Download Tacit Collusion and Voluntary Disclosure

Tacit Collusion and Voluntary Disclosure:
Theory and Evidence from the U.S. Automotive Industry
Jeremy Bertomeu, John Harry Evans III, Mei Feng, Jing Wu∗
Abstract
This study examines when and how industry peers can coordinate production
by sharing information in the form of production forecasts. In a tacit collusion
framework, we identify conditions under which firms will share private demand information in order to achieve “benefits” of voluntary disclosure. From the model we
establish the role of market demand and firm idiosyncratic financial stress as determinants of information sharing. Using monthly production forecast data for the Big
Three U.S. automobile manufacturers over a period of 31 years, we find empirical evidence that is generally consistent with predictions of our model. Demand forecasts
are shared among the Big Three auto manufacturers, but when demand increases,
forecast frequency and forecast accuracy decline. Likewise, forecast accuracy also
declines when individual firms experience financial stress.
1.
Introduction
Prior literature in accounting has analyzed information sharing among industry peers
within the context of a one-shot interaction. A one-shot interaction induces relatively
intense competition which can be softened by repeated interactions among firms. Each
firm anticipates that taking a more aggressive competitive stance in the present can
increase both current profits and the intensity of future competition, such as price wars.
An extensive research in industrial organization has studied how tacit agreements can
discipline firms to better coordinate their operating decisions. This study examines how
these long-term agreements affect information sharing among industry peers.
Regulatory concerns about information sharing have existed since the Sherman Antitrust of 1890, because information sharing among competitors can facilitate their cooperation.1 While tacit agreements are prohibited by the Sherman Antitrust Act, the
U.S. Supreme Court concluded in early rulings that information sharing alone provides
∗
J. Bertomeu is from Baruch College, City University of New York, 1 Bernard Baruch Way New York,
NY, 10010. J. Evans, M. Feng and J. Wu are from the Joseph M. Katz Graduate School of Business,
University of Pittsburgh Mervis Hall, Pittsburgh, Pennsylvania, 15260. Corresponding author: Jing Wu
([email protected]).
1
The early cases of American Column Co. v. United States (1921) and Maple Flooring Mfrs. Ass’n
v. United States (1925) provide early jurisprudence on antitrust actions against information sharing in
trade associations.
1
insufficient evidence of a tacit agreement. They stated that “the public interest is served
by the gathering and dissemination, in the widest possible manner, of information with
respect to the production and distribution, cost and prices in actual sales, of market commodities because the making available of such information tends to stabilize trade and
industry, to produce fairer price levels, and to avoid the waste which inevitably attends
the unintelligent conduct of economic enterprise." (p. 268, U. S. 583). Yet, we are not
aware of any formal testable theory that links information sharing to tacit agreements.
In this study we hypothesize that information sharing is more valuable with a tacit
agreement because it reduces the proprietary costs of revealing competitive information.
In a parsimonious model, we show that demand information that would not be shared in a
one-shot interaction is shared in the multi-period version of the game. Then, we conjecture
that factors that affect the disciplining role of reputations in the tacit agreement should
also affect the amount of information sharing. In the model, we formally establish that
information sharing is less in periods of high current demand, when the disciplining threat
of greater competition in future periods is relatively weaker. The model thus predicts that
information sharing should be more extensive during periods of industry slowdowns.
We test these predictions using U.S. automobile industry production over a 31 year
period. The U.S. automobile industry offers a relatively clean test the of the theory
because it features large players engaged in repeated communication, constitutes a sizable
component of the U.S. economy and identifies which information is shared. Prior to
1995, the U.S. market share was concentrated in three big large companies (GM, Ford
and Chrysler) accounting for more than 70% of total revenue (Figure 3). Throughout
most of the 31 years of the empirical sample period, the industry had been relatively
protected from entry by barriers ranging from economies scale, brand recognition, to
technological superiority. Further, the industry has its own trade publications, Ward’s,
through which a variety of business news and information are shared both for collaborating
among companies as well as informing stakeholders.2 The most important information
disclosed in Ward’s weekly newsletters, Ward’s Automotive Reports, is each company’s
monthly production forecast. This forecast implies each company’s predicted market
demand and rival firms then have direct use of such information when making their
operational decisions. As such, the automotive industry provides a perfect example of an
industry in which the existence of the tacit agreement is least controversial and where
voluntary disclosures are readily identifiable.3 The data is consistent with the model’s
predictions and reveals that precise information is shared frequently by the three main
2
Ward’s is an organization that covers the automotive industry since 1924. Automakers release their
information, including production forecasts, to Ward’s and Ward’s subsequently publishes them in its
weekly newsletters after information from all automakers have been received.
3
See Bresnahan (1987) for further evidence of collusion in the automotive industry. Consistent with the
benchmark model developed here, Bresnahan finds that periods of greater industry profitability weaken
the tacit agreement.
2
players and the quality of the shared information appears to increase when the industry is
doing worse. We document that, contrary to the non-sharing Nash equilibrium in a oneshot interaction, companies use information forecast by their industry peers to update
their own forecast and make adjustment to their actual production. Consistent with
our model’s prediction, the three main automobile manufacturers share relatively precise
information, particularly when demand is weak.
The study offers three main contributions. First, within the broader voluntary disclosure literature the study provides a more nuanced view of the proprietary costs of product
market disclosure. Contrary to traditional wisdom, we provide theoretical and empirical
evidence that information sharing can be important in concentrated industries. Second,
the study links the voluntary disclosure literature to the literature on product markets
and the business cycle by showing that disclosures vary inversely with the industry’s business cycle. Third, the study offers some insights as to how regulators might monitor tacit
agreements that are difficult to detect directly because they do not feature formal contracts. Similarly, a high profit margin may indicate an industry innovating or differentiate
products rather than a tacit agreement. Monitoring cyclical patterns of disclosure may
offer a regulatory tool to detect the existence of a tacit agreement.
Our formal model examines an oligopoly in which firms in an industry engage in repeated productive interactions. In each period, each firm observes a joint signal about
general conditions in the industry and a private signal about current demand. The common signal might represent a macroeconomic forecast that is likely to affect demand for
products produced by this industry. The private signal can represent firms’ private information about whether consumers will increase their spendings in this particular industry.
We assume that firms in the industry publicly choose whether to share their private signal
prior to making their production decisions for the period.
Information sharing has two effects on product market competition. First, it gives
firms the ability to adapt their quantity to precise demand forecasts, and thus tends to
coordinate the entire industry on more efficient production plans. Second, it allows each
firm to better forecast the quantities chosen by its competitor and, in doing so, raises
the payoff to a targeted aggressive move. In the classic case of quantity competition
Darrough (1993) shows that the second effect dominates and firms do not share demand
information.
The tacit agreement preserves both effects but alters their relative strength. When
engaging in the tacit agreement, firms tend to maximize total industry profits so that
the coordination benefits information sharing are still present if not magnified. On the
other hand, the competitive costs of more informed competition are reduced by the threat
of competition in future periods. This reduces the payoff to an increase in production
by an informed player, thereby neutralizing some of the costs of information sharing.
3
As a result, tacit agreements tend to feature more information sharing than a one-shot
interaction. Because concentrated, mature industries offer conditions conductive to tacit
agreements, this finding is contrary to the notion that more concentrated industries must
feature greater proprietary costs.
Factors that strengthen or weaken the tacit agreement should affect the quality of
the information sharing that emerges. Following Rotemberg and Saloner (1986), the
disciplining threat of competition in future periods is more effective when current demand
is low, reducing the returns to intense current period competition. Hence, the tacit
agreement is more (less) effective in periods of low (high) market demand, and more
(less) information is shared during periods of low (high) demand. Similarly, because firms
that are under greater financial stress should emphasize short-term cash flows we expect
less information sharing by such distressed firms.
The early accounting literature examines the determinants of voluntary disclosure
in the context of financial reporting, i.e., when the informed seller of a firm can make
a truthful disclosure to maximize market perceptions about the firm’s financial condition. The unraveling theory (Grossman 1981, Milgrom 1981) demonstrates conditions
under which full-disclosure result because firms with more favorable information in any
withholding region would always be better off disclosing their information. Empirical
evidence, however, seems to be at odds with this extreme prediction. Executives contend
that disclosed information is likely to be used by competitors in ways that are potentially harmful to the disclosing company’s future prospects. A large follow-up literature
thus argues that proprietary costs creates the tension that limits voluntary disclosures
(Verrecchia 1983, Dye 1986).
While proprietary costs were initially modeled as an exogenous fixed costs conditional
on a disclosure, the more recent literature has provided several important insights that
more closely tie characteristics of the product market to the magnitude and nature of
such proprietary costs. Darrough (1993) shows that in many competitive environments,
one should speak about proprietary “benefits” rather than proprietary costs. In specific
she shows that quantity and cost information are unilaterally shared by a firm who is
engaged in price competition. It is thus critical to understand the nature of the ongoing
competition before any claim to the existence of proprietary costs can be offered. Subsequent literature identifies features of the competitive environment that can affect the
magnitude of the proprietary costs, such as the regulatory regime in place or the implied
effect of proprietary costs on entry or exit (Feltham, Gigler and Hughes 1992, Chen and
Jorgensen 2012, Suijs and Wielhouwer 2012). Other studies have shown that reporting
and legal motives can interact with proprietary costs or benefits in the product market
(Gigler 1994, Evans and Sridhar 2002). One study that analyzes voluntary disclosure
within a tacit agreement is Bertomeu and Liang (2011). In their setting, a single firm ob4
serves a private signal and then decides whether or not to disclose its private information,
which implies that firms may use asymmetric disclosure strategies. Our approach is more
similar to Darrough (1993) in that firms choose the precision of the information system
and we do not focus on the asymmetry of disclosure.
While analytical models find that the nature and stage of competition affect voluntary
disclosure of product market information, empirical tests of these models have been carried
out by analyzing management earnings forecasts across different industry concentration
levels, a proxy for the intensity of existing competition, or capital structure and marketto-book ratios, proxies for possibility of new entrants (Bamber and Cheon 1998, Li 2010).
These studies find mixed results about the impact of competition: concerning potential
entrants, proxies for threats of entry are negatively correlated to disclosure venues in
which participants can press for more information (Bamber and Cheon 1998) but positively correlated with both quantity and quality of the forecast (Li 2010). With respect
to the intensity of existing competition, the level of concentration has not been found
to significantly affect the information release channel (Bamber and Cheon 1998) or the
accuracy of investment forecasting (Li 2010); on the other hand, concentration is shown
to negatively affect the number of forecasts (Li 2010) and the specificity of forecast numbers (Bamber and Cheon 1998), implying high (low) existing competition encourages
(discourages) disclosure.
There are many studies prior to ours that look at a particular firm or industry subject
to a set of economic conditions or observable information well-suited to test a theory
of voluntary disclosure. Bhojraj, Blacconiere and D’Souza (2004) examine the impact of
increased competition after the deregulation of the electric utility industry on information
content in firms’ annual financial report; their focus is primarily on the trade-off between
competition and reporting needs. Like the automotive industry, the electricity industry
features locally concentrated markets, but is less suited to testing the current theory
because it is less sensitive to business cycle changes and the nature of producing electricity
does not allow for advance production forecasts. The only accounting paper that we
are aware of and which analyzes trade association disclosures is by Uday, Procassini
and Waymire (1999). Focusing on the semi-conductor industry, they find that trade
association contain a significant amount of forward-looking information that is not fully
captured by other venues. Nagar and Rajan (2001) examine production information at
the plant-level from a large manufacturing group and show that many internal production
metrics are correlated to future sales. Curtis, Lundholm and McVay (2012) show that
a life-cycle model of store openings in the retail industry can predict future revenue.
Chapman (2012) presents a model that earnings management can affect competitors’
actions and provides evidence consistent with these predictions from promotions in a U.S.
supermarket chain. Finally, several recent studies such as Indjejikian and Matejka (2009),
5
Banker, Darrough, Huang and Plehn-Dujowich (2012) and Beyer, Guttman and Marinovic
(2012) combine analytic and empirical evidence to offer tests of economic theories.
2.
Model and Theory Development
This section develops the main research hypotheses within a simple model that illustrates the main tensions and intuitions. We assume two firms, indexed by i = 1, 2, are
engaged in repeated interactions and possess information that can be shared before operating decisions are made. The game takes place over an infinite horizon, with time
indexed by t = 0, . . . , +∞. Investors in the market value future cash flows with discount
rate 1/(1 + r) = β ∈ (0, 1).
The game begins at t = 0 and in each period a state of the economy st is realized,
representing the industry’s expected market demand condition. The state st is publicly
observable and varies over time. Each period, the state of the world is drawn from an
i.i.d. process with support over [s, +∞).4 In each period, firms compete on quantities
and achieve the following profit:
Πt,i = qt,i (st +
2
X
ut,i − qt,i − αqt,−i )
(1)
i=1
√
where qt,i is a quantity decision made in period t, α ∈ (2( 2 − 1), 1) represents the
substitution between products and ut,i represents firm i’s private signal about industry
conditions in period t.
Because we focus on incentives to share information related to the market condition, we
consider common-value demand information. Since all information would be shared even
without any tacit agreement if α is small or negative (Raith 1996), as for example in the
√
case of price competition, we focus on the more interesting case where α > 2( 2 − 1). For
similar reasons, we rule out perfect substitutes since, in that case, not sharing information
is always preferred. Each signal of the demand shock in a period ui is privately known
to firm i but unknown to its competitor. Specifically, we assume that ui is i.i.d. and
normally distributed with mean zero and variance σ 2 , p.d.f. g(.) and c.d.f. G(.).
In each period t, at t.1, firms can commit whether or not to share information by
revealing their observed ut,i to the trade association. The trade association circulates
information only if both firms have provided their information. This situation seems
descriptive of trade associations in which all participants must supply information to
receive information. At t.2, firms observe either the report made by the trade association
(ut,1 , ut,2 ) or the association makes no report. Firms then choose a quantity qt,i for the
4
In this type of model, Bagwell and Staiger (1997) discusses how the i.i.d. assumption can be extended
to a mean-reverting process.
6
period. If the information is not shared, firms then based on their own private information
ut,i to make their quantity decision. At t.3, firms realize their current profit Πt,i and
observe the quantity sold by the competitor.5 Period t + 1 then begins and the firms can
condition how they now play the game in t+1 as a function of their observations of period
t.
Similar to Darrough (1993), one limitation of this information sharing framework
is that it does not allow for ex-post changes in the sharing decision, i.e., a firm can
commit to the information system but cannot deviate to share (or not to share) after
it observes its private information. In the context of the data available, this allows us
to focus on precision, given that firms do not seem to selectively withhold high or low
production plans. Having noted this however, we do not model the decision to exit the
trade association after observing large unexpected private signals, as for example predicted
in Wagenhofer (1990) or Bertomeu and Liang (2011).
We present next the equilibrium concept. Each period features a strategic interaction
that features first an information sharing decision followed by an operating decision. As
is usual in this literature, we refer to this interaction over one period of the game as a
stage game and define first strategies for a stage game.
Definition 2.1 A stage-game strategy Γ is defined as:
(a) An information sharing decision H(s) ∈ {d, nd} which maps any state of the world to
a decision whether to share information (H = d) or not share information (H = nd)
and,
(b) a quantity choice decision Qnd (s, ui ) ∈ R+ if information is not shared and Qd (s,
if information is shared.
P2
j=1
Conditional on a vector of strategies (Γ1 , Γ2 ) and state s, denote V (Γ1 , Γ2 ; s) as the
payoff in the current period. In short-hand, we denote V (Γ; s) ≡ V (Γ, Γ; s) and V (Γ) ≡
E(V (Γ, Γ; s)).
We are interested here in stationary symmetric equilibria that are ex-ante preferred
by all firms in the industry. The equilibrium takes the following grim-trigger form: (i)
firms adopt the stage game strategy Γ∗ beginning at t = 0 and then for all periods, unless,
(ii) an action inconsistent with Γ∗ is observed, in which case, all firms no longer use their
reputations and play the Nash equilibrium of the single-period game. We denote the latter
the punishment path and, hereafter, refer to an equilibrium of the sort in the repeated
game as a tacit agreement.6
5
The assumption is relatively mild given that, if firms can share information prior to production, they
will also be able to share their information after production.
6
We use here the Nash equilibrium of the stage game as the punishment path for illustrative purpose,
but the results are unchanged if we use any other punishment path.
7
uj )
Definition 2.2 The preferred tacit agreement consists of a stage game strategy Γ∗ that
is the solution of the following program:
maxΓ V (Γ)
(Γn )
(Γ)
≥ maxΓ0 V (Γ0 , Γ; s) + β V1−β
s.t., for any s, V (Γ; s) + β V1−β
where Γn is the Nash equilibrium of the stage game, i.e., the strategy Γn that satisfies
the following equilibrium condition:
Γn ∈ argmaxV (Γ, Γn )
(2)
The repeated game has a nature that is slightly different from a single-period competitive game. In a single-period game, all firms play the stage-game strategy Γn , ignoring
the possible repercussions of an aggressive competitive stance on future periods. In a
tacit agreement, on the other hand, firms fully consider the potential loss of reputations
in future periods that follows a period of excessive competition. Stated differently, reputations discipline firms to implement higher quantities than what they could implement
if the interaction were one-shot.
Whether firms in an industry would be able to implement such a tacit agreement
is an empirical matter, but there are strong reasons to suspect that such an agreement
would naturally emerge in an organized industry such as the US automobile industry.
The US antitrust law explicitly prohibits any attempt to organize the industry as a cartel
but, in practice, the enforcement of antitrust requires proof of anticompetitive practices.
Another relevant fact about the US automobile industry is that it features a large number
of industry forums and meetings between participants (of which the trade association is
one example) that feature considerable informal communication. It seems, in this respect,
quite plausible to firms would naturally coordinate on the tacit agreement that would
maximize their industry profits.
It is of some importance to note that most examples of tacit agreements studied
in the industrial organization literature are not renegotiation-proof (Green and Porter
1984, Rotemberg and Saloner 1986, Bagwell and Staiger 1997, Athey and Bagwell 2001).
For example, in this model, a firm that deviates from the tacit agreement by producing
too much in one period would trigger the punishment path but could, then, solicit the
competitor to renegotiate toward the initial tacit agreement and, given that the deviation
occurred in the past, improve industry payoffs moving forward. Of course, such logic
would defeat any possible tacit agreement since, then, the disciplining mechanism would
be expected to be renegotiated away. This a limitation of this study and overall literature
which, we believe, may be understood as partly behavioral - i.e., people are unwilling to
candidly renegotiate with a trade partner that previously cheated - and partly due to the
8
specific context of industries under regulator monitoring - an open renegotiation away
from a price war would likely be suspect to antitrust authorities. Indeed, renegotiations
are more commonly analyzed in contexts where renegotiations are legal and organized, as
for the case of debt contracting (Magee and Sridhar 1996).7
As a benchmark, we begin by analyzing information sharing in the stage game Nash
equilibrium. While this is not new for the study (Darrough 1993, Raith 1996), we will later
on use this benchmark to show that the type of information sharing observed empirically
is inconsistent with this equilibrium but consistent with a tacit agreement.
Proposition 2.1 In the Nash equilibrium of the stage game Γn , firms do not share information for any st and choose an equilibrium quantity:8 Qnd (s, u) = s/(2 + α) + u/2.
2
s2
Firms achieve a per-period profit: V (Γn ; s) = σ4 + (2+α)
2.
The intuition for this classic no-sharing result is as follows. When α is close to one (i.e.,
firms’ products are good substitutes of one another), no-sharing of private information is
the dominating strategy.9 This shows that having each firm independently raise quantities
as a function of their own private demand does not necessarily lead to an inefficient use of
the information at the industry-level. In the extreme case of α ≈ 1 (perfect substitutes),
if (q1 , q2 ) were chosen by an industry planner to maximize total industry profit, the policy
qi = s/8 + ui /4 could attain the maximal possible industry profit even without sharing.
In the competitive stage game, disclosing or sharing information does not benefit industry
coordination but, as is intuitive, creates adverse incentives to compete more intensely.
The most relevant takeaway of the competitive stage game Nash equilibrium is that
the not-sharing information is always chosen, regardless the state of the market condition.
Indeed, firms in the industry should prefer not to share information regardless of the
realization of st ; the state of the world is only a scaling effect that raises the payoffs
from any sharing or no-sharing decision equally. In fact, Darrough (1993) and Raith
(1996) show that, within this framework and a single-period interaction, this findings
holds generally and the ex-ante expected level of market demand
We examine next the optimal strategy in a tacit agreement. The general approach of
the analysis will be to first examine the quantities chosen when sharing versus not sharing
to sustain tacit collusion. Next, noting that the optimal tacit agreement should always
7
Mailath and Samuelson (2006) give a few examples of games with asymmetric punishments that are
renegotiation-proof. One problem with such strategies, while intellectually more satisfying, seem to be
overly complicated (much more than the simple trigger strategies discussed here) and, for all practical
purposes, would require managements to lay out detailed plans about how to monopolize an industry that such strategies would be implemented naturally without a paper trail is unlikely. We do not know
if, in our game, asymmetric punishments could be made renegotiation-proof).
8
To save space, we omit the off-equilibrium path.
9
As α decreases below one, not sharing induces a cost in that, to maximize total industry surplus,
each firm should condition its quantities on all private signals. Yet, because of the
√ extreme nature of the
competition in the one-shot game, no-sharing is still preferred as long as α > 2( 2 − 1).
9
select the profit-maximizing strategy in each stage game, we compare profits and formally
derive whether information should be shared as a function of the state of the market st .
Assume first that the tacit agreement prescribes disclosing, H(s) = d, in the state
of s and let us derive the optimal quantity choices. Within a tacit agreement, firms
now implement a production quantity lower than the quantity in the single-period game
because reputations serve as a disciplining mechanism. Letting Qd (s, u1 + u2 ) be the
quantity prescribed in the tacit agreement when the trade association reports u1 + u2 , the
incentive-compatibility condition is stated as follows:
Qd (s, u1 + u2 )(s + u1 + u2 − (1 + α)Qd (s, u1 + u2 )) + β
V (Γ∗ )
1−β
V (Γn )
≥ max
q(s + u1 + u2 − q − αQd (s, u1 + u2 )) +β
q
1−β
|
{z
}
(3)
V dev
The left-hand side of this inequality is the profit if the firm sets its quantity following
the prescription of the tacit agreement and, thus, continues obtaining the profit of the
tacit collusion V (Γ∗ ) in future periods. The right-hand side of the inequality is the surplus
obtained by a firm deviating from the tacit agreement in the current period, after which
the agreement is broken and firms achieve their single-period profit V (Γn ) in all future
periods.
In the right-hand side of Equation (3). the current profit that can be achieved when
deviating for one period can be calculated explicitly as:
1
V dev = (s + u1 + u2 − αQd (s, u1 + u2 ))2
4
Substituting in this expression, the incentive-compatibility constraint in Equation (3) can
be written as a minimum quantity Qd (s, u1 + u2 ) that can be elicited,
1
Qd (s, u1 + u2 ) ≥
(s + u1 + u2 − 2
2+α
s
|
β(E(V (Γ) − V (Γn )))
)
1−β
{z
K
(4)
}
This condition includes two terms. The first term (s + u1 + u2 )/(2 + α) is the quantity
chosen in the single-shot game, as shown in Proposition 2.1. The second term 2K/(2 + α)
captures the reputation effect that arises from the repeated interaction. The role of this
reputation effect is to lower feasible quantities, possibly allowing quantities that would
maximize total industry profits.
In the next Proposition, we formally derive the choice of quantities in the tacit agreement after firms share information.
10
Proposition 2.2 Consider a tacit agreement in which information is shared for a realization of s,
K, firms implement their monopoly quantities:
(i) If s + u1 + u2 < 4 1+α
α
Qd (s, u1 + u2 ) =
1
(s + u1 + u2 )
2(1 + α)
(ii) Otherwise, firms implement a quantity higher than their monopoly quantity, as given
by:
1
Qd (s, u1 + u2 ) =
(s + u1 + u2 − 2K)
2+α
Quantity Qd
single-period game
Incentive-compatible
1.0
Industry profit
maximizing
0.5
Total demand s+u1+u2
1
2 4 K (1+α)/α
3
4
Figure 1: Production quantity with information sharing
The tacit agreement is illustrated graphically in Figure 1. The quantities that would
prevail in a single-period game are plotted as a dotted upper line; below this line, the
dotted region represents quantities that can be implemented in the repeated game. Lastly,
the solid line represent the quantities that maximize total industry profits (and is always
strictly lower than the quantity in the single-period game). When s + u1 + u2 is low, the
industry profit maximizing quantity lies within the dotted region and can be implemented.
In this case, the industry realizes its maximum profit and total rents to members of the
industry are realized. When s + u1 + u2 becomes larger, the industry profit maximizing
quantity is much lower from the single-period quantity and thus it is no longer incentivecompatible for the firms. Put differently, the larger size of the industry demand makes
a current deviation too tempting relative to the value of the reputation. To avoid such
deviations, the tacit agreement prescribes a higher quantity but at the cost of achieving
lower industry profits. This result is consistent with the classic results in Rotemberg and
Saloner (1986). In summary, the quantity chosen is always the maximum of the two solid
lines.
11
Next assume that the tacit agreement prescribes not disclosing or sharing information
among competing firms. As for the case of sharing, the presence of reputation concern
will tend to lead to lower quantities than those that would prevail in a single-period game.
Specifically, the optimal tacit agreement solves the following problem:
Qnd (s, u) ∈ maxm
Q(u),q
Z
Q(u)(s + u − Q(u) − αq m )g(u)du
s.t.
qm =
Z
Q(u)g(u)du
for any u, Q(u)(s + u − Q(u) − αq m ) + β
V (Γ)
V (Γn )
≥ max q(s + u − q − αq m ) + β
q
1−β
1−β
This program is similar to the case of information sharing with a few notable differences. First, the tacit agreement must now prescribe quantity choices Qnd (s, u) that
depend only a firm’s own private information, rather than the total market demand.
Given that products are imperfect substitutes, this tends to prevent firms from fully
responding to all demand shocks and reduce total industry profits. Second, the incentivecompatibility condition is now written in terms of the expected quantity produced by the
competitor given the distribution of the shocks, which also tends to make deviations less
attractive.
Proposition 2.3 Consider a tacit agreement in which information is not shared for a
realization of s, then:
(i) If s ≤ 4K(1 + α)/α, firms implement their monopoly quantities:
Qnd (s, u) =
u
1
s+
2(1 + α)
2
(ii) Otherwise, firms implement a quantity higher than their monopoly quantity, as given
by:
s
u
2
+ −
K
Qnd (s, u) =
2+α 2 2+α
In the absence of sharing, only the public demand signal s are used by both firms while
the privately observed shocks are used by each of them respectively. To be incentivecompatible, the tacit agreement must prescribe an expected quantity that is not too low
relative to the value of the reputation. This implies that if expected market demand is
low, the expected quantity that maximizes the total profit in an industry with no private
information disclosure can be implemented. On the other hand, when market demand is
high, the tacit agreement would prescribe a higher expected quantity; this takes the form
of firms producing more in response to each of their incomplete information about market
12
demand. A graphical representation of these findings is very similar to the former case of
information sharing in Figure 1, except that the horizontal axis must now be understood
as the expected market demand s and the vertical axis must be relabeled as the expected
quantity E(Qnd (s, u)).
Let us compare next sharing and not sharing, as a function of the market demand. As
noted earlier, the first-best monopoly industry profit can only be attained if firms share information and adapt their quantities to all available information. When expected demand
is low, the threat of future competition is sufficient to almost always enforce monopoly
quantities and thus the monopoly surplus becomes nearly feasible (Proposition 2.3). This
implies that sharing information must be preferred when low demand is expected. When
expected demand is high, however, the value of the reputation K is very small relative to
current potential profits and thus individual strategies will be similar to the single-shot
game. In this setting, as noted in Proposition 2.2, not sharing information is preferred
and firms realize in the current period that is the similar to the payoff in the single-shot
game. These are formalized in the next Proposition.
Proposition 2.4 In an optimal tacit agreement, there exists a threshold τ such that
information is shared when s < τ and information is not shared when s > τ .
The tacit agreement features two regimes. When market demand is low, so that industry profit maximizing quantities can be implemented using reputations as the disciplining
mechanism, the tacit agreement features information disclosure to allow for a more efficient
industry-wide use of information. When market demand is higher, the tacit agreement
prescribes to first soften competition by not disclosing or sharing private information
about demand shock. This allows for lower quantities to be implemented (following the
same intuition as the single-period game) and higher industry profits. This intuition is
further illustrated in Figure 2 where E(Πshare |s) is plotted against E(Πnoshare |s).
3.
3.1.
Empirical Analysis
Empirical Hypotheses
With Cournot competition, one-shot competition models predict non-disclosure of demand information is the equilibrium strategy for producers of close substitutes (GalOr 1985, Darrough 1993). The intuition behind that prediction is that without the disciplinary effect of reputation, non-disclosing company has more to gain from the available
information than the disclosing company. Contrary to this conventional prediction, information sharing through voluntary disclosure is documented by many studies (Doyle and
Snyder 1999, Raith 1996). Motivated by the fact that companies sharing a same prod-
13
Expected profit
670
No-Disclosure
660
650
Disclosing
640
5
10
15
20
Expected market conditions (s)
Figure 2: Sharing versus not sharing with the tacit agreement (σ = 50, K = 1, α = .9)
uct market feature long-run relation among each other, we examine the product market
disclosure strategy in a tacit collusion framework.
Intuitively repetitive interaction among peer firms in the same industry provides incentive through reputation mechanism to share information, which curbs the adverse
effect of competition on industry profitability. Our model suggests that information sharing through voluntary disclosure is attainable in a collusive industry. In this section,
we develop empirical tests that link information-sharing to the effectiveness of the tacit
agreement.
In a collusive industry, we assume that private information is credibly shared to enhance production quantity decisions. According to this assumption, actual production
is likely to be positively correlated to the forecast production quantity; and when there
are multiple rounds of forecasts the correlations with late rounds are likely to be greater
than early rounds. Furthermore, if each firm discloses and shares its private information
with its peers to maximize industry profit, the initial forecast of a firm is not correlated
to peer firms actual production while forecasts in later rounds are. These insights from
our model yield our first set of hypotheses:
Hypothesis 3.1 If private information is credibly shared among industry peers, the actual production of a firm reflects information shared by peer firms.
(a) Actual production of a company is positively correlated with its own forecasts and
the correlation is higher with later forecasts than earlier ones.
(b) Actual production of a company is positively correlated with its peers’ later forecasts.
In our model, the effectiveness of the tacit agreement depends on the current market condition since the threat of future competition is the key to discipline companies
14
and this threat becomes less powerful when the far future market is smaller relative to
the expected current market. Thus Proposition 2.4 predicts countercyclical information
sharing behavior: information sharing is more likely when market demand is expected to
be low, and vice versa. When the industry sharing information less actively, the total
number of forecast rounds for a production month increases as economic condition gets
worse. Similarly, the initial forecast is issued earlier and its accuracy is likely to be higher
under such condition. Following a recent finding that the unemployment rate accounts for
89% of variations in new vehicle sales and is the most important macroeconomic indicator
for vehicle demand (Sivak and Schoettle 2009), we use unemployment rate as our proxy
for macroeconomic demand condition in the automobile industry. Our main hypotheses
based on these predictions are:
Hypothesis 3.2 Information sharing among industry peers is less (more) likely when the
expected current market demand is high (low):
(a) The number of forecasts for a production month in the industry increases with the
unemployment rate.
(b) The horizon of the initial forecast for a production month in the industry increases
with the unemployment rate.
(c) The accuracy of the initial forecast for a production month in the industry decreases
with the unemployment rate.
We also present an additional hypothesis based on Proposition 2.4. In line with the
aforementioned impact of the relation between current and future demand on the disciplinary mechanism, our model implies that the effectivenss of tacit agreement depends on
how much firms value short term versus long term profitability. One cause of variations in
the relative value a firm placed on short term versus long term gains is financial distress:
if a firm is less likely to survive should discount future periods more heavily and focus
on making through the current period exclusively by collecting as much current cash flow
as possible. Consistent to the notion that companies under financial stress behave more
aggressively, a study of the airline industry shows that distressed firms are more likely to
enter price wars (Busse 2002). Thus we predict that when a company is under greater
financial stress and hence discount future more heavily, the tacit agreement ceases to
be effective to this company hence the company is less likely to share private demand
information. Hence we hypothesize:
Hypothesis 3.3 The accuracy of the initial forecast for a production month by a company
decreases with the level of financial stress the company experiences.
15
3.2.
Sample and Model Specification
Our sample includes domestic production forecasts of the Big Three U.S. automakers
(i.e., General Motors, Ford, and Chrysler) during the thirty-one years period 1965-95.
The data is collected by Doyle and Snyder (Doyle and Snyder 1999).10 These forecasts
are published in the automobile industry trade journal, Ward’s Automotive Reports, for
nearly all production months (369 out of 372 months) during the sample period starting
from as early as six months (198 days) prior to actual production. Following each initial
posting of the production forecasts, updated forecasts (if there is any) are published
in subsequent issues of Ward’s Automotive Reports until the actual production takes
place. The number of forecasts for each production month changes from month to month.
Although each release discloses production forecasts made by all three firms, the timing of
the announcement and the number of forecasts varies from month to month. The accuracy
of each forecast (with respect to actual production) varies both across companies and over
time.
We contacted Ward’s regarding the source of this information; they claim that the
production forecasts are voluntarily supplied by manufacturers and Ward’s releases this
information simultaneously once they receive forecasts from all member firms. This information is accessible to all members in the industry. Unlike press release or earnings
forecast, production forecast is not free to capital market investors. Moreover, the target
audience of production forecast is industry insiders, thus not known or used by nonprofessional investors. A study on the semiconductor industry’s trade-association releases
finds significant positive (negative) market reaction to good (bad) information (Uday et
al. 1999). Our results of actual abnormal returns are insignificant (untabulated). However, since we can not distinguish good news from bad news, the observed insignificant
abnormal returns may be due to positive and negative returns canceling out each other.
Using absolute abnormal market returns surrounding the three-day forecasting window
(-1 to 1 days), We find that production forecasts are informative to the capital market.
Table 2 presents the absolute cumulative returns during the three-day production forecast
announcement window: the absolute cumulative returns over the three-day window using
market model (equal weighted adjusted) return range from 1.9% (1.8%) to 3.0% (2.9%)
for the three automakers. The magnitude of the reaction is in line with a study of earnings forecast (Waymire 1984), suggesting information provided in production forecasts are
also valuable to the capital market. Nevertheless, production forecast is more relevant for
examining product market factors since the purpose of producing such information is to
facilitate operational budgeting and making production decision; and the release of such
information is primarily intented to be shared among industry peers through industry
10
We are extremely grateful to the authors, Chris Snyder and Maura Doyle, for generously sharing their
production forecast data. Their paper focuses on providing empirical evidence of information sharing
among product market competitors.
16
trade association.
Summary statistics of production forecast variables are provided in Table 3, Panel A.
Real productions from these three companies are quite different from each other: GM on
average produces (327, 012 units) more than Ford (155, 124 units) and Chrysler (87, 265
units) combined, which implies their different market standing/power in this industry. In
addition to the cross-sectional variation, the within company inter-temporal fluctuation
of production is substantial: the lowest production quantity is less than one tenth of
the highest. The inter-temporal variation could be due to macroeconomic demand and
supply-chain shocks, as well as changes in individual company financial condition. The
number of forecasts for a production month range from only 1 forecast to as many as
12 forecasts (the mean is 5); earliest forecast is released 198 days prior to actual production and updated forecasts continue until the actual production date.11 We examine
information sharing behavior in three dimensions: forecasting frequency (number of forecasts (NUMFORC)) for a production month, horizon of the initial forecast (number of
days between the date of initial forecast and the date of production (HORIZON)), and
the accuracy of initial forecast (the absolute value of percentage forecast error (ACCU)).
Overall forecast numbers are quite close to actual production quantity. Measuring at
(FORECAST − PROD)/PROD, the average forecasting error is about 6% and initial
forecasts are less accurate with percentage of error at around 15%.
Panel B of Table 3 presents the variables for measuring market or economic condition. Market demand is proxied by the monthly unemployment rate compiled by the
U.S. Bureau of Labor Statistics. Demand for new vehicles varies with macroeconomic
conditions. A recent study has shown that the unemployment rate accounts for 89% of
the variance in monthly vehicle sales and has significant negative effect on the seaonal
adjusted monthly sales (Sivak and Schoettle 2009). During the sample period, the unemployment rate fluctuates from its lowest point 3.4% to as high as 10.8%. The relative
price of new vehicles is measured by the monthly producer price index (PPI) of the motor
vehicle manufacturers scaled by consumer price index (CPI, a measure for the general
Producer Price Index
. Both seasonally adjusted producer price index and concost of living), Consumer
Price Index
sumer price index are obtained from the U.S. Bureau of Labor Statistics. This measure
captures the relative cost of production and is used in the analysis to control for the
impact of cost on production decision. However, the summary statistics show that this
measure does not change much overtime with a mean close to one (0.973). The monthly
capacity utilization of the automobile manufacturing industry is collected from the Federal Reserve Board statistical releases. The industrial organization theory argues that the
easier (harder) the firms can adjust their production quantities the better (worse) the fit
11
Some observations have negative horizons, implying forecasts release after actual production. We
bound the horizon by zero in our analysis.
17
of Bertrand (Cournot) model. 12 Thus when capacity utilization in this industry is high,
the industry is more likely to be in Cournot competition from the theoretical perspective.
We use this variable to test whether our model’s prediction on demand is robust to the
nature of competition. The percentage of capacity utilization in this industry fluctuates
between 44.271 and 104.033 during the sample period with an average usage of 78.767%
of the total capacity.
Financial ratios that are used to examine profitability, leverage ratios (the level of debt
financing), liquidity ratio (ability to meet near-term obligations) and operational efficiency
are obtained from COMPUSTAT Quarterly financial database. Accounting definitions of
the financial measures are given in Table 1: profitability is measured by return on asset;
leverage ratio is measured by the debt ratio and interest coverage ratio; liquidity ratio is
computed as the quick ratio, and efficiency is calculated inventory turnover ratio. These
financial ratios are widely used to measure a firm’s financial distress (Altman 1968). Using
a similar set of financial ratios a study of the airline industry has found that distressed
firms are more likely to enter price wars (Busse 2002). In this study we use these ratios
to examine whether a company’s financial conditions affect the management’s incentive
to disclose private information in participating industry coordination. If the firm is more
likely to deviate when it is under financial difficulties, then these ratios will have significant
impact on the forecasting behavior of the firm. Descriptive statistics of these financial
measure are presented in Table 3, Panel C. Among these financial ratios, interest-coverage
rate fluctuates the most, which suggests the level of financial stress borne by managers
indeed may vary over time in this industry. GM enjoys much higher ROAs than its
followers, Ford and Chrysler; which suggests its leader status in this industry.
Table 1: Definitions of Financial Ratios
12
Financial Measure
Definition
Return on asset
Operation Income
Total Asset
Debt ratio
Long Term Debt+Long Term Debt in Current Liability
Long Term Debt+Long Term Debt in Current Liability+Total Equity
Interest coverage ratio
Operation Income
Interest Expense
Quick ratio
Cash+Account Receivable
Total Current Liability
Inventory turnover ratio
Cost of Goods Sold
Inventory
We thank Professor Esther Gal-Or for her insight in the nature of competition.
18
We examine the credibility of forecast by looking at the coefficient between actual
production quantity and forecast quantity or the coefficient between subsequent forecast
quantity and preceding forecast quantity, controlling for the horizon of the forecast.
To investigate determinants of information sharing, we estimate the following model:
SHARING = f (DEMAND, STRESS, CONTROLS)
where the dependent variable is either SHARING is information sharing behavior measured by NUMFORC, HORIZON, or ACCU. We employ different regression models to
test our hypotheses: When NUMFORC is used as the dependent variable, we estimate
the equation using both OLS and ordered-response logit; the latter provides maximum
likelihood estimates that incorporates information embedded in the ordering of the dependent variable. When HORIZON is used as the dependent variable, we estimated the
equation using both OLS and Cox model. Cox model is also a maximum likelihood estimation that analyzes the probability of the spell between forecast and event (production)
taking certain amount of days. In all other cases OLS regressions are used. DEMAND is
proxied using unemployment rate. STRESS is proxied using financial ratios representing
financing pressure and profitability.
3.3.
Information Sharing through Voluntary Disclosure
To establish the role of production forecasts in industry coordination, We first examine
the credibility of these forecasts and their informativeness to other firms in this industry.
Credible forecasts are the ones that are accurate with respect to actual productions. The
regression analysis (Table 4) of the partial correlation between actual production and
initial or final forecast shows that both initial and final forecasts are highly correlated with
the actual production. Summary statistics (Table 3, panel A) of plan forecast accuracy
have already shown that plan forecasts are pretty accurate on average; here the regression
results confirm that production forecasts are highly correlated with actual output level,
suggesting information sharing and coordination among firms. Furthermore, the actual
output level correlates more (in terms of magnitude) to the final revision than the initial
forecast with parameter coefficients closer to one (increased from 0.935 to 0.995) and
higher model R2 (increased from 0.940 to 0.996), indicating strategic adjustments to
production plan after learning from initial forecasts.
A closer examination of the informativeness of the lagged forecasts to a firm’s forecast demonstrates the credibility of voluntarily shared information and the usage of the
information by its peers. Table 4 shows that individual company’s production quantity is
significantly correlated with its own initial forecast but not rival firms’ initial forecasts: a
company’s actual production is only positively and significantly correlated with it’s own
19
initial forecast (with coefficient 0.908) but not significantly correlated with its two peer
companies’ initial forecasts. Besides confirming the previous results about credibility this
also shows that initial forecast is based on the private information of each disclosing firm
rather than some common knowledge in this industry. More interestingly, when regressed
on each firm’s own initial forecast and rival firms’ final forecast revisions, the correlations
between its output level and own initial forecast and rivals’ updated forecasts are all
positive and statistically significant (with coefficients 0.857, 0.188 and 0.026 to its own
forecast and two peers’ forecasts respectively). In short, these results suggest that after
learning private information from their peers companies adjust their production quantities
accordingly.
Overall the results show that this industry has properties of a collusive framework
where firms voluntarily share their private information with their peers. Notwithstanding
the evidence here, collusion model does not necessarily apply to other voluntary disclosures
or forecasts without such supporting empirical evidence.13
3.4.
Market Demand and Information Sharing
The second hypothesis (Hypothesis 3.2) based on our model is that information sharing
among industry peers is less (more) likely when the expected current market demand is
high (low). In the automobile industry the adjustment costs are high to expand production in short amount of time (Ben-Shahar and White 2006); hence timely forecast is
critical for peer firms to react to the information. Moreover, firms on average announces
about 4-5 updated forecasts following the initial forecast with significant amount of both
downward and upward adjustments. This suggests that firms implicitly coordinate with
each other via multiple rounds of information disclosure and learning. Hence fewer number of forecasts leads to less learning and sharing. We examine how the market demand
(measured by UNEMP14 ) affects the number of forecasts and initial forecasting horizon
of a production month, after controlling for production cost (PPI_CPI) and industry
capacity utilization (CAP).
The number of forecasts issued for a production month indicates how many possible
rounds of communication among these industry peers. If high market demand discourages
information sharing then the predicted sign on UNEMP should be positive. Table 5 reports the regression results from both OLS and Ordered Logistic regressions. The number
of forecasts is shown to be negatively and significantly correlated with market demand
(a positive sign on UNEMP) after controlling for other market or industry conditions
13
(Stocken 2000) shows that management forecast is credible in repeated games with investors, as long
as the management is sufficiently patient such that the reputation mechanism is sufficiently effective.
14
Unemployment is shown to be the most important macroeconomic predictor for automobile
sales(Sivak and Schoettle 2009). Results are consistent when domestic automobile SALES are used
as the measure of demand (untabulated).
20
(PPI_CPI, CAP). Adding the interaction term, UNEMP interacting with a dummy variable indicating high capacity utilization (CAP > 80%), does not affect the positive and
significant demand effect in both OLS and LOGISTIC regressions. Furthermore, in the
LOGISTIC regression, the parameter estimate on the interaction term is also positive and
significant, suggesting the impact of UNEMP is even greater when capacity utilitzation is
high. These results show that the negative impact of demand on the number of forecasts
issued is robust to both low capacity and high capacity situations. Hence the empirical
results based on the number of forecasts are consistent with our model’s prediction: the
higher the demand, the less the incentive to voluntary private information to the industry.
Initial forecasting horizon is measured as the number of days between the date of
the first forecast of a production month and the date of actual production. Shorter
forecasting horizon leaves other companies with less time and higher costs to react to the
disclosed information. Therefore, when the market demand is high and companies are
reluctant to share information thus the predicted sign on UNEMP is positive. Both OLS
and Hazard Ratio regressions show that the initial forecasting horizon is negatively and
significantly correlated with market demand (Table 6). The hazard ratio (Hazard Ratio =
0.599) on the unemployment rate implies that the lower the market demand (the higher
the unemployment rate) the longer the time between the initial forecast and the actual
production, leaving more time for industry peers to learn and adjust operational plans.
Therefore, consistent with the model prediction that the incentive to disclose private
information declines with the growth of demand, market demand is negatively associated
with production forecast frequency and initial forecasting horizon.
We also examine whether the forecasting accuracy in this industry changes with the
market demand. The initial forecasting accuracy is proxied by percentage forecast error:
the difference between forecast and actual production scaled by the actual production. If
high demand reduces the incentive to release production forecasts the sign of the parameter estimate on UNEMP would be negative. The results show that the initial forecasting
accuracy is negatively correlated with market demand (Table 7, column (1) and (2)): the
lower the unemployment rate, the greater the initial forecast error. Thus the empirical
results are consistent with our model’s prediction that market demand negatively affects
forecasting accuracy.
3.5.
Discount Factor
The second prediction of our model is that when the discount factor β is high, the net
benefit from sharing information with peers is also high. In the voluntary disclosure literature, the underlying rationale for the incentive to disclose private information is that the
21
benefits (from attracting investment and reducing the cost of capital) outweigh the costs
(such as proprietary costs from the product market competition). Such costs typically are
assumed to negatively affect disclosing firm’s competitive advantage in achieving future
earnings. Many studies have addressed determinants of managers’ myopia that leads to
discretionary behaviors of managers including managing earnings or involving in accounting fraud (Narayanan 1985). We examine the impact of a firm’s financial distress on
information sharing to test our Hypothesis 3.3. Since there is no cross-sectional variations with respect to forecasting horizon and frequency in our setting, the discount factor
analysis is limited to the initial forecasting accuracy. We conjecture that when a firm is
under financial stress, the situation would impose pressure on the management to pursue
near-term performance (lower the β) and hence shift expected profits from future periods
to the current period.
In the airline industry, evidence about the association between firm’s financial stress
and incentive to instigate price war has been documented (Busse 2002). Follow similar
reasoning, individual firm that is in financial troubles would be less likely to share its
private demand information (in an attempt to deviate from collusive production level) in
order to improve current period return. We measure the level of financial distress using
four financial ratios that reflect the leverage, liquidity efficiency and profitability. The
accounting definitions of these measures are introduced at the beginning of Section 3.
As shown in the summary statistics (Table 3 Panel C). However, caution is warranted
to interpret insignificant association between these ratios and discretionary forecasting
behavior, as fluctuations of these ratios within certain thresholds (indicating a overall
health financial conditions) may not induce myopia behaviors. Table 7, column (3),
shows that among these financial ratios, the parameter estimate on DEBT is significant
and positive while on QUICK is significant and negative. Both indicate that when a
firm is weaker in its financial position (higher debt ratio or lower quick ratio), its initial
forecasting accuracy becomes lower; which is consistent with the prediction that the
managers are motivated to deviate when the firm is under stress. However, demand effect
dominates the impact of stress as shown in Table 7 (column (4), (5), and (6)).
There are two limitations of this analysis. First, previous analysis of credibility has
shown that Myerson’s revelation principle (Myerson 1982) applies and production forecasts in this industry are quite accurate on average. Therefore discretionary behavior may
be mainly shown up in other dimensions, such as timing and frequency. In our sample
however, firms simultaneously release their forecasts through the trade journal, there is
no cross sectional variation to exploit in this setting. Secondly, this is a single industry
study with only three major players in this industry. The mixed results we found here
may not be generalized to other settings.
22
3.6.
Extensions
Another layer of firm specific incentive comes from the product market standing of a
firm, i.e., the leader or the follower(s). GM is considered as the leader in this industry
according to both anecdotes (Adams 1994) and evidence from its output and profitability
(ROA). The summary statistics of the outputs of these three companies (Table 3) show
that GM produces more vehicles than the other two combined. Profitability analysis
over the sample period also shows that GM was the most profitable among these three
automakers. Industry leaders are considered less susceptible to losing its competitive
advantage since the followers are merely claiming residual demand; moreover, leaders
may be subjected to more reputation concerns hence are more pressured to be truthful.
Therefore individual firm’s discretions would be more pronounced among followers. We
examine the association between forecast accuracy and industry leader-follower status.
Results are displayed in Table 7 (Column (6) and (7)). We do not find significant effect
of a firm’s market standing on the accuracy of its initial production forecasts. It is worth
noting that the same caveats apply to this analysis as to the discount factor analysis:
forecast incentive may only manifest in timing and frequency but those variations are
suppressed in this setting.
4.
Conclusion
This paper examines how firm’s voluntary production forecasting behavior is affected by
the market demand in an implicitly collusive industry. Our model shows that in a concentrated industry tacit collusion increases industry profitability and repeated interactions
sustain collusion through reputation mechanism. However, the first-best production is
only implementable when demand is sufficiently low since the reputation mechanism becomes ineffective as the temptation to deviate increases along the market demand. To
sustain tacit collusion, restricting demand information by prescribing non-sharing in the
collusive agreement helps the industry soften competition. Therefore, we predict negative correlation between disclosure and the effectiveness of reputation mechanism that is
affected by the market demand condition and the patience of firms. Empirical evidence
in the U.S. automobile industry justifies the adoption of a collusive framework because
companies in this highly concentrated industry indeed voluntarily share their production
forecasts with their peers. We show that when the market is booming firms become reluctant to share their private information about the market demand the incentive to deviate
increases. Empirical results using unemployment as the measure for market demand condition are consistent with this prediction. As the market demand increases, companies
disclose their production forecasts less frequent, less timely and less accurate.
Our model also predicts that when a company discounts more of its future profits
23
the company is less willing to disclose its private information. Following the literature
we use a set of financial ratios to measure the financial distress. Higher level of financial
distress would lead to more discount of future profits. Hence the model predicts a negative
association between the incentive to disclose production forecasts and the level of financial
stress a firm is born. We find debt ratio is shown to negatively affect the accuracy of
forecasts while quick ratio is positively affect the accuracy, both support the prediction
by our model. Nevertheless, the effect of financial distress is dominated by the demand
effect.
In summary, this study makes the first attempt to analytically investigate voluntary
disclosure in a collusive framework and is the first study that provides empirical evidence
of the role of information sharing in this framework. In the collusive framework reputation mechanism sustains the tacit agreement, in which industry peers share information
about demand to coordinate production. Our model predicts that the effectiveness of the
tacit agreement inversely relates to the current demand, which is confirmed by our empirical findings. The accounting literature focuses on the “proprietary costs” of voluntary
disclosure based on the framework of one-shot competition. Hence our study shows that
there are “proprietary benefits” of voluntary disclosure and provides new insights about
voluntary information sharing in product market, particularly in concentrated industries.
Perhaps due to lack or strong evidence in academic research, antitrust regulators hold
the consensus that information sharing alone is not a sign of collusion. The analysis of
this paper thus fits as part of many tools for regulators to discover collusive conduct.
Future research could extend this analysis to other industries as well as other types of
management forecasts to reexamine the demand effect as well as investigate the impact
of discount factor in those settings.
24
Bibliography
Adams, Walter (1994) The Structure of American Industry (Macmillan Pub Co)
Altman, E.I. (1968) ‘Financial ratios, discriminant analysis and the prediction of corporate
bankruptcy.’ Journal of Finance 23, 589–609
Athey, Susan, and Kyle Bagwell (2001) ‘Optimal collusion with private information.’
RAND Journal of Economics 32, 428–465
Bagwell, Kyle, and Robert Staiger (1997) ‘Collusion over the business cycle.’ RAND Journal of Economics 28, 82–106
Bamber, Linda S., and Youngsoon S. Cheon (1998) ‘Discretionary management earnings
forecast disclosures: Antecedents and outcomes associated with forecast venue and
forecast specificity choices.’ Journal of Accounting Research 36(2), 167–190
Banker, Rajiv D., Masako Darrough, Rong Huang, and Jose M. Plehn-Dujowich (2012)
‘The relation between ceo compensation and past performance.’ Accounting Review,
forth.
Ben-Shahar, O., and J. J. White (2006) ‘Boilerplate and economics of power in auto
manufacturing contracts.’ Michigan Law Review 104, 953–982
Bertomeu, Jeremy, and Pierre Jinghong Liang (2011) ‘Voluntary disclosure and industry
fluctuations.’ Working Paper. Working paper 2011
Beyer, Anne, Ilan Guttman, and Ivan Marinovic (2012) ‘Optimal contracts with performance manipulation’
Bhojraj, Sanjeev, Walter G. Blacconiere, and Julia D. D’Souza (2004) ‘Voluntary disclosure in a multi-audience setting: An empirical investigation.’ The Accounting Review
79(4), 921–947
Bresnahan, Timothy F. (1987) ‘Competition and collusion inthe american automobile
industry.’ Journal of Industrial Economics 4(June), 457–82
Busse, Meghan (2002) ‘Firm financial condition and airline price wars.’ RAND Journal of
Economics 33(2), 298–318
Chapman, Craig (2012) ‘The effects of real earnings management on the firm, its competitors and subsequent reporting periods.’ Ph.D dissertation
Chen, Hui, and Bjorn N. Jorgensen (2012) ‘Firm exit through bankruptcy - the effect of
accounting bias on product market competition.’ KAT Accounting Conference 2012
25
Curtis, Asher, Russell J. Lundholm, and Sarah E. McVay (2012) ‘Forecasting sales: A
model and some evidence from the retail industry’
Darrough, M. (1993) ‘Disclosure policy and competition: Cournot vs. bertrand.’ The
Accounting Review
Doyle, M.P., and C.M. Snyder (1999) ‘Information sharing and competition in the motor
vehicle industry.’ Journal of Political Economy 107, 1326–1364
Dye, Ronald A. (1986) ‘Proprietary and nonproprietary disclosures.’ Journal of Business
59(2), 331–366
Evans, John H., and Sri S. Sridhar (2002) ‘Disclosure-disciplining mechanisms: Capital markets, product markets, and shareholder litigation.’ The Accounting Review
77(3), 595–626
Feltham, Gerald, F. Gigler, and J. Hughes (1992) ‘The effects of line-of-business reproting
on competition in oligopoly settings.’ Contemporary Accounting Research
Gal-Or, Esther (1985) ‘Information sharing in oligopoly.’ Econometrica 53(2), 329–343
Gigler, Frank (1994) ‘Self-enforcing voluntary disclosures.’ Journal of Accounting Research
32(2), 224–240
Green, Edward J., and Robert H. Porter (1984) ‘Noncooperative collusion under imperfect
price information.’ Econometrica 52(1), 87–100
Grossman, Sanford J. (1981) ‘The informational role of warranties and private discloure
about product quality.’ Journal of Law and Economics 24(3), 461–483
Indjejikian, Raffi, and Michal Matejka (2009) ‘Cfo fiduciary responsibilities and annual
bonus incentives.’ Journal of Accounting Research 47(4), 1061–1093
Li, Xi (2010) ‘The impacts of product market competition on the quantitiy and quality
of voluntary disclosures.’ Review of Accounting Studies 15(3), 663–711
Magee, Robert P., and Sri Sridhar (1996) ‘Financial contracts, opportunism, and disclosure management.’ Review of Accounting Studies 1(3), 225–258
Mailath, George J., and Larry Samuelson (2006) Repeated Games and Reputation: LongRun Relationship (Oxford University Press)
Milgrom, Paul R. (1981) ‘Good news and bad news: representation theorems and applications.’ The Bell Journal of Economics 12(2), 380–391
26
Myerson, R. B. (1982) ‘Optimal coordination mechanisms in generalized principal agent
problems.’ Journal of Mathematical Economics 10(1), 67–81
Nagar, Venky, and Madhav V. Rajan (2001) ‘The revenue implications of financial and
operational measures of product quality.’ The Accounting Review 76(4), pp. 495–513
Narayanan, M.P. (1985) ‘Managerial incentives for short-term results.’ Journal of Finance
40, 1469–1484
Raith, Michael (1996) ‘A general model of information sharing in oligopoly.’ Journal of
Economic Theory 71(1), 260–288
Rotemberg, Julio J., and Garth Saloner (1986) ‘A supergame-theoretic model of price
wars during booms.’ The American Economic Review 76(3), 390–407
Sivak, M., and B. Schoettle (2009) ‘Economic indicators as predictors of the number and
fuel economy of purchased new vehicles.’ Technical Report, University of Michigan
Transportation Research Institute
Stocken, Phillip C. (2000) ‘Credibility of voluntary disclosure.’ RAND Journal of Economics 31, 359–374
Suijs, Jeroen, and Jacco L. Wielhouwer (2012) ‘Disclosure regulation in duopoly markets:
Proprietary costs and social welfare’
Uday, Chandra, Andrew Procassini, and Gregory Waymire (1999) ‘The use of trade association disclosures by investors and analysts: Evidence from the semiconductor
industry.’ Contemporary Accounting Research 16(4), 643–670
Verrecchia, Robert E. (1983) ‘Discretionary discloure.’ Journal of Accounting and Economics 5(1), 179–194
Wagenhofer, Alfred (1990) ‘Voluntary disclosure with a strategic opponent.’ Journal of
Accounting and Economics 12, 341–363
Waymire, G. (1984) ‘Additional evidence on the information content of management earnings forecasts.’ Journal of Accounting Research 22(2), 703–718
27
A.
A.1.
Appendix
Operational Budgeting and Production Forecast
Operational budgeting deals with expenses, production, and sales over the coming months.
Management prepares a set of these budgets to control and manage various aspects of
the business. The typical textbook order of the budgeting process follows the sequence
below:
• Sales forecast
• Production forecast
• Manufacturing cost budget
• Cost of Goods Sold and ending inventory budgets
• Operating expense budget
which follows the natural order of production planning and finishing goods delivery. This
process shows that production forecast is prepared by the management after the sales
division analyzes the incoming demand for the goods to be produced. Capital expenditure
forecast, which often appears in management earnings forecast, is prepared based on all
aforementioned operational budgets over the earnings forecasting period.
A.2.
Appendix: Omitted Proofs
Proof of Proposition 2.1: Since the result is a special case of Raith (1996), we give
here only a short self-contained proof. Suppose information is not shared and denote
q m = E(Qnd (s, u)|s), then, Qnd (s, u) ∈ argmax q(s + u − q − αq m ). Differentiating with
respect to q, s + u − 2Qnd (s, u) − αq m = 0 and, taking expectations, s − 2q m − αq m = 0
which implies that q m = s/(2 + α). Substituting into the first-order condition and solving
for Qnd (s, u), we then have that Qnd (s, u) = s/(2 + α) + u/2. To obtain the expected
profit, note that E(Π|s) = E(Qnd (s, u)2 |s) = σ 2 /4 + s2 /(2 + α)2 . If, on the other hand,
firms share information, the choice of quantities will solve Qd (s, u1 + u2 ) ∈ argmax q(s +
u1 + u2 − q − αq 0 ) which implies that: s + u1 + u2 − αq 0 − 2q = 0 so that, in equilibrium,
Qd (s, u1 + u2 ) = (s + u1 + u2 )/(2 + α). The ex-ante profit from sharing information is
therefore given by: E(Π|s) = E(Qd (s, u1 + u2 )2 |s) = s2 /(2 + α)2 + 2σ 2 /(2 + α)2 which is
less than without sharing.2
Proof of Proposition 2.2: Suppose that the incentive-compatibility condition does
not bind. Then, the optimal quantity is given by:
Qd (s, u1 + u2 ) = argmax q(s + u1 + u2 − q − αq)
28
That is,
Qd (s, u1 + u2 ) = Q1d (s, u1 + u2 ) ≡
1
(s + u1 + u2 )
2(1 + α)
This quantity choice is lower than the quantity that prevails in the single-period game
and achieves the maximal total industry profit. Note that this quantity will satisfy the
incentive-compatibility condition if and only if:
1
1
(s + u1 + u2 ) ≥
(s + u1 + u2 − 2K)
2(1 + α)
2+α
That is,
1+α
K
α
As long as this inequality is satisfied, the monopoly achieves its maximal feasible profit,
i.e.,
1
Πshare =
(s + u1 + u2 )2
4(1 + α)
s + u1 + u2 ≤ 4
If s + u1 + u2 > 4(1 + α)/αK, the incentive-compatibility constraint binds. From
Equation (4),
Qd (s, u1 + u2 ) = Q2d (s, u1 + u2 ) ≡
1
(s + u1 + u2 − 2K)
2+α
Then, firms achieve the following profit:
Πshare =
1
2αK
4(1 + α) 2
(s + u1 + u2 )2 +
(s + u1 + u2 ) −
K
2
2
(2 + α)
(2 + α) )
(2 + α)2
2
Proof of Proposition 2.3: Denote λ the multiplier associated to the constraint
R
q m = Qnd (u)g(u)du. If one incentive-compatibility condition does not bind, the optimal
Qnd (s, u) is given by:
s + u − 2Qnd (s, u) − αq m − λ = 0
s + u − αq m − λ
2
As before, we substitute this Equation into the incentive-compatibility condition to check
whether it is satisfied:
Qnd (s, u) =
s + u − αq m − λ
s + u − αq m − λ
1
(s + u −
− αq m ) + K 2 ≥ (s + u − αq m )2
2
2
4
This Equation simplifies to:
λ
(5)
2
Note that this condition is not a function of u so that either all incentive-compatibility
K≥
29
conditions bind or none does.
We thus consider two possibilities. Assume that Equation (5) is satisfied, then:
Qnd (s, u) = (s + u − αq m − λ)/2
Taking expectations on both sides and solving for q m ,
1
(s − λ)
2+α
qm =
Therefore:
Qnd (s, u)(s+u−Qnd (s, u)−αq m ) =
1
(2s+(2+α)u−2λ)(2s+(2+α)u+2(1+α)λ)
4(2 + α)2
Reinjecting this expression into the objective function,
E(Πnoshare |s) =
=
Z
g(u)Qnd (u)(s + u − Qnd (s, u) − αq m )du
σ2
1
2
2
(αλs
+
s
−
(1
+
α)λ
)
+
(2 + α)2
4
Choosing the constant term λ to maximize the firm’s profit in the tacit agreement,
λ=
α
s
2(1 + α)
This implies that:
s
2(1 + α)
u
s
+
Qnd (s, u) =
2(1 + α) 2
2s2 + 4s(u1 + u2 ) + (1 + α)(u21 + 2(2 − α)u1 u2 + u22 ))
Πnoshare =
8(1 + α)
2
2
σ
s
+
E(Πnoshare |s) =
4(1 + α)
4
qm =
For these strategies to be incentive-compatible, Equation (5) must be satisfied, i.e.,
K≥
Assume next that K <
any u,
αs
.
4(1+α)
λ
αs
=
2
4(1 + α)
Then the incentive-compatibility condition binds for
Qnd (s, u) =
s + u − αq m
−K
2
30
Taking expectations on both sides and solving for q m ,
1
(s − 2K)
2+α
s
u
2
Qnd (s, u) =
+ −
K
2+α 2 2+α
1
σ2
2
2
E(Πnoshare |s) =
(−4(1
+
α)K
+
2αKs
+
s
)
+
(2 + α)2
4
qm =
(6)
(7)
(8)
2
Proof of Proposition 2.4: We compare the profit under sharing to the profit under
no sharing.
E(Π
share
|s) =
Z 4K(1+a)/a
√
σ 2
−∞
+
Z +∞
4K(1+a)/a
1
1+α
x
(s+x)(s+x−
(s+x))g( √ )dx
2(1 + α)
2(1 + α)
2
1
1+α
x
(s + x − 2K)(s + x −
(s + x − 2K))g( √ )dx
2(1 + α)
2(1 + α)
2
Differentiating this expression with respect to s,
√
4 + 4α + α2 G( 4(1+α)K−αs
)
∂ 2 E(Πshare |s)
2α
=
∂s2
2(1 + α)(2 + α)2
Define ∆ = E(Πshare |s) − E(Πnoshare |s) and consider first s ≤ 4K 1+α
, then:
α
E(Πnoshare |s) =
σ2
s2
+
4(1 + α)
4
Therefore:
√
4 + 4α + α2 G( 4(1+α)K−αs
)
∂ 2∆
1
4 + 4α
1
2α
=
−
<
−
=0
2
2
2
∂s
2(1 + α)(2 + α)
2(1 + α)
2(1 + α)(2 + α)
2(1 + α)
Note also that, for s small,
1
(s + u1 + u2 )2 )
4(1 + α)
1
∼
(s2 + 2σ 2 )
4(1 + α)
s2
σ2
1
∆ ∼
(s2 + 2σ 2 ) − (
+ )
4(1 + α)
4(1 + α)
4
2
2
∼ σ /(2(1 + α)) − σ /4 > 0
E(Πshare |s) ∼ E(
Consider next s > 4K 1+α
, then:
α
31
(9)
(10)
(11)
(12)
E(Πnoshare |s) =
1
σ2
2
2
(−4(1
+
α)K
+
2αKs
+
s
)
+
(2 + α)2
4
√
)
4 + 4α + α2 G( 4(1+α)K−αs
∂ 2∆
2
4 + 4α + α2
2
2α
=
−
>
−
=0
2
2
2
2
∂s
2(1 + α)(2 + α)
(2 + α)
2(1 + α)(2 + α)
(2 + α)2
It follows that ∆ is convex on (4K(1 + α)/α, +∞).
In addition, as s becomes large,
4(1 + α) 2
2αK
1
2
(s
+
u
+
u
)
−
(s
+
u
+
u
)
+
K |s)
1
2
1
2
(2 + α)2
(2 + α)2 )
(2 + α)2
4(1 + α) 2
2αK
1
2
2
s
−
(s
+
2σ
)
+
K
∼
(2 + α)2
(2 + α)2 )
(2 + α)2
1
4(1 + α) 2
2αK
2
2
∆ ∼
s
−
(s
+
2σ
)
+
K
(2 + α)2
(2 + α)2 )
(2 + α)2
σ2
1
2
2
(−4(1
+
α)K
+
2αKs
+
s
)
+
)
−(
(2 + α)2
4
(−α2 − 4α + 4) 2
∼
σ <0
4(2 + α)2
E(Πshare |s) ∼ E(
In summary, we know that (a) lims→−∞ ∆ > 0, (b) lims→+∞ ∆ < 0, (c) ∆ is concave
then convex. From (a) and (b), ∆ has at least one root. From (c), ∂∆/∂s can change
sign no more than twice, which implies one of the following cases: 1. ∆ is decreasing, 2.
∆ is decreasing, then increasing, 3. ∆ is increasing, then decreasing, 4. ∆ is increasing,
then decreasing, then increasing, 5. ∆ is decreasing, then increasing, then decreasing. All
of these cases jointly with the boundary conditions (a) and (b) imply a unique root.
To conclude the proof, we need to guarantee that the zero can occur for s > s. Indeed,
if the reputational factor is small (i.e., low discount factor), the equilibrium will still be
no-disclosure for all s, which would correspond to a threshold τ = s. However, one can
evaluate ∆ at s and let K become large (which is equivalent to β becoming close to one).
In that case, the function ∆ will necessarily be positive at s.2
32
Figure 3: Big Three U.S. Market Share, 1961-2010
33
Table 2: Capital Market Reactions to Production Forecasts, 1965-1995
Absolute Announcement Window Reaction
GM
FORD
CHRYSLER
Market Model Return
0.019***
(28.046)
0.021***
(28.804)
0.030***
(27.678)
Equal Weighted Adjusted Return
0.018***
(27.842)
0.021***
(26.804)
0.029***
(25.163)
n
476
481
479
The sample consists of three-day production forecasting windows (-1,1) for each
company between 1965 and 1995. For the period 1971 to 1995 the three-day windows
that overlap with earnings announcement dates are dropped from the analysis. Market
Model Return is the absolute difference from market model predicted return using (-260,
-60) rolling window to estimate beta. Equal Weighted Return is the absolute value of
subtracting from the contemporaneous equal weighted market index return. *(**)[***]
indicates statistical significance at the 0.10(0.05)[0.01] level under time series standard
deviation test. The P-values are provided in the brackets below parameter estimates.
34
Table 3: Summary Statistics
Panel A Actual and Planned Productions, 1965-1995
Variable
PROD
Mean
GM Actual Production (000)
Ford Actual Production (000)
Chrysler Actual Production(000)
NUMFORC Number of Forecasts
HORIZON Forecast Horizon
FORECAST
ACCU
ACCU_INIT
Variable
GM Forecasted Production (000)
Ford Forecasted Production (000)
Chrysler Forecasted Production (000)
GM Forecast Accuracy
Ford Forecast Accuracy
Chrysler Forecast Accuracy
Average Accuracy of Big Three
ROA
DEBT
INT
QUICK
INVT
Std. Dev Min
25th
Max
N
23.876
23.142
14.068
238.719 411.218 548.129
122.593 189.665 273.032
55.005 115.54 171.855
369
369
369
5.1951
93.1274
2.5991
44.3647
1
0
3
60
7
124
12
198
369
369
96.1112
42.7575
30.6839
0.1495
0.309
0.2224
0.1588
20
23.1
0
0
0
0
0
260
123
57.6
0.0095
0.01
0119
0.0219
405
175
108
0.0912
0.0951
0.116
0.107
572
288
183
2.1014
7.559
4.7577
2.5613
1917
1917
1917
1917
1917
1917
1917
0.1921
0.4435
0.3284
0.2219
0
0
0
0
0.0211
0.03
0.322
0.0444
0.1506
0.1397
0.2036
0.1642
2.0606
7.5559
4.7577
2.5613
369
369
369
369
75th
Max
N
5
94
331.6678 330
149.4507 146
83.6738 83
0.0819
0.034
0.1019
0.0375
0.11
0.0425
0.098
0.523
0.0603
0.0676
0.086
0.0869
Panel B Industry Demand, Cost, and Capacity, 1965-1995
Mean
Median Std. Dev Min
25th
6.2298
0.9733
78.7668
6
1.5947
0.9801 0.0885
79.8613 12.0208
3.4
0.8092
44.271
5.3
0.9042
74.1096
Panel C Individual Firm Efficiency (Quarterly), 1965-1995
Mean
Median Std. Dev Min
25th
GM ROA
Ford ROA
Chrysler ROA
GM Debt Ratio
Ford Debt Ratio
Chrysler Debt Ratio
GM Interest Coverage
Ford Interest Coverage
Chrysler Interest Coverage
GM Quick Ratio
Ford Quick Ratio
Chrysler Quick Ratio
GM Inventory Turnover
Ford Inventory Turnover
Chrysler Inventory Turnover
75th
327.0118 330.678 109.1074
155.1238 151.198 49.5854
87.265
88.091 35.2765
GM Initial Forecast Accuracy
0.1228
Ford Initial Forecast Accuracy
0.1496
Chrysler Initial Forecast Accuracy
0.1793
Average Initial Accuracy of Big Three 0.1506
UNEMP Unemployment
PPI_CPI Producer Cost
CAP Capacity Utilization (%)
Variable
Median
0.1109
0.073
0.0555
0.305
0.3764
0.509
35.9127
17.4461
11.3321
0.9367
0.5819
0.7238
3.0504
3.2994
3.1445
0.0495
0.0387
0.0346
0.1558
0.235
0.3633
11.5013
8.7559
5.4543
0.9843
0.5676
0.6399
2.533
2.8778
2.5406
0.1158
0.0713
0.0579
0.3099
0.2784
0.27
51.5929
28.8057
16.0061
0.2397
0.131
0.337
1.4557
1.488
1.7809
-0.0013
-0.0168
-0.0485
0.0274
0.1196
0.1531
-0.3139
-3.5446
-4.6441
0.3597
0.3772
0.2553
1.1164
1.1113
0.9864
0.0281
0.025
0.018
0.0862
0.1688
0.3187
3.0097
2.4464
1.6477
0.7184
0.4748
0.4629
1.9562
1.8575
1.7399
7.3
10.8
1.0571 1.1202
86.7666 104.033
369
369
369
75th
Max
N
0.1596
0.1083
0.0977
0.7139
0.7647
0.706
41.1197
18.9461
12.8696
1.1567
0.6841
0.9612
4.5167
4.919
4.476
0.3561
0.2176
0.1882
0.9324
0.8779
1.3064
225.7079
142.2009
59.3688
1.2803
0.8658
1.6172
8.9535
5.9898
6.5788
124
124
124
124
124
120
120
124
124
92
92
124
124
124
124
The sample consists of 369 forcasted months between 1965 and 1995 with forecast data available for the three automakers:
GM, FORD, and CHRYSLER. PROD (in thousands) is the actual car production in the forecasted month. NUMFORC is the
total number of forecasts disclosed via Ward’s weekly reports for a production month. HORIZON is the number of days
between the forecasting date and the date of actual production. FORECAST (in thousands) is the projected production for a
given month as disclosed in the production forecast. ACCU is the forecast accuracy calculated as the absolute value of the
difference between forecast and actual production scaled by the actual production number. ACCU_INIT is the forecast
accuracy (as for ACCU) of the initial forecast for a production month. UNEMP is the monthly unemployment rate computed
by the U.S. Bureau of Labor Statistics. PPI_CPI is the ratio of producer price index to consumer price index, both indices are
computed by the U.S. Bureau of Labor Statistics. CAP is the automobile industry percentage capacity utilization constructed
by the Federal Reserve Board. ROA is the return on asset calculated as the operation income divided by total asset. DEBT is
the debt ratio calculated as (long term debt + long term debt in current liability)/(long term debt + long term debt in current
liability + total equity). INT is the interest coverage calculated as the operation income divided by interest expense. QUICK is
the quick ratio calculated as (cash + account receivable)/total current liability. INVT is the inventory turnover ratio calculated
as cost of good sold divided by inventory.
35
Table 4: Production Forecasts and Actual Production
Variable Name
Production
Production
Production
Production
Production
Intercept
2.504
(0.709)
0.0463
(0.972)
-1.460
(0.298)
8.570
(0.274)
9.346
(0.221)
0.026
(0.104)
0.908***
(<.0001)
0.857***
(<.0001)
FORECAST_INIT 0.936***
(<.0001)
PEER1
FORECAST_INIT
0.008
(0.459)
PEER2
FORECAST_INIT
0.054
(0.118)
0.996***
(<0.0001)
FORECAST_LAST
0.973***
(<.0001)
PEER1
FORECAST_LAST
0.026**
(0.017)
PEER2
FORECAST_LAST
0.188***
(<.0001)
Adj-R sqrd
n
0.940
0.996
0.996
1107
0.941
0.944
The sample consists of 1,107 monthly production forecasts by the three automobile
companies (GM, FORD, and CHRYSLER) from 1965 to 1995. FORECAST_INIT is
the first time forecast number by a company. FORECAST_LAST is the last forecast
number by a company for a production month. *(**)[***] indicates statistical
significance at the 0.10(0.05)[0.01] level under the t-test based on MacKinnon and White
(1985) heterscedastic-robust standard error. The P-values are provided in the brackets
below parameter estimates. Fixed-effect is included.
36
Table 5: Forecast Frequency
Variable Name
(1)
OLS
(2)
Ordered Logit
(4)
(3)
Intercept
6.552***
(<0.0001)
8.203***
(0.0001)
UNEMP
0.831***
(<0.0001)
0.781***
(<0.0001)
0.845***
(<0.0001)
0.780***
(<0.0001)
PPI_CPI
-3.517***
(0.003)
-3.640***
(0.002)
-3.362***
(0.002)
-3.567***
(0.001)
CAP
-0.040***
(0.0004)
-0.058***
(0.001)
-0.038***
(0.0002)
-0.062***
(<0.0001)
0.088
(0.121)
UNEMP*CAP_H
R sqrd
n
0.400
0.403
0.108**
(0.017)
0.458
0.462
369
The sample consists of 369 monthly production forecasts by each one of the three
automobile companies (GM, FORD, and CHRYSLER) from 1965 to 1995.
UNEMP*CAP_H is the interaction of UNEMP and a dummy variable indicating high
capacity utilization (CAP>80%). The remaining variables definitions are specified in
the note of Table 1. *(**)[***] indicates statistical significance at the 0.10(0.05)[0.01]
level under t test based on MacKinnon and White (1985) heterscedastic-robust standard
eror. Adjusted (Psedo) R-squared are reported for OLS (Logistic) regression. The
P-values are provided in the brackets below parameter estimates.
37
Table 6: Initial Forecast Timing
Variable Name
(1)
OLS
(2)
(3)
Intercept
54.452*
(0.063)
74.600**
(<0.037)
UNEMP
17.761***
(<0.0001)
17.150***
(<0.0001)
PPI_CPI
-77.599***
(0.0001)
-79.107***
(<0.0001)
0.045
(0.815)
-0.181
(0.539)
CAP
0.108
(0.271)
UNEMP*CAP_H
Adj-R sqrd
n
-0.523***
(<0.0001)
0.593
3.448***
(<0.0001)
31.446
-0.005
(0.378)
0.995
0.390
0.391
-0.428
369
Cox Model
(4)
-0.513***
(<0.0001)
0.599
3.489***
(<0.0001)
32.747
-0.002***
(0.802)
0.998
-0.012***
(0.232)
0.988
-0.429
The sample consists of 369 monthly production forecasts by each one of the three
automobile companies (GM, FORD, and CHRYSLER) from 1965 to 1995.
UNEMP*CAP_H is the interaction of UNEMP and a dummy variable indicating high
capacity utilization (CAP>80%). The remaining variables definitions are specified in
the note of Table 1. *(**)[***] indicates statistical significance at the 0.10(0.05)[0.01]
level under t test based on MacKinnon and White (1985) heterscedastic-robust standard
eror. In Cox Model (column (3) and (4)), the third row for each variable is the value of
Hazard Ratio. R-Squared for Cox Model is computed as 1 − exp (LRT /n), where LRT
is the log-likelihood statistics. The P-values are provided in the brackets below
parameter estimates.
38
Table 7: Forecast Accuracy (Percentage Forecast Error)
Variable Name
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Intercept
0.950***
(<.0001)
1.040***
(<.0001)
0.238***
(<.0001)
1.219***
(<.0001)
1.323***
(<.0001)
1.191***
(0.0002)
1.295***
(0.0001)
UNEMP
-0.031***
(<.0001)
-0.034***
(<.0001)
-0.055***
(<.0001)
-0.057***
(<.0001)
-0.054***
(<.0001)
-0.057***
(<.0001)
0.222**
(0.0453)
0.215*
(0.0528)
0.161
(0.4404)
0.147
(0.4850)
0.189
(0.3787)
0.174
(0.4180)
-0.010***
(<.0001)
-0.011***
(<.0001)
-0.010***
(<.0001)
-0.011***
(<.0001)
-0.010***
(<.0001)
-0.012***
(<.0001)
PPI_CPI
CAP
0.005
(0.3056)
UNEMP*CAP_H
0.005
(0.3787)
0.005
(0.3767)
DEBT
0.174***
(0.0037)
0.070
(0.2635)
0.069
(0.2685)
0.045
(0.6131)
0.044
(0.6200)
INT
0.0003
(0.6616)
0.001
(0.1795)
0.001
(0.1370)
0.001
(0.1792)
0.001
(0.1366)
QUICK
-0.111**
(0.0171)
-0.039
(0.4909)
-0.041
(0.4729)
-0.020
(0.7671)
-0.021
(0.7506)
INV
-0.011
(0.4571)
-0.018
(0.3065)
-0.017
(0.3408)
-0.020
(0.2740)
-0.019
(0.3045)
ROA
-0.314
(0.4182)
-0.535
(0.1697)
-0.572
(0.1449)
-0.490
(0.2240)
-0.526
(0.1940)
Chrysler
0.026
(0.6285)
0.026
(0.6258)
Ford
0.023
(0.5688)
0.023
(0.5639)
0.098
0.0978
Adj-R sqrd
n
0.0926
0.0927
1107
0.0395
0.0997
0.0995
891
The sample consists of 1,107 monthly production forecasts by the three automobile companies (GM, FORD,
and CHRYSLER) from 1965 to 1995; among them there are 891 observations have corresponding quarterly
financial data from COMPUSTAT quarterly data. Chrysler and Ford are dummy variables for individual
companies. Remaining variables are specified in notes of Table 1 and 5. *(**)[***] indicates statistical
significance at the 0.10(0.05)[0.01] level under two-tailed t test. The P-values are provided in the bracket
below parameter estimates.
39