Debt financing: Does it boost or hurt firm performance in product markets?

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Journal of Financial Economics 82 (2006) 135–172 www.elsevier.com/locate/jfec

Debt financing: Does it boost or hurt firm performance in product markets?$ Murillo Campello Department of Finance, University of Illinois at Urbana-Champaign, 430A Wohlers Hall, 1206 South Sixth Street, Champaign, IL 61820, USA Received 27 August 2004; received in revised form 30 December 2004; accepted 5 April 2005 Available online 18 October 2005

Abstract Previous research seeks to establish whether debt boosts or hurts a firm’s product market performance. This paper proposes that both of these outcomes can be observed: debt can boost and hurt performance. I first model a nonmonotonic relation between debt-like finance and competitive conduct. I then empirically examine the within-industry relation between leverage and sales performance using data from 115 industries over 30 years. My tests deal with the endogeneity of debt in a novel fashion: I use creditors’ valuation of assets in liquidation to identify financial leverage. I find that moderate debt taking is associated with relative-to-rival sales gains; high indebtedness, however, leads to product market underperformance. r 2005 Elsevier B.V. All rights reserved. JEL classification: G31; G32; L11 Keywords: Capital structure; Product market competition; Industry concentration; Endogeneity; GMM

1. Introduction A large literature suggests that firm financing decisions are influenced not only by conflicts among agents inside the firm, but also by the actions of parties outside of the $

I thank Heitor Almeida, Dan Bernhardt, Diemo Dietrich, Charlie Hadlock, Charlie Kahn, Gordon Phillips, Bill Schwert (the editor), Sheri Tice, and the referee for their very helpful suggestions. The usual disclaimer applies. E-mail address: [email protected]. 0304-405X/$ - see front matter r 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.jfineco.2005.04.001

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firm’s boundaries, such as industry rivals and consumers.1 The research on this topic has revolved around the hypothesis that certain financial policies—in particular, the use of debt financing—should either boost or hinder firm competitive performance. Beginning with Brander and Lewis (1986), a string of theoretical papers have focused on risk-shifting incentives provided by debt usage to equity holders, and on the strategic consequences of such incentives (see also Maksimovic, 1986). In these articles, firms have an incentive to take out debt and increase production to gain a strategic advantage over their industry rivals. Crucially, these models typically examine full-information environments in which contracts cannot be renegotiated, and they rule out asymmetries in rivals’ financial status (e.g., differential access to credit). Such features lead, in turn, to a Prisoner Dilemma’s-type problem in which capital structure ultimately has no impact on individual firms’ relative product market performance, but only on their industries’ overall debt and output levels. The research puzzle has been to reconcile these theoretical predictions with the empirical evidence presented by Chevalier (1995), Phillips (1995), and Kovenock and Phillips (1997), which shows that the performance of recapitalizing firms that choose significantly higher debt levels is markedly different from that of rivals that do not recapitalize. This paper proposes that the empirical research on the interaction between capital structure and product markets should not be reduced to the task of establishing whether debt hurts or boosts a firm’s competitive performance: both of these outcomes can obtain in the data. In addressing the existing puzzle in the literature, I first analyze a simple contracting–product market model. In it, access to external, debt-like finance is related to the amount of funds that creditors can recover from firms in liquidation. Assuming information asymmetries between the parties to a financial contract, I show that firms with greater access to external financing can threaten their rivals with lender-financed product market overinvestment. Facing this threat, those rivals may accommodate. Therefore, consistent with the previous evidence on firms implementing leveraged recapitalizations, debt can initially provide for a product market advantage for those firms with greater access to credit. However, in a world in which parties can renegotiate contracts, debt taking cannot support aggressive market strategies after a certain threshold—ex post inefficiencies associated with excessive debt taking are renegotiated away. The simple model I present essentially implies a nonmonotonic association between firm debt and competitive performance. I test the intra-industry predictions of my model using firm-level data from a panel of 115 well-defined product markets over three decades. In doing so, I design a testing strategy that is simple and general enough to nest the predictions of various theories relating firm financing and performance. My approach differs significantly from those of previous studies in that it allows for the marginal effect of debt policies on product market outcomes to vary according to the level of firm indebtedness. Focusing on relative-to-industry firm sales growth—debt sensitivities, I report a new set of findings on the interplay between debt financing and competitive performance. In a nutshell, the central results of this paper suggest that moderate (excessive) firm debt is associated with market share gains (losses) that obtain at the expense of (that benefit) 1

Papers in the theoretical literature include, among others, Benoit (1984), Titman (1984), Allen (1986), Brander and Lewis (1986, 1988), Maksimovic (1986, 1988), Bolton and Scharfstein (1990), Maksimovic and Titman (1991), Chevalier and Scharfstein (1996), Maurer (1999), and Campello and Fluck (2004). See Maksimovic (1995) for a review.

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industry rivals. More precisely, I find that firms with markedly higher debt than their industry-year average rivals expand their sales relatively more than those rivals in future years—those firms display significantly positive (relative-to-rival) sales growth–debt sensitivities. This ‘‘leverage effect’’ is, however, nonmonotonic: after a threshold, more debt leads to highly significant sales underperformance. Finally, incremental debt has mostly negligible performance consequences for firms with relatively low leverage. The robustness of the results I report is highlighted through a series of estimation checks that include the use of alternative sampling procedures, instrumentation schemes, lagging structures, and econometric techniques. Importantly, the testing strategy I use addresses concerns that unobservable, time-varying industry effects could affect my inferences.2 In effect, all industry-specific factors are expunged from my estimates in each year of the period sampled: a firm’s indebtness and performance are measured relative to that of its industry-year rivals. Since rivals’ financial policies and competitive conduct are ultimately outside of the firm’s control set, the issue of reverse causality is minimized in my relative-torival estimations. I further tackle the issue of debt endogeneity using an instrument suggested by the theoretical framework I study. To wit, in a model of lending in the presence of contracting imperfections (and in the real world), creditors typically request collateral in exchange for financing. The amount of financing that can be supported by contracts with outside creditors is thus correlated with those creditors’ valuation of the firm’s hard, transferable assets in liquidation (‘‘asset tangibility’’). Crucially, while a firm’s asset tangibility might correlate with its financing, the tangible attributes of a firm’s assets should not determine the firm’s relative sales performance (other than through the association with financing). Asset tangibility therefore serves as a natural instrument for debt financing in sales performance equations. Accordingly, in the tests performed in this study I use the predicted values from a regression of leverage on asset tangibility (the expected resale value of assets such as fixed capital and inventory) in order to estimate firms’ relative-to-rival sales–debt sensitivities. It is based on empirical tests of this nature that I draw inferences about capital structure–product market interactions. To my knowledge, this is the first study to identify an instrument for debt taking that is borne out of the very contracting frictions that allow debt to play a role in product market competition.3 In the final part of the paper, I examine whether the results I find are particularly more pronounced in concentrated markets and/or among leader firms.4 Most theoretical models in the relevant literature assume that the market environment is concentrated and that the actions of some firms more heavily influence the behavior of their industry counterparts. Hence, this final set of tests provides an additional mapping of the theory into the data. My 2 The importance of controlling for underlying industry effects in capital structure–product markets interactions has been emphasized by Kovenock and Phillips (1997), Campello (2003), MacKay and Phillips (2003), and Campello and Fluck (2004). 3 The identification strategy used in most previous studies revolves around ‘‘natural experiments’’ such as changes in industry regulation (Zingales, 1998), market entry (Khanna and Tice, 2000), and macroeconomic shocks (Campello, 2003). Kovenock and Phillips (1997) is a notable exception, as they use an instrumental variables approach. 4 In the existing literature, Haskel and Scaramozzino (1997) examine whether the link between debt and performance differs across leaders and followers in three U.K. industries. Kovenock and Phillips (1997) and MacKay and Phillips (2003) also look at the impact of concentration on capital structure–product market interactions.

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evidence points to noticeable asymmetries in leader/follower debt-induced outcomes across different competitive settings. I find that leader (follower) firms in concentrated markets underperform (outperform) their rivals when those leaders’ (followers’) indebtedness exceeds their industry standard. In contrast, relatively unleveraged leaders in those same industries observe positive (relative-to-rival) sales–debt sensitivities. Firm leadership has a much weaker effect on the association between debt and performance in less concentrated industries. Overall, my findings suggest that variations in market concentration and firm leadership play an important role in shaping financing–performance interactions. The findings of this paper add to the evidence on the interplay between financial structure and product markets presented in the original work of Chevalier (1995), Phillips (1995), and Chevalier and Scharfstein (1996). While these studies report mostly time- and industry-specific results, the current paper shows that financing–performance interactions are very pervasive, being present in a wide cross-section of markets and over a long period of time. In that regard, this paper’s approach is most related to Opler and Titman (1994), Kovenock and Phillips (1997), Campello (2003), and MacKay and Phillips (2003), who also look at a number of different industries over time. In contrast to these studies, however, I use a very flexible functional approach to model the impact of leverage on competitive performance. This allows my tests to accommodate a broader spectrum of theoretical predictions.5 At the same time, my empirical analysis also explores contrasts between leader and nonleader firms in competitive as well as in concentrated industries. Finally, this paper is the first to propose a theoretically motivated empirical instrumental strategy to tackle the issue of debt endogeneity. The remainder of the paper is organized as follows. In Section 2, I study a simple model of commitment through contracts to propose a nonmonotonic relation between debt financing and product market outcomes. Section 3 describes the data collection process, variable construction methods, and empirical testing design. Section 4 contains the paper’s central empirical findings. In Section 5, I examine if (and how) my results change when tests explicitly allow for variation across concentrated and competitive markets, and between industry leaders and nonleaders. Section 6 concludes.

2. A simple model of commitment through financial contracting In this section, I consider a simple model to show that the introduction of third-party contracting in an environment with financing frictions gives rise to an active and complex interplay between firm capital structure and product market performance. The setup and timing of my model draw on Dewatripont (1988), who uses the idea of commitment through inefficient third-party contracting (between an incumbent firm and its workers) in a model of entry deterrence. While a full-blown theory of financial contracting and industry competitive equilibrium is outside the scope of this paper, the model motivates the empirical analysis that follows, which shows that a firm’s use of debt and its (relative-torival) sales performance are not monotonically related in the data. 5

Although MacKay and Phillips (2003) do not focus on nonlinearities in financing–performance interactions, their empirical design also allows for nonstandard inferences regarding capital structure and the dynamics of industry competition. As in this study, their evidence reveals a complex set of interactions between a firm’s financing and real decisions and those of its industry rivals.

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2.1. Preliminaries Similar to Bolton and Scharfstein (1990), Gertner et al. (1988), and Benoit (1984), I emphasize the role of financial contracting in influencing a firm’s ability to invest in product markets without fully characterizing the industry environment (the analysis focuses on reduced-form revenue functions). Working out the industry competitive environment as, say, Cournot competition, would impose more structure on the analysis at the cost of additional length and loss of generality. This consideration is important for the goals of this paper in that I want to empirically examine the issue of nonmonotonicity in debt–performance relations very generally. Indeed, in my empirical tests, I measure product market performance with a proxy that can be used in a broad set of industry environments. This performance measure incorporates information from the combined effects of various product market strategies, which include—but are not restricted to— pricing and output decisions. Of course, some minimum financing and competitive structure is needed. As such, the model can be seen as one of the Stakelberg type with respect to initial investment choice, where financial contracts are debt-like. 2.2. Structure 2.2.1. Product markets Consider a setup in which two firms, Firm A and Firm B, compete in a product market. Assume that the revenue a firm receives from selling its product in the market is a general function of both the market share ‘‘investment’’ made by the firm itself and the investment made by its rival. Denote a firm’s revenue function by RðI j ; I k Þ, for j; k ¼ fA; Bg, where I j is firm j’s investment in the product market. The analysis takes I as the ‘‘amount of funds’’ invested in its product market (say, in product promotion or output capacity). Firm j’s (and its rival k’s) investment influences j’s revenue function Rð
Þ as follows: RI j 40; RI k o0; and RI j I k oRI j I j o0. Standard Inada conditions further guarantee an interior solution. Similar sets of assumptions can be found in the recent literature, e.g., Maurer (1999) and Faure-Grimaud (2000). Funds for product market investment can be either internal to the firm or acquired in the credit markets. Suppose firm j has some amount I of internal funds at its disposal and that it faces a firm-specific opportunity cost (per unit of capital) of pursuing its product market investment denoted by yj . This opportunity cost can be thought of as the gains from pursuing investment opportunities outside of the firm’s industry (e.g., through unrelated subsidiaries or new ventures). It can take one of two values, yhj and ylj ðoyhj Þ, with probabilities m and 1  m, respectively. Finally, assume that firms are endowed with assets in place. The value of firm j’s assets (under j’s management control) equals T j . The payoff function of firm j is Pj ¼ RðI j ; I k Þ þ yij ðI  I j Þ

for j ¼ fA; Bg; i ¼ fh; lg.

(1)

Absent any frictions, a firm would optimally tradeoff the benefits of investing in its product market and in the alternative opportunity. Firm A would maximize profits by choosing I A according to RI A ðI A ; I B Þ ¼ yiA

for i ¼ fh; lg.

(2)

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In trying to capture a larger share of the market, Firm A could announce I A 4I A , so as to induce Firm B to invest I B oI B . However, by Eq. (2), Firm A’s overinvestment threat is not credible and Firm B will rationally invest I B . As I show next, the contracting of external funds in the presence of informational and contractual frictions can lead to alternative product market outcomes. 2.2.2. Credit markets As in Bolton and Scharfstein (1990), I consider an environment in which operating profits are observable, but unverifiable by the parties to a financial contract. In this setting, a firm and its financiers cannot write contracts contingent on profits since the firm’s manager can always divert profits to herself. Financial contracts, however, can be made contingent on the liquidation value of the firm if one assumes that the proceeds from the sale of assets in receivership are verifiable by a court. Under sensible assumptions (e.g., about the parties’ relative bargaining powers), the optimal contractual outcome in this environment is such that creditors will only lend up to the expected value of the firm’s assets in liquidation (Hart and Moore, 1994). This amount of credit can be sustained by a promised payment equal to the value of the firm’s underlying assets under creditors’ control (as opposed to manager’s control) and a covenant establishing a transfer of ownership to creditors in the event the manager does not make the promised payment. As in Myers and Rajan (1998), I further assume that if a firm’s assets are seized by its creditors, a fraction l 2 ð0; 1Þ of the asset value T is lost. The most natural interpretation for ð1  lÞT is that it reflects the value creditors can recoup from the sales of a firm’s physical assets in the market for liquidated assets (I call this ‘‘asset tangibility’’). Denote credit market financiers by ‘‘lenders’’ and assume that a lender’s utility function is quasi-linear in money transfers. For a given interest rate r, a lender’s profit function is simply p ¼ L  rI,

(3)

where I is the amount loaned to the firm and L ðpð1  lÞTÞ is the face value of the loan. I introduce additional firm heterogeneity into the model by assuming that l is determined by firm-specific characteristics. Firms with low l are able to borrow more because their assets are worth more to external creditors. In contrast, firms whose assets are difficult to pledge ðl ! 1Þ will receive no external financing. In this simple two-firm setting, I assume that Firm B falls into the latter category. Firm A’s assets, on the other hand, are assumed offer enough collateral to secure a loan.6 In capturing this feature of the model, my empirical tests use a measure of expected asset liquidation values in order to identify a firm’s ability to contract external financing. Finally, one last condition is needed for outside financing to provide for product market overinvestment: that the financier is asymmetrically informed about the realization of a borrower’s y. In this case, to screen borrowers, the lender must offer a menu of contracts that allow for efficient trade-offs between funds borrowed and product market investment for ‘‘high-state’’ borrowers (those who privately observe yh ), but inefficient fund 6

Bolton and Scharfstein (1990) and Maurer (1999) introduce heterogeneity across firms by assuming that one of the firms has less internal funds than the other, i.e., only one firm is cash-constrained. Theories explaining why firms in the same industry display, in equilibrium, different levels of accumulated tangible capital are provided in Jovanovic (1982) and Hopenhyan (1992); these differences arise from random, firm-specific shocks.

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allocations for ‘‘low-state’’ borrowers (those who observe yl ). I restrict the analysis to the optimal contracting mechanism that arises when a firm and its lender can commit to carry out the contract throughout its duration, but cannot commit not to renegotiate it. This gives creditors incentives to care not only about the firm’s existing assets, but also about the firm’s investment policies, since they stand to gain from renegotiations that avoid wasteful investment. I characterize the structure of the financing game in turn. Information structure and timing of the external financing game. Firms may borrow only to fund product market investment. A typical financial contract would specify the amount of funds granted to the creditworthy firm (Firm A) and the face value of the obligation. However, in tying the model to my empirical tests, I also assume that the parties agree that the external funds will amount to a proportion a of the total firm investment (‘‘financial leverage’’). I later determine the support for a 2 ð0; 1Þ that allows external financing to be a valuable competitive tool. The distribution of y’s, preferences, and technologies are common knowledge. The timing is as follows (see Dewatripont, 1988):



 



At date 0, Firm A signs a contract with the lender. This contract specifies pairs fðLhA ; aI hA Þ; ðLlA ; aI lA Þg that correspond to yhA and ylA , respectively. The contract makes it optional for Firm A to take down the funds. Firm B observes the existence of the contract. At date 1, yA and yB are realized. The realization of yA ðyB Þ is privately observed by Firm A (Firm B) and cannot be credibly communicated. The internally funded rival, Firm B, sets I B . At an interim date, the lender observes I B . Before Firm A invests, the lender has the opportunity to propose a ‘‘take it or leave it’’ offer in order to change the terms of the contract that was signed at date 0.7 The renegotiation game has the following structure: (i) any Pareto-improving proposal will be accepted by Firm A, with gains from renegotiation captured by the lender (denote the expected value of such gains by Z); (ii) any proposal that leaves Firm A worse-off can be rejected at no cost, and the original contract remains in effect. At date 2, Firm A decides whether to implement any contractual changes in the agreement with the lender and announces its type. Production takes place and payoffs are made.

Definition 1. A renegotiation-proof contract is a contract signed at date 0 that remains in effect through date 2 with each of its original terms. The unique renegotiation-proof contract of this setup is the contract that emerges as a Bayesian-perfect equilibrium solution of the game that starts at date 1 and ends with the announcement of the borrower’s type. The subsequent analysis focuses on the usefulness of this contract.

7

Having the party who suffers from the information asymmetry lead the renegotiation ensures that the information structure of the game is not altered by the content of the newly proposed terms.

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2.3. Analysis Contract terms are contingent on the investment decision of Firm B and the state announced by Firm A, which will coincide with the true state by virtue of the revelation principle. An optimal contract between Firm A and the lender maximizes the firm’s payoff function subject both to the incentive compatibility of the firm and the individual rationality of the lender. These constraints are, respectively, of the form: ykA 2 arg max RðI jA ; I B Þ  LjA þ yiA ðI  ð1  aÞI jA Þ for j; i 2 S ¼ fh; lg,

(4)

ð1  rÞ½mðLhA  raI hA Þ þ ð1  mÞðLlA  raI lA ÞXZ,

(5)

where r is the probability that Firm B will choose I B oI B (i.e., that the rival firm accommodates). In what follows, I describe contracts that support overinvestment in at least some states of the world. However, I first show that the information asymmetry between the firm and its financiers is a key element of the argument I study. This is done via a proposition. (All proofs are in the Appendix.) Proposition 1. If the opportunity cost of investing in product markets, yA , is fully observed by Firm A’s financiers, then there does not exist a renegotiation-proof contract that allows for overinvestment. Proposition 1 simply implies that ‘‘informed’’ finance (e.g., insiders’ equity) is ineffective in providing a firm with a credible commitment to overinvest in its product market, and thus it is ineffective in inducing a firm’s rival to modify its conduct. This happens because full information about Firm A’s true opportunity costs will allow its financiers to make ex post Pareto-improving offers that preclude value-destroying overinvestment. Since this is known ex ante by Firm B, there is no reason for Firm B to deviate from I B (cf. Eq. (2)). The next proposition specifies the conditions under which contracting ‘‘uninformed’’ finance (that is, borrowing from asymmetrically informed financiers) works as a credible competitive threat. Proposition 2. There exits an optimal renegotiation-proof contract under asymmetric information about yA that is incentive compatible for Firm A and individually rational for its financiers, inducing: (i) optimal product market investment when (a) Firm B accommodates, or (b) Firm A’s opportunity costs are high (i.e., the state is yhA ); (ii) overinvestment in product markets when Firm A’s opportunity costs are low (i.e., ylA obtains) and Firm B does not accommodate. Proposition 2 specifies a renegotiation-proof contract that a firm can secure in the credit markets in order to threaten its industry rivals with value-destroying overinvestment. The threat will be credible so long as the firm approaches financiers with which it enjoys some degree of informational asymmetry. To wit, external financing induces Firm A to invest optimally in its product market either when Firm B accommodates (i.e., the rival cuts its

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investments)8 or when Firm A’s opportunity costs are high (due to renegotiation with the financier). If those opportunity costs are low, however, Firm A will invest inefficiently in its product market when Firm B does not accommodate. Importantly, notice that Firm A’s opportunity cost realization is stochastic and that it is costly for Firm B to forgo investing at the optimum level when Firm A’s opportunity costs are high (that is, when Firm A does not overinvest). This implies that with some probability, 1  r, Firm B will not accommodate and Firm A will overinvest. In other words, it is not the case that the product market dynamics reduce to a static outcome in which Firm B always accommodates and thus Firm A never has to take on debt in the first place (see also Benoit, 1984).9 Note that the product market outcomes described above are feasible not because Firm A takes advantage of creditors under limited liability a` la Brander and Lewis (1986), but rather because those creditors (although always made whole) offer a menu of contracts that minimizes (yet does not eliminate) inefficient investment allocations. Creditors profit from contract renegotiation and thus grossly inefficient outcomes are avoided, yet by necessity, the external financing contract allows for inefficiencies in product market investment. The previous proposition employs a classical inefficiency outcome—in this case overinvestment—that obtains when a principal is to elicit truthful revelation at an informational disadvantage. A key condition for this result is that the incentive compatibility condition for the firm in the high state will bind soon enough as investment in the low state is reduced (see Mussa and Rosen, 1978). The next proposition determines the maximum magnitude of product market overinvestment that can be supported by a renegotiation-proof contract with asymmetrically informed financiers. Proposition 3. The upper bound for I lA (Firm A’s investment in the low state of opportunity costs) is equal to a level F that obtains when Firm A’s surplus equals the minimum informational rent needed to maintain incentive compatibility, where F is given by
 m l h l h ðy  y Þ ð1  aÞF  RðF; I B Þ40. LA ðFÞ þ yA  (6) ð1  mÞ Proposition 3 formally establishes the claim that the effect of debt financing on product market performance is nonmonotonic. The proposition shows that lender financing will not support excessive overinvestment. This happens because overinvestment destroys value surplus, yet informational rents must be retained by the borrowing firm in order to keep this party committed to the truthful revelation of private information. In the absence of those rents, external financing has no strategic implications for product market behavior. Simply put, uninformed (debt-like) financing can be used for competitive purposes, but only to a limited extent. 8

Note that when Firm B accommodates, Firm A’s total product market investment will be higher, yet it will be optimal in the sense of the (new) trade-off between investing in its product market and pursuing its alternative opportunities (see proof of Proposition 1). 9 This result is consistent with the empirically observed dispersion in debt usage by firms in the same industry. Comprehensive evidence of this regularity is provided by MacKay and Phillips (2003). Importantly, this solution sidesteps the prisoner’s dilemma-like problem of papers such as Brander and Lewis (1986) in which capital structure ultimately has no impact on individual firms’ relative product market performance (rival firms will seek the same financial policies), but only on their industry’s overall output levels.

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One can use Eq. (6) to determine a range for the proportion of outside finance supported by contracts that allow for credible overinvestment (leverage). For external financing to be a useful competitive tool, the portion of investment that is financed by outside lenders, a, has to satisfy the condition 0oaoa, with a given by a¼

m=ð1  mÞDyA rhA

þ m=ð1  mÞDyA  ylA

,

(7)

where DyA  yhA  ylA , and rhA is the loan interest rate for the contract pair ðLhA ; aI hA Þ.

2.4. Robustness The model I analyze is admittedly stylized. It is meant to study the claim that debt–performance interactions are likely to be more complex (‘‘less linear’’) than commonly considered. As I discuss below, the model’s main implications are robust to variations in some of the assumptions used. Dealing with these variations, of course, may require more structure and/or additional steps for the results to follow. Other variations are more difficult to deal with. This happens whenever one brings general theoretical models into a specific, applied context.10 This section discusses a couple of stylized features of the model. In the model, the parameter y refers to alternative outside opportunities that Firms A and B face; i.e., opportunities other than their common product market. In that sense, the y’s need not be the same across rival firms, nor need they be correlated. For example, these firms may have subsidiaries in different industries to which they can transfer investment funds, or they may be endowed with the opportunity to start new ventures in other lines of business. It thus seems reasonable to assume that Firm B might know far less about yA than Firm A itself, and hence convey little or no information about yA through its investment choice. However, one could question the model’s results on the grounds that Firms A and B could knowingly have somewhat related y’s, and that, after learning yB , Firm B could try to reveal information about yA to Firm A’s lender through its choice of I B . In considering this possibility, I note that the previous results will go through unless I B fully reveals the realization of yA . More precisely, so long as there is a difference in the information sets of lenders and their borrowers, optimal contracting will still require a menu choice; one that allows for inefficient outcomes. Now consider the case of an information structure under which I B could potentially provide a one-to-one mapping into yA . In that case, Firm B could then use I B to try to influence Firm A’s lender for strategic gains. If all parties know about this, however, I B will become a noncredible signal—an action choice that is not fully informative of the true realization of yA . And we are thus back to the case in which the lender does not know the true yA —at best, it has only a noisy signal about yA —and therefore has to offer Firm A a menu of contracts; a menu that still allows for inefficient investment.11 10

In essence, this paper applies Dewatripont’s (1988) theory of commitment through renegotiation-proof contracts to a product market context. 11 If the lender will not believe Firm B’s action because it knows of Firm B’s incentives, then it will make no sense for Firm B to set I B in ways that are not value-maximizing in the absence of a response by the lender—these alternative choices are costly and may accomplish nothing in terms of the contractual relationship between Firm A and its lender.

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Another issue that deserves discussion is the timing of investment. Allowing for sequential investment is an important feature of the theoretical setting I study (namely, that of ex post renegotiable contracts). Crucially, the very asymmetry created by the possibility of uninformed financial contracting brings into the industry dynamics a reason for the rivals to invest sequentially rather than simultaneously. To wit, at date 1, the firm that uses internal funds only (Firm B) has all the information it needs to set its investment policy. Waiting to invest gives no advantages to Firm B if Firm A is the only player with the ability to renegotiate product market financing in the credit markets. Rather, as in any quantity setting environment, Firm B has an incentive to move first, effectively becoming a Stakelberg leader (which is an advantageous strategy), when it anticipates that Firm A might wait on its investment decision. This fits naturally with my setup in that valueenhancing renegotiations between Firm A and its lender will only make sense if Firm B has invested.12 2.5. Taking it to the data The analysis of Section 2.3 shows that debt-like financing can lead to multiple competitive outcomes—some more beneficial than others for the leveraged firm. Notice, though, that it does not formalize the idea that excessive debt taking (i.e., a4a) may have detrimental consequences for performance. This might be unnecessary given the ample amount of research already devoted to these effects. Aside from the traditional ‘‘predation’’ stories of Telser (1966) and Bolton and Scharfstein (1990), additional theoretical rationales for the detrimental effects of debt are articulated by Titman (1984), Maksimovic and Titman (1991), Chevalier and Scharfstein (1996), and Campello and Fluck (2004). These papers consider the negative attitude of consumers towards indebted firms’ products. Somewhat closer to my model (also under asymmetric information), Faure-Grimaud (2000) shows that the output-boosting limited liability effect of Brander and Lewis (1986) can be offset (and reversed) by the costs of debt-contracting inefficiencies. Kovenock and Phillips (1995, 1997) discuss conditions under which a leveraged firm’s investments and profits decline at exceedingly high levels of leverage, while the performance of unleveraged rivals improves. At the cost of added structure, some of these arguments can be incorporated into the preceding analysis in order for debt to be strictly detrimental to performance after the threshold given by a. While I do not formally model the detrimental effects of ‘‘excessive’’ debt taking, my empirical test design will allow for these sorts of countervailing effects in debt–performance interactions to manifest themselves. At first glance, the model of Section 2.3 might seem to imply that a firm could boost its performance by simply taking on some additional debt. However, the argument is subtler than this. The analysis pertains to a multifirm setting in which the effect of a firm’s debt on its competitive performance is determined by its rivals’ actions. Taking the model to the data is therefore more involved than just, say, gauging the overall impact of debt taking on revenues or sales. What the model implies is that when a firm relies more heavily on debt 12

Other papers in the literature also use the Stakelberg sequential model to make their case. For instance, Dasgupta and Titman (1998) discuss the fact that only the Stakelberg sequential move model—and not the Nash, simultaneous move model—would allow their theory to explain why prices sometimes fall after leverage increases in an industry (a well-known empirical finding reported in Chevalier, 1995; Phillips, 1995).

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than its industry rivals, then it may be in a position to take (limited) advantage of the inefficiencies of debt contracting in order to influence competitive outcomes. Although a firm can determine its own reliance on external funding, note that the firm cannot ultimately set the capital structure of its rivals. To the extent that those rivals’ financial policies change—because of reasons both related and unrelated to competition—a firm’s relative-to-rival capital structure will change beyond the firm’s control. A novel feature of the above analysis is that it allows parties to renegotiate the contracts they sign. Explicitly recognizing that many conduct–commitment contracts cannot survive renegotiation places a limit on the extent to which external finance might influence competitive outcomes. More precisely, the optimal contracting analysis shows that the impact of debt financing on product market investment is nonmonotonic: some leverage is needed for a credible commitment to overinvest in product markets, but excessive reliance on debt is ineffective for such commitment. In essence, the preceding theoretical analysis proposes that relative-to-rival debt taking and performance should be related in a nonmonotonic fashion in the data. In what follows, I empirically test this prediction. Anticipating my strategy, in the remainder of the paper I develop measures of relative-to-rival debt usage and sales performance to draw inferences about capital structure–product market interactions through an empirical model that allows for nonlinearities in those interactions. 3. Empirical research design I study the interplay between financial structure and competitive outcomes looking at data from a large cross-section of industries over a number of years, as opposed to exploring stylized industry/time settings. In that sense, the subsequent analysis is related to the work of Opler and Titman (1994), Kovenock and Phillips (1997), Campello (2003), and MacKay and Phillips (2003) since, to my knowledge, these are the only studies in the literature that are not restricted to a small set of industries or time-specific events. In turn, I discuss the issues of sample selection, variable construction, and testing design. 3.1. Sample selection and variable construction 3.1.1. Identification of relevant industry groupings (product markets) All firm-level data are from COMPUSTAT’s Primary/Supplementary/Tertiary, Full Coverage, and Research annual tapes over the 1971–2000 period. Observations from financial institutions, governmental enterprises, and not-for-profit organizations are excluded. I retain only those firm-years with valid information on total assets (COMPUSTAT’s item #6), long-term debt (item #34), and sales (item #12). I also eliminate firms with sales or asset growth in excess of 200% in any one year. All financial data are CPI-deflated. A relevant issue for those studying product market dynamics using COMPUSTAT concerns the criteria used by S&P to assign companies to finely defined industry codes. As discussed in Clarke (1989), Guenther and Rosman (1994), and Kahle and Walkling (1997), some of the three- and four-digit SIC codes employed by COMPUSTAT are not useful in identifying economically meaningful markets. For example, some firms may be too diversified or lack enough counterparts to make up a particular four-digit SIC code. These firms are typically assigned to SIC codes ending with ‘‘0.’’ Other SIC codes (typically

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ending with a ‘‘9’’) combine ‘‘miscellaneous’’ and ‘‘not elsewhere classified’’ businesses, and are thus unfit for my tests. Accounting for changes in the SIC definitions, I follow the industry selection criteria proposed by Clarke (1989) and retain only those firm-years that are assigned to well-defined product markets using four-digit SIC codes. After this pass of the data selection process, 168 four-digit SIC industries remain in the sample. 3.1.2. Proxies for product market performance and capital structure In examining the link between product market performance and capital structure, empirical research has often linked price-setting behavior with some aspect of debt financing (see, e.g., Chevalier, 1995). In some settings, pricing decisions may reasonably reflect how a firm’s financial status might affect its competitive behavior. More generally, though, firms can implement a number of alternative policies that influence their performance, but that may not be reflected in how they price their products. Examples of such policies are decisions about capital outlays, research and development spending, plant or store location, distribution network, etc. One way to build a practical measure of performance that summarizes information from the combined effects of pricing and other competitive strategies is to look at changes in firms’ share of industry sales. In what follows, I use a firm’s relative-to-industry sales growth to gauge its performance in the product market (see also Campello, 2003). Different from most other performance proxies, this measure can be consistently estimated across many industries and periods. The capital structure proxy I use, denoted Leverage, is computed as the ratio of longterm debt to total assets, all measured at book values (COMPUSTAT’s items #9 and #6, respectively). Using ‘‘long-term book leverage’’ helps reduce the potential for reverse causality between performance and capital structure in at least two ways. First, in contrast to market values, long-term book values are less sensitive to capital markets’ assessments about performance in the near future. Second, while leverage changes (e.g., LBOs) are likely to reflect changes in expectations about ensuing product market outcomes, leverage levels carry the cumulative effect of past financing decisions. I restrict the sample’s debt-toasset distribution to the ½0; 1 range. This implies the deletion of firm-years with negative book equity (nearly bankrupt firms) from the sample. 3.1.3. Control variables In a classical paper, Myers and Majluf (1984) argue that firms with favorable growth prospects will exhaust their internal sources of funds before soliciting outside financing, implying a negative correlation between debt and profitability. In contrast, Jensen (1986) proposes a positive association between profits and leverage, arguing that debt may discipline managerial behavior. Because sales growth is likely to be associated with profitability and debt ratios may correlate with profitability, one must control for profitability in any empirical model that gauges the effect of debt on sales performance. In a similar vein, one may argue that capital spending in one period can lead to sales growth in the next, and that investment is more likely to take place under lower debt burdens (Myers, 1977). In this case, the relation between firm sales growth and leverage should account for fixed investment spending. Accordingly, as in previous papers in the product market literature, lags of firm Profitability (proxied by operating earnings over assets, or item #18 plus item #14, divided by item #6), Investment (item #172 divided by item #6), and Size (the log of total assets) are used as controls in my regressions of sales growth on leverage.

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Finally, to the extent that a firm’s sales performance may be influenced by its past sales efforts (e.g., advertising and use of promotions) and that those efforts might correlate with financial leverage, one needs to introduce controls for sales-related expenditures in a model designed to capture the association between sales performance and debt.13 Hence, my baseline sales performance model also includes the sum of advertising expenses (item #45) plus selling expenses (item #189) scaled by total sales as an added control variable (denoted SellExpenses). 3.2. Testing design The pooled cross-sectional–time series regression models I estimate resemble those of Opler and Titman (1994), Campello (2003), and Campello and Fluck (2004); they have the following general form: Sales Growthi;t ¼ a þ bSizei;t þ

2 X k¼1

þ

2 X

fk Profitabilityi;tk þ

2 X

lk Investmenti;tk

k¼1

gk SellExpensesi;tk þ dLeveragei;t2ðor;t3Þ þ
i;t .

ð8Þ

k¼1

In the estimations performed, I correct the regression error structure for within-firm residual clustering and heteroskedasticity using Huber-White’s covariance estimator (see Rogers, 1993). To purge idiosyncratic effects from my estimates at a low cost, prior to the estimations, I adjust all of the realizations of the variables in Eq. (8) by removing their mean industry effects in each year. This adjustment serves two main purposes. First, I can more safely ascribe relations among the variables of interest to the dynamics of competition in the sample firms’ industry-years (‘‘product markets’’). The second advantage refers to the interpretation of the estimates. By adjusting observations of a firm’s sales growth in this manner, I obtain a variable that measures the firm’s sales growth relative to that of its industry rivals in a given year; this roughly gauges a firm’s market share growth. Likewise, the average rivals’ leverage becomes the metric used to measure a firm’s indebtedness. To ensure that the empirical industry-year mean represents a reliable measure of centrality, I require that a minimum of ten firms be present in each industry-year.14 The final sample covers 115 different four-digit SIC codes, yielding 3,201 industry-years. Table 1 reports descriptive statistics of the variables used in the paper (before industry-year adjustments), including an instrument for firm debt (asset tangibility). In gauging the extent to which a firm’s financial position differs from that of its industry peers, I not only adjust Leverage for industry-year mean effects, but also standardize (i.e., 13

An empirical link between financial structure and sales effort is established in Titman and Wessels (1988), who use selling expenses over sales as a proxy for product uniqueness. A theoretical link between financial structure and sales effort (more precisely, the determination of product markup) is proposed by Chevalier and Scharfstein (1996). 14 Campello and Fluck (2004) also require a minimum of ten firms, while Opler and Titman (1994) require four. My inferences are unchanged if I use Opler and Titman’s four-firm cutoff criteria.

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Table 1 Descriptive statistics for the overall sample This table reports summary statistics for the main variables used in the regression estimations (before industryyear adjustments). The data are collected annually from COMPUSTAT. Firm Size is total assets (COMPUSTAT item #6). Sales Growth is the annual gross sales growth at time t, given by ðSalest  Salest1 Þ=Salest1 , where Sales is item #12. Leverage is the ratio of long-term debt (item #9) to total assets. Profitability is operating earnings plus depreciation (item #18 + item #14) over assets. Investment is capital expenditures (item #172) over assets. SellExpenses is the ratio of advertising (item #45) and selling expenses (item #189) to total sales. Tangibility is computed as a weighted sum of cash holdings, accounts receivables, inventories, and net fixed capital, divided by total assets ð¼ ðitem #1 þ 0:715  item #2 þ 0:547  item #3 þ 0:535  item #8Þ=item #6Þ. The sample period is 1971–2000. Included firms are from industries selected at the four-digit SIC level following Clarke (1989). Only observations from industry-years containing at least ten firms are used.

Size ($ Millions) Sales Growth Leverage Profitability Investment SellExpenses Tangibility

Mean

Median

Std. Dev.

Pct. 25

Pct. 75

N. Obs.

528.19 0.0609 0.2029 0.0215 0.0840 0.2145 0.5421

34.77 0.0437 0.1710 0.0853 0.0601 0.1597 0.5498

2695.33 0.2675 0.1774 0.4561 0.0829 0.3719 0.1127

7.20 0.0525 0.0538 0.0299 0.0306 0.0511 0.4978

178.84 0.1790 0.3016 0.1287 0.1097 0.3579 0.5975

44,556 44,556 44,556 44,133 44,001 43,514 42,994

z-score) it within each industry-year.15 In other words, I take the approach that it is not the absolute size of the difference from the industry average that matters, but rather the relative size of that deviation (see also Almazan and Molina, 2002; MacKay and Phillips, 2003). The upshot of standardizing deviant behavior is that it generalizes the test of the proposition that debt systematically affects performance across different types of industries at different points in time. Put differently, standardization allows me to use the same metric across different industry environments. Consider, for example, the information that a firm’s debt-to-asset ratio deviates by 5% from its industry average. Clearly, that deviation will be relevant if the industry average is just 10% and most firms’ debt ratios are distributed within a 1% band around the 10% average. On the other hand, the same 5% deviation is likely to be uninformative when the industry average leverage is 40% and rivals’ debt ratios vary widely around that number. While most previous studies looking at relative-to-rival debt positions overlook cross-industry differences in debt dispersion, the rescaling approach used here accounts for that property of the data, identifying truly informative deviant behavior. The standardization approach may raise other potential concerns, however. If one wants to get reliable estimates of the within-industry-year standard deviation of leverage (so that the z-scores are sound), one may need to require a minimum number of firms for an industry-year to qualify for the final sample. If the underlying distribution of leverage ratios within each industry-year is well behaved (i.e., unimodal and not severely skewed), then requiring a minimum of ten observations (as I do) is sufficient. Otherwise, 30 or 40 observations should be considered for a minimum cutoff. Since skewness could distort the 15 To be exact, ‘‘z-scoring’’ Leverage within an industry-year amounts to subtracting the industry-year mean debt-to-asset ratio from Leverage and dividing that difference by the industry-year standard deviation of the debtto-asset ratio.

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standardization of leverage across different industry-years, when checking the robustness of my findings, I rerun my tests using (alternatively): (1) observations from industry-years in which leverage skewness is in the ½1; þ1 range and a minimum of ten firms are present, and (2) observations from industry-years with a minimum of 40 firms. 3.3. Estimation biases Kovenock and Phillips (1997) argue that the potential for simultaneity between capital structure decisions and product market performance stems largely from time-varying factors that affect all of the firms in an industry, such as capacity utilization, demand conditions, etc. Fortunately, the empirical strategy that best suits my tests naturally addresses these concerns in that all industry-specific factors are expunged from my estimations in each sample year. In effect, a firm’s indebtness and performance are measured relative to those of its industry-year counterparts. To the extent that rivals’ financial and product market policies are outside of the firm’s control set, the issue of endogeneity is minimized in my relative-to-rival sales–debt regressions. In designing an empirical test of the interplay between debt and product market performance, however, one would like to gauge the effect of financing on competitive outcomes when financing varies along some dimension that is orthogonal to those outcomes. It is not easy to find an ‘‘instrument’’ for debt that does not belong in the performance equation, but the theory of Section 2 suggests an identification strategy. In the presence of contracting imperfections, lenders typically request collateral in exchange for financing. The amount of financing that can be supported by contracts with outside financiers therefore correlates with the creditors’ valuation of the firm’s transferable, hard assets (‘‘asset tangibility’’). Crucially, while a firm’s asset tangibility may correlate with its financing, the tangible attributes of a firm’s assets should not influence its relative sales performance other than through the association with financing itself. Asset tangibility is therefore a candidate instrument for debt usage in sales performance equations. Exploring this insight, in the estimations below (all via IV and GMM), I use the predicted values from a regression of leverage on asset tangibility in models that include linear and nonlinear versions of Leverage in the explanatory variable set. The proxy that I use for asset tangibility borrows from Berger et al. (1996) and gauges the expected resale value of a firm’s assets in liquidation. In determining whether investors rationally value their firms’ abandonment option, Berger et al. gather data on proceeds from discontinued operations reported by a sample of COMPUSTAT firms over the 1984–1993 period. The authors find that a dollar’s book value produces, on average, 72 cents in exit value for total receivables, 55 cents for inventories, and 54 cents for fixed assets. Similar to those authors, I estimate liquidation values for the firm-years in my sample using the following computation: Tangibility ¼ 0:715  Receivables þ 0:547  Inventory þ 0:535  Fixed Capital, where Receivables is COMPUSTAT’s item #2, Inventory is item #3, and Fixed Capital is item #8. As in Berger et al., I also add the firm’s total cash holdings (item #1) to the computation of Tangibility and scale the result by total book assets. A regression of Leverage on Tangibility yields a slope coefficient of 1.283 (t-statistic of 4.22). Finally, suppose one is concerned with the case of an unmodeled variable capable of introducing biases into the estimation of Eq. (8). Econometrically, this would only be a

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problem if the omitted variable’s dispersion was systematically correlated with the distance of the included variables’ realizations from their means for each industry in each year, and along the lines of the lagging scheme used in Eq. (8). Such a case would require a very involved story. The omitted variable bias case is even more difficult to articulate in the main tests below, which employ a nonlinear version of Eq. (8) in which the marginal effect of leverage on sales varies across different levels of indebtedness. 4. Results 4.1. Revisiting previous findings Before conducting my tests, I verify whether my final sample can be used to replicate results from existing comparable studies. In my first estimation, I follow Opler and Titman (1994) and define Leverage in Eq. (8) as a dummy variable that assigns the value of one to firms with leverage ratios in the overall (across all years and all industries) top three deciles of the leverage distribution, and zero otherwise. In this particular estimation, leverage is neither instrumented nor z-scored. In the second model estimation, Leverage is the industry-year standardized projection of the debt-to-asset ratio onto Tangibility. This model resembles that of Campello and Fluck (2004), where leverage is also industry-year adjusted and instrumented, but not z-scored. The results from these first-pass estimations are reported in Table 2, where I use a couple of different lagging structures for leverage in order to approximate the design of those two studies. I leave the interpretation of the estimated coefficients for the next subsection. The replication of Opler and Titman (1994) (under Models 1 and 2) shows that ‘‘absolutely highly leveraged’’ firms strongly underperform their industry counterparts. Even though my tests differ somewhat from that of Opler and Titman, both with respect to data selection and model specification, the estimates returned for the high leverage dummy variable (Top3Deciles) are similar to those reported in Table 4 of their paper. The leverage estimates under Models 3 and 4, on the other hand, suggest that ‘‘relatively highly leveraged’’ firms outperform their industry rivals. This result resembles that of Table 7 in Campello and Fluck (2004), who use more comparable sampling criteria and specification. Besides allowing for a direct comparison between my inferences and those in the prior literature, I take the ability to replicate existing results as indicative that the findings below do not stem from biases introduced by my treatment of the data and variable construction. More importantly, the subsequent analysis is able to reconcile these diverse debt– performance outcomes. 4.2. New basic findings 4.2.1. A flexible functional form for financial leverage I now propose an empirical functional form that relaxes the assumption of monotonicity in debt–performance interactions. My test design differs significantly from that of previous studies (such as those just revisited) in that it allows for the marginal effect of debt policies on product market outcomes to vary according to the level of firm indebtedness. Importantly, the data can easily reject my proposed cross-sectional test design if indeed debt has a near-monotonic effect on competitive performance (tests for linearity are performed in all of the estimations below).

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Table 2 Nonlinearities in debt–performance interactions: prior studies Pooled time-series cross-section instrumental least square regressions. The dependent variable is firm annual sales growth at time t, given by ðSalest  Salest1 Þ=Salest1 . Size is the natural logarithm of assets. Investment is capital expenditures over assets. Profitability is operating earnings over assets. SellExpenses is the ratio of advertising and selling expenses to total sales. In Models 1 and 2, Leverage is a dummy variable for firm-years with a total debt-to-asset ratio in the top three deciles of the overall sample distribution, denoted Top3Deciles. In Models 3 and 4, Leverage is computed from the predicted values from a regression of the ratio of long-term debt to assets on a proxy for asset tangibility. All variables are adjusted for their four-digit SIC industry-year means, with leverage in Models 3 and 4 further standardized by dividing it by its industry-year standard deviation (zscored), denoted z-Leverage. The sample period is 1971–2000. Included firms are from industries selected at the four-digit SIC level following Clarke (1989). Only observations from industry-years containing at least ten firms are used. The estimations correct the error structure for heteroskedasticity and within-firm error clustering using the Huber-White estimator. t-statistics are in parentheses. Dep. var.: Sales Growtht

Model 1 2-Year lagged Leverage

Model 2 3-Year lagged Leverage

Model 3 2-Year lagged Leverage

Model 4 3-Year lagged Leverage

P2

0.0065** (7.26) 0.1311**

0.0062** (6.66) 0.1594**

0.0076** (8.43) 0.1225**

0.0072** (7.65) 0.1509**

P2

(8.90) 0.4455**

(8.73) 0.4461**

(8.26) 0.4333**

(8.28) 0.4318**

Sizet k¼1 Profitabilitytk k¼1 Investmenttk

P2

k¼1 SellExpensestk

Leverage: Top3Deciles

(14.69) 0.0078

(13.57) 0.0103

(14.02) 0.0046

(12.75) 0.0085

(0.62)

(0.79)

(0.38)

(0.62)

0.0140** (7.82)

0.1266** (6.48)

0.0192** (4.02)

0.0305** (2.87)

z-Leverage Adjusted-R2 N. observations

0.03 33,113

0.04 28,893

0.02 32,578

0.04 27,443

Note: ** and * indicate statistical significance at the 1% and 5% (two-tailed) test levels, respectively.

Denote a firm’s standardized predicted value of leverage by z-Leverage. Suppose the number of different intervals over which debt bears a distinct relation with sales growth is n. One can partition z-Leverage in these many intervals and rewrite it as z-Leveragek , with k ¼ 1; . . . ; n, implying that n variables will replace the original one. To maintain continuity in the functional form, these variables must be joined at knots. Denote these knots by l k , with k ¼ 1; . . . ; n  1. The following expressions show how the normalized leverage is transformed into n new variables (leverage spline): z-Leverage1 ¼ min½z-Leverage; l 1 ; and z-Leveragek ¼ max½min½z-Leverage; l k ; l k1   l k1 ;

for k ¼ 2; . . . ; n.

ð9Þ

In the remainder of the paper, I estimate Eq. (8) defining Leverage as in (9). Recall, the theoretical analysis suggests a nonlinear association between relative-to-rival capital structure and product market performance. For empirical testing, I conjecture that if capital structure matters in strategically influencing performance, then one should be able

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to establish a link between a firm’s finances and its performance when the firm’s debt ratio is at (and varies along) a level that is markedly different from that of its rivals. To capture this effect under standard parametric assumptions, I hypothesize that the knots of the leverage spline given by Eq. (9) should be placed somewhere a few standard deviations ðsÞ from the industry average. However, I leave room for the data to suggest where these knots should be placed and, of course, whether the knots (i.e., nonlinearities) matter at all. Tables 3 and 4 below are dedicated to this identification process.

4.2.2. Nonlinearities in debt– performance interactions Results from Models 3 and 4 in Table 2 show that if one imposes a single-line form upon the association between relative indebtness and sales growth, then a positive coefficient obtains for debt. In this section, I look for deviations from a linear monotonic relation

Table 3 Descriptive statistics for firm debt This table provides detailed statistics for absolute and relative-to-rival debt usage among sample firms. For each one of the various breakdowns of z-Leverage, the table reports the number of individual firms, the number of firmyear observations, the mean and median raw leverage ratio, and the mean and median industry-year-adjusted leverage ratio. The raw leverage ratios are presented in percentage terms and the adjusted leverage ratios are presented in terms of standard deviations from the industry-year mean of Leverage. The sample period is 1971–2000. Included firms are from industries selected at the four-digit SIC level following Clarke (1989). Only observations from industry-years containing at least ten firms are used. Breakdown of z-Leverage by range

Number of firms

Number of firm-years

Raw leverage in % terms mean [Median]

Adjusted leverage in s’s terms mean [Median]

1oz-Leveragep0

5,257

22,556

0oz-Leverageo1

4,697

20,428

1oz-Leveragep  1:5s

1,301

3,274

1:5soz-Leveragep þ 1:5s

6,012

35,780

þ1:5soz-Leveragep þ 1

1,253

3,940

1oz-Leveragep  2s

810

2,412

2soz-Leveragep þ 2s

6,267

37,393

þ2soz-Leverageo þ 1

742

3,189

1oz-Leveragep  3s

388

1,121

3soz-Leveragep þ 3s

6,679

39,424

þ3soz-Leverageo þ 1

394

2,449

17.573 [13.951] 23.052 [20.372] 10.821 [4.753] 20.204 [17.303] 25.348 [21.909] 9.157 [2.988] 20.146 [17.110] 26.154 [22.330] 7.100 [1.558] 20.125 [17.005] 28.755 [25.66]

0.781 [0.582] 0.683 [0.530] 2.147 [1.958] 0.047 [0.0739] 1.978 [1.842] 2.659 [2.408] 0.038 [0.071] 2.443 [2.311] 3.732 [3.433] 0.016 [0.064] 3.499 [3.309]

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Table 4 Nonlinearities in debt–performance interactions: base tests Pooled time-series cross-section instrumental least square regressions. The dependent variable is firm annual sales growth at time t, given by ðSalest  Salest1 Þ=Salest1 . Size is the natural logarithm of assets. Investment is capital expenditures over assets. Profitability is operating earnings over assets. SellExpenses is the ratio of advertising and selling expenses to total sales. Leverage is computed from the predicted values from a regression of the ratio of long-term debt to assets on a proxy for asset tangibility. All variables are adjusted for their four-digit SIC industry-year means, with leverage further standardized by dividing it by its industry-year standard deviation (z-scored), denoted z-Leverage. The sample period is 1971–2000. Included firms are from industries selected at the four-digit SIC level following Clarke (1989). Only observations from industry-years containing at least ten firms are used. The estimations correct the error structure for heteroskedasticity and within-firm error clustering using the Huber-White estimator. t-statistics are in parentheses. Dep. var.: Sales Growtht Sizet P2

k¼1 Profitabilitytk

P2

k¼1 Investmenttk

P2

k¼1 SellExpensestk

Leveraget2 : z-Leverage

Model 1 0.0076** (8.43) 0.1225** (8.26) 0.4333**

Model 2 0.0078** (8.62) 0.1234** (8.33) 0.4423**

Model 3 0.0079** (8.78) 0.1227** (8.30) 0.4357**

Model 4

Model 5

0.0078** (8.72) 0.1224** (8.29) 0.4353**

0.0077** (8.61) 0.1222** (8.27) 0.4341**

(14.02) 0.0046

(14.22) 0.0063

(14.06) 0.0047

(14.07) 0.0043

(14.05) 0.0042

(0.38)

(0.52)

(0.39)

(0.35)

(0.35)

0.0140** (7.82)

1oz-Leveragep0 0oz-Leverageo1

0.0066* (2.48) 0.0222** (6.43)

1oz-Leveragep  1:5s

0.0189** (–2.92) 0.0200** (8.92) 0.0037 (0.30)

1:5soz-Leveragep þ 1:5s þ1:5soz-Leveragep þ 1 1oz-Leveragep  2s

0.0315** (4.10) 0.0179** (9.04) 0.0190 (0.90)

2soz-Leveragep þ 2s þ2soz-Leverageo þ 1 1oz-Leveragep  3s

0.0389** (5.54) 0.0158** (8.79) 0.0895** (3.83)

3soz-Leveragep þ 3s þ3soz-Leverageo þ 1 Partial F-tests for Leverage coefficients F -statistic (null of joint insignificance) F -statistic (null of linearity) 0.02 Adjusted-R2 N. observations 32,578

34.60** 7.74** 0.04 32,578

28.28** 14.32** 0.05 32,578

28.95** 18.33** 0.05 32,578

34.58** 32.03** 0.06 32,578

Note: ** and * indicate statistical significance at the 1% and 5% (two-tailed) test levels, respectively.

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between debt and performance by examining a number of alternative forms for Eq. (9). Specifically, I consider a number of segment breakdowns for z-Leverage, which are listed in Table 3 below. This table is useful in that it also provides a sense for how many individual firms and observations (firm-years) one may find in each of the debt categories I derive from (9). Table 3 also shows statistics for both the raw and industry-adjusted leverage in each z-Leverage breakdown.16 Inspection of Table 3 shows a very consistent and stable pattern in the distribution of leverage across various segments of z-Leverage. Firms in the lower (upper) ranges of zLeverage have lower (higher) raw and industry-adjusted leverage. Also, the number of observations declines smoothly as we move to more extreme segments of z-Leverage. Importantly, firms in the higher branches of z-Leverage are sufficiently many and they do not exhibit extremely high levels of absolute indebtedness. In other words, the table shows that (relative-to-industry) highly leveraged firms should not be taken as a small group of troubled firms with extremely high leverage. For instance, the most extreme industryadjusted leverage range in the table ðþ3soz-Leverageo þ 1Þ contains as many as 2,449 observations from 388 different firms whose average debt-to-asset ratio is below 29%. In testing for departures from the monotonicity rule, I first fit a spline with a knot set at zero; i.e., I use a version of Eq. (9), where n ¼ 2 and l 1 is set at the industry-year mean leverage. The results are presented under Model 2 in Table 4. (To facilitate comparisons, Model 1 replicates the results from the single-line specification.) The instrumental least square estimates suggest that sales growth–debt sensitivities are much weaker for leverage levels below the industry average than when a firm’s leverage exceeds that of the average rival. The coefficient for the positive branch of z-Leverage is over three times larger than that returned for the negative branch of z-Leverage; the difference in the slope coefficient for these two branches is statistically significant at better than 1% test level. In Models 3–5, I allow two knots to be placed symmetrically around zero. Since skewness dampens the significance levels of standard parametric hypotheses tests, I place my first two knots relatively far from zero, at 1:5s from the industry-year mean.17 The results for this specification are reported under Model 3 of Table 4. The new estimates suggest that among the firms with higher-than-average leverage—recall from Model 2, these firms exhibit strongly positive sales–debt sensitivities—those with leverage ratios above þ1:5s display weakly negative sales–debt sensitivities. Next, the two knots are placed at 2s from the mean. The new results (given under Model 4) show no significant differences from the previous estimation, except that those firms with comparatively very high debt levels (i.e., z-Leverage4 þ 2s) display much lower (more negative) sales–debt sensitivities. Finally, I place the knots further away, at 3s from the mean (see Model 5). This specification shows that while leverage seems to boost competitive performance at moderately high levels of debt usage, sales–debt sensitivities become strongly negative after an upper threshold (more on this below). At the bottom of Table 4, the F-statistics for the null hypothesis that the variables derived from the piecewise approach are not jointly significant given the model’s control 16

While the number of firm-year observations is held fixed at 42,994 across all sets of partitions (this happens because the data on Tangibility limits the construction of z-Leverage), the number of individual firms can vary across partitions since a firm can move across debt categories over the 30-year sample period. 17 In the presence of skewness, the usual interpretations ascribed to observations located at more than two or three standard deviations from the mean do not apply exactly. For example, for highly skewed distributions with some 30 or 40 observations, a 2s range may determine confidence levels of as low as 70% (rather than 95%).

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set validate the leverage spline variables in each of the estimations performed. More importantly, the table also reports the F-statistics for the test that Leverage attracts about the same slope coefficient over the intervals defined by the various spline models. Uniform linearity (monotonicity) is strongly rejected in every model. Finally, the adjusted-R2 ’s suggest that the piecewise form markedly improves the overall fit of the empirical model relating debt and product market performance. To summarize, the results from Models 2–5 in Table 4 show evidence of a positive sales growth response to marginal debt taking for firms with significantly—but not excessively— higher-than-average leverage in their industry-years. In other words, at the margin, debt taking by firms with distinctively higher levels of debt in their capital structure (as implied by their own industry standard) is associated with market share gains that come at the expense of their rivals. Crucially, though, the addition of more debt does not monotonically lead to sales expansions at the expense of rivals. In fact, at very high relative-to-industry levels of leverage there are no performance gains associated with further (tangibilitysupported) debt taking. Rather, large losses seem to be observed by these highly leveraged firms. These findings are consistent with the nonmonotonicity implications of the model of Section 2. At a minimum, the results presented thus far strongly suggest that the data reject the notion that financial structure invariably leads to one particular type of competitive outcome: debt can hurt and boost performance. The analysis, however, still lacks a more complete characterization of the interplay between debt and product sales performance. The next set of tests shed more light on debt–performance interactions, highlighting the statistical reliability and economic significance of these effects. 4.3. Characterizing the debt– performance interplay I take the results of Table 4 as initial evidence of nonlinearities in the empirical association between a firm’s capital structure and its competitive performance. To more fully characterize the nonmonotonic nature of debt–performance interactions and to check whether the data offer robust support for my previous inferences, I estimate alternative versions of the spline model that: (1) allow for additional ranges for the marginal impact of debt on sales, (2) adopt different lagging schemes, (3) tackle the issue of skewness in industry-year leverage, (4) use a different definition of firm debt, and (5) further address concerns about empirical biases in the estimated parameters through the use of alternative econometric methods (firm-fixed effects and GMM). In what follows, I also provide a detailed economic characterization of the main empirical estimates of this study. Results from a number of additional tests are tabulated below. To save space, the reported outputs focus on alternative versions of a four-segment leverage spline. This spline function builds on the results from Table 4 and has symmetric knots at three standard deviations away from firms’ industry-year means (i.e., at 3s) as well as at the industry-year mean (i.e., at 0).18 For ease of exposition, I rename the variables derived from the new spline function. Standardized leverage levels that lie far from the industryyear average, that is, below 3s and above þ3s from the normalized mean, are denoted 18

I also experimented with knots placed at somewhat different positions (e.g., 2:5s) and obtained results that are very similar to those reported below. Additionally, I examined results from splines containing up to six different segments and these lead to the same conclusions provided by the current (four-segment) spline model. These results are omitted to save space.

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VeryLowLev and VeryHighLev, respectively. Leverage in the range of 3s to 0 is denoted LowLev. Likewise, the spline segment representing leverage in the 0 to þ3s range is labeled HighLev. 4.3.1. Descriptive statistics Before discussing the new results, it is important to look at the characteristics of the firms under each of the debt categories I consider. Relevant firm information is displayed in Table 5, which reports the summary statistics for relative-to-industry-year-average (‘‘demeaned’’) size, sales growth, profitability, investment, selling expenditures, and asset tangibility for firms assigned to the VeryLowLev, LowLev, HighLev, and VeryHighLev debt categories. The statistics of Table 5 suggest that most of the firms in the LowLev category are larger than their average industry peers (see median row). In contrast, most of the firms in the other debt categories seem to be smaller than their average rivals. The mean and median Sales Growth of firms in the VeryLowLev, LowLev, and VeryHighLev debt categories are all negative, while those same statistics show that both the average and the median firms in the HighLev category outperform their industry-year average rivals. A noteworthy set of statistics in Table 5 concerns firm profitability. One could wonder whether very unprofitable—perhaps nearly bankrupt—firms could be driving some of the previous results in that those firms might both carry higher-than-average debt and register lower-than-average sales growth. The inclusion of profitability in the regression specification should tackle any such stories; however, an interesting insight into this case can be gained by examining summary statistics from relative-to-average-rival Profitability. In the data, most HighLev and VeryHighLev firms are more profitable than their average rivals (see median row). There are indeed some firms with relatively very high debt that are unprofitable (note the decline from the median to the mean estimate of Profitability under the VeryHighLev category), but these seem to be the exception rather than the rule. In fact, mean–median comparisons suggest that many more troubled firms seem to be located in the VeryLowLev and LowLev categories. The remaining variables in the table display less pronounced patterns; however, notice that HighLev and VeryHighLev firms seem to invest more in fixed capital than their less leveraged competitors. 4.3.2. IV results Table 6 reports the results from six different estimations of a four-segment spline version of Eq. (8) using the same instrumental least squares approach of Table 4. Column 1 displays the results from my ‘‘baseline’’ spline model. As in prior regressions, the estimates suggest that firms that were more profitable, invested more in fixed capital, and spent more in advertising/selling in previous years tend to outperform their industry rivals in the current year. More importantly, the results highlight a very noticeable pattern in the way capital structure influences firm sales performance. While at low leverage levels more debt is either detrimental (see VeryLowLev row) or mildly beneficial (see LowLev) to performance, on the margin, debt taking seems to significantly boost sales growth at relatively high leverage levels (see HighLev). According to the baseline model’s estimates, marginal debt increases at the HighLev range have over three times the impact of similar increases within the LowLev range (with over 99% confidence). At the same time, however, additional debt taking at very high leverage levels (within the VeryHighLev range) is highly detrimental to competitive performance. Column 2 of Table 6 reports results for the same

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Table 5 Summary statistics for (within-industry-year) debt categories This table reports summary statistics for the main variables used in estimations (after industry-year adjustments) separately for each of the four debt categories considered in the baseline four-segment spline model. Sales Growth is the firm annual sales growth. Size is the natural logarithm of assets. Investment is capital expenditures over assets. Profitability is operating earnings over assets. SellExpenses is the ratio of advertising and selling expenses to total sales. Tangibility is computed as a weighted sum of cash holdings, accounts receivables, inventories, and net fixed capital, divided by total assets. Leverage is computed from the predicted values from a regression of the ratio of long-term debt to assets on a proxy for asset tangibility. All variables are adjusted for their four-digit SIC industry-year means. The debt categories VeryLowLev and VeryHighLev group firms with debt-to-asset ratios below 3s and above þ3s from the industry-year average, respectively. LowLev and HighLev group firms whose leverage are in the 3s to 0 range and in the 0 to þ3s range of the industry-year standardized leverage distribution, respectively; 0 is the industry-year average. Observations are from industry-years with at least ten firms. Industry-year adj. variables

Debt categories (ranked within industry-years) VeryLowLev ð# Firms ¼ 388Þ ð# Obs: ¼ 1; 121Þ

LowLev ð# Firms ¼ 4; 855Þ ð# Obs: ¼ 21; 445Þ

HighLev ð# Firms ¼ 4; 333Þ ð# Obs: ¼ 17; 979Þ

VeryHighLev ð# Firms ¼ 394Þ ð# Obs: ¼ 2; 449Þ

Size Mean Median Std. Dev. Interq. range

1.2080 1.1452 2.4500 3.4712

0.3219 0.2635 2.0682 2.8204

0.2683 0.2710 1.8441 2.3892

0.0912 0.0119 2.0561 2.6706

Sales Growth Mean Median Std. Dev. Interq. range

0.0921 0.0596 0.2473 0.2425

0.0200 0.0128 0.2412 0.2068

0.0185 0.0051 0.2545 0.2246

0.0062 0.0016 0.2160 0.1785

Profitability Mean Median Std. Dev. Interq. range

0.1507 0.0459 0.4556 0.1830

0.0077 0.0106 0.3312 0.1136

0.0041 0.0312 0.4750 0.1100

0.0025 0.0229 0.4752 0.1236

Investment Mean Median Std. Dev. Interq. range

0.0652 0.0940 0.0652 0.0821

0.0051 0.0125 0.0650 0.0523

0.0052 0.0033 0.0700 0.0588

0.0043 0.0038 0.0962 0.0670

SellExpenses Mean Median Std. Dev. Interq. range

0.0201 0.0422 0.4475 0.1710

0.0125 0.0436 0.3012 0.1742

0.0123 0.0268 0.2978 0.1853

0.0290 0.0463 0.3451 0.1359

Tangibility Mean Median Std. Dev. Interq. range

0.1352 0.1260 0.0777 0.0622

0.0705 0.0468 0.0712 0.0795

0.0632 0.0430 0.0642 0.0663

0.0998 0.0859 0.0880 0.0952

Investmenttk

Profitabilitytk

(11.60) 0.0088 (0.68)

(14.19) 0.0057 (0.47)

0.05 26.25** 32,578

0.04 18.83** 27,443

0.0372** (3.01) 0.0087** (2.91) 0.0190** (5.31) 0.1699** (4.56)

(5.53) 0.4350**

(8.32) 0.4411**

0.0520** (5.55) 0.0069* (2.22) 0.0228** (6.75) 0.0988** (4.27)

0.0073** (7.80) 0.0884**

Model 2 3-Year lag Lev

0.0079** (8.73) 0.1230**

Model 1 Baseline model

0.05 27.66** 15,870

0.0520** (5.50) 0.0077 (1.77) 0.0283** (6.04) 0.0952** (5.03)

(11.91) 0.0233 (1.37)

(4.85) 0.4442**

0.0077** (6.83) 0.0794**

Model 3 # Firms X40

0.05 18.30** 23,598

0.0460** (5.04) 0.0067* (2.30) 0.0236** (5.33) 0.0969** (2.80)

(13.16) 0.0213 (1.41)

(7.14) 0.5163**

0.0065** (5.88) 0.2136**

Model 4 1p Skewness p 1

Note: ** and * indicate statistical significance at the 1% and 5% (two-tailed) test levels, respectively.

Adjusted-R2 Partial F-statistic (linearity) N. observations

VeryHighLev

HighLev

LowLev

Leveraget2 : (spline segments) VeryLowLev

k¼1 SellExpensestk

P2

k¼1

P2

k¼1

P2

Sizet

Dep. var.: Sales Growtht

0.04 20.20** 32,582

0.0520** (5.55) 0.0094** (3.23) 0.0227** (6.74) 0.0988** (4.27)

(14.20) 0.0063 (0.52)

(8.35) 0.4413**

0.0078** (8.72) 0.1234**

Model 5 Total debt

0.04 27.82** 32,578

0.0480** (5.45) 0.0087** (2.79) 0.0233** (6.80) 0.1048** (4.58)

(13.98) 0.0037 (0.30)

(8.81) 0.4353**

0.0033** (4.06) 0.1486**

Model 6 Firm effects

Table 6 Nonlinearities in debt–performance interactions: detailed characterization (IV estimations) Pooled time-series cross-section regressions. The dependent variable is firm annual sales growth at time t, given by ðSalest  Salest1 Þ=Salest1 . Size is the natural logarithm of assets. Investment is capital expenditures over assets. Profitability is operating earnings over assets. SellExpenses is the ratio of advertising and selling expenses to total sales. Leverage is computed from the predicted values from a regression of the ratio of long-term debt to assets on a proxy for asset tangibility. All variables are adjusted for their four-digit SIC industry-year means, with leverage further standardized by dividing it by its industry-year standard deviation (z-scored), z-Leverage. VeryLowLev and VeryHighLev denote leverage levels below 3s and above þ3s from the industry-year average, respectively (i.e., z-Leverage 4j3sj). LowLev and HighLev denote leverage levels in the 3s to 0 range and in the 0 to þ3s range of the industry-year standardized leverage distribution, respectively; 0 is the industry-year average. The sample period is 1971–2000. The estimations correct the error structure for heteroskedasticity and within-firm error clustering using the Huber-White estimator. t-statistics are in parentheses.

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specification when firm leverage is measured with a three-year lag (Model 2). The results are similar to those of the baseline model, but the performance decline that is observed at very high levels of leverage (i.e., z-Leverage4 þ 3s) is considerably more pronounced than in the previous estimation. The next two sets of estimations address the concern that skewness in the empirical distribution of leverage ratios within industry-years could distort results. These estimations restrict the sample to observations from industry-years with at least 40 firms (under Model 3), and from industry-years with leverage skewness in the ½1; 1 range (Model 4). Although both these data restrictions significantly reduce the number of valid observations, the estimates from the spline model remain largely unchanged. The only noticeable difference from the regressions that explicitly treat skewness is an increase in the gap between the coefficients returned for relatively high and low levels of leverage. In these new regressions, the marginal impact of leverage increases on performance within the HighLev range have about four times the impact of similar changes within the LowLevrange. Model 5 uses long-plus short-term debt (i.e., total debt) in the computation of firm debtto-asset ratio. None of the previous conclusions are affected by this change in the definition of leverage. The last set of estimates in the table is returned from an empirical specification that includes firm-fixed effects in the set of explanatory variables. This additional set of effects should wipe out any biases stemming from unobserved firm-specific characteristics that could influence both capital structure and competitive performance. Results under Model 6 show that the inclusion of firm effects does not alter my conclusions. 4.3.3. GMM results One potential criticism of the experiments performed thus far is that they fail to recognize that, similarly to leverage, the inclusion of variables such as fixed capital investment and advertising/selling expenditures might also introduce endogeneity biases in the empirical estimates of Eq. (8). In addressing this issue, I reestimate each one of the regression models of Table 6 using a two-step efficient GMM estimator. In addition to Tangibility, the instrumental set used in those estimations includes lags two through four of stock fixed capital (or PPE/assets) as instruments for Investment—exploring the argument that investment in an specific asset category should depend negatively on the initial stock of that asset due to a decreasing marginal valuation associated with stock levels—as well as lags three through five of SellExpenses. Following Bound et al. (1995), instrument relevance is verified by looking at the significance of the squared partial correlation between the excluded instruments and the endogenous regressors in the system (the firststage partial F-statistics).19 Instrument validity is checked through Hansen’s (1982) J-statistic. The results from the GMM estimations are reported in Table 7. Although there are noticeable changes in the point estimates of the newly instrumented variables, there are virtually no changes in the estimates associated with the leverage spline. Simply put, none of the central inferences of the paper are modified when I use the method of moments estimator. The diagnostic test statistics at the bottom of Table 7 speak to the consistency of the GMM estimator. The instruments used are both significant and valid. The lowest 19

In examining instrument relevance below, I only report the lowest of the first-stage F-statistics—i.e., to ensure robustness, I gauge the quality of the instruments I use based on the weakest of their associated test statistics.

SellExpensestk

Investmenttk

Profitabilitytk

(2.07) 0.1934* (2.51)

(2.21) 0.1918*

(2.48)

0.76 71.45** 18.73** 23,258

0.68 71.38** 8.99** 23,255

0.0246 (1.17) 0.0071* (2.12) 0.0201** (2.88) 0.2377** (4.41)

(1.59) 0.5228*

(1.62) 0.4976*

0.0411** (2.87) 0.0069* (2.04) 0.0214** (3.08) 0.1639** (6.58)

0.0040** (3.51) 0.1623

Model 2 3-Year lag Lev

0.0041** (3.51) 0.1643

Model 1 Baseline model

0.62 96.99** 19.87** 11,411

0.0326** (2.77) 0.0099 (1.92) 0.0361** (2.97) 0.1595** (6.50)

(2.73)

(1.11) 0.1381**

(1.00) 0.2426

0.0039** (3.09) 0.0904

Model 3 # Firms X40

Note: ** and * indicate statistical significance at the 1% and 5% (two-tailed) test levels, respectively.

Hansen’s J-statistic p-value Lowest first-stage F-statistic Partial F-statistic (linearity) N. observations

VeryHighLev

HighLev

LowLev

Leveraget2 : (spline segments) VeryLowLev

k¼1

P2

k¼1

P2

k¼1

P2

Sizet

Dep. var.: Sales Growtht

0.88 87.43** 8.98** 17,998

0.0217 (1.45) 0.0069 (1.62) 0.0211** (2.98) 0.1814** (4.47)

(1.92)

(3.55) 0.3728

(2.79) 0.7229**

0.0030** (3.08) 0.2254**

Model 4 1p Skewness p 1

0.84 152.92** 6.48** 23,258

0.0333** (2.87) 0.0050 (1.20) 0.0264* (2.09) 0.2210** (2.94)

(0.93)

(2.10) 0.0928

(1.62) 0.4462*

0.0030** (3.21) 0.1340

Model 5 Total debt

0.77 171.44** 18.72** 23,258

0.0411** (3.08) 0.0088* (2.20) 0.0215** (3.09) 0.1739** (6.78)

(2.27)

(2.20) 0.1912*

(1.58) 0.4965*

0.0023* (1.98) 0.1631

Model 6 Firm effects

Table 7 Nonlinearities in debt–performance interactions: detailed characterization (GMM estimations) Pooled time-series cross-section regressions. The dependent variable is firm annual sales growth at time t, given by ðSalest  Salest1 Þ=Salest1 . Size is the natural logarithm of assets. Investment is capital expenditures over assets. Profitability is operating earnings over assets. SellExpenses is the ratio of advertising and selling expenses to total sales. Leverage is computed from the predicted values from a regression of the ratio of long-term debt to assets on a proxy for asset tangibility. All variables are adjusted for their four-digit SIC industry-year means, with leverage further standardized by dividing it by its industry-year standard deviation (z-scored), z-Leverage. VeryLowLev and VeryHighLev denote leverage levels below 3s and above þ3s from the industry-year average, respectively (i.e., z-Leverage4j3sj). LowLev and HighLev denote leverage levels in the 3s to 0 range and in the 0 to þ3s range of the industry-year standardized leverage distribution, respectively; 0 is the industry-year average. The sample period is 1971–2000. The estimations correct the error structure for heteroskedasticity and within-firm error clustering using the Huber-White estimator. t-statistics are in parentheses. The table displays diagnostic statistics for instrument overidentification restrictions (p-values for Hansen’s Jstatistics reported) and instrument first-stage partial F-statistics (lowest F-statistics reported).

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F-statistic from the (first-stage) regression of the endogenous regressors on the set of excluded instruments is highly significant in each of the six models estimated. Moreover, the p-values associated with the tests for overidentifying restrictions (J-statistics) are all well above 10%. Finally, note that monotonicity in debt–performance sensitivity is once again robustly rejected across all models (see partial F-tests). 4.3.4. Economic interpretation I now discuss the economic and statistical significance of the results from the baseline four-segment leverage spline in further detail. To facilitate this discussion I recast the key estimates of the paper (those in Tables 6 and 7) in terms of their economic impact. Specifically, based on the IV and GMM estimations of the baseline model, I compute the implied impact of a one-standard deviation increase in Leverage on Sales Growth for each of the four ranges of relative-to-rival indebtness I use (VeryLowLev, LowLev, HighLev, and VeryHighLev). These economic estimates are reproduced in Table 8 below, along with their associated p-values. To save space, I focus my discussion on GMM results (under Model 2 in Table 8). To illustrate this discussion I plot the estimated impact of leverage on sales growth using the coefficients from the GMM model. This is shown in Fig. 1. Results from GMM estimations suggest that, net of the effects of size, profitability, capital expenditures, and advertising/selling expenses, a one-standard deviation increase in (tangibility-supported) leverage as of year t within the range of 0 and þ3 standard deviations from the industry-year mean (HighLev) leads to sales growth of some 2.1% above the industry average growth between years t þ 1 and t þ 2, with better than 99% confidence. This result is consistent with the idea that debt-increasing policies can have a positive strategic effect on product market performance. In contrast, similar leverage increases at lower levels of relative-to-industry indebtness lead to either sales growth underperformance (about 4.1% in the VeryLowLev range) or to economically small and statistically marginal gains (0.7% in the LowLev range). Finally, the coefficient returned Table 8 Nonlinearities in debt–performance interactions: economic magnitudes This table synthesizes the economic and statistical significance of the impact of debt taking on product market performance. Each of the cells represents the response of a firm’s relative-to-rival sales growth (in percentage terms) to a one-standard deviation change in the firm’s tangibility-predicted leverage ratio across different debt categories. The four debt categories of the baseline specification are derived from industry-year normalized debtto-asset ratios. Model 1 reports estimates obtained from the baseline four-segment spline IV estimation of Table 6. Model 2 reports similar results from the GMM estimation of Table 7. p-values are in square brackets. Leverage spline segments

Model 1 IV estimates % sales growth response to one-Std. Dev. change in debt

Model 2 GMM estimates % sales growth response to one-Std. Dev. change in debt

VeryLowLev

5.20 [0.00] 0.69 [0.03] 2.28 [0.00] 9.88 [0.00]

4.11 [0.00] 0.69 [0.04] 2.14 [0.00] 16.39 [0.00]

LowLev HighLev VeryHighLev

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163

10%

5%

0%

-5%

-3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Within Industry-Year z-scored Leverage

Fig. 1. Sales growth response to debt at varying levels of leverage (spline function). The figure displays the estimated expected industry-year adjusted sales response to the lagged industry-year normalized, instrumented leverage at four different segments of the normalized leverage: ð1; 3s; ð3s; 0; ð0; þ3s; ðþ3s; þ1Þ. The slopes are the GMM-estimated coefficients from Model 1 of Table 5. The initial point of the function is set at the average adjusted sales growth for the unleveraged (zero-debt) firms in the sample, 2.4%.

for VeryHighLev shows that the marginal benefits of leverage seem to vanish after an upper threshold. For firms with markedly high leverage ratios—i.e., in excess of þ3 standard deviations from the industry-year mean—further indebtedness is associated with large sales underperformance (16.4%) in future years. For these firms, the estimated relative-to-rival sales–debt sensitivity agrees with the prediction that excessive indebtedness cannot commit the firm to an aggressive competitive conduct in its product market. On the contrary, additional debt taking may trigger detrimental responses by industry rivals and/ or other product markets participants (such as consumers). To summarize, the analysis of this section shows that debt taking has a systematic, nonmonotonic effect on product market performance. The empirical findings are consistent with the nonmonotonicity implications of the model developed earlier in the paper and agree with some of the theoretical predictions in the extant literature. More generally, the evidence presented suggests that debt taking may engender a rich variety of markedly distinct performance implications for firms in their industries—these are likely to be even more complex than what is described here. Important additional characterizations of financing–performance dynamics are presented in turn. 5. Market concentration and firm leadership Although most of the theoretical arguments relating capital structure and performance are derived from imperfect markets’ dynamics, researchers argue that their models’ implications should carry over other market structures (see, e.g., Brander and Lewis, 1986). In the previous section, I report evidence of some of those theories’ implications for various industries meeting minimum selection criteria. However, a natural question to ask is whether those results are more pronounced in concentrated markets. Based on those same theoretical analyses, one might also expect to find particularly strong results for the interplay between capital structure and product market outcomes when one focuses on market leaders, i.e., firms with large market shares (see Bolton and Scharfstein, 1990). In this section, I examine whether my previous results change when I allow the estimations to

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capture differences in sales–debt sensitivities across concentrated and competitive markets, and between leader and nonleader firms. 5.1. Measuring market concentration and leadership I measure market concentration using industry-level data on Herfindahl-Hirschman concentration ratios (HH-index). These data are hand-collected from the 1982, 1987, and 1992 U.S. Economic Censuses, which only cover manufacturing industries (SICs 20003999). Following the Department of Justice’s guidelines, I denote as ‘‘concentrated’’ those industries for which the HH-index is greater than 1800, and as ‘‘competitive’’ those industries for which the index is less than 1000. In assigning firms to either concentrated or competitive markets, I use the most timely information on their industries’ HH-index within five-year windows from 1980 through 1994. To be precisely, I use the 1982 Census data for COMPUSTAT firm-fiscal years in the 1980–1984 period, the 1987 Census data for firms in the 1985–1989 period, and the 1992 Census data for firms in the 1990–1994 period. The Census–COMPUSTAT matching reduces the number of observations used in the estimations of this section. The IO literature commonly denotes as ‘‘market leaders’’ those firms whose sales account for a sizable percentage of the total gross sales in their industries. Following Haskel and Scaramozzino (1997), I classify as leader firms those firm-years whose beginning-of-period sales account for more than 15% of their industry-years’ total sales. In the estimations below, firms that are leaders in their industry-years are assigned to the indicator variable Leader.20 This variable is then interacted with each one of the branches of the four-segment leverage spline, yielding an augmented version of the baseline specification. Perhaps not surprisingly, none of the leader firms in the sample is in the VeryHighLev range of standardized leverage; VeryHighLev  Leader is hence dropped from the regressions. 5.2. Results Before examining the effect of firm leadership, I illustrate the impact of market concentration (alone) on debt–performance outcomes. I do so by estimating my baseline spline model via GMM over subsamples of concentrated and competitive manufacturing industries. Fig. 2 plots the estimated impact of leverage on sales growth using the coefficients from that model (the regression output is omitted). The figure suggests that the types of nonlinear effects discussed above seem to be particularly more pronounced in more concentrated industries: there are more sales gains associated with moderately high levels of debt in those industries as well as more acute sales losses associated with excessive relative-to-rival indebtness. The next set of estimations demonstrates that the differences in concentrated–competitive debt-led outcomes illustrated in Fig. 2 are indeed economically and statistically significant. Table 9 reports results from estimations that resemble those of column 1 in Tables 6 and 7. The only difference is the inclusion of the interaction terms for firm leverage and 20 To the extent that COMPUSTAT might not have data on every single firm in the industries sampled, my measure of market shares may be inflated. In that case, my tests will be biased against finding a significant effect for Leader.

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10%

165

Concentrated Industries Competitive Industries

5%

0%

-5%

-3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Within Industry-Year z-scored Leverage

Fig. 2. Sales growth response to debt at varying levels of leverage: concentrated versus competitive industries. The figure displays the estimated expected industry-year adjusted sales response to the lagged industry-year normalized, instrumented leverage at four different segments of the normalized leverage: ð1; 3s; ð3s; 0; ð0; þ3s; ðþ3s; þ1Þ. The slopes are estimated via GMM (separately) over samples of concentrated and competitive manufacturing industries.

leadership. Results in Panel A are returned from IV regressions, whereas those in Panel B are from GMM regressions. The results from the two panels are very similar. The coefficients under Model 1 (in both panels) obtain when I estimate the augmented baseline regression model over the entire sample of firm-years with valid HH-index figures. While the estimated sales–debt sensitivities resemble those reported for the previous (larger) sample, they highlight more strongly the sorts of nonlinearities I have discussed: (1) no sales gains associated with debt in the LowLev range, (2) more positive sales–debt sensitivity in the HighLev range, and (3) more severe sales underperformance in the VeryHighLev range of relative-to-industry leverage. Another noteworthy feature of the results under Model 1 is that firm leadership would not appear to have a statistically significant effect on the association between debt and sales performance. Table 9 also allows for contrasts between concentrated and competitive industries. Model 2 replicates the estimation of Model 1 for industry-years in which the HH-index is greater than 1800 (concentrated industries). Model 3 is similarly specified when the HHindex is less than 1000 (competitive industries). The estimates under Model 2 reveal strong evidence regarding the nonlinear effects of debt on sales growth in concentrated industries, as seen from the magnified estimates for the coefficients associated with HighLev and VeryHighLev. The results from Model 2 also make a strong case for firm leadership as a key element in the interplay between capital structure and performance in concentrated industries. In effect, while for ordinary firms additional debt taking at moderately high leverage levels (HighLev) is associated with improved sales performance, the same is not true for market leaders with similar capital structures. Indeed, relative to other rivals in the same debt category, moderately highly leveraged leaders lose market share as debt increases. The overall effect of debt on relative-to-industry sales growth for moderately highly leveraged leaders (given by HighLev þ HighLev  Leader) is, nonetheless, statistically insignificant. At the same time, compared to ordinary firms in the same debt category, leader firms whose financial structures are conservative (by their industry standard) observe improved sales performance associated with debt increases (note the coefficient for LowLev  Leader).

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Table 9 Nonlinearities in debt–performance interactions: industry concentration and firm leadership Pooled time-series cross-section regressions. The dependent variable is firm annual sales growth at time t, given by ðSalest  Salest1 Þ=Salest1 . Size is the natural logarithm of assets. Investment is capital expenditures over assets. Profitability is operating earnings over assets. SellExpenses is the ratio of advertising and selling expenses to total sales. Leverage is computed from the predicted values from a regression of the ratio of long-term debt to assets on a proxy for asset tangibility. All variables are adjusted for their four-digit SIC industry-year means, with leverage further standardized by dividing it by its industry-year standard deviation (z-scored), z-Leverage. VeryLowLev and VeryHighLev denote leverage levels below 3s and above þ3s from the industry-year average, respectively (i.e., z-Leverage4j3sj). LowLev and HighLev denote leverage levels in the 3s to 0 range and in the 0 to þ3s range of the industry-year standardized leverage distribution, respectively; 0 is the industry-year average. Observations are from industry-years with at least ten firms. Concentrated industries are those with HerfindahlHirschman (HH) index X1800, while competitive industries are those with HH-index p1000. A market leader (Leader) is a firm with total sales accounting for more than 15% of the total industry-year sales. The sample period is 1980–1994 and sampling is restricted to manufacturing industries (SICs 2000-3999) due to limitations in the Bureau of Census coverage on industry concentration. The estimations correct the error structure for heteroskedasticity and within-firm error clustering using the Huber-White estimator. t-statistics are in parentheses. GMM estimations display diagnostic statistics for instrument overidentification restrictions (pvalues for Hansen’s J-statistics reported) and instrument first-stage partial F-statistics (lowest F-statistics reported). Dep. var.: Sales Growtht

Panel A: IV regressions Sizet P2

k¼1

P2

k¼1

P2

k¼1

Profitabilitytk Investmenttk SellExpensestk

VeryLowLevt2 LowLevt2 HighLevt2 VeryHighLevt2 VeryLowLev  Leadert2 LowLev  Leadert2 HighLev  Leadert2 Adjusted-R2 N. observations Panel B: GMM regressions Sizet

Model 1 All industries

Model 2 Concentrated inds. HH-index X1800 (A)

Model 3 Competitive inds. HH-index p1000 (B)

0.0045* (2.37) 0.1551**

0.0071 (1.74) 0.1987*

0.0031 (1.21) 0.1390**

(5.01) 0.4256** (6.27) 0.0054

(2.98) 0.4494* (2.26) 0.0301

(3.68) 0.3158** (3.70) 0.0091

(0.27) 0.1092** (2.80) 0.0030 (0.53) 0.0269** (4.44) 0.4483 (1.26) 0.0161 (1.92) 0.0192 (1.88) 0.0075 (0.49) 0.04 8,923 0.0025 (1.63)

(0.55) 0.0448 (1.44) 0.0191 (1.46) 0.0621** (4.35) 0.8227** (15.47) 0.0730** (4.97) 0.0895** (4.44) 0.0801** (2.96) 0.07 1,559 0.0051 (1.81)

(0.39) 0.2694 (1.45) 0.0059 (0.78) 0.0173* (2.27) 0.0958 (1.29) 0.0047 (0.44) 0.0027 (0.21) 0.0027 (0.11)

Diff. p-value (A) – (B)

0.41 0.44 0.54 0.51 0.23 0.11 0.01 0.00 0.00 0.00 0.02

0.02 5,466 0.0011 (1.19)

0.18

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Table 9 (continued ) Dep. var.: Sales Growtht

P2

k¼1

P2

k¼1

P2

k¼1

Profitabilitytk Investmenttk SellExpensestk

VeryLowLevt2 LowLevt2 HighLevt2 VeryHighLevt2 VeryLowLev  Leadert2 LowLev  Leadert2 HighLev  Leadert2 Hansen’s J-statistic p-value Lowest first-stage F-statistic N. observations

Model 1 All industries

Model 2 Concentrated inds. HH-index X1800 (A)

0.2636*

0.2415*

(2.22) 0.5554* (1.98) 0.2622*

(2.07) 0.6140 (1.87) 0.2222**

(2.00) 0.1001* (2.25) 0.0025 (0.16) 0.0273* (2.25) 0.4651** (3.59) 0.0118 (1.74) 0.0170* (2.33) 0.0029 (0.15) 0.67 92.68** 7,085

(2.91) 0.0699 (1.55) 0.0133 (0.97) 0.0726** (4.64) 0.7501** (8.66) 0.0355* (2.27) 0.0771** (4.71) 0.0350** (2.83) 0.20 24.14** 1,233

Model 3 Competitive inds. HH-index p1000 (B) 0.2822** (2.88) 0.4199 (1.79) 0.3123 (1.89) 0.1062* (2.45) 0.0038 (0.24) 0.0217 (1.56) 0.0217 (1.05) 0.0084 (0.76) 0.0067 (0.55) 0.0070 (0.35)

Diff. p-value (A) – (B)

0.79 0.63 0.62 0.56 0.65 0.02 0.00 0.02 0.00 0.06

0.68 44.17** 4,331

Note: ** and * indicate statistical significance at the 1% and 5% (two-tailed) test levels, respectively.

The dynamics of firm leadership and debt–performance interactions are somewhat different when one looks at less concentrated industries (Model 3). While debt taking at moderate levels seems to have a positive effect on sales performance in more competitive markets (see HighLev), one also finds: (1) less underperformance for very highly indebted firms (VeryHighLev), and (2) firm leadership has no relevant impact on the sensitivity of sales to debt in those markets. The differences in debt-induced product market dynamics across concentrated and competitive industries can be gauged from the last column of Table 9, which displays the p-values from standard Wald tests of cross-equation coefficient equality. While the estimates returned for Size, Profitability, Investment, and SellExpenses are virtually identical across the two subsamples, most of the coefficients associated with Leverage (as well as with its interactions with Leader) are significantly different both in economic and in statistical terms. Altogether, the results of this section suggest that concentration and market share play a significant role in shaping debt–performance interactions. As a final robustness check, I reestimate IV and GMM versions of my spline model separately over subsamples of low and high debt dispersion industries. Since MacKay and Phillips (2003) report that concentrated industries have somewhat tighter leverage

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dispersions, one could wonder whether my results are driven by debt dispersion as opposed to concentration. As it turns out, I find no evidence that results are significantly different across levels of industry debt dispersion, as measured by the within-industry-year standard deviation of leverage (results available upon request). Yet, as in MacKay and Phillips, the results above show that debt-related strategic substitution effects are stronger in concentrated industries. 6. Concluding remarks Studies on the interaction between a firm’s financing decisions and its product market performance often conclude that debt taking either hurts or boosts competitive performance. This paper proposes that both types of associations are likely to be manifested in the data: debt can hurt and boost performance. To motivate this case, I study a simple model of strategic commitment through renegotiable-proof contracts. The model implies a nonmonotonic association between external (debt-like) financing and product market outcomes. I then empirically examine the relation between firm relative-to-rival debt and sales performance using data from a panel of 135 well-defined product markets over three decades. The results I find suggest that moderate debt taking by a firm may, on the margin, yield market share gains. After some point, however, additional indebtedness leads to significant sales underperformance. I further investigate whether financing–performance linkages vary along the lines of industry concentration and firm leadership. I find that leader firms in concentrated industries cannot expand their sales through leverage if their indebtedness already exceeds their industry standard. In contrast, less leveraged leader firms in those same industries have significantly positive (relative-to-rival) sales growth–debt sensitivities. The findings of this paper are meant to invite further research on the multifaceted character of financing–performance interactions. In exploring the complex relation between a firm’s financing and its product market conduct, future researchers might consider looking for evidence that goes beyond the association between debt and sales. Arguably, the next logical step in pinning down the effect of financing decisions on real business performance should involve the more difficult task of identifying the precise product market strategies—e.g., output, pricing, R&D, marketing, etc.—that firms might implement given their own as well as their rivals’ finances. Some early evidence suggests that understanding these intra-industry interactions can be important not only for researchers, but also for economic policy makers. Appendix Proof of Proposition 1. If I B oI B then there is no need for Firm A to take down funds from outside financiers; the firm simply invests at an new optimal level I A 4I A (from Eq. (2)) given RI A I B oRI A I A o0. Otherwise, provided that the contract is individually rational for the lender, this party can always make Pareto-improving proposals to Firm A at date 1. As a result, Firm A’s investment is optimally set at I A according to RI A ðI A ; I B Þ ¼ ariA þ ð1  aÞyiA

for i ¼ fh; lg,

where riA is the loan interest rate for a contract pair ðLiA ; aI iA Þ.

(10) &

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Proof of Proposition 2. (i) That no inefficient investment will occur when Firm B accommodates is clear. Firm A will not take down any money and will use only its internal funds to increase its investment by an amount proportional to what Firm B withdraws from the market. To see that in the high state of opportunity costs no overinvestment can be credibly supported in the equilibrium of this game, consider any contract with I A 4I A , where the optimum investment is given by (10). At the interim date, the lender can offer a 0 0 new contract inducing less overinvestment of the form fðLhA ; aI hA Þ; ðLlA ; aI hA Þg, where 0 0 I hA ¼ I hA  dI hA , and LhA ¼ LhA ðqLhA =qI hA ÞdI hA , with dI hA indicating a small disturbance around I hA . Under the newly proposed terms, an incentive compatible contract will yield Firm A the following in the high state: 0

0

0

RðI hA ; I B Þ  LhA þ yhA ðI  ð1  aÞI hA Þ, which in the small neighborhood of

I hA

(11)

can be approximated by

RðI hA ; I B Þ  RI h ðI hA ; I B ÞdI hA  LhA þ arhA dI hA þ yhA ðI  ð1  aÞI hA Þ þ yhA ð1  aÞdI hA , A

or, RðI hA ; I B Þ  LhA þ yhA ðI  ð1  aÞI hA Þ  ½RI h ðI hA ; I B Þ  arhA  yhA ð1  aÞdI hA . A

(12)

For any I A 4I A , while the term outside the brackets merely replicates the terms of the initial contract, the term inside the brackets is negative. Since the outside investors have all the bargaining power, they are entitled to these Pareto-improving gains and renegotiation will take place. Notice, however, that lowering I hA via renegotiation is only feasible if such action does not violate Firm A’s incentive constraint (given by (4)) in the low state (hereinafter IC l ). One can check that this constraint is not violated by observing that while the profits obtained with truthful revelation in this state remain constant (since the pair ðLlA ; aI lA Þ is unchanged), misrepresentation under the alternative contract can only leave Firm A worse-off. To see this, note that a false announcement in this state yields 0

0

RðI hA ; I B Þ  LhA þ ylA ðI  ð1  aÞI hA Þ,

(13)

which can be approximated by RðI hA ; I B Þ  LhA þ ylA ðI  ð1  aÞI hA Þ  ½RI h ðI hA ; I B Þ  arhA  ylA ð1  aÞdI hA . A

(14)

Since the term inside the brackets is positive, misrepresenting is unattractive. (ii) Consider the case of overinvestment in the low state with the incentive compatibility constraint being slack, i.e., IC l 40. One can use the same argument of (i) to show that a small decrease in I lA leading a decrease inLlA will benefit the outside financiers and still assure incentive compatibility in the low state. However, for yhA high enough, the decrease in investment levels allowed by the alternative contract will make it more attractive for Firm A to misrepresent in the high state, causing IC h to bind. By standard arguments, if IC h binds IC l will be neglected. Therefore, one has an incentive compatible contract with overinvestment when the state is ylA . & Proof of Proposition 3. Start with the highest value of I lA for which it is individually rational for Firm A to seek a contract that allows for credible overinvestment (recall (10)). By incentive compatibility in the low state, it follows that RðI lA ; I B Þ  LlA þ ylA ðI  ð1  aÞI lA ÞXRðI hA ; I B Þ  LhA þ ylA ðI  ð1  aÞI hA Þ.

(15)

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This can be rewritten as ½RðI hA ; I B Þ  LhA   ½RðI lA ; I B Þ  LlA  þ ylA ð1  aÞðI lA  I hA Þp0,

(16)

which shows that efficiency losses induced by misrepresentation surpass the gains from investment savings when opportunity costs are low. All else constant, any sufficiently small cut in I lA associated with a reduction in LlA such that IC l is not violated will be Paretoimproving. In this case, renegotiation will take place. However, a cut in I lA will also raise profits when yjA ¼ yhA , possibly violating IC h . This last problem can be avoided if LlA is slightly decreased, but this in turn hurts optimality in the high state. One then needs to determine how low LlA can be without violating IC h . In the region where IC h weakly slacks one gets RðI hA ; I B Þ  LhA þ yhA ðI  ð1  aÞI hA ÞXRðI lA ; I B Þ  LlA þ yhA ðI  ð1  aÞI lA Þ.

(17)

Using the fact that Firm A’s individual rationality binds in the low state, one can substitute for LlA in the right-hand side of (17) to get RðI hA ; I B Þ  LhA þ yhA ðI  ð1  aÞI hA Þ þ ylA ðI  ð1  aÞI lA Þ  yhA ðI  ð1  aÞI lA ÞX0, or, RðI hA ; I B Þ  LhA þ ylA ðI  ð1  aÞI hA Þ þ DyA ð1  aÞðI lA  I hA ÞX0,

(18)

where DyA  ðyhA  ylA Þ. Consider now reducing I lA . So long as the gains from (16) exceed the losses from (18), renegotiation will take place. That is, no renegotiation-proof contract between Firm A and the outside financiers obtains so long as the following holds: ( ) qðRðI hA ; I B Þ  LhA  RðI lA ; I B Þ  LlA þ ylA ð1  aÞðI lA  I hA ÞÞ ð1  mÞ dI hA qI lA ( ) qðRðI hA ; I B Þ  LhA þ ylA ðI  ð1  aÞI hA Þ þ DyA ð1  aÞðI lA  I hA ÞÞ 4m ð19Þ dI hA . qI lA The highest possible I lA (denote it by F) guaranteeing renegotiation-proofness is implicitly given by
 m l h DyA ð1  aÞ. RI l ðF; I B Þ ¼ arA þ yA  (20) A ð1  mÞ Integrating (20) with respect to I lA and rearranging yields an expression similar to Myerson’s (1981) ‘‘virtual surplus’’
 m l h DyA ð1  aÞF  RðF; I B Þ. LA ðFÞ þ yA  (21) ð1  mÞ That (21) cannot be negative means that the portion of the surplus retained by Firm A cannot be smaller than the rent necessary to induce truthful revelation. That is, value destruction cannot be high enough as to make the contract infeasible. &

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