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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. 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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