Product Market Competition and Firms’ Voluntary Disclosure Behavior:
Evidence from a Quasi-natural Experiment
This Version
August 17, 2014
Chen Lin and Lai Wei *
The University of Hong Kong
Abstract
This paper establishes a casual relation between product market competition and key dimensions
of voluntary disclosure using a quasi-natural experiment. We resort to exogenous variation of U.S.
import tariff rate to identify changes in competition level and employ a difference-in-differences
design to estimate the treatment effect. We find that firms tend to make less voluntary disclosure
(proxied by management earnings forecasts) when competition intensifies; and they choose less
precise form as well as shorter horizon to make forecasts. The negative effect is more pronounced
for firms hit by larger competitive shock, rendering support for the proprietary cost argument.
Furthermore, the negative impact of competition on voluntary disclosure is augmented if firms
have stronger capital market incentives. When firms are financially constrained or dependent on
external financing, they become even more unwilling to reveal information for fear of
disappointing the market with negative news caused by competition. This evidence manifests the
role of capital market as an indirect channel through which competition can potentially influence
firms’ voluntary disclosure behavior; it also highlights the interaction between capital market
incentives and product market competition, which jointly shape the information environment of a
firm.
JEL classification: D80; F30; G10; G30; L10
Keywords: Information environment; Voluntary disclosure; Management forecast; Product market
competition; Trade Liberalization; Capital market
*
Chen Lin is with The University of Hong Kong: [email protected]; Lai Wei is with The University of Hong
Kong: [email protected].
1
I. Introduction
Information environment is an indispensible part of a firm. It is jointly shaped by the demand and
supply of corporate information. Along the supply side, firms’ voluntary disclosure is one of the
major sources (Beyer et al. 2010).1 Since managers typically have superior information over
outsiders about current condition and future prospect of the firm, their disclosure plays a crucial
role to mitigate information asymmetry ex ante and moral hazard problem ex pose (Beyer et al.
2010). More broadly speaking, effective disclosure of information is key to optimal allocation of
resources in the market, which is a critical challenge to fulfill investment opportunities and to fuel
economic growth (Healy & Palepu 2001). Interestingly, firms do not always voluntarily disclose
their private information in face of such compelling demand.2 Apart from disclosure mandated by
the regulators, firms make very different decisions as for whether to disclose and what do disclose
from the remaining set of private information. Their divergent disclosure practices have been
intriguing researchers to explore the determinants of voluntary disclosure for several decades.
Traditional wisdoms have identified a wide range of determinants of firms’ voluntary
disclosure. Motives for making voluntary disclosure include facilitating capital market access via
reducing cost of capital (e.g. Botosan 1997; Botosan & Plumlee 2002) and improving liquidity
(e.g. Balakrishnan et al. 2014), boosting stock-based compensation (e.g. Noe 1999; Cheng & Lo
2006; Cheng et al. 2013), maintaining corporate control and lowering litigation risks (e.g. Skinner
1994); while in terms of disincentives, proprietary cost from competition is among the top-hit (See
Healy & Palepu 2001; Beyer et al. 2010 for thorough reviews). In light of infrequent voluntary
disclosure in the real world, analyzing deterring forces is of particular importance. Earlier
theoretical works including Verrecchia (1983) have formalized the concept of proprietary cost.
Any information, of which the disclosure may expose a firm to certain competitive disadvantage,
could be perceived as proprietary. When the proprietary cost of disclosing information is large, a
firm may end up with limited or no disclosure to avoid being taken advantage by its competitors.
Although the proprietary cost argument is pervasive, neither theory nor empirical evidence has
reached a unanimous conclusion regarding the impact of competition on voluntary disclosure. The
ongoing debate largely motivates us to explore the topic further, aiming to identify a clear causal
relation between product market competition and firms’ voluntary disclosure behavior.
The relation between competition and voluntary disclosure has attracted immense theoretical
attention. The first group of theories predicts a positive association between competition intensity
and firms’ voluntary disclosure behavior. In a model with fixed proprietary cost, Verrecchia (1983,
1990b) conclude that firms in more competitive industries tend to disclose less information for
fear of rendering unintended assistance to their competitors. Similar implications can be drawn
from Dye (1986), where the manager is endowed with both non-proprietary and proprietary
information, the latter of which will decrease cash flow to the firm once disseminated. Lanen and
Verrecchia (1987) show in their model that higher proprietary costs divert firms from direct
disclosure to real operating activities as an indirect communication mechanism, which offers
additional support that greater competition leads to less voluntary disclosure. The second group of
theories point to negative association between competition and voluntary disclosure. For example,
Darrough and Stoughton (1990) suggests that firms may voluntarily disclose all the information,
1
The other two main sources are disclosure mandated by regulators and reporting decisions by analysts.
According to the recent international study by Radhakrishnan, Tsang and Yang (2014), only 37.59% (30.68%) of U.S.
(non-U.S.) listed firms provide management forecasts, a major form of voluntary disclosure, from 2004 to 2010.
2
1
particularly bad news, to deter potential entry to the product market (other similar theories see
Wagenhofer 1990; Feltham & Xie 1992). Moreover, the conclusions regarding the relation
between competition and voluntary disclosure can be very sensitive to model assumptions
(Verrecchia 2001). As manifested in Darrough (1993) , firms competing in Cournot style (quantity
setting) can end up completely revealing information about market demand whereas those in
Bertrand style (price setting) have little incentives to release their information about production
cost. Although the theories provide diverse or even conflicting predictions, they nevertheless
highlight the necessity to establish a credible relation between competition and voluntary
disclosure. No wonder a huge wave of studies are inspired to search for empirical evidence.
However, empirical investigation to date produces mixed evidence as well, for which two
challenges might be responsible (Beyer et al. 2010; Berger 2011). First of all, there is no direct
and precise measurement of proprietary cost. Industry concentration is a widely used proxy for
proprietary cost; yet researchers disagree on the direction of association, thereby drawing contrary
inferences. Bamber and Cheon (1998) associates higher proprietary cost to more concentrated
market, where firms usually issue less specific disclosure to avoid revealing too much proprietary
information. Similarly, Harris (1998) concludes that firms are less likely to separately report
information of segments in more concentrated market in order to protect abnormal profit and
market share.3 Verrecchia and Weber (2006) Verrecchia and Weber (2006) Verrecchia and Weber
(2006) Verrecchia and Weber (2006) Verrecchia and Weber (2006) find the contrary that, in a
competitive industry characterized with lower Herfindahl-Hirschman Index (HHI) of market
concentration, firms have higher propensity to modify filing materials with U.S Security and
Exchange commission (SEC) that contain proprietary information. Using HHI and four-firm
concentration ratio, Li (2010) also documents a negative relation between existing competition
and quantity of voluntary disclosure. Apart from indeterminate association between market
concentration and proprietary cost, additional concern has been raised about the construction of
concentration ratio. As most prior studies compute the ratio within Compustat universe, private
firms are not systematically covered, leaving the measurement a poor proxy for actual market
concentration (Ali et al. 2009). Ali et al. (2009) hence propose the measurement to be based on
U.S. Census data and reexamine the relation between competition and voluntary disclosure (see
also Ali et al. 2014). Interestingly, the relation implied in some prior literature (e.g. Harris 1998;
Verrecchia & Weber 2006 ) no longer holds or almost reverses using the newly constructed
concentration measurement. Efforts are also extended to survey data. According to Graham et al.
(2005), more than 60% of survey respondents agree or strongly agree that incurring proprietary
cost is a large concern that prevent them from voluntary disclosure. Using measurement of
competition constructed from survey of managers, Dedman and Lennox (2009) observe higher
tendency to withhold information among managers who perceive current or potential competition
to be strong. Albeit various options, there is still lack of consensus regarding the correct
measurement of competition or proprietary costs.
Identification is the other commonly recognized challenge that hinders the literature from a
definite conclusion. Extant literature mostly analyzes cross-sectional relation between competition
and voluntary disclosure, which is likely to be confounded with some unobserved omitted
variables or reverse causality. For instance, non-disclosure can result from conflict of interests
3
See Botosan and Stanford (2005) for two other examples that explore firms’ incentives to report segment information.
2
between managers and shareholders besides proprietary cost, but many studies fail to consider the
aspect of agency cost in their analysis (Berger & Hann 2007; Bens et al. 2011). Moreover, as both
disclosure policy and product market strategies are endogenous to a firm, voluntary disclosure can
be used to facilitate desirable product market outcomes that shape the competitive environment
rather than the other way round.
In this paper, we address the two major challenges using a quasi-natural experiment. The core
idea follows Fresard (2010), who employs exogenous import tariff reduction to identify varying
intensity of competition. In the 1990s, the U.S. has witnessed a series of large import tariff
reduction, echoing the worldwide trend of trade liberalization. Remarkable events include the
establishment of the North America Free Trade Agreement (NAFTA) on January 1, 1994. Reduced
import tariff help stimulate import penetration and increase overall competition in the product
market (Bernard et al. 2006). These tariff reduction events scattered across industries and over
time, capturing both cross-sectional and time-series changes in competition intensity without
reference to any specific proxies that suffer from measurement problems. In addition, as we
demonstrate below, import tariff reduction is unlikely to be driven by firms' existing disclosing
strategies, thereby qualifies as an unexpected shock to product market competition and proprietary
cost level. Based on the U.S. import dataset compiled by Feenstra (1996), Feenstra et al. (2002)
and Schott (2010), we calculate import tariff rate for all the manufacturing industries at three-digit
SIC level (starting with 2 or 3) and defines a tariff-cut event in an industry-year when the annual
percentage change of tariff rate falls below the industry median reduction over the sample period.
We identify a total of 78 industries that ever experienced an import tariff reduction between 1995
and 2005. We apply a difference-in-differences approach to pin down the effect on voluntary
disclosure around each tariff cut event. For every firm-year affected by a tariff cut event, we add
one year before and one year after the event to construct an event episode. All the unaffected firms
in a certain year serve as benchmark group, for which we construct so-called falsified event
episode, or rather non-cut episode. By identifying changes in voluntary disclosure from before to
after the tariff cut and comparing the changes between the treated and benchmark group, we are
able to obtain a clean estimate on the impact of competition on firms’ voluntary disclosure
behavior.
Apart from measurement problem and endogeneity concerns, failure to incorporate the
interaction among different disclosure determinants might also contribute to mixed empirical
evidence. As mentioned by Beyer et al. (2010), a major limitation of empirical research is omitted
correlation between cost of disclosure and capital market incentives. Failure to consider the
correlation can bias the relation inferred between competition and voluntary disclosure. Albeit
indirectly, competition can affect voluntary disclosure via the capital market, a channel that is
non-negligible but under-studied in an interactive context. In this regard, we feel it necessary to
examine the role of capital market conditional on the direct effect that competition exerts on firms
through proprietary cost channel. Specifically, we entertain two competing arguments. The first
argument is that strong capital market incentives will ameliorate the negative impact of
competition on voluntary disclosure. Since product market competition can increase the cost of
capital (Hou & Robinson 2006; Valta 2012), firms of greater external financing need are expected
to disclose more information to mitigate information asymmetry and thus to obtain favorable
financing terms (Brown 1979; Barry & Brown 1985; Barry & Brown 1986; Merton 1987). On the
other hand, capital market incentives can possibly augment the negative impact of competition on
3
voluntary disclosure. The second argument follows because firms may become reluctant to
disclose information when their profitability deteriorates in face of intensified competition (Xu
2012). As reporting negative news may depress the capital market investors and increase financing
cost (Kasznik & Lev 1995), firms can choose to refrain further from voluntary disclosure if they
are financially weak. In short, how the indirect channel works via capital market is an empirical
question; and understating its mechanism is definitely crucial for an overall evaluation of the
effect of competition on voluntary disclosure.
In terms of proxy of voluntary disclosure, we focus on management earnings forecasts, the
most common type of voluntary disclosure. According to Healy and Palepu (2001), management
forecasts have two significant advantages, namely, typically known disclosure timing and precise
measurement. Therefore, information conveyed through management forecasts is easily accessible
and processed by different information users. Unlike product or customer information that reveals
one aspect of the firm, management forecasts provide earnings estimates that are indicative of a
variety of information including market demand, operating cost and product quality. Moreover,
management forecasts are forward-looking, allowing sufficient time for competitors to respond.
Lastly, non-disclosure of management forecasts could not be confounded with non-existence of
information, which, nevertheless, may be a serious problem to other types of disclosure (see
Hribar 2004). That is to say, whatever changes we observe from management forecasts reflect
discretion and choices of the managers. In particular, we examine three major dimensions
associated with disclosure choices of managers. The first is the tendency or frequency of
disclosure, respectively measured by the existence or total number of forecasts in a year. Upon the
decision to make earnings forecasts, additional dimensions to consider are the precision of
delivery and horizon of disclosure. For example, a firm may choose from very precise form of
reporting (e.g. a point estimate like “Earnings-per-share (EPS) of $2”) to very qualitative
expression (e.g. “we expect non-negative EPS”); and the firm shall also decide on the horizon of
disclosure, that is, how many days ahead of the announcement of actual earnings to disclose
corresponding forecasts.
Based on a sample of 1,839 manufacturing firms from 1995 to 2005, we identify a negative
causal relation between competition and voluntary disclosure. A firm is less likely to make
earnings forecasts when there is an import tariff reduction in the industry it belongs to. Even if a
firm decides to disclose, it does so less frequently. A tariff cut event on average reduces 0.17
forecasts made by a firm, which is an 18% decrease from the average number of forecasts made
by treated firms prior to the event year. Moreover, they choose to report in a less precise form and
within shorter horizon when competition heightens. A coarser form of forecast prevents
competitors from inferring critical messages while a shorter horizon leaves them less time to take
actions before the information goes stale. All three dimensions have rendered consistent support
for the proprietary cost argument that firms refrain from disclosing information that reveals too
much proprietary content to their competitors. As a further confirmation, we find the impact to be
more negative for firms experiencing larger tariff reduction or sharing a smaller pie of the market
before the tariff cut shock. When firms are of inferior competitive position, they become more
susceptible to the shock because the marginal proprietary cost is larger for any given amount of
information. Consequently, they retreat further from making disclosure than firms that possess
greater existing market share. In addition to the direct impact of competition on voluntary
disclosure through proprietary cost channel, we also explore how capital market can affect firm’s
4
voluntary disclosure behavior as an indirect channel. We found that firms that are more financially
constrained or dependent on external financing tend to disclose less frequently, in less precise
language and within shorter horizon. This renders support for the argument that capital market
incentives augment the negative relation between competition and voluntary disclosure. It is the
incentive to hold back bad news that dominates firms in greater financial needs when competition
heightens.
Our contributions to the literature are three-fold. Foremost, we provide strong evidence for
the unsettled debate using a clean setting to test the impact of competition on firms’ voluntary
disclosure behavior. The exogenous import tariff reduction helps us identify increase of
competition level without suffering much from measurement errors and endogeneity concerns. A
contemporaneous paper by Burks et al. (2014) shares the same spirits to overcome the
endogeneity issue where they resort to release of the U.S. interstate branching restriction during
the 1990s. The key difference is that their setting is to test the entry-deterrence motives of
voluntary disclosure while we shed light on a general and interactive competitive environment
rather than an entry game. Second, empirical studies on proprietary cost of voluntary disclosure
often focus on the single determinant without incorporating other forces at the same time (Berger
2011), but we consider the interaction between proprietary costs and capital market incentives
when studying the impact of competition on voluntary disclosure. We explore the conditional role
of capital market incentives in shaping firm’s disclosure behavior on top of the direct proprietary
cost channel. More importantly, the conditional role of capital market is different from the
often-discussed tension between capital market incentives and product market concerns. Unlike
the effect of stand-alone capital market incentives that encourage voluntary disclosure, the effect
is muted when analyzed in an interactive context with strong competition. The results shall raise
our vigilance that the prior belief about the role of certain factor may not necessarily hold if
conditional on other factors. This is critical for future model development not only to build the
tension but also the “collusion” between capital market and product market. Finally, we add
another niche to the competition literature, especially to the dark side (Shleifer 2004). In addition
to increased earnings manipulation induced by heightened competition (e.g. Lin et al. 2014),
reduced voluntary disclosure is another corporate behavior that can dampen the transparency of a
firm’s information environment as well as the efficiency of capital allocation in the market.
The remaining part of the paper proceeds as follows. Section II reviews related literature and
develops hypothesis. Section III introduces the identification strategy, data source and sample
construction. We present and explain major results in section IV and conclude in section V.
II. Theories and Hypothesis Development
The seminal work on voluntary disclosure is characterized with an “unraveling” feature where all
private information is fully disclosed (Grossman & Hart 1980; Grossman 1981; Milgrom 1981).
This is clearly inconsistent with what we observe in reality though. One contributing factor is the
key assumption that disclosure of information is costless. To reconcile the wedge, a series of
follow-up work introduces cost into disclosure model (Jovanovic 1982; Verrecchia 1983; Dye
1986; Verrecchia 1990b), among which, Verrecchia (1983) proposes the notion of proprietary
costs. Proprietary costs have a broad definition encompassing any disutility arising from
disclosure, let it be related to shareholder litigation, labor union negotiation or product market
competition. As in most disclosure literature that evaluates proprietary cost argument, we choose
5
to focus on competitive environment in which proprietary content of information weighs
significantly.
In this section, we review related literature and develop main hypotheses about the relation
between general competition and voluntary disclosure. Specifically, we form conjectures with
respect to direct and indirect impact of competition on the key dimensions of voluntary disclosure,
namely, likelihood/frequency, precision and horizon.
2.1 Direct impact of competition on voluntary disclosure – proprietary cost channel
Theoretical implications for the likelihood of voluntary disclosure largely divide along the nature
of competition. The general discussion of proprietary costs suggests that firms in more
competitive industries are prone to withhold information. For example, Verrecchia (1983) does
not associate the constant proprietary cost to any specific competitive context and Dye (1986)
materializes the impact of disclosing proprietary information as reduction in cash flows to a firm.
Both of them hold a flexible perception regarding the source of proprietary cost, hence conveying
general implications for firms’ voluntary disclosure behavior. Several other models condition
proprietary costs on the action of existing competitors in a mature market. Darrough and
Stoughton (1990) derives that Cournot competition with private information about demand
stimulates greater amount of disclosure than Bertrand competition with private cost information
on cost. He suggests a possible interpretation about the results that higher level of competition
constrains firms from full disclosure because Bertrand can be viewed as more competitive than
Cournot in terms of greater product substitutability. Clinch and Verrecchia (1997) also models
competition in a post-entry duopoly game. They explore how competitive disadvantage of a firm
in an industry may depress its incentive to disclose. In line with these theories modeling on
general competition in the product market, we tend to hypothesize that higher competition leads to
less frequent voluntary disclosure of firms.
Another tranche of literature highlights the specific motive of disclosure to deter entry
(Darrough & Stoughton 1990; Wagenhofer 1990; Feltham & Xie 1992). By construction of an
entry game, proprietary cost is higher when no information is disclosed. In this regard, firms are
more likely to reveal bad news so as to expel potential entrants; and the full disclosure equilibrium
may be unique when it is imperative to deter entry (i.e. entry cost is very low). Admittedly, these
models are helpful in that they manifest the role of voluntary disclosure in a lineate setting of an
entry game; yet they cannot escape critics as for the specificity of the model design. Verrecchia
(1990a) casts concerns that the entry game fails to incorporate interdependency among existing
firms when confronted with potential entrants. He shares similar comment with Darrough (1993)
that bad news has been placed disproportionate weight in an entry game and accordingly gives rise
to prevailing full disclosure equilibrium. In this regard, we come to the first hypothesis.
Hypothesis 1: Firms are less likely to make voluntary disclosure when the level of general
competition increases.
When making disclosure decisions, form of precision and horizon are two other dimensions a firm
needs to consider. Extension of “cheap-talk” model to disclosure literature provides good
reference for the choice of reporting precision. When firms are not restricted to truthful reporting,
there exists a partitioned reporting strategy in equilibrium where firms choose to reveal his private
6
information in a coarse form.4 In the context of management forecast, one may observe a firm
presenting a range for an EPS forecast (“between $2 and $3.5”), an open interval (“at least $2”) or
a qualitative description (“positive EPS”) instead of a precise figure (“$2”). As modeled by
Newman and Sansing (1993), firms balance between conflicting objectives from different
information users such as stockholders and potential entrants. Despite the willingness to reveal
complete private information to shareholders, firms may still report in an imprecise form to avoid
relevant information being inferred by potential entrants. In a more general competitive setting,
Gigler (1994) concludes that when the relative importance of proprietary cost exceeds that of the
capital market, firms holding favorable information tend to disclose in a less precise form.
Therefore, firms are more likely to adopt an imprecise disclosure form when proprietary costs
increases, irrespective of the exact source of competition.
When it comes to the choice of disclosure horizon, gauged by the time span between
disclosure and realization of outcomes5, generalization of proprietary costs by Verrecchia (1983)
has provided some guidance for analysis. If we allow proprietary cost to vanish over time together
with the value of information, disclosure of longer horizon would embody greater proprietary
content and permit longer response time on the part of competitors. It is reasonable to infer that
firms may strategically delay disseminating information when faced with increased competition.
Here is what we posit in Hypothesis 2.
Hypothesis 2: Firms will deliver voluntary disclosure in a less precise form and of shorter horizon
when the level of general competition increases.
2.2 Indirect impact of competition on voluntary disclosure – capital market channel
While competition can exert impact on voluntary disclosure via direct proprietary cost channel, it
can also affect voluntary disclosure indirectly through capital market incentives. Nevertheless,
there are two possibilities about how capital market incentives can play a role in shaping the
relation between competition and voluntary disclosure. The first possibility suggests that capital
market incentives can ameliorate the negative impact of competition on voluntary disclosure. On
one hand, product market competition can adversely affect financing environment of a firm. Hou
and Robinson (2006) document a negative relation between product market concentration and
stock return. They argue that competition introduces more undiversifiable distress or innovation
risk, thereby commanding higher expected return. Debt financing also becomes costlier when
competition heightens, because competition could increase cash flow risk, default risk, as well as
change liquidation value of the firms (Valta 2012). On the other hand, there is both theoretical and
empirical support that more voluntary disclosure can help mitigate information asymmetry and
hence reduce cost of capital to some extent (See theoretical models by Brown 1979; Barry &
Brown 1985; Barry & Brown 1986; Merton 1987; empirical evidence by Botosan 1997; Botosan
& Plumlee 2002); and firms indeed issue more voluntary disclosure before seasonal equity
offering activities (Lang & Lundholm 2000). Taken together, it is plausible that firms, if in greater
financial needs, will not reduce voluntary disclosure too much when competition intensifies. In
other words, the capital market incentives may work against the direct proprietary cost channel in
4
Credible disclosure is assumed in theories used to arrive at hypothesis 1. Such a constraint can be ensured by feasible
verification ex post and potential litigation risk (Verrechia, 2001).
5
The content varies with different context. For example, in a management earnings forecast, horizon is the number of days
between forecast date and earnings announcement date; in an M&A deal, horizon is the number of days between announcement
and closure date.
7
terms of altering the disclosure behavior of firms.
However, more voluntary disclosure may not necessarily reduce cost of capital, especially when
the news to be disclosed is negatively perceived by the market. As documented in Xu (2012) ,
profitability of firms deteriorates when competition intensifies. It is hence likely that earnings
forecasts, if made, can negatively surprise the market as they mainly consist of pessimistic news.
According to Kasznik and Lev (1995) , a large number of firms remain silent when confronted
with earnings disappointments. Since the market tends to interpret earnings warning as permanent
earnings disappointment, it responds more negatively to the “warning” firms than to the “silent”
ones. This logic leads to the second possibility that capital market incentives can augment the
negative relation between competition and voluntary disclosure. The greater the need for capital,
the less likely that firms disclose to depress the market, even though more disclosure so can
reduce asymmetric information.
In light of the two possible mechanisms, we do not have a definite hypothesis regarding the
indirect effect of competition on voluntary disclosure via capital market incentives. This is left as
an empirical question which we derive no hypothesis for.
III. Sample, Data and Empirical Setup
In this section, we introduce the setting of import tariff reduction and the identification strategy we
employ to quantify the relation between competition and voluntary disclosure. We also detail the
construction of regression samples and corresponding measurement of key variables. Definition
and construction of all the variables are elaborated in Appendix A.
3.1 Setting and identification strategy
Most empirical literature examines the relation between competition and voluntary disclosure via
a simple cross-sectional regression, a methodology that is nevertheless plagued with measurement
problem of competition as well as endogeneity concerns. As a commonly used proxy for
competition level, industry concentration ratio does not necessarily captures the overall intensity
of product market competition if it fails to include private firms (Ali et al. 2009), nor does it
correlate with competition level in an unambiguous increasing/decreasing manner. Simple
cross-sectional design does not take enough care of endogeneity problem either. For one thing,
reverse causality from the real impact of disclosure on product market cannot be ruled out; while
for another, it does not fully control for unobserved firm characteristics which may drive
disclosure decision in the same direction as competition does. For example, conflicting interest
between managers and shareholders can also prevent managers from voluntary disclosure (Berger
& Hann 2007; Bens et al. 2011).
To address the measurement problem and reverse causality issue, we resort to a series of
quasi-natural experiments that spread over time and occur independently from firms’ information
environment. Following Fresard (2010) , we identify large import tariff reduction at industry level
to capture exogenous increase in product market competition. As documented by vast literature on
trade barriers, the overall landscape of market competitiveness has been reconfigured during
globalization and trade liberalization (Tybout 2003), where the reduction in trade costs such as
import tariff rate plays a prominent role to intensify competition from foreign rivals (Bernard et al.
2006). As a result, we expect the level of proprietary cost for any given amount of disclosure to be
8
shifted exogenously when import tariff drops. The major analysis is conducted at firm-year level
despite the industry-wide events, because heterogeneity across firms ought to be accounted as
crucial determinants of corporate disclosure decisions. In particular, we adopt a
difference-in-differences methodology to articulate the exact effect from the competitive shocks.
We tag firms as treated whenever they experience a large import tariff reduction; and make them
part of non-treated group during years without a tariff cut. In order to gauge changes from before
to after each tariff cut event, we construct an event episode for each firm-year observation. Each
episode consists of three firm-year observations from one year before (t-1) to one year after (t+1)
the event. The notion of event episode also applies to firm-years in non-treated group, where
changes around a falsified event (i.e. a firm-year without tariff cut) serve as a benchmark. This
construction allows us to carry out the difference-in-differences analysis using the following
regression specification, where
captures the treatment effect from competition on disclosure
choices.
(1)
In addition, we include firm fixed-effect to control for time-invariant, unobserved firm
characteristics that may lead to alternative explanation. We further add year fixed-effect to address
potential correlation across firms in a given year. Standard errors are clustered at industry level to
account for some inherent correlation among firms in the same industry.
3.2 Sample construction
To implement the identification strategy stated above, we start with the product-level U.S. import
data compiled by Feenstra (1996), Feenstra et al. (2002) and Schott (2010). Since the import data
covers only manufactured products and runs up to year 2005, we restrict our sample to
manufacturing industries (classified by three-digit SIC code between 200 and 399) and to the
decade between 1995 and 2005.6 For each three-digit SIC industry, we calculate annual ad
valorem tariff rate as duties collected at the U.S. Customs divided by total Free-On-Board custom
value of general imports. We then calculate annual percentage change of the import tariff rate and
obtain the median industry change over the sample period. We defines a tariff cut “event” to occur
in a specific industry-year whenever the industry witnesses a negative change of tariff rate that
exceeds the median industry change in terms of absolute magnitude. We further follow Fresard
(2010) to exclude tariff cuts that are reversed by equivalently large increases in tariff rate within
subsequent two years. This is to ensure that the identified tariff cuts do not just reflect temporary
changes in the competitive environment. To make the setting cleaner, we retain only the last event
in a consecutive series of tariff cuts, otherwise the measured change due to the event would be
overstated when the post-event observation is affected by an additional shock. Similarly, an
industry-year without a tariff reduction is not qualified as a falsified event in the control group if it
is subsequently followed by a tariff cut event.
Next, we match the cleaned event profile with firm-level measurement of management
earnings forecasts (the construction is detailed in the next subsection). By construction of the
difference-in-differences design, we require availability of forecast measurement in all three years
6
We do not start from years before 1995 because of the incomplete coverage of First Call Historical Database that provide us
with data on management earnings forecast. We are faced with a tradeoff to decide on the staring year: on one hand, we hope to
incorporate early periods in the 1990s when the U.S. lessens trade barriers significantly; while on the other hand, we cannot
move too far to remain unbiased on sample selection when there is only limited coverage in the First Call Historical Database.
9
around a tariff cut event (or a falsified event) to construct an event episode (or a falsified event
episode). With the whole set of restrictions, we end up with a sample of 1,839 manufacturing
firms from 112 three-digit SIC industries, among which 78 have ever experienced a tariff cut event
within our sample period. Moreover, the total of 192 tariff cut events are scattered along the
sample period with different industries going through a cut at different times; some industries have
also experienced a tariff cut for multiple times. Referring to specification (1) above, we now have
12,987 (falsified) event episodes, each consisting of three firm-year observations. 1,464 episodes
are characterized with a tariff cut event (Treat=1) while the remaining serve as non-treated
benchmark (Treat=0). We further define Post as an indicator variable that turns from 0 to 1 since a
tariff cut event (or a falsified event). It helps delineate two concurrent disclosure changes
undergone by the treated and non-treated firms respectively. Our variable of interest, undoubtedly,
is the interaction term (Post×Treat) that pins down the effect of competition resulting from import
tariff reduction.
3.3 Measuring key dimensions of voluntary disclosure
Among various types of voluntary disclosure, we focus on management earnings forecast for its
accessibility, measurability and comprehensiveness. Management earnings forecasts are usually
disseminated through press releases, conference calls, and meetings with analysts (Li 2010),
which can be easily spread to end-users of information. Earnings figures are presented in standard
form (e.g. $2.5/share) that facilitates measurement from different aspects and, unlike specialized
product terminology, are mostly comparable across firms. Moreover, forward-looking earnings
often present rich proprietary content in which both firm-level operating condition and
market-wide environment have implicitly embedded. Taken all, management earnings forecasts
provide a suitable context for our discussion on the general relation between competition and
voluntary disclosure. Throughout the rest of the paper, we use voluntary disclosure and
management earnings forecasts interchangeably.
Data on management earnings forecasts has been retrieved from Company Issued Guidance
section under First Call Historical Database (FCHD) by Thompson Reuters. In order to address
the coverage issue of FCHD (Chuk et al. 2013), we include only firms that have ever appeared in
the dataset within our sample period.7 Given that firms in our sample indeed provide earnings
forecasts, we are more confident to interpret non-existence of a forecast record as non-disclosure
rather than omission by the dataset. We consider both annual and quarterly forecasts of earnings
per share (EPS) but remove pre-announcement of earnings when constructing each of the
disclosure measurements.8 All the measurements are then aggregated at firm-year level. The first
set of measurements depicts first-order choices of managers as for whether to disclose and how
much to disclose. Disclosure_dummy is set to one whenever a firm discloses an earnings forecast
in that year; it is used to infer the likelihood of making disclosure. Frequency is the total number
of earnings forecasts made during a year, which quantify the intensity of disclosure activities. The
7
Chuk at al. (2013) find that FCHD does not cover all the firms that make earnings forecasts. It tends to include firms followed
by greater analysts, with larger institutional holding, and suffering from fewer prior losses. In an alternative construction, we
include firm-years after a firm had its first appearance in the dataset. This construction is suitable for analysis of intensive margin
where we examine changes in disclosure behaviors of firms that have a history of disclosure rather than firms that become a
forecaster for the first time (i.e. the “extensive margin”) (see Balakrishnan, Billings, Kelly and Ljungqvist (2014) for a discussion
on the sample selection issue).
8
Earnings forecasts released after the corresponding fiscal year end but before the announcement of actual earnings are called
“pre-announcement”. Managers are believed to possess almost certain information regarding the actual earnings after the fiscal
year end, which represents a different informational setting compared to forecasts made during the fiscal year.
10
second group of measurements casts light on two additional dimensions of management forecasts,
namely, precision and horizon. We assign a score of precision to each forecast made in a year
where higher score suggests greater precision. A forecast receives a score of 4 if it delivers a point
estimate, a score of 3 for range estimate, 2 for open-end estimate, 1 for qualitative statement, and
0 for non-disclosure. Precision is then the average score of all the management earnings forecasts
made during a year. Horizon is defined as the average number of days between a forecast and the
fiscal year end date it covers. For example, if a firm makes a forecast of EPS on September 30,
2001 for the fiscal year ending December 31, 2001, the horizon for the forecast made in
September is 92 days. The horizon is set to 0 if we a firm discloses no earnings forecast in a
certain year. This is essentially making a forecast at the same time the actual EPS is known, hence
result in 0 days between the two dates. We adopt the natural log of one plus the measurement in
the regression analysis to facilitate more sensible interpretation.
The four basic measurements help with the first two hypotheses regarding the direct impact of
competition on voluntary disclosure. They are core dimensions of voluntary disclosure that we
need to demonstrate in the baseline analysis.
3.4 Measuring capital market incentives
Capital market incentives are jointly determined by financing constraints and financing needs. To
measure financial constraints, we construct WW_index following Whited and Wu (2006) , and
HP_index following Hadlock and Pierce (2010); the higher the index, the more financially
constrained a firm is. We also introduce an indicator variable Dividend_dummy, which equals one
if a firm has dividend distribution in a year. Firms that distribute dividends are generally less
financially constrained. Additionally, we follow Rajan and Zingales (1998) as well as Duchin et al.
(2010) to develop a bunch of proxies for external financing dependence (EFD) at 3-digit SIC
industry level. Specifically, we define Ind_EFD, Ind_EED, Ind_capx_ppe, which measures
industry level external financing dependence, external equity dependence as well as investment
intensity (i.e. capital expenditure over net property plant and equipment). We first calculate the
median value of each firm in an industry for out-of-sample period during the 1980s, and then we
use the median firm value within each industry as the industry level measurement. We also follow
Raddatz (2006) to construct a measurement on liquidity needs at the same industry level using the
same approach. The measurement is the ratio of inventory to sales (Ind_inv_sales), which captures
the capability to finance inventory investment with ongoing revenue. The larger the Ind_inv_sales,
the weaker the liquidity position and greater financing need of the firms. We additionally include
Firm_age as proxy for financing needs because younger firms are generally believed to have
higher demand for capital to fuel investment.9
3.5 Control variables
By design of our difference-in-differences specification, we are able to add time-varying firm
characteristics to control for observable differences between treated and non-treated group. We
include standard control variables that are commonly used in literature to analyze determinants of
voluntary disclosure. We include firm size measured by market value of equity at the end of each
fiscal year (e.g. Lev & Penman 1990; Bamber & Cheon 1998; Li 2010; Ali et al. 2014). The
9
Actually, firm age is separated incorporated in the construction of WW index and HP index respectively. As Whited and Wu
(2006) states, firms with large growth potential may want to invest enough to be constrained. However, we feel it clearer by
pulling these factors out so as to depict a more specific picture about a firm’s financing need and investment opportunities.
11
natural log form of one plus market capitalization (Ln_Size) is used in our regression analysis. We
include book leverage (Leverage) to account for possible interaction between capital structure and
product market (Chevalier 1995; Campello 2003), which is constructed as the ratio of liabilities
net of deferred taxes to total value of assets (e.g. Li 2010). We also include Book-to-market equity
ratio (BTM) to infer growth potential of a firm that captures both proprietary information and
forecasting difficulty (see Bamber & Cheon 1998 for the former; Li 2010 for the latter). To
incorporate important factors in the informational environment, we further control for analyst
coverage (Analyst_coverage) (e.g. Baginski & Hassell 1997; Balakrishnan et al. 2014). We define
analyst coverage as the maximum number of unique analysts following a firm in a particular year
and we use the natural log of one plus the number (Ln_coverage) in the regression analysis. As
institutional investors also play a role to configure a firm’s disclosure policy, we incorporate the
percentage of shares held by institutional investors (Institution_holding) to take care of the
governance effect (e.g. Ajinkya et al. 2005; Karamanou & Vafeas 2005). We additionally include
stock return volatility (Ret_vol) measured as standard deviation of daily stock return over a year,
because firms are less likely to disclose information when there is high uncertainty (e.g. Li 2010;
Ali et al. 2014; Balakrishnan et al. 2014).
3.6 Summary statistics
Table 1 shows the summary statistics of all the variables used in the regression analysis. All the
variables are measured on the basis of fiscal year and detailed construction is presented in
Appendix A. For an average firm in the base sample, it discloses 1.7 earnings forecasts per year
with an average precision score of 1.3 (i.e. between a qualitative statement and an open-ended
estimate). Earnings forecasts on average are issued 2 months before the corresponding fiscal
period end. In terms of firm characteristics, an average firm has a market capitalization of 400
million U.S. dollar, a BTM ratio of 0.63 and a leverage ratio of 0.42. About 7 analysts are
following a firm and institutional investors hold around 48% of shares in a year. Average annual
volatility of daily stock return is about 0.04.
[Insert table 1 about here]
IV. Results
This section contains primary empirical tests on the hypotheses proposed in Section II. Apart from
establishing a direct baseline relation between general competition and voluntary disclosure, we
also examine how competition can indirectly affect voluntary disclosure via capital market
channel. Furthermore, we provide validity check of our identification strategy and carry out a
series of robustness checks.
4.1 Direct effect of competition on voluntary disclosure
In this subsection, we establish a direct baseline relation between the extent of competition and the
key dimensions of voluntary disclosure. Specifically, we follow the difference-in-differences
specification (1) proposed in Section III. We first qualify the relation by examining the tendency
to make disclosure. In the first two columns in Table 2, we carry out Probit and Logit regressions
respectively on Disclosure_dummy, a binary response variable that help imply the probability of
making disclosure. As shown in column (2), there is no significant difference between the treated
and non-treated firms before the tariff cut event; neither is there a pronounced change in the
disclosure tendency among non-treated firms. However, there exists a much more negative change
12
in the disclosure tendency of treated firms, as evident by the coefficient of the interaction term
between Treat and Post. Translated from the coefficient of -0.3422, a firm with tariff cut has odds
ratio of making an earnings forecast 30% lower than that of a firm without. We then quantify the
relation between competition and voluntary disclosure by replacing the dependent variable with
Frequency, which is the total number of earnings forecasts made in a year. In the specifications
under column (3) and (4), a firm on average discloses 0.16 forecasts fewer than the non-treated
firms after a tariff cut event. Compared with an average of 0.88 forecasts of treated firms in the
pre-event year, this change amounts to 18% decrease of disclosure frequency. Moreover, given
Frequency is a discrete count variable, we adopt a Poisson regression model and find similar
results shown in column (5). Across all these specifications, we have added firm fixed effects to
control for time-invariant, unobservable components of firm characteristics including litigation
risk and agency cost. We also include year fixed effects to address cross-sectional correlation in
each particular year, where the effect from the passage of Fair Disclosure Regulation (Reg FD) in
2000 is taken care of. In results not tabulated, we further include a bunch of variables such as
discretionary accruals to measure earnings manipulation, earnings volatility to proxy for
performance uncertainty as well as net equity issuance dummy to document financing activities;
our result remain robust with additional controls.10 The empirical results are consistent with
Hypothesis 1, where there is a negative causal relation between general competition level and the
intensity of voluntary disclosure activities. There is no direct evidence that firms strategically
disclose more information to deter entry. For one thing, our analysis is carried out at year level, a
window possibly too long to detect any strategic moves particularly for “entry-deterrence”
purpose ex ante; for another and more importantly, the identification from import tariff reduction
has brought about overall changes to the competitive landscape that affects both existing players
and potential entrants. Instead of isolating the competitive forces from existing and potential
opponents, we hold a view of interdependency and derive a more general conclusion that firms are
less likely to make voluntary disclosure when competition heightens.
[Insert table 2 about here]
Having established the impact of competition on the intensity of voluntary disclosure, we turn to
two other important dimensions that managers shall consider when making disclosure decisions.
The first two columns of Table 3 document the effect of competition on managers’ choice of
disclosure precision. We observe a large decrease in precision of disclosure made by treated firms
after the event compared to the changes in the non-treated group. We found similar results for
disclosure horizon. Conditional on an increase of horizon in the non-treated group, firms having
experienced a tariff reduction choose to report forecasts at closer date to the fiscal period end. We
also include firm fixed effects and year fixed effects to address within-firm and within-year
correlation respectively. Taken together, the changes in disclosure precision and horizon render
sufficient support for the proprietary cost argument implied by Hypothesis 2. Since forecasts with
more precise form may have proprietary information inferred by competitors more easily, and
forecasts made in greater advance to the fiscal period end can provide longer period for
competitors to respond, they are less likely to be adopted when overall competition hikes.
[Insert table 3 about here]
To further confirm the causal relation between competition and major dimensions of voluntary
10
For concision of presentation and endogeneity concerns on the measurement of additional control variables, we stick to the
original set of control variables to report main results.
13
disclosure, we explore the variation of responses from firms experiencing different level of
competitive shock. We resort to two variables to classify treated firms into Large_shock group and
Small_shock group, namely, the percentage change of import tariff rate and market share by sales.
Percentage change in import tariff rate (%Δtariff) is calculated as the annual change in import
tariff rate scaled by the tariff rate in the previous year; market share (Market_share) is defined as
the share of firm-level sales over total industry sales at three-digit SIC level. Within treated group,
we obtain the median value of percentage change in tariff rate (%Δtariff=-16.08%) at the year of
event and the median value of market share in the year before the event (Market_share=0.87%).
Then we assign a treated firm to Large_shock group if its value is smaller than the median. The
more negative the percentage change of tariff rate is or the smaller pie of the market a firm shares,
the higher competitive pressure the firm faces. In order to facilitate cross-group hypothesis test,
we modify the difference-in-differences specification (1) into the following form:
(2)
Treat(Large_shock) is an indicator variable equal to one for all the event episodes (or treated
firm-years) corresponding to a large competitive shock and zero otherwise; accordingly,
Treat(Small_shock) is set equal to one for all the event episodes (or treated firm-years)
corresponding to small shock and zero otherwise. Falsified event episodes (or non-treated
firm-years) have a value of 0 for both indicators. This specification has essentially disentangled
the treatment effect on firms that are hit by different levels of competitive shock, each captured by
one interaction term.
[Insert table 4 about here]
As presented in Table 4, the negative effect of competition on frequency, precision and horizon of
voluntary disclosure are all concentrated on firms suffering from larger shock, specifically firms
seeing larger reduction in import tariff rate (Panel A) or those ending up with smaller market share
(Panel B). The magnitude of impact in the Large_shock group is also greater than that in the
pooled panel regressions. This is expected because firms faced with larger reduction in tariff rate
are more susceptible to the predation from new entrants or to the squeeze by flood of imports. In
addition, the existing competitive position is also impaired as evident by lower market share, so
that they are less capable of resisting the pressure from intensified competition; holding back
further from making disclosure, especially that of precise and prompt style, become the optimal
choice of these firms. In short, the heterogeneous effect of competition on voluntary discourse
substantiates the baseline causal relation established above.
4.2 Indirect effect of competition on voluntary disclosure via capital market
Admittedly, competition alone does not shape the entire voluntary disclosure policy of a firm. It
might correlate with other determinants to exert additional influence on voluntary disclosure.
Given the interaction between product market and capital market, we would expect firms with
various capital market incentives to respond differently to heightened competition. As we posit in
Section II, greater capital market incentives could either ameliorate or augment the negative
impact of competition on voluntary disclosure, depending on whether it is the incentive to mitigate
information asymmetry or the motive to hold bad news that dominates. We employ two groups of
measurements to gauge a firm’s capital market incentives: the first is financial constraints (proxied
by WW_index, HP_index and dividend_dummy) and the second is financing need (proxied by
14
Ind_EFD, Ind_EEF, Ind_capx_ppe, Ind_inv_sales, and Firm_age). We consider firms as having
strong capital market incentives if they are featured with higher financial constraint index, having
no dividend distribution activity, higher external financing dependence, higher liquidity
dependence, or shorter listing history.
To implement the test, we first divide treated firms into Large_incentive group and
Small_incentive group according the median value of WW_index, HP_index, Ind_EFD, Ind_EED,
Ind_capx_ppe, Ind_inv_sales, and Firm_age respectively or whether a firm distributes dividends
(dividend_dummy equals 1 or not). The median value of WW_index, HP_index, Firm_age, and
dividend distribution activity is determined within treated firms one year before the tariff cut event;
while the median value of Ind_EFD, Ind_EED, Ind_capx_ppe, Ind_inv_sales are determined at
industry level in the treated group. Then we refer to the partitioned form of
difference-in-differences specification to unwind the heterogeneous effect on voluntary disclosure
across divergent capital market incentives.
(3)
Similar to specification (2), Treat(Large_incentive) is set to one for event episodes (or treated
firm-years) that belong to the Large_incentive group and zero otherwise. Treat(Small_incentives)
is equal to one among event episodes (or treated firm-years) that are part of Small_incentive group.
All the falsified event episodes (or non-treated firm-years) have been assigned a value of 0 for
both indicator variables.
[Insert table 5 about here]
Results are summarized in Table 5. Across the set of measurements of capital market incentives,
firms falling into Large_incentive group significantly decrease the frequency of voluntary
disclosure compared to those in the Small_incentive group.11 For instance, firms with high
external equity dependence (Ind_EED) on average make 0.34 fewer management earnings
forecasts after an import tariff reduction; the decrease approximately amounts to a 50% reduction
from the average level of disclosure frequency. While on the other hand, their counterparties in the
low incentive group almost remain unchanged in terms of disclosure frequency, as manifested by
the insignificant coefficient before Post. It is interesting to notice that, before an import tariff
reduction takes place (Post=0), firms with larger capital market incentives are more likely to make
voluntary disclosure. This is consistent with the unconditional role of capital market incentives,
with which firms usually make more disclosure to reduce asymmetric information component and
to lower cost of financing. However, such benefit of disclosure is dwarfed by the effect of
competition. After a material import tariff reduction (Post=1), firms that are more susceptible to
external financing environment (Large_incentive group) retreat from reporting voluntarily for fear
that disclosing bad news may disappoint the market. Although information asymmetry remains a
concern, the potential cost of non-disclosure is not as large as the cost that would be borne by the
firms for reporting unexpected negative news. Similar patterns are observed for choices of
disclosure precision and horizon (Panel B and Panel C). Firms with greater financial constraints or
11
We also construct these measurements of external financial dependence using our sample period 1995-2005, or
construct industry level measurement annually, the empirical results remain robust.
15
larger external financing dependence become strategically vague in their forecast language and
they leave even shorter period of time for their opponents to learn about their condition and to take
actions correspondingly. These results have cast light on the conditional role of capital market
incentives, or rather, the indirect effect on voluntary disclosure that competition may exert through
capital market.
4.3 Setting validity and robustness check
In this subsection, we first provide support for the validity of the identification strategy. To ensure
that the tariff cut event qualifies as an exogenous shock, existing voluntary disclosure practice
shall not have predictive power for the occurrence of tariff cut event. Hence we aggregate the
measurements of voluntary disclosure at three-digit SIC industry level one year prior to the tariff
cut event, and then regress the event indicator (Tariff_cut) over lagged average industry frequency,
precision, horizon, along with other controls. As demonstrated in Table 6, lagged measurements of
voluntary disclosure do not have any significant relation with the tariff cut event. There is no
evidence that firms have expected the occurrence of tariff reduction and thereby act ex ante to
alter their disclosure policy as well as to influence product market competition.
[Insert table 6 about here]
The second issue relates to the sample selection process. To address the incomplete coverage of
FCHD, we previously focus on firms that have ever made a forecast during our sample period. Yet
we could step further to examine the intensive margin of disclosure behaviors by including firms
only after they have made the very first forecast (i.e. first record in the database) (See
Balakrishnan et al. 2014 for explanation of intensive and extensive margin). In this way, we are
able to concentrate on the changes in voluntary disclosure behavior of firms that have already built
up a history of providing earnings guidance. The alternative construction has rendered us a smaller
sample of firms and the new results are shown in Table 7. The effect of competition is larger on all
the listed dimensions of voluntary disclosure, and there is a clear decreasing pattern among
non-treated firms. This is expected because the new sample has excluded firms with less
inclination to change their disclosure practices, which somewhat contribute to the less prominent
effect we identified in the baseline analysis above.
[Insert table 7 about here]
V. Concluding Remarks
We have established an unambiguous causal relation between product market competition and
firms’ voluntary disclosure behaviors. Unlike most prior literature that focuses on a single or two
facets, we provide a comprehensive evaluation on four key dimensions of voluntary disclosure,
namely, tendency, frequency, precision and horizon. On the part of competition, we use variation
in import tariff rate as an exogenous shock to the competition level, which addresses the
measurement problem of competition on one hand, while largely mitigates endogeneity concerns
on the other hand. Based on a difference-in-differences specification, we find that firms tend to
make less frequent voluntary disclosure when they are confronted with large tariff reduction.
Since reduced tariff rate is appealing to import penetration and at the same time shaping the
existing competitive landscape, firms are less willing to reveal information that might render
unintended assistance to their competitors. Furthermore, firms reduce disclosure precision and
horizon accordingly with intensified competition, squeezing out possible opportunities for their
16
competitors to grab useful proprietary information and to act against them. The negative effect on
voluntary disclosure practices is more pronounced if the tariff reduction is larger or the firm has
smaller market share beforehand. The overall results have provided sufficient support for the
proprietary cost argument, particularly from an interdependency view on competition where no
specific theory or model is targeted.
We have also explored the interaction between product market and capital market in terms of
their joint effect on voluntary disclosure behavior. Although many theoretical models have
considered the tension between the two markets, little empirical research has managed to address
the two forces in an interactive context. With a partitioned difference-in-differences design, we
have estimated the indirect effect of competition on voluntary disclosure when interacted with
different level of capital market incentives. For firms with greater external financing dependence
or larger financial constraints, they usually make voluntary disclosure less frequently, in less
precise form and within shorter horizon as responses to intensified competition. The marginal
choice based on capital market incentives have complemented firms’ baseline decision to cut
down disclosure intensity and to lower reporting precision. The results suggest that, mitigating
information asymmetry is not always the priority for firms in greater financial need. In the context
featured with competition shocks and deteriorating performance, capital market incentives induce
firms to retreat from making voluntary disclosure so as not to suffer from even worse capital
market outcomes. After all, firms should have optimally incorporated the dynamic interaction
among different forces when formulating their disclosure strategies. It would be very promising
for future research to explore the real effect of voluntary disclosure on both the financial market
and the product market, which may help verify the ex ante disclosure incentives as well as assess
the effectiveness of disclosure strategies.
17
Appendix A Variable Definition
This table presents the definition of all the variables used in the paper. Variables are classified into
four groups respectively: tariff cut event features, management earnings forecast / voluntary
disclosure, basic firm characteristics and capital market incentives.
Tariff Cut Event Features / Competitive Envrionment
Tariff_rate
Total duties collected by U.S custom divided by total Free-On-Board value
of imports at the three-digit SIC industry level; Source: Peter Schott's
International Economics Resource Page
http://faculty.som.yale.edu/peterschott/sub_international.htm
%ΔTariff
Current change in tariff rate scaled by tariff rate in the previous year
Tariff_cut
An indicator variable equals one if the percentage change of tariff rate is
negative and below the median reduction in the three-digit SIC industry
over the sample period 1995-2005
Market_share
Annual sales (Compustat variable sale) as a share of total sales in the
three-digit SIC industry; Source: Compustat
HHI
Herfindahl-Hirschman Index at three-digit SIC industry defined as the sum
of squared market shares by annual sales (Compustat variable sale); Source:
Compustat
No_firms
The total number of Compustat firms in a three-digit SIC industry each
year; Source: Compustat
Ind_sales
The sum of sales (Compustat variable sale) at three-digit SIC industry each
year divided by 1,000,000; Source: Compustat;
Management Earnings Forecast / Voluntary Disclosure
Frequency
Total number of annual and quarterly EPS forecasts made by a firm in a
fiscal year; Source: First Call Company Issued Guidance (CIG)
Precision
Average score of precision for all the annual and quarterly EPS forecasts
made by a firm in a fiscal year: for each forecast, we assign a score of 0 if
no estimate, 1 for qualitative estimates, 2 for open estimates, 3 for range
estimates and 4 for point estimates; Source: First Call Company Issued
Guidance (CIG)
Horizon
Average horizon of all the annual and quarterly EPS forecasts made by a
firm in a fiscal year: for each forecast, horizon is the number of calendar
days between the forecast announcement data and the forecast period end
date; Source: First Call Company Issued Guidance (CIG)
Basic Firm Characteristics
Ln_Size
Natural log of 1 plus market value of equity calculated as the product of
closing stockprice at fiscal year end (Compustat variable prcc_f) and the
number of common shares outstanding at fiscal year end (Compustat
variable csho); Source: Compustat
18
Book-to-market equity The ratio of book equity to market equity, where book equity is calculated as
ratio (BTM)
the sum of stockholder's equity (Compustat variable seq), deferred taxes
(Compustat variable txdb), and investment tax credit (Compustat variable
itcb), net of preferred stock (Compustat variable pref), while market equity
caculated as the product of closing stockprice at fiscal year end (Compustat
variable prcc_f) and the number of common shares outstanding at fiscal year
end (Compustat variable csho); Source: Compustat
Leverage
The ratio of long-term liability (Compustat variable lt) net of deferred taxes
(Compustat variable txdb) over total assets (Compustat variable at) at fiscal
yearend; Soucrce: Compustat
Analyst_coverage
Total number of unique analysts following a firm in a fiscal year; Source:
I/B/E/S unadjusted detail files
Institution_holding
The average percentage of shares owned by institutional investors of a firm
in a fisacal year; Source: Thompson Reuters Institutional Holding (13F)
Database
Ret_vol
Standard deviation of daily stock return (CRSP variable ret) over last fiscal
year; Source: CRSP
Capital Market Incentives
Dividend_dummy
An indicator variable equals to one if a firm distributes dividend (Compustat
variable dvpsx_f) in a fiscal year; Source: Compustat
WW_index
Financial constraint index constructed following Whited and Wu (2006)
with the following equation WW index= －0.091×Cash_flow－
0.062×Dividend_dummy+0.021×D/A－
0.044×Size+0.102×Industry_sale_growth－0.035×Sale_growth, where
Cash_flow is the sum of earnings before extraordinary items (Compustat
variable ib) and depreciation (Compustat variable dep) scaled by lagged
total assets (Compustat variable at), Dividend_dummy is an indicator
variable that equals one if a firm distributes dividend (Compustat variable
dvpsx_f), D/A is the ratio of long-term debt (Compustat variable dltt) over
total assets (Compustat variable at), Size is natural log of total assets
(Compustat variable at), Industry_sale_growth is annual growth of industry
sales at three-digit SIC level, Sale_growth is annual change of sales scaled
by lagged sales (Compustat variable sale); Source: Compustat
HP_index
Financial constraint index constructed following Hadlock and Pierce (2010)
with the following equation: HP index=－0.737×Size+0.043×Size^2－
0.040×Age, where Size is the natural log of total assets capped at $4.5
billion and Age is the total number of years a firm has been on Compustat
and is capped at thirty-seven years ; Source: Compustat
Firm_age
The total number of years a firm has been on Compustat; Source:
Compustat
Ind_EFD
3-digit SIC industry level external financial dependence (EFD) is defined as
the median firm EFD over the 1980s, following Rajan and Zingales (1998)
as well as Duchin, Oxbas and Sensoy (2010). Firm level EFD= Capital
19
expenditures (Compustat variable capx) – funds from operations
(Compustat variable fopt)) / capital expenditures (Compustat variable capx).
When fopt is missing, funds from operations is defined as the sum of the
following variables: Income before extraordinary items (Compustat variable
ibc), depreciation and amortization (Compustat variable dpc), deferred taxes
(Compustat txdc), equity in net loss / earnings (Compsutat variable esubc),
sale of property, plant, and equipment and investments – gain / loss
(Compusat variable sppiv), and funds from operations – other (Compustat
variable fopo); Source: Compustat
Ind_EED
3-digit SIC industry level external equity dependence (EED) is defined as
the median firm EED over the 1980s, following Rajan and Zingales (1998)
as well as Duchin, Oxbas and Sensoy (2010). Firm level EED is constructed
as the ratio of the net amount of equity issued (sale of common and
preferred stock (Compustat variable: sstk) – purchase of common and pref.
stock (Compustat variable: prstkc)) to capital expenditures (Compustat
variable: capx); Soruce: Compustat
Ind_capx_ppe
3-digit SIC industry level investment intensity is defined as the median firm
investment intensity over the 1980s, following Rajan and Zingales (1998).
Firm level investment intensity is the ratio of capital expenditure
(Compustat variable: capx)to net property plant and equipment (Compustat
variable: ppent); Soruce: Compustat
Ind_inv_sales
3-digit SIC industry level ratio of inventory to sales is defined as the median
firm level inventory to sales ratio over the 1980s, following Raddatz (2006).
firm level inventory to sales ratio is calculated as inventory (Compustat
variable: invt) over total sales (Compustat variable: sale); Soruce:
Compustat
20
References:
Ajinkya, B., Bhojraj, S., Sengupta, P., 2005. The Association between Outside Directors, Institutional
Investors and the Properties of Management Earnings Forecasts. Journal of Accounting
Research 43, 343-376
Ali, A., Klasa, S., Yeung, E., 2009. The Limitations of Industry Concentration Measures Constructed
with Compustat Data: Implications for Finance Research. Review of Financial Studies 22,
3839-3871
Ali, A., Klasa, S., Yeung, P.E., 2014. Industry Concentration and Corporate Disclosure Policy. Journal
of Accounting and Economics, Forthcoming
Baginski, S.P., Hassell, J.M., 1997. Determinants of Management Forecast Precision. The Accounting
Review 72, 303-312
Balakrishnan, K., Billings, M.B., Kelly, B., Ljungqvist, A., 2014. Shaping Liquidity: On the Causal
Effects of Voluntary Disclosure. Journal of FInance, Forthcoming
Bamber, L.S., Cheon, Y.S., 1998. Discretionary Management Earnings Forecast Disclosures:
Antecedents and Outcomes Associated with Forecast Venue and Forecast Specificity Choices.
Journal of Accounting Research 36, 167-190
Barry, C.B., Brown, S.J., 1985. Differential Information and Security Market Equilibrium. The Journal
of Financial and Quantitative Analysis 20, 407-422
Barry, C.B., Brown, S.J., 1986. Limited information as a source of risk. The Journal of Portfolio
Management 12, 66-72
Bens, D.A., Berger, P.G., Monahan, S.J., 2011. Discretionary Disclosure in Financial Reporting: An
Examination Comparing Internal Firm Data to Externally Reported Segment Data. The
Accounting Review 86, 417-449
Berger, P.G., 2011. Challenges and opportunities in disclosure research—A discussion of ‘the financial
reporting environment: Review of the recent literature’. Journal of Accounting and Economics
51, 204-218
Berger, P.G., Hann, R.N., 2007. Segment Profitability and the Proprietary and Agency Costs of
Disclosure. The Accounting Review 82, 869-906
Bernard, A.B., Jensen, J.B., Schott, P.K., 2006. Trade costs, firms and productivity. Journal of
Monetary Economics 53, 917-937
Beyer, A., Cohen, D.A., Lys, T.Z., Walther, B.R., 2010. The financial reporting environment: Review of
the recent literature. Journal of Accounting and Economics 50, 296-343
Botosan, C.A., 1997. Disclosure Level and the Cost of Equity Capital. The Accounting Review 72,
323-349
Botosan, C.A., Plumlee, M.A., 2002. A Re-examination of Disclosure Level and the Expected Cost of
Equity Capital. Journal of Accounting Research 40, 21-40
Botosan, C.A., Stanford, M., 2005. Managers' Motives to Withhold Segment Disclosures and the Effect
of SFAS no. 131 on Analysts' Information Environment. The Accounting Review 80, 751-771
Brown, S., 1979. The Effect of Estimation Risk on Capital Market Equilibrium. The Journal of
Financial and Quantitative Analysis 14, 215-220
Burks, J.J., Cuny, C., Gerakos, J., Granja, J., 2014. Competition and voluntary disclosure: Evidence
from deregulation in the banking industry. Chicago Booth Research Paper No. 12-29
Campello, M., 2003. Capital structure and product markets interactions: evidence from business cycles.
21
Journal of Financial Economics 68, 353-378
Cheng, Q., Lo, K.I.N., 2006. Insider Trading and Voluntary Disclosures. Journal of Accounting
Research 44, 815-848
Cheng, Q., Luo, T., Yue, H., 2013. Managerial Incentives and Management Forecast Precision. The
Accounting Review 88, 1575-1602
Chevalier, J.A., 1995. Capital Structure and Product-Market Competition: Empirical Evidence from the
Supermarket Industry. American Economic Review 85, 415-435
Chuk, E., Matsumoto, D., Miller, G.S., 2013. Assessing methods of identifying management forecasts:
CIG vs. researcher collected. Journal of Accounting and Economics 55, 23-42
Clinch, G., Verrecchia, R.E., 1997. Competitive Disadvantage and Discretionary Disclosure in
Industries. Australian Journal of Management 22, 125-137
Darrough, M.N., 1993. Disclosure Policy and Competition: Cournot vs. Bertrand. The Accounting
Review 68, 534-561
Darrough, M.N., Stoughton, N.M., 1990. Financial disclosure policy in an entry game. Journal of
Accounting and Economics 12, 219-243
Dedman, E., Lennox, C., 2009. Perceived competition, profitability and the withholding of information
about sales and the cost of sales. Journal of Accounting and Economics 48, 210-230
Duchin, R., Ozbas, O., Sensoy, B.A., 2010. Costly external finance, corporate investment, and the
subprime mortgage credit crisis. Journal of Financial Economics 97, 418-435
Dye, R.A., 1986. Proprietary and Nonproprietary Disclosures. The Journal of Business 59, 331-366
Feenstra, R.C., 1996. U.S. imports, 1972-1994, data and concordances. NBER Working Paper 5515
Feenstra, R.C., Romalis, J., Schott, P.K., 2002. U.S imports, exports and tariff data, 1989-2001. NBER
Working Paper 9387
Feltham, G.A., Xie, J.Z., 1992. Voluntary financial disclosure in an entry game with continua of types.
Contemporary Accounting Research 9, 46-80
Fresard, L., 2010. Financial Strength and Product Market Behavior: The Real Effects of Corporate
Cash Holdings. The Journal of Finance 65, 1097-1122
Gigler, F., 1994. Self-Enforcing Voluntary Disclosures. Journal of Accounting Research 32, 224-240
Graham, J.R., Harvey, C.R., Rajgopal, S., 2005. The economic implications of corporate financial
reporting. Journal of Accounting and Economics 40, 3-73
Grossman, S.J., 1981. The Informational Role of Warranties and Private Disclosure about Product
Quality. Journal of Law and Economics 24, 461-483
Grossman, S.J., Hart, O.D., 1980. Disclosure Laws and Takeover Bids. The Journal of Finance 35,
323-334
Hadlock, C.J., Pierce, J.R., 2010. New Evidence on Measuring Financial Constraints: Moving Beyond
the KZ Index. Review of Financial Studies 23, 1909-1940
Harris, M.S., 1998. The Association between Competition and Managers' Business Segment Reporting
Decisions. Journal of Accounting Research 36, 111-128
Healy, P.M., Palepu, K.G., 2001. Information asymmetry, corporate disclosure, and the capital markets:
A review of the empirical disclosure literature. Journal of Accounting and Economics 31,
405-440
Hou, K., Robinson, D.T., 2006. Industry Concentration and Average Stock Returns. The Journal of
Finance 61, 1927-1956
Hribar, P., 2004. Discussion of Competitive Costs of Disclosure by Biotech IPOs. Journal of
22
Accounting Research 42, 357-364
Jovanovic, B., 1982. Truthful Disclosure of Information. The Bell Journal of Economics 13, 36-44
Karamanou, I., Vafeas, N., 2005. The Association between Corporate Boards, Audit Committees, and
Management Earnings Forecasts: An Empirical Analysis. Journal of Accounting Research 43,
453-486
Kasznik, R., Lev, B., 1995. To Warn or Not to Warn: Management Disclosures in the Face of an
Earnings Surprise. The Accounting Review 70, 113-134
Lanen, W.N., Verrecchia, R.E., 1987. Operating Decisions and the Disclosure of Management
Accounting Information. Journal of Accounting Research 25, 165-189
Lang, M.H., Lundholm, R.J., 2000. Voluntary Disclosure and Equity Offerings: Reducing Information
Asymmetry or Hyping the Stock?*. Contemporary Accounting Research 17, 623-662
Lev, B., Penman, S.H., 1990. Voluntary Forecast Disclosure, Nondisclosure, and Stock Prices. Journal
of Accounting Research 28, 49-76
Li, X., 2010. The impacts of product market competition on the quantity and quality of voluntary
disclosures. Review of Accounting Studies 15, 663-711
Lin, C., Officer, M.S., Zhan, X., 2014. Does competition affects earnings management? Evidence form
a natural experiment.
Merton, R.C., 1987. A Simple Model of Capital Market Equilibrium with Incomplete Information. The
Journal of Finance 42, 483-510
Milgrom, P.R., 1981. Good News and Bad News: Representation Theorems and Applications. The Bell
Journal of Economics 12, 380-391
Newman, P., Sansing, R., 1993. Disclosure Policies with Multiple Users. Journal of Accounting
Research 31, 92-112
Noe, C.F., 1999. Voluntary disclosures and insider transactions. Journal of Accounting and Economics
27, 305-326
Raddatz, C., 2006. Liquidity needs and vulnerability to financial underdevelopment. Journal of
Financial Economics 80, 677-722
Radhakrishnan, S., Tsang, A., Yang, Y.G., 2014. Management Forecasts around the World. Working
paper
Rajan, R.G., Zingales, L., 1998. Financial Dependence and Growth. The American Economic Review
88, 559-586
Schott, P.K., 2010. U.S. manufacturing exports and imports by SIC and NAICS category and partner
country, 1972-2005. In: Unpublished working paper. Yale School of Management
Shleifer, A., 2004. Does Competition Destroy Ethical Behavior? American Economic Review 94,
414-418
Skinner, D.J., 1994. Why Firms Voluntarily Disclose Bad News. Journal of Accounting Research 32,
38-60
Tybout, J., 2003. Plant- and firm- level evidence on "new" trade theories. In: Choi EK & Harrigan J
(eds.) Handbook of International Economics. Basil-Blackwell, Oxford.
Valta, P., 2012. Competition and the cost of debt. Journal of Financial Economics 105, 661-682
Verrecchia, R.E., 1983. Discretionary disclosure. Journal of Accounting and Economics 5, 179-194
Verrecchia, R.E., 1990a. Endogenous proprietary costs through firm interdependence. Journal of
Accounting and Economics 12, 245-250
Verrecchia, R.E., 1990b. Information quality and discretionary disclosure. Journal of Accounting and
23
Economics 12, 365-380
Verrecchia, R.E., 2001. Essays on disclosure. Journal of Accounting and Economics 32, 97-180
Verrecchia, R.E., Weber, J., 2006. Redacted Disclosure. Journal of Accounting Research 44, 791-814
Wagenhofer, A., 1990. Voluntary disclosure with a strategic opponent. Journal of Accounting and
Economics 12, 341-363
Whited, T.M., Wu, G., 2006. Financial Constraints Risk. Review of Financial Studies 19, 531-559
Xu, J., 2012. Profitability and capital structure: Evidence from import penetration. Journal of Financial
Economics 106, 427-446
24
Table 1 Summary Statistics for the Panel Regression Sample
This table reports summary statistics of dependent and control variables used in the baseline panel
regressions. For each firm-year with a tariff cut event, we construct an event episode of three
firm-years by additionally including one year before and one year after the event. We similarly
construct falsified event episodes for firm-years without a tariff cut. The sample includes 12,987
episodes of tariff cut event (or corresponding non-cut falsified event) used in the analysis of
disclosure frequency, precision and horizon. N is the total number of firm-years used in the
regressions. All variables are defined in Appendix A.
Variable
N
Q1
Median
Mean
Q4
Std. dev
Freq
31728
0
0
1.690
2
2.843
Precision
31728
0
0
1.302
3
1.512
Horizon
31728
0
0
57.569
96
89.498
0Ln_Size
31728
4.555
5.889
5.990
7.282
2.024
BTM
31728
0.274
0.468
0.628
0.750
0.993
Leverage
31728
0.235
0.414
0.418
0.575
0.215
Analyst_coverage
31728
2
5
7.253
10
8.056
Institution_holding
31728
25.95%
50.66%
48.18%
69.79%
0.264
Ret_vol
31728
0.023
0.034
0.038
0.049
0.021
25
Table 2 Baseline Effect of Competition on Voluntary Disclosure Likelihood and Frequency
–Difference-in-differences Analysis in Pooled Panel Regressions
This table presents the difference-in-differences estimation of competition on the likelihood and
frequency of voluntary disclosure (proxied by management earnings forecasts). Variation in
competition is identified by a series of tariff cut events, which are defined as instances where the
annual percentage decrease of tariff rate in a three-digit SIC manufacturing industry exceeds the
median reduction in the industry over the sample period from 1995 to 2005. For each firm-year
with a tariff cut event, we construct an event episode of three firm-years by additionally including
one year before and one year after the event. We similarly construct falsified event episodes for
firm-years without a tariff cut. We use the following difference-in-differences specification for
pooled panel regressions:
. Treat is an indicator variable set to one for event episodes and zero for
falsified event episodes. Post is an indicator variable that turns from zero to one since the tariff cut
event or the falsified event. The dependent variable Disclosure_choice is evaluated as either
Disclosure_dummy or Frequency. Dislcosure_dummy is an indicator variable that is equal to one if
a firm issues earnings forecast in a year and zero otherwise. Frequency is a count variable that is
equal to the total number of earnings forecasts made by a firm in a particular year. Other variables
are defined in Appendix A. All the regression specifications include both firm fixed effects and
year fixed effects. Robust t-statistic (OLS) and robust z-statistic (Probit, Logit, Poisson) are
displayed in parenthesis. t-statistics in column (1) – (3) are robust to clustering at three-digit SIC
industry level; t-statistic in column (4) is robust to two-way clustering at three-digit SIC industry
level and year level. *, **, *** denote 10%, 5%, and 1% significance level respectively.
26
Dependent variable
Treat×Post
Treat
Post
Ln_Size
BTM
Leverage
Institution_holding
Ln_coverage
Ret_vol
Constant
Number of observations
Adj. / Pseudo R-squared
Firm Fixed Effects
Year Fixed Effects
S.E. Clustered by
Regression Model
Disclosure_dummy
Frequency
(1)
(2)
(3)
(4)
(5)
-0.2020**
(-2.43)
0.1550*
(1.65)
0.0127
(1.00)
0.0028
(0.11)
0.0149
(1.09)
-0.2594*
(-1.75)
0.4923**
(1.98)
0.7792***
(15.35)
-6.5079***
(-2.84)
-3.5105***
(-12.36)
-0.3422**
(-2.34)
0.2585
(1.57)
0.0193
(0.87)
0.0181
(0.38)
0.0255
(1.02)
-0.4556*
(-1.71)
0.8782**
(2.05)
1.3617***
(14.85)
-11.7043***
(-2.85)
-6.5791***
(-12.09)
-0.1568**
(-2.34)
0.0833
(0.72)
0.0088
(0.63)
0.2432***
(5.65)
0.0557*
(1.88)
-0.2356
(-1.29)
0.3591
(0.92)
0.4907***
(7.12)
-4.1572**
(-2.03)
-2.0451***
(-8.30)
-0.1568***
(-3.78)
0.0833
(0.77)
0.0088
(0.65)
0.2432**
(2.57)
0.0557*
(1.81)
-0.2356
(-1.25)
0.3591
(0.87)
0.4907***
(5.47)
-4.1572*
(-1.78)
-2.4681***
(-3.31)
-0.1349**
(-2.53)
0.0692
(1.44)
0.0077
(0.61)
0.1843***
(10.89)
0.0419***
(2.69)
0.0226
(0.30)
0.5065***
(6.64)
0.5499***
(27.36)
-5.9236***
(-7.99)
-5.1694***
(-12.10)
29,356
0.346
Yes
Yes
Industry
Probit
29,356
0.348
Yes
Yes
Industry
Logit
31,728
0.532
Yes
Yes
Industry
OLS
31,728
0.532
Yes
Yes
Ind & Year
OLS
31,728
0.483
Yes
Yes
Robust
Poisson
27
Table 3 Baseline Effect of Competition on Voluntary Disclosure Precision and Horizon
–Difference-in-differences Analysis in Pooled Panel Regressions
This table presents the difference-in-differences estimation of competition on the precision and
horizon of voluntary disclosure (proxied by management earnings forecasts). Variation in
competition is identified by a series of tariff cut events, which are defined as instances where the
annual percentage decrease of tariff rate in a three-digit SIC manufacturing industry exceeds the
median reduction in the industry over the sample period from 1995 to 2005. For each firm-year
with a tariff cut event, we construct an event episode of three firm-years by additionally including
one year before and one year after the event. We similarly construct falsified event episodes for
firm-years without a tariff cut. We use the following difference-in-differences specification for
pooled panel regressions:
. Treat is an indicator variable set to one for event episodes and zero for
falsified event episodes. Post is an indicator variable that turns from zero to one since the tariff cut
event or the falsified event. The dependent variable Disclosure_choice is evaluated as either
Precision or Ln_Horizon. Precision is the average of scores assigned to each forecasts made by a
firm in a year: 0 for non-disclosure, 1 for qualitative estimate, 2 for open interval estimate, 3 for
range estimate and 4 for point estimate. Horizon is the average number of days between forecast
date and corresponding fiscal period end date associated with each forecast made by a firm in a
particular year. Ln_Horizon, defined as the natural log of one plus horizon, is used in the
regression analysis. Other variables are defined in Appendix A. All the regression specifications
include both firm fixed effects and year fixed effects. Robust t-statistics are displayed in
parenthesis. t-statistics in column (1) and (3) are robust to clustering at three-digit SIC industry
level; t-statistics in column (2) and (4) are robust to two-way clustering at three-digit SIC industry
level and year level. *, **, *** denote 10%, 5%, and 1% significance level respectively.
28
Dependent variable
Treat×Post
Treat
Post
Ln_Size
BTM
Leverage
Institution_holding
Ln_coverage
Ret_vol
Constant
Number of observations
Adj. / Pseudo R-squared
Firm Fixed Effects
Year Fixed Effects
S.E. Clustered by
Regression Model
Precision
Ln_horizon
(1)
(2)
(3)
(4)
-0.1572***
(-2.66)
0.1285*
(1.87)
0.0087
(0.92)
0.0278
(1.39)
0.0142
(1.32)
-0.1212
(-1.08)
0.5654***
(3.14)
0.4794***
(13.16)
-2.9723***
(-2.81)
-0.8639***
(-5.73)
-0.1572**
(-2.38)
0.1285*
(1.66)
0.0087
(0.97)
0.0278
(0.57)
0.0142
(0.92)
-0.1212
(-0.92)
0.5654***
(3.27)
0.4794***
(8.60)
-2.9723***
(-3.31)
-1.1547***
(-5.01)
-0.1992**
(-2.51)
0.1236
(1.24)
0.0251*
(1.78)
0.0583**
(2.08)
0.0125
(0.85)
-0.1446
(-0.71)
0.8014***
(3.12)
0.7300***
(15.09)
-4.2386***
(-3.04)
-1.4159***
(-5.26)
-0.1992***
(-2.94)
0.1236
(1.15)
0.0251**
(2.05)
0.0583
(0.80)
0.0125
(0.60)
-0.1446
(-0.72)
0.8014***
(3.21)
0.7300***
(8.84)
-4.2386***
(-3.17)
-1.2521**
(-2.43)
31,728
0.4264
Yes
Yes
Industry
OLS
31,728
0.4264
Yes
Yes
Ind & Year
OLS
31,728
0.4350
Yes
Yes
Industry
OLS
31,728
0.4350
Yes
Yes
Ind & Year
OLS
29
Table 4 Heterogeneous Effect of Competition on Voluntary Disclosure Frequency, Precision and Horizon with Varying Shock Intensity
– Difference-in-differences Analysis in Partitioned Panel Regressions
This table displays heterogeneous treatment effect of competition on three dimensions of voluntary disclosure following the specification below:
For each firm-year with a tariff cut event (treated firm-years), we construct an event episode of three firm-years by additionally including one
year before and one year after the event. We similarly construct falsified event episodes for firm-years without a tariff cut (non-treated firm-years). Treated firm-years
are divided into Large_shock group and Small_shock group based on median percentage change in tariff rate (%ΔTariff) within treated group at the year of event
(Panel A) or by the median market share (Market_share) within treated group in the year prior to the event (Panel B). Treat(Large_Shock) is an indicator variable
equal to one for treated firm-years (and corresponding event episodes) in Large_shock group (i.e. below median), while zero otherwise. Treat(Small_shock) is an
indicator variable equal to one for treated firm-years (and corresponding event episodes) in Small_shock group (i.e. above median), while zero otherwise. Non-treated
firm-years (and corresponding falsified event episodes) have a value of zero for both indicators. Post is an indicator variable that turns from zero to one since the
tariff cut event or the falsified event. Disclosure_choice is evaluated to be Frequency, Precision or Ln_Horizon. Frequency is a count variable that is equal to the total
number of earnings forecasts made by a firm in a particular year; Precision is the average of scores assigned to each forecast made by a firm in a year: 0 for
non-disclosure, 1 for qualitative estimate, 2 for open interval estimate, 3 for range estimate and 4 for point estimate; Horizon is the average number of days between
forecast date and corresponding fiscal period end date associated with each forecast made by a firm in a particular year; Ln_Horizon, defined as the natural log of one
plus horizon, is used in the regression analysis. Control variables include Ln_size, BTM, Leverage, Institution_holding, Ln_coverage and Ret_vol, which are
suppressed to conserve space. Detailed variable definitions are presented in Appendix A. All the regression specifications include both firm fixed effects and year
fixed effects. Robust t-statistic (OLS) and robust z-statistic (Poisson) are displayed in parenthesis. t-statistics in column (1) (4) and (6) are robust to clustering at
three-digit SIC industry level; t-statistics in column (2) (5) and (7) are robust to two-way clustering at three-digit SIC industry level and year level. *, **, *** denote
10%, 5%, and 1% significance level respectively.
30
Panel A: Shock by %ΔTariff
Dependent variable
Frequency
Precision
Ln_horizon
(1)
(2)
(3)
(4)
(5)
(6)
(7)
-0.2225***
-0.2225***
-0.2358***
-0.2200**
-0.2200**
-0.2705***
-0.2705***
(-2.70)
(-2.78)
(-2.89)
(-2.47)
(-2.18)
(-2.82)
(-2.59)
-0.0950
-0.0950***
-0.0750
-0.0980
-0.0980**
-0.1313
-0.1313
(-0.97)
(-4.19)
(-1.09)
(-1.42)
(-2.25)
(-1.13)
(-1.55)
0.1174
0.1174
0.0950
0.1609
0.1609
0.1438
0.1438
(0.72)
(0.74)
(1.30)
(1.46)
(1.36)
(1.04)
(0.97)
0.0531
0.0531
0.0562
0.0999
0.0999
0.1080
0.1080
(0.36)
(0.43)
(0.90)
(1.35)
(1.61)
(0.83)
(0.90)
0.0088
0.0088
0.0077
0.0088
0.0088
0.0254*
0.0254**
(0.62)
(0.65)
(0.61)
(0.91)
(0.96)
(1.77)
(2.03)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
31,728
31,728
31,728
31,728
31,728
31,728
31,728
Adj. / Pseudo R-squared
0.532
0.532
0.483
0.4264
0.4264
0.4350
0.4350
Firm Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Year Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
S.E. Clustered by
Industry
Ind & Year
Robust
Industry
Ind & Year
Industry
Ind & Year
Regression Model
OLS
OLS
Poisson
OLS
OLS
OLS
OLS
Treat(Large_shock)×Post
Treat(Small_shock)×Post
Treat(Large_shock)
Treat(Small_shock)
Post
Controls
31
Panel B: Shock by Market_share
Dependent variable
Frequency
Precision
Ln_horizon
(1)
(2)
(3)
(4)
(5)
(6)
(7)
-0.3380***
-0.3380***
-0.2974***
-0.2654***
-0.2654***
-0.3721***
-0.3721***
(-6.07)
(-4.73)
(-2.93)
(-3.17)
(-2.85)
(-3.80)
(-4.68)
-0.0054
-0.0054
-0.0786
-0.0544
-0.0544
-0.0379
-0.0379
(-0.05)
(-0.11)
(-1.27)
(-0.77)
(-0.89)
(-0.40)
(-0.67)
-0.3525**
-0.3525*
0.0456
0.0329
0.0329
-0.0510
-0.0510
(-2.11)
(-1.95)
(0.82)
(0.41)
(0.38)
(-0.41)
(-0.37)
0.5972***
0.5972***
0.1402
0.2379***
0.2379**
0.3251***
0.3251***
(4.93)
(4.73)
(1.53)
(2.79)
(2.42)
(3.10)
(3.18)
0.0084
0.0084
0.0075
0.0087
0.0087
0.0250*
0.0250**
(0.57)
(0.62)
(0.59)
(0.91)
(0.95)
(1.74)
(1.98)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
31,721
31,721
31,721
31,721
31,721
31,721
31,721
Adj. / Pseudo R-squared
0.534
0.534
0.483
0.427
0.427
0.435
0.435
Firm Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Year Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
S.E. Clustered by
Industry
Ind & Year
Robust
Industry
Ind & Year
Industry
Ind & Year
Regression Model
OLS
OLS
Poisson
OLS
OLS
OLS
OLS
Treat(Large_shock)×Post
Treat(Small_shock)×Post
Treat(Large_shock)
Treat(Small_shock)
Post
Controls
32
Table 5 Heterogeneous Effect of Competition on Voluntary Disclosure Frequency, Precision and Horizon via Varying Capital Market Incentives
– Difference-in-differences Analysis in Partitioned Panel Regressions
This table displays indirect effect of competition on three key dimensions of voluntary disclosure via varying capital market incentives. The following specification is
adopted:
. For each firm-year with a tariff cut event (treated firm-years), we construct an event episode of three
firm-years by additionally including one year before and one year after the event. We similarly construct falsified event episodes for firm-years without a tariff cut
(non-treated firm-years). Treated firm-years are divided into Large_incentive group and Small_incentive group based on dividend_dummy, the median of WW_index,
HP_index, and Firm_age respectively within treated group in the year prior to the event; or based on the median value of industry level measurements such as
Ind_EFD, Ind_EED, Ind_capx_ppe, Ind_inv_sales within the treated group. Treat(Large_incentive) is an indicator variable equal to one for treated firm-years (and
corresponding event episodes) in Large_incentive group (i.e. dividend_dummy = 0; HP_index, WW_index, Ind_EFD, Ind_EED, Ind_capx_ppe, Ind_inv_sales above
median; Firm_age below median), while zero otherwise. Treat(Small_incentive) is an indicator variable equal to one for treated firm-years (and corresponding event
episodes) in Small_incentive group (i.e. dividend_dummy = 1; HP_index, WW_index, Ind_EFD, Ind_EED, Ind_capx_ppe, Ind_inv_sales below median; Firm_age
above median), while zero otherwise. Non-treated firm-years (and corresponding falsified event episodes) have a value of zero for both indicators. Post is an
indicator variable that turns from zero to one since the tariff cut event or the falsified event. Disclosure_choice is evaluated to be Frequency (Panel A), Precision
(Panel B) or Ln_Horizon (Panel C). Frequency is a count variable that is equal to the total number of earnings forecasts made by a firm in a particular year; Precision
is the average of scores assigned to each forecast made by a firm in a year: 0 for non-disclosure, 1 for qualitative estimate, 2 for open interval estimate, 3 for range
estimate and 4 for point estimate; Horizon is the average number of days between forecast date and corresponding fiscal period end date associated with each forecast
made by a firm in a particular year; Ln_Horizon, defined as the natural log of one plus horizon, is used in the regression analysis. Control variables include Ln_size,
BTM, Leverage, Institution_holding, Ln_coverage and Ret_vol, which are suppressed to conserve space. Detailed variable definitions are presented in Appendix A.
All the regression specifications include both firm fixed effects and year fixed effects and are estimated using OLS. Robust t-statistics are displayed in parenthesis.
t-statistics are robust to clustering at three-digit SIC industry level. *, **, *** denote 10%, 5%, and 1% significance level respectively.
33
Panel A
Dependent variable
Incentives Partitioned by
Frequency
Dividend
dummy
(1)
Treat(Large_inceitive)×Post
Treat(Small_inceitive)×Post
Treat(Large_incentive)
Treat(Small_incentive)
Post
Controls
Observations
Adj. / Pseudo R-squared
Firm Fixed Effects
Year Fixed Effects
SE Clustered by
Model
HP_index WW_index Firm_age
(2)
(3)
(4)
Ind_EFD
Ind_EED
(5)
(6)
Ind
capx_ppe
(7)
Ind
inv_sales
(8)
-0.2336*** -0.3145*** -0.2780*** -0.2620*** -0.1937*** -0.3387*** -0.2074*** -0.2074***
(-3.08)
(-4.55)
(-4.23)
(-2.85)
(-3.07)
(-4.05)
(-2.92)
(-2.92)
-0.0632
-0.0448
-0.0471
-0.0899
-0.0742
-0.0213
-0.0498
-0.0498
(-0.58)
(-0.49)
(-0.49)
(-1.03)
(-0.48)
(-0.24)
(-0.38)
(-0.38)
0.3680*** 0.5447*** 0.5082*** 0.2973** 0.2132* 0.4220*** 0.1634
0.1634
(3.11)
(4.25)
(4.22)
(2.23)
(1.87)
(2.92)
(1.17)
(1.17)
-0.2805
-0.2901* -0.3126*
-0.0497
-0.2228
-0.1769
-0.0845
-0.0845
(-1.57)
(-1.92)
(-1.98)
(-0.36)
(-0.88)
(-1.25)
(-0.47)
(-0.47)
0.0091
0.0068
0.0073
0.0087
0.0092
0.0070
0.0086
0.0086
(0.66)
(0.49)
(0.53)
(0.63)
(0.65)
(0.51)
(0.62)
(0.62)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
31,722
0.5328
Yes
Yes
Industry
OLS
31,722
0.5332
Yes
Yes
Industry
OLS
31,658
0.5333
Yes
Yes
Industry
OLS
31,722
0.5322
Yes
Yes
Industry
OLS
31,728
0.5322
Yes
Yes
Industry
OLS
31,728
0.5324
Yes
Yes
Industry
OLS
31,728
0.5320
Yes
Yes
Industry
OLS
31,728
0.5320
Yes
Yes
Industry
OLS
34
Panel B
Dependent variable
Incentives Partitioned by
Precision
Dividend
dummy
(1)
Treat(Large_inceitive)×Post
Treat(Small_inceitive)×Post
Treat(Large_incentive)
Treat(Small_incentive)
Post
Controls
Observations
Adj. / Pseudo R-squared
Firm Fixed Effects
Year Fixed Effects
SE Clustered by
Model
HP_index WW_index Firm_age
Ind_EFD
Ind_EED
(3)
(6)
(7)
(2)
(5)
Ind
capx_ppe
(8)
Ind
inv_sales
(9)
-0.2357*** -0.2545*** -0.2367*** -0.2321*** -0.1925*** -0.2809*** -0.2356*** -0.1973**
(-3.29)
(-3.27)
(-3.04)
(-2.66)
(-2.81)
(-3.14)
(-3.73)
(-2.22)
-0.0468
-0.0685
-0.0785
-0.1003
-0.0725
-0.0572
0.0176
-0.1154
(-0.65)
(-1.02)
(-1.15)
(-1.54)
(-0.77)
(-0.81)
(0.16)
(-1.57)
0.2077** 0.2153** 0.2024** 0.1611* 0.1712** 0.2324** 0.1631*
0.2005*
(2.48)
(2.36)
(2.48)
(1.72)
(2.14)
(2.01)
(1.91)
(1.90)
0.0264
0.0569
0.0605
0.1080
0.0266
0.0465
0.0499
0.0493
(0.34)
(0.84)
(0.81)
(1.60)
(0.22)
(0.62)
(0.46)
(0.60)
0.0089
0.0087
0.0087
0.0089
0.0088
0.0084
0.0086
0.0084
(0.94)
(0.91)
(0.92)
(0.94)
(0.92)
(0.87)
(0.91)
(0.87)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
31,722
0.4267
Yes
Yes
Industry
OLS
31,722
0.4267
Yes
Yes
Industry
OLS
31,658
0.4269
Yes
Yes
Industry
OLS
31,722
0.4266
Yes
Yes
Industry
OLS
31,728
0.4265
Yes
Yes
Industry
OLS
31,728
0.4265
Yes
Yes
Industry
OLS
31,728
0.4266
Yes
Yes
Industry
OLS
31,728
0.4265
Yes
Yes
Industry
OLS
35
Panel C
Dependent variable
Incentives Partitioned by
Ln_horizon
Dividend
dummy
(1)
Treat(Large_inceitive)×Post
Treat(Small_inceitive)×Post
Treat(Large_incentive)
Treat(Small_incentive)
Post
Controls
Observations
Adj. / Pseudo R-squared
Firm Fixed Effects
Year Fixed Effects
SE Clustered by
Model
HP_index WW_index Firm_age
Ind_EFD
Ind_EED
(3)
(6)
(7)
(2)
(5)
Ind
capx_ppe
(8)
Ind
inv_sales
(9)
-0.3214*** -0.3538*** -0.3301*** -0.3184*** -0.2728*** -0.4019*** -0.2880*** -0.2600**
(-3.18)
(-3.49)
(-3.17)
(-2.66)
(-3.17)
(-4.02)
(-3.21)
(-2.56)
-0.0302
-0.0638
-0.0727
-0.1144
-0.0239
-0.0362
-0.0000
-0.1357
(-0.30)
(-0.70)
(-0.80)
(-1.29)
(-0.18)
(-0.32)
(-0.00)
(-1.15)
0.2698** 0.3099** 0.2659** 0.2362* 0.2282** 0.3058**
0.1498
0.2253
(2.37)
(2.45)
(2.23)
(1.81)
(2.19)
(2.00)
(1.18)
(1.54)
-0.0646
-0.0290
-0.0090
0.0532
-0.1255
-0.0199
0.0609
0.0119
(-0.54)
(-0.27)
(-0.08)
(0.53)
(-0.65)
(-0.17)
(0.45)
(0.10)
0.0254*
0.0247*
0.0249*
0.0253*
0.0254*
0.0245*
0.0251*
0.0247*
(1.79)
(1.74)
(1.76)
(1.79)
(1.77)
(1.72)
(1.78)
(1.73)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
31,722
0.4352
Yes
Yes
Industry
OLS
31,722
0.4352
Yes
Yes
Industry
OLS
31,658
0.4352
Yes
Yes
Industry
OLS
31,722
0.4351
Yes
Yes
Industry
OLS
31,728
0.4351
Yes
Yes
Industry
OLS
31,728
0.4351
Yes
Yes
Industry
OLS
31,728
0.4351
Yes
Yes
Industry
OLS
31,728
0.435
Yes
Yes
Industry
OLS
36
Table 6 Validity Check of Event Setting – Regression of Event Dummy over Lagged Industry Average Voluntary Disclosure Measurement
This table shows the regression results of tariff cut event dummy over lagged voluntary disclosure measurements average across firms within each three-digit SIC
industry. Tariff_cut is an indicator variable if there is a tariff cut event in the industry-year. Ind_frequency, Ind_precision, Ind_horizon are average measurement of
Frequency, Precision, Horizon across all the firms in an industry-year. Industry controls include lagged Ind_sales, lagged HHI, lagged No_firms, lagged Tariff_rate
and a year trend, definition of which are presented in Appendix A. Column (1)-(5) present results from Probit regressions and columns (6) – (10) presents results
from Logit regressions. z-statistics are displayed in parenthesis. *, **, *** denote 10%, 5%, and 1% significance level respectively.
Dependent variable
Tariff_Cut
Lagged Control
(1)
Ind_frequqncy
(4)
(5)
(6)
-0.0012
0.0430
0.0617
(-0.04)
(1.16)
(1.54)
-0.0525
-0.0301
-0.0067
(-0.92)
(-0.29)
(-0.06)
-0.0007
-0.0008
(-0.70)
Ind_precision
(2)
Ind_horizon
Industry controls
(3)
(7)
(8)
(9)
(10)
-0.0108
0.0877
0.1195
(-0.17)
(1.28)
(1.65)
-0.0858
-0.0399
-0.0210
(-0.83)
(-0.22)
(-0.10)
-0.0011
-0.0015 -0.0021
-0.0026
(-0.47)
(-0.65)
(-0.78)
(-0.68)
(-0.79)
No
No
No
No
Yes
No
No
No
No
Yes
Observations
1,200
1,200
1,200
871
834
1,200
1,200
1,200
871
834
Pseudo R-Squared
0.124
0.125
0.125
0.138
0.209
0.125
0.125
0.125
0.140
0.205
Regression Model
Probit
Probit
Probit
Probit
Probit
Logit
Logit
Logit
Logit
Logit
37
Table 7 Effect of Competition on Voluntary Disclosure Likelihood, Frequency, Precision and Horizon within Intensive Margin
This table presents the difference-in-differences estimation of competition on the four dimensions of voluntary disclosure based on an alternative sample which only
include firms after they have make the first ever forecast. Control variables include Ln_size, BTM, Leverage, Institution_holding, Ln_coverage and Ret_vol, which
are suppressed to conserve space. Detailed variable definitions are presented in Appendix A. Robust t-statistic (OLS) and robust z-statistic (Probit, Logit, Poisson) are
displayed in parenthesis. t-statistics in column (1) – (3), (6), (8) are robust to clustering at three-digit SIC industry level; t-statistics in column (4), (7), (9) are robust
to two-way clustering at three-digit SIC industry level and year level. *, **, *** denote 10%, 5%, and 1% significance level respectively.
Dependent variable
Disclosure_dummy
(1)
Treat×Post
Treat
Post
(2)
-0.3600*** -0.6944***
Frequency
(3)
-0.2880**
(4)
Precision
(5)
-0.2880** -0.1554***
(6)
Ln_horizon
(7)
-0.3082*** -0.3082***
(8)
(9)
-0.3481**
-0.3481***
(-2.84)
(-2.97)
(-2.32)
(-2.04)
(-2.88)
(-3.47)
(-2.69)
(-2.61)
(-2.78)
0.3375**
0.6544***
0.2125
0.2125
0.0993**
0.2963***
0.2963***
0.2977**
0.2977***
(2.53)
(2.63)
(1.15)
(1.23)
(2.09)
(3.33)
(3.45)
(2.26)
(2.81)
-0.2329***
-0.2329***
-0.3028*** -0.4990***
-0.1069*** -0.1069**
-0.0293**
-0.1443*** -0.1443***
(-9.90)
(-9.53)
(-4.17)
(-2.39)
(-2.33)
(-8.54)
(-3.20)
(-7.77)
(-3.20)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
16,555
16,555
20,719
20,719
20,719
20,719
20,719
20,719
20,719
Adj. / Pseudo R-squared
0.332
0.333
0.5953
0.5953
0.460
0.4777
0.4777
0.4914
0.4914
Firm Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Year Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Industry
Industry
Industry
Ind & Year
Robust
Industry
Ind & Year
Industry
Ind & Year
Probit
Logit
OLS
OLS
Poisson
OLS
OLS
OLS
OLS
Control
SE Clustered by
Regression Model
38