 3-digit NAICS industry within the manufacturing sector, we report the median values of the HerfindahlHirschman index for the component 6-digit NAICS industries. The Herfindahl-Hirschman index for a 6-digit NAICS industry
is the average of the 1997 and 2002 Census year values. In Panel B, quintiles are based on 6-digit NAICS HerfindahlHirschman index values. # of firms per industry is the number of public and private firms in a 6-digit NAICS industry as
reported by the Census of Manufactures. Net sales is defined as net sales in millions in year t. Market capitalization is defined as
the market value of equity in millions at the end of year t. Book assets is the book value of total assets at the end of year t. Net
sales, Market capitalization, and Book assets are inflation-adjusted. Median values are reported using firm-year observations
over the 1995-2004 sample period.
Panel A: Industries sorted by industry concentration
3-digit NAICS code
Industry name
323
321
332
333
337
314
339
313
326
315
331
324
327
322
335
311
334
312
325
316
336
Printing and Related Support
Wood Products
Fabricated Metal Products
Machinery
Furniture and Related Products
Textile Product Mills
Miscellaneous
Textile Mills
Plastics and Rubber Products
Apparel
Primary Metal
Petroleum and Coal Products
Nonmetallic Mineral Products
Paper
Electrical Equipment, Appliances, and Components
Food
Computer and Electronic Products
Beverage and Tobacco Products
Chemicals
Leather and Allied Products
Transportation Equipment
Median of 6-digit NAICS industry
Herfindahl-Hirschman index
112.0
206.2
230.5
328.0
353.1
361.1
370.3
415.2
420.4
451.2
595.5
615.6
648.0
733.4
748.8
778.6
805.9
923.0
943.9
988.8
1054.2
Panel B: Industry Characteristics by Quintiles of Industry Concentration
Quintile
Herfindahl-Hirschman Index
1
2
3
4
5
81.4
270.2
572.9
952.2
1677.9
# of firms per
industry
1077
482
293
185
135
46
Net sales
Market capitalization
282
398
611
830
863
218
287
493
628
635
Book
assets
266
349
583
770
858
Table 2: Industry concentration and the frequency of management forecasts
The table reports results of one-sided Tobit regression models. The dependent variable is the number of management forecasts made during a
fiscal year for the earnings of that fiscal year. We use a Tobit model because the dependent variable is truncated at zero. The sample consists of
firms in the manufacturing sector during the years 1995-2004, with necessary data available for the variables used in the regression models.
Management forecast data are obtained from First Call‟s Company Issued Guidelines (CIG) database. The industry Herfindahl-Hirschman index is
divided by 1000. Industry mean for the number of years since a firm‟s IPO is for the 6-digit NAICS industry and is calculated using monthly
CRSP listing data. Industry asset-weighted mean of the net-debt-to-asset ratio is for the 6-digit NAICS industry, where net-debt is defined as longterm debt minus cash. Ratio of the number of public to private firms in the industry is calculated as the number of firms on Compustat in the 6digit NAICS industry divided by the difference between this number and the total number of firms in the industry as reported in Census of
Manufactures publications. Stock return volatility is calculated with monthly stock return data over the firm‟s fiscal year. Absolute change in annual
earnings per share/stock price is the absolute value of the annual change in earnings per share deflated by stock price at the beginning of the fiscal
year. Market-adjusted stock return is the firm‟s buy-and-hold 12-month fiscal year stock return minus the CRSP value-weighted stock return over
the same period. Research and development expense/book assets is the R&D expense for the fiscal year divided by the book value of assets at the
beginning of the fiscal year. Analyst coverage is the average number of analysts making earnings forecasts for the firm during the fiscal year.
Institutional fractional ownership is institutional holdings at the beginning of the fiscal year. The Post-Regulation Fair Disclosure dummy takes the
value of one for years 2001 and beyond, and zero otherwise. Year dummies are included in all the models. z-statistics (reported in parentheses) are
based on standard errors clustered by industry. ***, **, and * indicate significance levels for two-tailed tests at the 1, 5 and 10 percent levels,
respectively.
47
Model
Industry Herfindahl-Hirschman index
1
2
-0.490
(-1.26)
-1.159**
(-2.10)
Industry Herfindahl-Hirschman index × Industry
mean of the number of years since a firm‟s IPO
3
-0.621**
(-1.99)
0.032**
(2.17)
Industry Herfindahl-Hirschman index × Industry
asset-weighted mean of the net-debt-to-asset ratio
3.568***
(2.94)
Industry mean of the number of years since a firm‟s
IPO
0.001
(0.06)
Industry asset-weighted mean of the net-debt-toasset ratio
-1.199
(-1.04)
Ratio of the number of public to private firms
in the industry
-5.278***
(-2.75)
-4.716**
(-2.37)
-4.551**
(-2.44)
Stock return volatility
-5.647**
(-2.02)
-4.706*
(-1.83)
-4.032
(-1.64)
Absolute change in annual earnings per
share/stock price
-2.700***
(-4.65)
-2.699***
(-4.69)
-2.814***
(-4.85)
0.198***
(4.09)
0.178***
(3.68)
0.183***
(3.69)
-5.534***
(-3.89)
-5.067***
(-3.65)
-4.584***
(-3.45)
Natural logarithm of market value of equity
0.366***
(4.20)
0.302***
(3.82)
0.328***
(3.99)
Analyst coverage
0.048
(1.40)
0.057*
(1.70)
0.060*
(1.80)
Institutional fractional ownership
3.495***
(9.29)
3.518***
(9.28)
3.508***
(9.30)
Post-Regulation Fair Disclosure dummy
-3.426***
(-5.11)
-3.470***
(-5.14)
-3.084***
(-4.51)
Pseudo-R2
N
0.086
9,145
0.089
9,145
Market-adjusted stock return
Research and development expense/book assets
48
0.090
9,145
Table 3: Industry concentration and the horizon of management forecasts of earnings
The table reports results of OLS regression models. The dependent variable is the average number of days between a management forecast date
for a firm‟s fiscal year earnings and the end date of the fiscal year. The sample consists of firms in the manufacturing sector during the years 19952004, with necessary data for the variables in the models. Management forecast data are from First Call‟s Corporate Investor Guidelines (CIG)
database. If a firm does not make a management forecast during a given year that firm-year is dropped from the analysis. The industry HerfindahlHirschman index is divided by 1000. Industry mean for the number of years since a firm‟s IPO is for the 6-digit NAICS industry and is calculated
using monthly CRSP listing data. Industry asset-weighted mean of the net-debt-to-asset ratio is for the 6-digit NAICS industry, where net-debt is
defined as long-term debt minus cash. Ratio of the number of public to private firms in the industry is calculated as the number of firms on
Compustat in the 6-digit NAICS industry divided by the difference between this number and the total number of firms in the industry as reported
in Census of Manufactures publications. Stock return volatility is calculated with monthly stock return data over the firm‟s fiscal year. Absolute change
in annual earnings per share/stock price is the absolute value of the annual change in earnings per share deflated by stock price at the beginning of
the fiscal year. Market-adjusted stock return is the firm‟s buy-and-hold 12-month fiscal year stock return minus the CRSP value-weighted stock
return for the same period. Research and development expense/book assets is the R&D expense for the fiscal year divided by the book value of
assets at the beginning of the fiscal year. Analyst coverage is the average number of analysts making earnings forecasts for the firm during the
fiscal year. Institutional fractional ownership is institutional holdings at the beginning of the fiscal year. The Post-Regulation Fair Disclosure
dummy takes the value of one for years 2001 and beyond, and zero otherwise. z-statistics (reported in parentheses) are based on standard errors
clustered by year and industry. ***, **, and * indicate significance levels for two-tailed tests at the 1, 5 and 10 percent levels, respectively.
49
Model
Intercept
Industry Herfindahl-Hirschman index
1
2
3
16.185
(1.15)
23.241
(1.38)
14.183
(0.98)
-11.941*
(-1.75)
-25.304**
(-2.49)
-12.664**
(-2.55)
Industry Herfindahl-Hirschman index × Industry
mean of the number of years since a firm‟s IPO
0.643**
(2.15)
Industry Herfindahl-Hirschman index × Industry
asset-weighted mean of the net-debt-to-asset ratio
63.415***
(3.43)
Industry mean of the number of years since a firm‟s
IPO
-0.241
(-0.65)
Industry asset-weighted mean of the net-debt-to-asset
ratio
Ratio of the number of public to private firms
in the industry
-9.906
(-0.47)
79.541
(1.58)
92.064*
(1.83)
105.755**
(2.08)
-189.960**
(-2.29)
-174.947**
(-2.20)
-161.327**
(-2.09)
Absolute change in annual earnings per share/stock
price
-8.859
(-0.82)
-8.600
(-0.78)
-12.775
(-1.32)
Market-adjusted stock return
10.127***
(5.61)
9.702***
(5.63)
9.570***
(5.51)
Research and development expense/book assets
-3.393
(-0.07)
2.409
(0.05)
24.632
(0.60)
Natural logarithm of market value of equity
12.020***
(6.56)
11.316***
(6.02)
10.704***
(6.70)
Analyst coverage
-1.116
(-1.64)
-1.041
(-1.53)
-0.798
(-1.39)
Institutional fractional ownership
12.763
(1.13)
13.997
(1.28)
16.400
(1.50)
Post-Regulation Fair Disclosure dummy
24.418***
(2.82)
24.909***
(2.88)
27.088***
(2.73)
R2-adjusted
N
0.086
3,917
0.091
3,917
Stock return volatility
50
0.098
3,917
Table 4: Industry concentration and the private placement versus seasoned equity offering decision
The table reports results of probit regression models. The dependent variable equals one if a firm sells new shares through a private placement
and equals zero if it sells new shares through a seasoned equity offering. The sample consists of firms in the manufacturing sector for the years
1997 and 2002 with necessary data for variables in the models. The industry Herfindahl-Hirschman index is divided by 10,000. Industry mean
for the number of years since a firm‟s IPO is for the 6-digit NAICS industry and is calculated using monthly CRSP listing data. Industry assetweighted mean of the net-debt-to-asset ratio is for the 6-digit NAICS industry, where net-debt is defined as long-term debt minus cash. Ratio of
the number of public to private firms in the industry is calculated as the number of firms on Compustat in the 6-digit NAICS industry divided
by the difference between this number and the total number of firms in the industry as reported in Census of Manufactures publications. Analyst
coverage is the average number of analysts making earnings forecasts for the firm during the fiscal year. Industry-adjusted sales growth is
calculated as the natural logarithm of the difference between annual sales growth for the firm in the year prior to the event year and the
corresponding median sales growth in the firm‟s 6-digit NAICS industry. Change in industry-adjusted market-to-book equity is divided by 1000.
It is the difference between 6-digit NAICS industry-adjusted market-to-book equity at the end of the event fiscal year and at the end of the
previous fiscal year. Operating cash flow/book assets is operating income before depreciation divided by lagged book assets. Cash flow volatility
is the standard deviation of operating income, calculated with data for at least 12 and up to 20 quarters prior to the event quarter. Altman-Z
score, a bankruptcy likelihood measure, is calculated as in Altman (1968), and is scaled by 100. Stock return volatility is calculated with monthly
stock return data for the firm‟s fiscal year. One-year market-adjusted stock return is the firm‟s stock return net of the value-weighted CRSP
index for the year prior to the announcement of the equity issuance. Year dummies are included in all the models. The table presents estimates
of marginal effects. z-statistics are in parentheses. ***, **, and * indicate significance levels for two-tailed tests at the 1, 5, and 10 percent levels,
respectively.
51
Model
Industry Herfindahl-Hirschman index
1
2
1.315*
(1.84)
Industry Herfindahl-Hirschman index × Industry
mean of the number of years since a firm‟s IPO
2.099**
(2.46)
-6.842*
(-1.69)
Industry mean of the number of years since a firm‟s
IPO
0.008
(1.46)
Industry asset-weighted mean of the net-debt-toasset ratio
Natural logarithm of book value of assets
0.791
(0.97)
-0.112*
(-1.74)
Industry Herfindahl-Hirschman index × Industry
asset-weighted mean of the net-debt-to-asset ratio
Ratio of the number of public to private firms
in the industry
3
0.094
(0.28)
0.008
(0.03)
0.037
(0.13)
-0.110***
(-3.23)
-0.118***
(-3.52)
-0.111***
(-3.18)
Analyst coverage
0.003
(0.43)
Number of years since a firm‟s IPO
0.002
(0.81)
Industry-adjusted sales growth
0.031
(0.64)
0.032
(0.67)
0.040
(0.83)
Change in industry-adjusted
market-to-book equity
-0.002
(-0.76)
-0.002
(-0.94)
-0.002
(-0.74)
Operating cash flow/book assets
-0.565***
(-3.54)
-0.404***
(-3.25)
-0.428***
(-3.40)
Cash flow volatility
-0.266
(-0.62)
-0.138
(-0.33)
-0.130
(-0.32)
Altman-Z score
-0.000
(-0.38)
-0.001
(-1.11)
-0.001
(-0.93)
Stock return volatility
0.006
(0.53)
-0.126
(-0.40)
0.003
(0.25)
0.003
(0.86)
1.591**
(2.17)
1.417**
(1.97)
1.136
(1.50)
One-year market-adjusted stock return
-0.100*
(-1.69)
-0.111*
(-1.91)
-0.116**
(-2.00)
The offering takes place within one week of an
earnings announcement
-0.364***
(-3.21)
-0.339***
(-3.06)
-0.338***
(-3.06)
Pseudo-R2
N
0.361
280
0.365
280
52
0.369
280
Table 5: Industry concentration and the likelihood that a bidder acquires a private target
The table reports results of probit regressions. The dependent variable equals one if a bidder acquires a private target and equals zero if a bidder
acquires a public target. The sample consists of firms in the manufacturing sector for the years 1996-2004 with necessary data for the variables in
the models. The industry Herfindahl-Hirschman index is divided by 10,000. Target and bidder are in different industries dummy takes a value of
one if the target and bidder firm are not in the same 6-digit NAICS industry, and zero otherwise. Industry mean for the number of years since a
firm‟s IPO is for the 6-digit NAICS industry and is calculated using monthly CRSP listing data. Industry asset-weighted mean of the net-debt-toasset ratio is for the 6-digit NAICS industry, where net-debt is defined as long-term debt minus cash. Ratio of target to bidder firm size is the
market value of the target firm divided by the market value of the bidder firm. Ratio of the number of public to private firms in the bidder‟s
industry is calculated as the number of firms on Compustat in the 6-digit NAICS industry divided by the difference between this number and the
total number of firms in the industry as reported in Census of Manufactures publications. Stock payment dummy takes the value of one if the
payment for a target consists only of stock, and zero otherwise. Year dummies are included in all the models. The table presents estimates of
marginal effects. z-statistics are reported in parentheses. ***, **, and * indicate significance levels for two-tailed tests at the 1, 5 and 10 percent
levels, respectively.
Model
Industry Herfindahl-Hirschman index
1
2
0.658***
(3.99)
1.134***
(4.63)
Industry Herfindahl-Hirschman index × Target and
bidder firm in different industries dummy
3
1.019***
(4.09)
0.558***
(3.26)
-0.832***
(-2.64)
Industry Herfindahl-Hirschman index × Industry
mean for the number of years since a firm‟s IPO
-0.020**
(-2.01)
Industry Herfindahl-Hirschman index × Industry
asset-weighted mean for the net-debt-to-asset ratio
-2.700**
(-3.08)
Industry mean for the number of years since a firm‟s
IPO
-0.038***
(-3.62)
Industry asset-weighted mean for the net-debt-toasset ratio
Target and bidder firms are in different
industries dummy
4
-0.046
(-0.50)
0.331***
(16.22)
0.395***
(12.73)
0.337***
(10.69)
0.334***
(17.07)
Natural logarithm of market value of equity
of the bidder
-0.076***
(-16.57)
-0.076***
(-16.60)
-0.067***
(-14.34)
-0.075***
(-16.47)
Ratio of target to bidder firm size
-0.283***
(-10.96)
-0.282***
(-10.94)
-0.276***
(-10.69)
-0.269***
(-10.39)
-0.131**
(-2.54)
-0.131**
(-2.54)
-0.166***
(-3.19)
-0.218***
(-3.80)
Stock payment dummy
0.038
(1.63)
0.036
(1.52)
0.009
(0.38)
0.016
(0.65)
Pseudo-R2
N
0.166
3,088
0.168
3,088
0.183
3,088
0.173
3,088
Ratio of the number of public to private firms
in the bidder‟s industry
53
Table 6: Multinomial logit model of the likelihood of a bidder acquiring a small private target versus
a large private target or a public target
The table reports results of multinomial logit regressions. The dependent variable equals zero if the target is a publicly traded company, one if the
target is private and its size is at least 20 percent of the size of the bidder, and two if the target is private and its size is less than 20 percent of the
size of the bidder. The first column presents the regression results for small private targets versus large private targets. The second column
presents the regression results for small private targets versus public targets. The sample consists of firms in the manufacturing sector for the years
1996-2004 with necessary data for the variables in the models. The industry Herfindahl-Hirschman index is divided by 10,000. Target and bidder
are in different industries dummy takes a value of one if the target and bidder firm are not in the same 6-digit NAICS industry, and zero otherwise.
Ratio of target to bidder firm size is the market value of the target firm divided by the market value of the bidder firm. Ratio of the number of
public to private firms in the bidder‟s industry is calculated as the number of firms on Compustat in the 6-digit NAICS industry divided by the
difference between this number and the total number of firms in the industry as reported in Census of Manufactures publications. Stock payment
dummy takes the value of one if the payment for the target consists only of stock, and zero otherwise. Year dummies are included in all the
models. The table presents estimates of marginal effects. z-statistics are reported in parenthesis. ***, **, and * indicate significance levels for twotailed tests at the 1, 5 and 10 percent levels, respectively.
Dependent variable = 0 if public, 1 if large private, and 2 if small private
Small vs. large private target
Small private vs. public target
Industry Herfindahl-Hirschman index
0.648**
(3.76)
0.254***
(3.24)
Target and bidder firms are in different
industries dummy
0.151***
(10.80)
0.196***
(16.01)
-0.068***
(-16.31)
-0.052***
(-17.78)
-0.590**
(-2.38)
-2.071***
(-18.39)
Ratio of the number of public to private firms
in the bidder‟s industry
0.079
(0.31)
-0.208***
(-4.13)
Stock payment dummy
0.109***
(4.24)
0.120*
(1.93)
Pseudo-R2
N
0.268
3,088
0.268
3,088
Natural logarithm of market value of equity
of the bidder
Ratio of target to bidder firm size
54
Table 7: The effect of industry age and industry leverage on the association between industry
concentration and whether a bidder acquires a small private versus a large private or public target
The table reports results of multinomial logit regressions. The dependent variable equals zero if the target is a publicly traded company, one if the
target is private and its size is at least 20 percent of the size of the bidder, and two if the target is private and its size less than 20 percent of the size
of the bidder. The sample consists of firms in the manufacturing sector for the years 1996-2004 with necessary data for the variables in the
models. The industry Herfindahl-Hirschman index is divided by 10,000. Target and bidder are in different industries dummy takes a value of one if
the target and bidder firm are not in the same 6-digit NAICS industry, and zero otherwise. Industry mean for the number of years since a firm‟s
IPO is for the 6-digit NAICS industry and is calculated using monthly CRSP listing data. Industry asset-weighted mean of the net-debt-to-asset
ratio is for the 6-digit NAICS industry, where net-debt is defined as long-term debt minus cash. Ratio of target to bidder firm size is the market
value of the target firm divided by the market value of the bidder firm. Ratio of target to bidder firm size is the market value of the target firm
divided by the market value of the bidder firm. Ratio of the number of public to private firms in the bidder‟s industry is calculated as the number
of firms on Compustat in the 6-digit NAICS industry divided by the difference between this number and the total number of firms in the industry
as reported in Census of Manufactures publications. Stock payment dummy takes the value of one if the payment for a target consists only of stock,
and zero otherwise. Year dummies are included in all the models. The table presents estimates of marginal effects. z-statistics are reported in
parenthesis. ***, **, and * indicate significance levels for two-tailed tests at the 1, 5 and 10 percent levels, respectively.
Dependent variable = 0 if public, 1 if large private, and 2 if small private
Small vs. large private target
Model
Industry Herfindahl-Hirschman index
Industry Herfindahl-Hirschman index × Target
and bidder firm in different industries dummy
1
1.177***
(5.13)
2
0.931***
(3.73)
0.421***
(3.78)
-0.129
(-1.37)
2
0.346**
(2.99)
Industry asset-weighted mean of the net-debt-toasset ratio
-0.002**
(-3.08)
0.025
(0.50)
-0.068***
(-16.38)
0.265***
(3.10)
-1.394***
(-2.68)
-0.003**
(-2.39)
0.206***
(9.05)
3
-0.003
(-1.10)
-2.265*
(-3.08)
Industry mean of the number of years since a
firm‟s IPO
Ratio of target to bidder firm size
1
-0.348**
(-2.35)
Industry Herfindahl-Hirschman index × Industry
asset-weighted mean of the net-debt-to-asset ratio
Natural logarithm of market value of equity
of the bidder
0.621***
(3.93)
-0.907***
(-3.15)
Industry Herfindahl-Hirschman index × Industry
mean of the number of years since a firm‟s IPO
Target and bidder firms are in different
industries dummy
3
Small private vs. public target
0.151***
(10.95)
0.065
(0.91)
0.155***
(10.99)
0.215***
(11.88)
0.203***
(16.13)
0.198***
(16.11)
-0.062***
-0.067***
(-14.87)
(-16.29)
-0.052***
(-17.79)
-0.049***
(-16.18)
-0.052***
(-17.75)
-0.585**
(-2.39)
-0.590*
(-2.31)
-0..600*
(-2.11)
-2.074***
(-18.39)
-2.080***
(-18.21)
-2.074***
(-18.26)
Ratio of the number of public to private firms
in the bidder‟s industry
0.081
(0.36)
0.059
(0.19)
0.034
(0.61)
-0.211***
(-4.15)
-0.226***
(-4.52)
-0.219***
(-4.20)
Stock payment dummy
0.105***
(4.08)
0.087***
(3.19)
0.090***
(3.35)
0.013
(0.91)
0.025
(0.04)
0.015
(0.57)
Pseudo-R2
N
0.270
3,088
0.277
3,088
0.271
3,088
0.270
3,088
0.277
3,088
0.271
3,088
55
Table 8: Industry concentration and disclosure ratings
The table reports results of OLS regressions. The dependent variable is analysts‟ overall ratings of a firm‟s disclosure practices from the Report of the
Association for Investment Management and Research; the rating is divided by 100. The sample consists of firms in the manufacturing sector for the years
1995 and 1996 with necessary data for the variables in the models. The industry Herfindahl-Hirschman index is divided by 10,000. Industry mean
for the number of years since a firm‟s IPO is for the 6-digit NAICS industry and is calculated using monthly CRSP listing data. Industry assetweighted mean of the net-debt-to-asset ratio is for the 6-digit NAICS industry, where net-debt is defined as long-term debt minus cash. Ratio of
the number of public to private firms in the industry is calculated as the number of firms on Compustat in the 6-digit NAICS industry divided by
the difference between this number and the total number of firms in the industry as reported in Census of Manufactures publications. Standard
deviation of return on equity is computed using annual return on equity of the preceding 10 years, with a minimum of three preceding years of
data. Absolute change in annual earnings per share/stock price is the absolute value of the annual change in earnings per share deflated by stock
price at the beginning of the fiscal year. Return on assets is earnings before extraordinary items scaled by assets at the beginning of the year. Sales
growth is sales divided by lagged sales. Analyst forecast dispersion is the 12-month average of the standard deviation of analysts‟ forecasts, deflated
by stock price at the beginning of the fiscal year. Analyst forecast error is the 12-month average of the absolute values of analyst forecast errors,
defined as actual earnings minus median forecast, deflated by stock price at the beginning of the fiscal year. Analyst forecast revision volatility is
the standard deviation of forecast revisions deflated by stock price at the beginning of the fiscal year, where forecast revision is defined as current
month‟s median forecast minus previous month‟s median forecast. The S&P 500 firm dummy takes the value of one if the firm is included in the
S&P 500, and zero otherwise. Year dummies are included in all the models. t-statistics are reported in parentheses. ***, **, and * indicate
significance levels for two-tailed tests at the 1, 5 and 10 percent levels, respectively.
56
Model
Industry Herfindahl-Hirschman index
1
2
-0.654**
(-2.33)
Industry Herfindahl-Hirschman index × Industry
mean of the number of years since a firm‟s IPO
-1.60**
(-2.47)
3
-1.833***
(-2.83)
0.032**
(2.06)
Industry Herfindahl-Hirschman index × Industry
asset-weighted mean of the net-debt-to-asset ratio
7.623**
(2.15)
Industry mean of the number of years since a
firm‟s IPO
-0.006**
(-2.53)
Industry asset-weighted mean of the net-debt-toasset ratio
-0.835**
(-2.34)
Ratio of the number of public to private firms
in the industry
-0.112
(-0.62)
-0.101
(-0.57)
-0.310
(-1.57)
Natural logarithm of market value of equity
-0.052*
(-1.67)
-0.035
(-0.11)
-0.039
(-1.26)
Standard deviation of return on equity
0.619**
(2.57)
0.594**
(2.51)
0.607**
(2.41)
Absolute change in annual earnings per
share/stock price
-0.219
(-1.08)
-0.171
(-0.85)
-0.222
(-1.11)
Book-to-market-equity
-0.038
(-0.36)
0.023
(0.22)
0.019
(0.17)
Return on assets
0.153
(0.72)
0.204
(0.97)
0.247
(1.14)
Sales growth
0.111
(1.03)
0.154
(1.43)
0.143
(1.34)
Analyst coverage
0.400
(0.97)
0.000
(0.48)
0.000
(0.51)
-11.215**
(-2.11)
-7.863
(-1.47)
-5.52
(-0.94)
Analyst forecast error
0.085
(0.05)
-0.096
(-0.05)
-0.727
(-0.40)
Analyst forecast revision volatility
4.087
(1.43)
2.889
(1.01)
0.417
(1.46)
S&P 500 firm dummy
0.085**
(2.36)
R2-adjusted
N
0.106
123
Analyst forecast dispersion
0.072**
(2.03)
0.142
123
57
0.077**
(2.11)
0.135
123
Table 9: Industry concentration and analyst forecast properties
The table reports results of OLS regressions. The sample consists of firms in the manufacturing sector for the years 1995-2004, with necessary
data for the variables in the models. The dependent variable in model 1 is the 12-month average of the standard deviation of analysts‟ forecasts,
deflated by stock price at the beginning of the fiscal year. The dependent variable in model 2 is the 12-month average of the absolute values of
analyst forecast errors defined as actual earnings minus median forecast, deflated by stock price at the beginning of fiscal year. The dependent
variable in model 3 is the standard deviation of forecast revisions deflated by stock price at the beginning of the fiscal year, where forecast revision
is defined as current month‟s median forecast minus previous month‟s median forecast. The industry Herfindahl-Hirschman index is divided by
10,000. Ratio of the number of public to private firms in the industry is calculated as the number of firms on Compustat in the 6-digit NAICS
industry divided by the difference between this number and the total number of firms in the industry as reported in Census of Manufactures
publications. Research and development expense/book assets is the R&D expense for the fiscal year divided by book value of assets at the
beginning of the fiscal year. Analyst coverage is the average number of analysts making earnings forecasts for a firm during the fiscal year.
Correlation between return on equity and stock returns is computed using annual data of the preceding 10 years, with a minimum of three
preceding years of data. Standard deviation of return on equity is computed using annual return on equity of the preceding 10 years, with a
minimum of three preceding years of data. Absolute change in annual earnings per share/stock price is the absolute value of the annual change in
earnings per share deflated by stock price at the beginning of the fiscal year. Market-adjusted stock return is the firm‟s buy-and-hold 12-month
fiscal year stock return minus the CRSP value-weighted stock return for the same period. Average proportion of new monthly forecasts is the
fiscal year average of the proportion of analyst forecasts at the end of a month that are either first-time forecasts or are revised during the month.
t-statistics based on standard errors clustered by year and industry are reported in parentheses. ***, **, and * indicate significance levels for twotailed tests at the 1, 5 and 10 percent levels, respectively.
Model
Dependent variable
1
Analyst forecast dispersion
2
Analyst forecast errors
3
Analyst forecast revision volatility
Intercept
1.450***
(5.56)
6.90***
(7.07)
1.065***
(5.96)
Industry Herfindahl-Hirschman index
0.926*
(1.69)
2.971*
(1.66)
0.805**
(2.13)
Ratio of the number of public to private
firms in the industry
1.031*
(1.93)
Natural logarithm of market value of
equity
Research and development
expense/book assets
-0.626
(-0.33)
-0.173***
(-5.47)
-0.899***
(-7.53)
0.514
(0.95)
-0.539
(-0.33)
0.084
(0.30)
-0.182***
(-7.42)
0.417
(1.47)
Analyst coverage
-0.000
(-0.04)
0.050**
(2.25)
-0.002
(-0.40)
Correlation between return on equity and
stock returns
-0.042
(-0.80)
0.422***
(2.68)
0.055
(1.53)
Standard deviation of return on equity
0.587***
(3.21)
1.917***
(3.19)
0.479**
(3.88)
Stock return volatility
2.509*
(1.67)
11.405**
(2.31)
3.222**
(2.12)
Absolute change in annual earnings per
share/stock price
1.532***
(7.07)
7.262***
(4.68)
1.472***
(5.90)
Market-adjusted stock return
-0.316***
(-5.11)
-1.505***
(-5.32)
-0.346***
(-5.33)
Average proportion of new monthly
forecasts
-0.104
(-0.38)
-0.718
(-0.95)
2.177***
(9.13)
R2-adjusted
N
0.275
10,141
0.257
11,944
0. 253
11,852
58
Table 10: The effect of industry age and industry leverage on the associations between industry
concentration and analyst forecast properties
The table reports results of OLS regressions. The sample consists of firms in the manufacturing sector for the years 1995-2004 with necessary data for
the variables in the models. The dependent variable in models 1 and 2 is the 12-month average of the standard deviation of analysts‟ forecasts, deflated
by stock price at the beginning of the fiscal year. The dependent variable in models 3 and 4 is the 12-month average of the absolute values of analyst
forecast errors defined as actual earnings minus median forecast, deflated by stock price at the beginning of the fiscal year. The dependent variable in
models 5 and 6 is the standard deviation of forecast revisions, deflated by stock price at the beginning of the fiscal year, where forecast revision is
defined as current month‟s median forecast minus previous month‟s median forecast. The industry Herfindahl-Hirschman index is divided by 10,000.
Industry mean for the number of years since a firm‟s IPO is for the 6-digit NAICS industry and is calculated using monthly CRSP listing data. Industry
asset-weighted mean of the net-debt-to-asset ratio is for the 6-digit NAICS industry, where net-debt is defined as long-term debt minus cash. Ratio of
the number of public to private firms in the industry is calculated as the number of firms on Compustat in the 6-digit NAICS industry divided by the
difference between this number and the total number of firms in the industry as reported in Census of Manufactures publications. Research and
development expense/book assets is the R&D expense for the fiscal year divided by book value of assets at the beginning of the fiscal year. Analyst
coverage is the average number of analysts making earnings forecasts for a firm during the fiscal year. Correlation between return on equity and stock
returns is computed using annual data of the preceding 10 years, with a minimum of three preceding years of data. Standard deviation of return on
equity is computed using annual return on equity of the preceding 10 years, with a minimum of three preceding years of data. Absolute change in annual
earnings per share/stock price is the absolute value of the annual change in earnings per share deflated by stock price at the beginning of the fiscal year.
Market-adjusted stock return is the firm‟s buy-and-hold 12-month fiscal year stock return minus the CRSP value-weighted stock return for the same
period. Average proportion of new monthly forecasts is the fiscal year average of the proportion of analyst forecasts at the end of a month that are either
first-time forecasts or are revised during the month. t-statistics based on standard errors clustered by year and industry are reported in parentheses. ***,
**, and * indicate significance levels for two-tailed tests at the 1, 5 and 10 percent levels, respectively.
59
Model
Dependent variable
1
Analyst forecast
dispersion
2
Analyst forecast
dispersion
3
Analyst forecast
errors
4
Analyst forecast
errors
5
Analyst forecast
revision volatility
6
Analyst forecast
revision volatility
Intercept
1.374***
(5.72)
1.449***
(5.57)
6.663***
(7.00)
6.893***
(7.06)
1.033***
(5.93)
1.062***
(5.97)
Industry Herfindahl-Hirschman
index
1.201
(1.64)
0.930
(1.63)
3.916
(1.45)
3.010
(1.58)
1.054**
(2.00)
0.812**
(2.05)
Industry Herfindahl-Hirschman
index × Industry mean of the
number of years since a firm‟s
IPO
-0.011
(-1.06)
Industry Herfindahl-Hirschman
index × Industry asset-weighted
mean of the net-debt-to-asset
ratio
Industry mean of the number of
years since a firm‟s IPO
-1.043
(-0.90)
Natural logarithm of market
value of equity
0.018**
(1.17)
0.092
(0.92)
1.079***
(2.24)
1.047**
(1.98)
-0.183***
(-5.51)
-0.172***
(-5.50)
-2.013**
(-2.05)
0.002
(0.55)
0.630*
(1.98)
-0.507
(-0.28)
-0.529
(-0.28)
-0.920***
(-7.61)
-0.896***
(-7.53)
-0.004
(-0.28)
-0.497
(-0.30)
Research and development
expense/book assets
0.562
(1.10)
0.521
(0.96)
Analyst coverage
0.001
(0.15)
-0.000
(-0.05)
0.052**
(2.29)
-0.035
(-0.67)
-0.044
(-0.84)
Correlation between return on
equity and stock returns
-0.010
(-1.11)
-7.257*
(-1.79)
0.007**
(1.84)
Industry asset-weighted mean of
the net-debt-to-asset ratio
Ratio of the number of public to
private firms in the industry
-0.039
(-0.90)
0.177**
(2.07)
0.084
(0.31)
-0.183***
(-7.18)
0.114
(0.42)
-0.181***
(-7.50)
0.424
(1.56)
0.430
(1.54)
0.049**
(2.21)
-0.002
(-0.35)
-0.002
(-0.43)
0.435***
(2.64)
0.410***
(3.65)
0.055
(1.49)
0.052
(1.44)
Standard deviation of return on
equity
0.594***
(3.30)
0.589***
(3.22)
1.932***
(3.25)
1.924***
(3.19)
0.479***
(3.93)
0.481***
(3.86)
Stock return volatility
2.577*
(1.69)
2.489*
(1.67)
11.501**
(2.31)
11.284**
(2.30)
3.220**
(2.11)
3.187*
(2.11)
Absolute change in annual
earnings per share/stock price
1.522***
(7.06)
1.532***
(7.05)
7.245***
(4.67)
7.255***
(4.68)
1.471***
(5.88)
1.470***
(5.90)
Market-adjusted stock return
-0.318***
(-5.05)
-0.316***
(-5.13)
-1.509***
(-5.29)
-1.503***
(-5.33)
-0.346***
(-5.32)
-0.345***
(-5.35)
Average proportion of new
monthly forecasts
-0.123
(-0.45)
-0.119
(-0.44)
-0.739
(-0.97)
-0.805
(-1.05)
2.169***
(9.07)
2.154***
(9.20)
R2-adjusted
N
0.277
10,141
0.275
10,141
0.257
11,944
0.257
11,944
0.253
11,852
0.253
11,852
60
Appendix
Table A1: The effect of data requirements on sample sizes
Compustat/CRSP/IBES initial sample refers to the number of firms over our sample period, 1995-2004, that are included on Compustat, CRSP, and IBES. For Table 4, the initial sample consists of firms
that sold new equity via private placements or seasoned equity offerings in 1997 or 2002, and which are included on Compustat, CRSP, and IBES. Only the year(s) in which a firm sells new shares is (are)
included in this initial sample. For Table 5, the initial sample consists of firms that acquire public or private firms between 1996-2004, and which are included on Compustat. Only the year(s) in which a firm
makes an acquisition is (are) included in this initial sample. For Table 8, the initial sample consists of firms whose disclosure practices are rated in the Report of the Association for Investment Management and
Research in 1995 or 1996, and which are in Compustat and IBES. Only the 130 year(s) for which a firm receives a disclosure rating is (are) included in this initial sample. N/A stands for not applicable.
Dependent variable
Compustat/CRSP/IBES
initial sample
Table 2
Management
forecast frequency
Table 3
Management
forecast horizon
Table 4
Private placement
vs. SEO
Table 5
Private vs.
public target
Table 8
Disclosure rating
Table 9
Analyst forecast
dispersion
Table 9
Analyst forecast
error
Table 9
Analyst forecast
revision volatility
19,579
19,579
408
3,211
130
19,579
19,579
19,579
Number of firms dropped due
to missing IBES data items
3,796
3,796
41
N/A
0
3,796
1,993
2,085
Number of firms dropped due
to missing data on institutional
ownership
5,759
5,759
N/A
N/A
N/A
N/A
N/A
N/A
Number of firms dropped due
to missing data to calculate the
correlation between return on
equity and stock returns
N/A
N/A
N/A
N/A
N/A
2,686
2,686
2,686
Number of firms dropped due
to missing data for remaining
variables
879
879
87
123
7
2,956
2,956
2,956
Number of firms dropped due
to no management forecasts
0
5,288
N/A
N/A
N/A
N/A
N/A
N/A
9,145
3,917
280
3,088
123
10,141
11,944
11,852
Final sample
61
Table A2: Descriptive statistics of the variables in the models in Tables 2 to 10, for our sample firms,
and also for all manufacturing Compustat firms and all non-financial Compustat firms
Manufacturing firms consist of all manufacturing sector firms, for which necessary data to construct the relevant variables are available on
Compustat. All Compustat firms consist of all non-financial firms (SIC codes not beginning with „6‟), for which necessary data to construct the
relevant variables are available on Compustat. Descriptive statistics of the variables are computed for the sample periods used for estimating the
models. For the „Manufacturing Firms‟ and „All Compustat Firms‟ samples, within each panel the number of observations varies for each variable
depending on data availability..
Sample Firms
Mean
Median
Panel A: Frequency of management forecasts (Table 2)
Dependent Variable:
Number of management forecasts
Independent Variables:
Industry Herfindahl-Hirschman index (scaled by 1000)
Industry mean of the number of years since a firm‟s IPO
Industry asset-weighted mean of the net-debt-to-asset ratio
Ratio of the number of public to private firms in the industry
Stock return volatility
Absolute change in annual earnings per share/stock price
Market-adjusted stock return
Research and development expense/book assets
Natural logarithm of market value of equity
Analyst coverage
Institutional fractional ownership
Post-Regulation Fair Disclosure dummy
Variables used in robustness tests discussed in Section 4.6
Fraction of a firm‟s sales that relate to manufacturing
Fraction of a firm‟s sales that relate to its primary industry
Fraction of a firm‟s manufacturing sales relating to its primary industry
Sales-weighted Herfindahl-Hirschman index (scaled by 10,000)
Compustat Herfindahl-Hirschman index (scaled by 10,000)
Panel B: Horizon of management forecasts (Table 3)
Dependent Variable:
Horizon of management forecasts
Independent Variables:
Industry Herfindahl-Hirschman index (scaled by 1000)
Industry mean of the number of years since a firm‟s IPO
Industry asset-weighted mean of the net-debt-to-asset ratio
Ratio of the number of public to private firms in the industry
Stock return volatility
Absolute change in annual earnings per share/stock price
Market-adjusted stock return
Research and development expense/book assets
Natural logarithm of market value of equity
Analyst coverage
Institutional fractional ownership
Post-Regulation Fair Disclosure dummy
Variables used in robustness tests discussed in Section 4.6
Fraction of a firm‟s sales related to manufacturing
Fraction of a firm‟s sales related to its primary industry
Fraction of a firm‟s manufacturing sales related to its primary industry
Sales-weighted Herfindahl-Hirschman index (scaled by 10,000)
Compustat Herfindahl-Hirschman index (scaled by 10,000)
Panel C: Private placement vs. seasoned equity offering decision (Table 4)
Dependent Variable:
Firm sells new shares through private placement instead of seasoned
equity offering
Independent Variables:
Industry Herfindahl-Hirschman index (scaled by 10,000)
Industry mean of the number of years since a firm‟s IPO
Industry asset-weighted mean of the net-debt-to-asset ratio
62
Manufacturing Firms
Mean
Median
All Compustat Firms
Mean
Median
0.725
0.000
0.273
0.000
0.289
0.000
0.770
16.228
0.042
0.105
0.042
0.109
0.189
0.097
6.142
6.062
0.445
0.602
0.660
13.043
0.032
0.044
0.021
0.035
-0.015
0.038
5.969
3.833
0.467
1.000
0.700
14.358
0.131
0.029
0.047
0.358
0.063
0.089
5.075
3.734
0.162
0.254
0.560
11.957
0.139
0.011
0.022
0.045
-0.127
0.033
4.863
1.833
0.000
0.000
N.A.
13.084
0.127
N.A.
0.050
0.465
0.054
0.057
5.142
3.963
0.156
0.248
N.A.
11.109
0.136
N.A.
0.022
0.046
-0.125
0.000
4.976
1.917
0.000
0.000
0.986
0.920
0.932
0.076
0.296
1.000
1.000
1.000
0.061
0.214
0.982
0.934
0.941
0.070
0.332
1.000
1.000
1.000
0.056
0.248
N.A.
0.943
N.A.
N.A.
0.316
N.A.
1.000
N.A.
N.A.
0.227
Mean
Median
Mean
Median
Mean
Median
105.691
102.167
202.637
203.292
205.132
205.500
0.790
17.515
0.061
0.075
0.033
0.067
0.118
0.058
6.572
7.827
0.552
0.649
0.630
13.486
0.066
0.032
0.017
0.029
-0.005
0.031
6.404
5.500
0.602
1.000
0.700
14.358
0.131
0.029
0.047
0.358
0.063
0.089
5.075
3.734
0.162
0.254
0.560
11.957
0.139
0.011
0.022
0.045
-0.127
0.033
4.863
1.833
0.000
0.000
N.A.
13.084
0.127
N.A.
0.050
0.465
0.054
0.057
5.142
3.963
0.156
0.248
N.A.
11.109
0.136
N.A.
0.022
0.046
-0.125
0.000
4.976
1.917
0.000
0.000
0.987
0.911
0.923
0.077
0.314
1.000
1.000
1.000
0.061
0.235
0.982
0.934
0.941
0.070
0.332
1.000
1.000
1.000
0.056
0.248
N.A.
0.943
N.A.
N.A.
0.316
N.A.
1.000
N.A.
N.A.
0.227
Mean
Median
Mean
Median
Mean
Median
0.357
0.000
N.A.
N.A.
N.A.
N.A.
0.070
12.648
0.017
0.062
10.092
-0.017
0.070
14.175
0.123
0.056
11.674
0.135
N.A.
12.995
0.119
N.A.
10.985
0.133
Ratio of the number of public to private firms in the industry
Natural logarithm of book value of assets
Analyst coverage
Number of years since a firm‟s IPO
Industry-adjusted sales growth
Change in industry-adjusted market-to-book equity
Operating cash flow/book assets
Cash flow volatility
Altman-Z score
Stock return volatility
One-year market-adjusted stock return
The offering takes place within one week of an earnings announcement
Variables used in robustness tests discussed in Section 4.6
Fraction of a firm‟s sales related to manufacturing
Fraction of a firm‟s sales related to its primary industry
Fraction of a firm‟s manufacturing sales related to its primary industry
Sales-weighted Herfindahl-Hirschman index (scaled by 10,000)
Compustat Herfindahl-Hirschman index (scaled by 10,000)
0.123
4.966
3.776
10.357
0.175
0.078
-0.061
0.057
9.520
0.052
0.456
0.164
0.069
4.623
2.750
6.000
0.025
-0.209
0.092
0.030
3.045
0.015
0.335
0.000
0.030
4.906
3.626
14.062
0.055
-0.229
0.011
0.090
4.987
0.040
N.A.
N.A.
0.011
4.631
1.769
9.000
0.000
-0.194
0.098
0.025
3.624
0.022
N.A.
N.A.
N.A.
5.099
3.831
12.923
0.056
0.083
0.025
0.140
3.852
0.040
N.A.
N.A.
N.A.
4.884
1.857
8.000
0.000
-0.106
0.098
0.023
3.148
0.021
N.A.
N.A.
0.955
0.825
0.862
0.068
0.281
1.000
1.000
1.000
0.061
0.193
0.985
0.931
0.945
0.068
0.325
1.000
1.000
1.000
0.056
0.248
N.A.
0.950
N.A.
N.A.
0.312
N.A.
1.000
N.A.
N.A.
0.228
Panel D: The likelihood that bidder acquires a private target (Tables 5, 6, 7)
Dependent Variable:
Bidder acquires a private target instead of a public target
Independent Variables:
Industry Herfindahl-Hirschman index (scaled by 10,000)
Industry mean of the number of years since a firm‟s IPO
Industry asset-weighted mean of the net-debt-to-asset ratio
Target and bidder firms are not in the same industry dummy
Natural logarithm of market value of equity
Ratio of target to bidder firm size
Ratio of the number of public to private firms in the bidder‟s industry
Stock payment dummy
Variables used in robustness tests discussed in Section 4.6
Fraction of a firm‟s sales related to manufacturing
Fraction of a firm‟s sales related to its primary industry
Fraction of a firm‟s manufacturing sales related to its primary industry
Sales-weighted Herfindahl-Hirschman index (scaled by 10,000)
Compustat Herfindahl-Hirschman index (scaled by 10,000)
Mean
Panel E: Disclosure ratings (Table 8)
Dependent Variable:
AIMR disclosure rating
Independent Variables:
Industry Herfindahl-Hirschman index (scaled by 10,000)
Industry mean of the number of years since a firm‟s IPO
Industry asset-weighted mean of the net-debt-to-asset ratio
Ratio of the number of public to private firms in the industry
Natural logarithm of market value of equity
Standard deviation of return on equity
Absolute change in annual earnings per share/stock price
Book-to-market-equity
Return on assets
Sales growth
Analyst coverage
Analyst forecast dispersion
Analyst forecast errors
Analyst forecast revision volatility
S&P 500 firm dummy
Variables used in robustness tests discussed in Section 4.6
Fraction of a firm‟s sales related to manufacturing
Fraction of a firm‟s sales related to its primary industry
Fraction of a firm‟s manufacturing sales related to its primary industry
Sales-weighted Herfindahl-Hirschman index (scaled by 10,000)
Compustat Herfindahl-Hirschman index (scaled by 10,000)
Mean
63
Median
Mean
Median
Mean
Median
0.629
1.000
N.A.
N.A.
N.A.
N.A.
0.075
16.945
0.039
0.659
6.574
0.201
0.122
0.202
0.063
12.400
0.156
1.000
6.348
0.059
0.048
0.000
0.060
14.491
0.132
N.A.
5.138
N.A.
N.A.
N.A.
0.034
12.000
0.142
N.A.
4.928
N.A.
N.A.
N.A.
N.A.
13.166
0.128
N.A.
5.213
N.A.
N.A.
N.A.
N.A.
11.188
0.137
N.A.
5.060
N.A.
N.A.
N.A.
0.984
0.910
0.924
0.074
0.273
1.000
1.000
1.000
0.061
0.193
0.982
0.924
0.941
0.069
0.331
1.000
1.000
1.000
0.056
0.248
N.A.
0.942
N.A.
N.A.
0.315
N.A.
1.000
N.A.
N.A.
0.225
Median
Mean
Median
Mean
Median
69.340
71.220
N.A.
N.A.
N.A.
N.A.
0.081
33.084
0.137
0.128
8.525
0.111
0.064
0.412
0.086
1.096
20.370
0.005
0.011
0.005
0.554
0.049
34.486
0.111
0.088
8.281
0.095
0.037
0.382
0.073
1.089
13.583
0.002
0.004
0.002
1.000
0.063
13.012
0.124
0.030
4.721
0.230
0.377
0.525
-0.046
1.193
3.662
0.009
0.038
0.010
0.058
0.053
10.306
0.126
0.012
4.532
0.137
0.046
0.432
0.043
1.112
1.778
0.004
0.009
0.004
0.000
N.A.
12.075
0.119
N.A.
4.720
0.223
0.532
0.558
-0.041
1.185
3.787
0.009
0.041
0.009
0.056
N.A.
9.875
0.127
N.A.
4.533
0.131
0.047
0.458
0.035
1.099
1.750
0.003
0.008
0.003
0.000
0.955
0.825
0.862
0.078
0.256
1.000
1.000
1.000
0.047
0.130
0.985
0.930
0.943
0.062
0.330
1.000
1.000
1.000
0.053
0.255
N.A.
0.952
N.A.
N.A.
0.321
N.A.
1.000
N.A.
N.A.
0.238
Panel F: Analyst forecast properties (Tables 9,10)
Dependent Variables:
12-month average of the standard deviation of analysts' forecasts
12-month average of the absolute values of analyst forecast errors
Standard deviation of forecast revisions
Independent Variables:
Industry Herfindahl-Hirschman index (scaled by 10,000)
Industry mean of the number of years since a firm‟s IPO
Industry asset-weighted mean of the net-debt-to-asset ratio
Ratio of the number of public to private firms in the industry
Natural logarithm of market value of equity
Research and development expense/book assets
Analyst coverage
Correlation between return on equity and stock returns
Standard deviation of return on equity
Stock return volatility
Absolute change in annual earnings per share/stock price
Market adjusted stock return
Average proportion of new monthly forecasts
Variables used in robustness tests discussed in Section 4.6
Fraction of a firm‟s sales related to manufacturing
Fraction of a firm‟s sales related to its primary industry
Fraction of a firm‟s manufacturing sales related to its primary industry
Sales-weighted Herfindahl-Hirschman index (scaled by 10,000)
Compustat Herfindahl-Hirschman index (scaled by 10,000)
Mean
64
Median
Mean
Median
Mean
Median
0.008
0.032
0.010
0.003
0.009
0.004
0.011
0.048
0.013
0.003
0.010
0.004
0.012
0.052
0.013
0.004
0.009
0.004
0.072
18.605
0.058
0.097
6.040
0.082
6.257
0.222
0.196
0.033
0.104
0.096
0.259
0.058
15.167
-0.020
0.040
5.820
0.033
3.833
0.293
0.131
0.017
0.035
-0.071
0.261
0.070
14.358
0.131
0.029
5.075
0.089
3.734
0.237
0.236
0.047
0.358
0.063
0.253
0.056
11.957
0.139
0.011
4.863
0.033
1.833
0.311
0.148
0.022
0.045
-0.127
0.254
N.A.
13.084
0.127
N.A.
5.142
0.057
3.963
0.245
0.231
0.050
0.465
0.054
0.251
N.A.
11.109
0.136
N.A.
4.976
0.000
1.917
0.317
0.139
0.022
0.046
-0.125
0.250
0.987
0.919
0.930
0.068
0.287
1.000
1.000
1.000
0.061
0.205
0.982
0.934
0.941
0.070
0.332
1.000
1.000
1.000
0.056
0.248
N.A
0.943
N.A.
N.A.
0.316
N.A.
1.000
N.A.
N.A.
0.227