Creditor Conflict and the Efficiency of Corporate Reorganization*
Mark Jenkins
The Wharton School, University of Pennsylvania
David C. Smith
McIntire School of Commerce, University of Virginia
May 2014
*We have received helpful comments from Ken Ayotte, Ralph Brubaker, Robert Lawless, and Richard Levin, as well as participants at the ABI/University of Illinois Symposium on Secured Debt in Chapter 11, the Wharton Micro
Lunch Seminar, and the Darden Brown Bag Lunch Seminar.
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Creditor Conflict and the Efficiency of Corporate Reorganization
Abstract
We develop a bargaining model that assumes a senior creditor can exert strong control over whether a firm reorganizes as a going-concern or liquidates during the bankruptcy process. The estimable parameters of the model allow us to gauge the efficiency of bankruptcy outcomes using a large sample of U.S. corporate bankruptcy cases over the period 1989 to 2011. The main result of the paper is an estimate of the value loss that results from inefficient liquidations in bankruptcy. We estimate these losses to be up to 0.28 percent of the going-concern value of the firm, on average, across all bankrupt firms in our sample. As predicted by theory, these losses primarily are realized by firms with asset values that are close to the face value of secured debt.
Our estimate of efficiency losses is driven by several auxiliary findings, including estimates of the fraction of firms that are efficiently reorganized, the fraction of firms that are efficiently and inefficiently liquidated, and the average liquidation discount faced by firms in bankruptcy.
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1.
Introduction
According to modern capital structure theory, incentive conflicts between different capital providers can distort efficient investment outcomes, creating potentially adverse effects from debt financing. These conflicts become particularly acute in bankruptcy, where capital providers with lower priority claims on cash flows – such as equityholders – have an incentive to run a firm as a going concern even when the value from liquidating the assets is higher, whereas capital providers with senior priority – such as bank lenders and secured creditors – may push to sell assets early when the expected value is higher from waiting and reorganizing the firm. The literature on optimal debt structure (e.g., Rajan (1992), Diamond (1993), Bolton and Scharfstein
(1996), and Hart and Moore (1998), among others) and bankruptcy policy (e.g., White 1980,
1983) study scenarios in which biases towards “excess continuations” or “excess liquidations” distort ex post decisions away from the efficient outcome.
While a rich set of theories make clear that incentive conflicts between senior and junior claimants may lead to inefficient outcomes, empirical evidence on how often these conflicts do so has been limited. In this paper, we study the incentives of senior claimants to force inefficient liquidations, or liquidations in which a firm’s assets are sold for less than the firm’s value as a going concern. For inefficient liquidations to appear in the data, several conditions must hold.
First, senior claimants must have the control rights to impact the decision of whether to liquidate or continue. Second, the benefits of continuation must be sufficiently small that the gains to the senior claimant do not exceed the benefit of inefficiently exploiting these control rights at the expense of junior claimants. Third, frictions must exist that prevent the senior and junior claimants from bargaining to reach the efficient outcome, as in Coase (1960). Such frictions could include exogenous liquidity constraints (Rajan, 1992), information asymmetries (Myers and Majluf, 1984), or coordination failure (Bolton and Scharfstein, 1996).
In this paper, we formulate a simple model of creditor decision-making that incorporates the three conditions described above. We then apply the model to data on over 700 bankruptcy filings of U.S. firms between 1989 and 2011 to study the frequency and cost of inefficient liquidations. In a bankruptcy setting, senior claimants have a natural interpretation as secured creditors, who hold a first-priority claim on the value of their collateral, and the junior claimants
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have a natural counterpart as unsecured creditors and equityholders, who collectively hold a residual claim.
The main result of the paper is an estimate of the amount of value lost due to inefficient liquidations in bankruptcy. We estimate these losses to be up to 0.28 percent of the goingconcern value of the firm, on average, across all bankrupt firms in our sample. This result is driven by several related findings. First, we estimate that liquidation is the efficient outcome in
19 percent of cases. In 41 percent of cases, the benefits of continuation are sufficiently large that secured creditors are incentivized to reorganize on their own. Among the remaining 40 percent of cases where inefficient liquidation is possible, 32 percent of these are reorganized by junior creditors, leaving 8 percent of firms inefficiently liquidated. Finally, while we estimate an average liquidation discount across all firms of 11 percent, the value lost from inefficient liquidation is only 4 percent because firms that are inefficiently liquidated are precisely those firms for which liquidation is not “too” costly.
Quantifying the impact of the liquidation incentives of secured creditors in bankruptcy is advantageous because the three necessary conditions for inefficient liquidation are likely to hold in this setting. As documented by Baird and Rasmussen (2002, 2010), Skeel (2003), and Ayotte and Morrison (2009), among others, the U.S. Bankruptcy Code provides a number of protections that give secured creditors rights to exert control over the bankruptcy process, including the decision to liquidate or force a sale of the firm early, rather than waiting to restructure it through a reorganization. Liquidation values in bankrupt firms are likely to be higher, and continuation values more volatile, compared to healthy firms, making these rights particularly valuable in bankruptcy. Moreover, the presence of dispersed junior creditors, including trade creditors and public bondholders, suggests that bargaining frictions related to liquidity constraints and coordination failures may prevent parties from reaching the efficient outcome posited by the
Coase Theorem. This combination of factors leads to the possibility of inefficient liquidations.
Our findings are of particular interest because the use of secured debt by speculativegrade U.S. corporate borrowers has increased significantly in recent years, touching off a renewed debate among bankruptcy policymakers about the relation between secured credit and bankruptcy outcomes. Figure 1 shows the growing importance of secured debt in the capital structures of corporate borrowers that file for bankruptcy. Secured debt represented less than
45% of the debt of Moody’s-rated firms filing for bankruptcy in 1991; by 2012, secured debt
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accounted for more than 70% of the debt of Moody’s-rated bankruptcy filers. Citing the increased use of secured credit, a number of bankruptcy policymakers advocate amending laws to curtail the rights of secured creditors during the Chapter 11 bankruptcy process.
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These policymakers, as well as a large representation of bankruptcy professionals, argue that secured creditors take actions that diminish the overall efficiency of the process.
Our estimates of the frequency and costs of inefficient liquidation derive from a simple model of creditor decision-making in bankruptcy. The model is based on the idea that the incentives for secured creditors to deviate from efficient decisions are largest when the value of the assets of the firm (V) – the collateral backing the secured claim -- is close to the size of the claim (S). When V is significantly lower than S, i.e., when the secured claim is deeply impaired and “under-secured”, junior creditors play no role and secured creditors have an incentive to choose the value-maximizing outcome to satisfy their claim, be it liquidation or reorganization.
When V is much larger than S, i.e., when the secured claim is unimpaired and “oversecured”, secured creditors can be compensated in full by junior creditors, who pay a transactions cost to take control of the bankruptcy process. Junior creditors then choose the bankruptcy outcome that maximizes value, net of the transactions cost. However, in the region in which the claim is “nearly impaired”, that is, V is close to S, senior creditors will opt for an inefficient liquidation, and the transactions cost may preclude junior creditors from pushing for the efficient outcome. Thus, excess liquidations are most likely in the region in which the firm value V just covers the secured creditor’s claim S.
The model incorporates important features of the modern U.S. bankruptcy process, namely, that Ch. 11 bankruptcies play out as a bargaining game among a set of sophisticated investors who hold claims against the bankrupt firm of varying payment priority. Because the bankrupt firm is often insolvent, some (or all) of these claims will not be payable in full and must be renegotiated as part of the bankruptcy restructuring. Bankruptcy law respects the payment priorities, but also allows investors to bargain to an outcome that may deviate from absolute priority. Moreover, within the bounds of Ch. 11 law and practice, investors can negotiate to reorganize the firm, sell the firm, liquidate its assets piecemeal, or engage in some combination
1 For instance, Klee (2012) states that “Just because commercial lawyers have crafted non-bankruptcy laws to favor secured creditors . . . does not mean that a business reorganization law should respect those laws inviolate.”
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of these activities. The bargaining power of claimants will depend, among other things, on their priority in the capital structure and the value of the underlying firm, given its next best use.
A particularly appealing feature of our model is that we can estimate the likelihood of reorganization (versus liquidation) as a function of the secured debt asset coverage ratio, V/S. In the absence of incentive conflicts, the function should be monotonic over the range of observed values for V/S. Observed deviations from monotonicity over a given interval for V/S provide evidence of distortions away from efficient decisions. We estimate V/S using observed measures of enterprise value based on Moody’s “ultimate” recovery rates, measured at bankruptcy exit, and the face value of secured claims in a company’s capital structure. We also calculate a bankruptcy outcome indicator that equals one when a case concludes with the reorganization of the bankrupt firm as a stand-alone entity, and zero for cases that ended in a liquidation or sale.
Using these data, we first produce reduced-form evidence on the relationship between the probability of reorganization and the secured debt asset coverage ratio, V/S. Our primary estimates of interest are derived from non-parametric regressions, which allow us to trace out the probability of reorganization over a wide range of values for V/S and to estimate the magnitude of deviations from the smooth function that would result under the null hypothesis that bankruptcy outcomes are always efficient. As predicted by our model, we observe our largest deviations towards inefficient liquidation in a relatively tight band around V/S = 1. For the range of estimates below or above V/S =1, our evidence indicates that distortions to an efficient decision to reorganize versus liquidate are economically and statistically small.
We next seek to quantify the efficiency implications of our reduced-form findings by formally estimating our model of creditor decision-making. We employ moment-matching techniques to back out estimates of the structural parameters in our theoretical model. The estimated parameters are those that allow the model to best match key moments from the data, including the distribution of reorganization recovery values, the distribution of liquidation recovery values, the average reorganization rate, the correlation between the reorganization rate and recovery values, and any non-monotonicity in the reorganization rate around V/S=1. Our parameter estimates suggest that junior creditors pay transactions costs equal to approximately 7 percent of the face value of secured claims to preclude inefficient liquidations when it pays to do so, and that expected cost of inefficient liquidation across all firms in our sample is 0.28 percent of the value that would be achieved if assets were put to their efficient use.
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The rest of the paper is organized as follows. Section 2 places our paper in the recent literature related to senior debt financing and bankruptcy, and discusses the institutional features of secured lending and Ch. 11 bankruptcy. Section 3 introduces our theoretical framework and model and Sections 4 and 5 contain our empirical results. Section 6 concludes.
2. Literature and Institutional Background
This first part of this section places our paper within two empirical literatures: (1) studies examining the benefits and costs of senior debt financing, and (2) research that relates capital structure, in particular, secured lending, to bankruptcy outcomes. The second part of the section provides some legal and institutional context for our model.
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2.1 Related literature
Our paper is related to a number of other studies that examine the influence of senior and secured lenders on the efficiency of distressed restructurings, and the concomitant impact on the ex post costs of financial distress. Gilson (1990), Gilson, John, and Lang (1990), and Asquith, Gertner, and Scharfstein (1993) use 1980s-era U.S. data to study how the presence of senior bank lenders impacts the likelihood that firms restructure out-of-court versus through a bankruptcy filing.
These studies find that less-expensive out-of-court restructurings are more likely when senior bank lenders hold a prominent position in the debt to be restructured. James (1996) also uses
U.S. data from the 1980s on debt restructurings to shows that secured lenders often take significant equity positions in a company following a restructuring. He argues that allowing senior lenders to take equity mitigates banks incentive to force an inefficient liquidation.
More recently, Benmelech and Bergman (2008) use information on aircraft lease renegotiations to examine how liquidation values influence bargaining between lenders and financially distressed borrowers. Using an incomplete contracting model, they argue that bargaining power should transfer to borrowers as liquidation values decline, increasing the likelihood of observing a negotiated outcome over liquidation. Consistent with the model, they
2 Extensive discussions of the institutional and legal framework for bargaining in Chapter 11 bankruptcy are available in Baird and Rasmussen (2002, 2010), Skeel (2003), and Ayotte and Morrison (2009).
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find that aircraft leases held by financially distressed airlines are more likely to be renegotiated in favor of the borrower when aircraft liquidation values are relatively low. Our model and findings lend further support to the Benmelech and Berghman (2008) results. We show that reorganizations are more likely when liquidation discounts are high, and inefficient liquidations occur when the delta between reorganization and liquidation values is small.
The two papers most closely related to our work are Strömberg (2000) and Ayotte and
Morrison (2009). Strömberg (2000) develops a cash auction model of bankruptcy that incorporates potential incentive conflicts between senior and junior creditors and tests the model using data from the Sweden’s liquidation-based bankruptcy system. He shows that inefficient liquidations can be circumvented by selling assets of a valuable bankrupt firm back to its original owners, and that sale-backs are often financed by the original secured lenders, who roll their original claim over into the new firm. Like Strömberg (2000), we develop and estimate a model in which incentive conflicts can predispose secured creditors towards inefficient liquidations, and actions by junior claimholders (equityholders and managers in Strömberg, 2000) mitigates these incentives. Moreover, our paper shares with Strömberg (2000) the notion that incentive conflicts become particularly acute when asset values are close to the face value of the senior creditor’s claim. Besides examining this question on a more recent sample of bankruptcies in the U.S., our paper extends the analysis of Strömberg (2000) by estimating parameters that provide direct insight into the deadweight costs associated with the incentive conflicts.
Ayotte and Morrison (2009) employ detailed data on the bankruptcies of 153 large firms during 2001 to study the growing importance of creditor control in Chapter 11 bankruptcies.
Among other things, Ayotte and Morrison (2009) show that incumbent management is often ousted prior to bankruptcy, that deviations from absolute priority away from senior creditors is rare, and that senior creditors exert significant contractual control over the bankruptcy process through debtor-in-possession (DIP) loan agreements. Their findings in large part motivate our assumption of secured creditor control. Ayotte and Morrison (2009) consider a model based on a judicial decision-making process, and as in our study, explore the relation between the degree to which secured lenders in bankruptcy firms are over- or under-secured and the likelihood of observing that the firm is reorganized versus liquidated. We structure our bargaining model to yield estimable parameters than can deliver insight into the costs of creditor control and estimate
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these parameters on a relatively large sample of bankruptcies (700+), spanning 23 years and three credit cycles.
2.2 Institutional background
2.2.1 Secured debt and bankruptcy
Secured debt is debt backed by collateral. Specifically, the issuance of secured debt involves the execution of an additional legal agreement in which the debt provider receives property or
“security” interests, typically through a “lien”, in assets held by the borrower. Today, secured loans made to corporate borrowers are typically made against “substantially all assets” of the firm, including all real estate property, buildings, equipment, machines, fixtures, inventory, accounts receivable, intellectual property, and most other forms of tangible and intangible assets that are not already encumbered by previous liens. Outside of bankruptcy, the security interests give the lender the right to foreclose on the assets – that is, take possession of the assets for purposes of selling them – when the borrower is in default under the debt agreement. It is in this sense that secured debt has a senior priority; in case of default the secured lender has the first right to exercise foreclosure proceedings against the assets backing the loan, and to receive payment in full from the proceeds, before any distribution is made to other creditors, claimants, and equityholders.
In bankruptcy, secured creditor efforts to foreclose on assets are automatically stayed; all collection efforts must stop once a company has filed for bankruptcy. However, bankruptcy law accords a number of special protections to secured creditors that are generally unavailable to unsecured claimants. The protections can provide considerable bargaining clout to the secured lender vis-à-vis other negotiating parties, include the debtor management, unsecured creditors, and the firms original shareholders.
The primary set of protections come under the U.S. Bankruptcy Code’s requirement that secured lenders receive “adequately protection” during bankruptcy, in the sense that the lenders must be compensated in full for any loss or diminution of their interest in the collateral. This constrains actions, such as assets sales and access to competitive financing, that might improve
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ex post bargaining but endangers the secured lender’s first-priority claim on the collateral.
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That said, adequate protection extends only to protecting the smaller of: (a) the amount of the secured claim and (b) the value of the collateral in which the creditor has an interest. For instance, if the lender is under-secured because the market value of the collateral falls below the amount of the secured claim, adequate protection stops at the value of the collateral; the residual amount of the secured claim left uncovered by the collateral is treated by the court as an unsecured claim.
Bargaining advantages during bankruptcy can also accrue to secured lenders through their role as interim lenders to the debtor during the bankruptcy case. Section 364 of the
Bankruptcy Code enables the bankrupt firm to raise senior-priority “Debtor-in-possession” (DIP) financing to fund operations while in bankruptcy. But adequate protection requirements often make it difficult for outside lenders to provide DIP financing because there are often few unencumbered assets to lend against in a first lien position. Therefore, the original secured lenders – termed the “prepetition” lenders because they were present prior to the filing– are often in the best position to offer DIP loans.
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DIP loan agreements can provide the lender with substantial contractual control over the bankruptcy process through strict covenants and
“milestones” that require a case to proceed on a timeline set by the lender. While a DIP agreement must be approved by the bankruptcy judge, who can entertain objections to burdensome DIPs by other parties, the DIP lender is often in a position to threaten to withdraw financing unless the agreement it puts forward is approved.
In sum, bankruptcy law provides protections to secured lenders that are unavailable to other unsecured creditors and other claimants. These protections increase the bargaining clout of secured creditors and potentially increases their incentives to take actions that maximize the value of their own claim at the expense of the ex post value of the company as a whole.
2.2.2 Strategies for junior creditors
While the U.S. bankruptcy law contains significant protections for secured creditors, the law also affords a number of opportunities for unsecured creditors and other junior interests to challenge
3 Adequate protection is defined in Section 361 of the U.S. Bankruptcy Code and is when considering relief from the automatic stay, at times when assets of the debtor are sold during bankruptcy, and in the process of approving the use of existing cash or new (debtor-in-possession, or “DIP”) under Sections 362, 363, and 364 of the Code, respectively.
4 For instance, in a study of DIP lending practices by the law firm Wilmer, Cutler, Hale, Pickering, and Dorr LLP found that 74% of all DIPs to 113 large firms during the period 2006-2012 were provided by the prepetition lenders.
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secured creditors that attempt to force an inefficient outcome. Under Section 1102, the
Bankruptcy Code requires that an Official Committee of Unsecured Creditors, composed of the debtor’s largest unsecured creditors, be appointed to represent the interests of unsecured creditors before the court. Official committees can be appointed by request to represent other interests in the debtor, including equityholders. All official committees approved by the court hire legal counsel and financial advisors whose fees are paid by the bankrupt firm. “Ad hoc” committees can also be formed to represent specialized groups of junior creditors (e.g., holders of a specific issue of unsecured bonds). The ad hoc committees have similar standing before the court as the official committees, but must pay their own legal and advisory fees. During the case, these committees can motion the court in favor of actions that favor junior creditors, and object to motions made by other parties that are detrimental to junior creditors. Ultimately, the committees can lobby wider sets of creditors to vote to reject a plan put forth by the debtor
(including a plan to liquidate) that does not maximize the value of the distributions paid to all parties.
Unsecured creditors can utilize particular sections of the U.S. Bankruptcy Code to reduce directly the influence of secured creditors on the bankruptcy process. Under Section 1124 of the
Code, junior creditors may reinstate the senior debt, leaving it unimpaired and unable to vote on a plan of reorganization or liquidation. However, this strategy relies on the ability of the debtor to cure all defaults on the secured debt, which may be infeasible in many cases. A second strategy is a “cram up”. Under Section 1129, the junior creditors may cram up a plan of reorganization on secured creditors by giving them new debt with a present value equal to the face value of the secured claim. Both of these strategies are costly, in the sense that they typically are completed only following a significant amount of litigation. Both also are feasible only if the firm is reorganized and the value of the firm is greater than the face value of the secured claim. A third strategy for unsecured creditors is a “pay to play” strategy in which they simply pay off secured creditors. This leaves secured creditors unimpaired and unable to vote on a plan of reorganization or liquidation. This third strategy requires that unsecured creditors are able to raise capital to pay off the senior creditors.
In today’s bankruptcy market, sophisticated and well-capitalized investors often buy claims of financially distressed firms to participate in the bargaining that occurs during bankruptcy and to bet on the outcome of the restructuring. These investors specialize in utilizing
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the legal and contractual remedies available to bankruptcy claimants to maximize their return, and often deploy significant resources to litigate in favor of those remedies. The competition that occurs between distressed debt investors could serve to balance the interests of senior and junior creditors in a way that maximizes firm value, or could create large deadweight inefficiencies through excessive litigation.
Consistent with idea that distressed debt investors improve bankruptcy outcomes,
Hotchkiss and Mooradian (1997) show that 1980s-era distressed firms in which “vulture investors” bought debt claims to gain control of the firm performed better following bankruptcy than firms with no vulture investor present. More recently, Jiang, Li, and Wang (2011) find that when hedge funds hold junior claims, Chapter 11 restructurings reduce debtor exclusivity in formulating a restructuring plan, increase CEO turnover, and reduce management compensation plans. Ivashina, Iverson, and Smith (2013) present evidence that the presence of distressed debt positions across the capital structure prior to a bankruptcy filing are associated with quicker and potentially less costly restructurings. However, they also find that purchases of junior “trade claims” by investors during the bankruptcy are associated with a more prolonged bankruptcy and higher chance of liquidation. Hotchkiss, Smith, and Strömberg (2014) find that distressed firms that receive capital injections from equityholders (the most junior claimants) prior to a restructuring are more likely to restructure quickly and exit as a going concern with existing equity holders still in control, compared to firms that receive no capital injection. Given this background, we now turn to a simple, testable theoretical model that attempts to capture the behavior of self-interested senior creditors with strong decision rights in bankruptcy that must bargain with junior creditors when asset values exceed the amount of their senior claim.
3. Model
Our model features a bankrupt firm with a senior creditor and a junior creditor. The senior creditor holds a claim of size S, and the junior creditor holds a residual claim. Both creditors are risk-neutral. The key decision the creditors face is whether to reorganize or liquidate the firm.
We assume that the value of the firm’s assets, V, evolve as a geometric Brownian motion (GBM) with volatility parameter . We refer to expectation of V as the firm’s reorganization value. If the firm is liquidated, its value is L = (1-
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value. We assume that is realized at the time the company files for bankruptcy. If 0, then
< 0, then liquidation is efficient.
We assume that the absolute priority rule holds exactly, so that if the firm is liquidated, the senior creditor receives a payoff of min(L,S) and the junior creditor receives a payoff of max(0,L-S). If the firm is reorganized, junior creditors hold a call option on the value of the firm’s assets, C(V, S, , T), with a strike price of S and maturity of T, where T is the bankruptcy confirmation date. In the case of reorganization, senior creditors receive V - . In other words, senior creditors hold a short “covered call” position, similar debtholders in the models of
Merton (1974). Our assumption that strict absolutely priority holds embodies the strong protections that secured creditors receive up to the value of the collateral in bankruptcy.
This simple setup yields a friction that is well understood in both the law and finance literature. If the senior creditor exercises full control over the reorganization decision, inefficient liquidations may occur for certain combinatio because the senior creditor bears all of the losses of a failed reorganization, but does not capture all of the gains of a successful one because his payoff is capped at S. That is, the senior creditor bears a payoff similar to being short a call option on V with exercise price S.
In the remainder of this section, we use this simple model to derive predictions that are testable in the data. In particular, we define the ratio of the firm’s reorganization value to face value of the senior claim, V/S, to be the level of “asset coverage” or “collateral coverage” of the senior claim and , T) where incentive conflicts are likely to occur. In later sections, we seek to quantify how often firms fall into these regions of the parameter space and evaluate the resulting implications for efficiency.
3.1. The reorganization decision of senior creditors
We begin by examining the reorganization decision under the assumption that senior creditors have complete control over the reorganization decision. The senior creditor will reorganize whenever it pays to do so:
( , , , ) > min( , ), (1)
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where L = (1reorganization decision depends importantly on the parameter values V,
, and The call option value can be computed using the Black-Scholes formula for a
European call option with strike price S and underlying value V.
Going forward, it is convenient to normalize our measures of asset value by the size of the senior creditor’s claim. The normalized value, V/S, represents a measure of how well the value of firm assets cover the senior claim, and will often be referred to as a “senior debt” or
“secured debt” asset coverage ratio. Much of our focus will concentrate on how incentive conflicts change in the neighborhood around V/S = 1; that is, when asset values just covers the senior debt claim. For modeling and estimation purposes, it is often convenient to examine the natural logarithm of V/S, in which asset values just cover the senior debt claim at a value of zero.
Figure 2(a) illustrates the senior creditor’s decision to reorganize or liquidate according to equation (1) above as function ln(V/S) setting the total volatility of the reorganization process, , equal to 0.30. The senior creditor will always liquidate a firm
-axis. The senior creditor will also efficiently reorganize a firm when V is low relative to S, and when liquidation in Figure 2(a) as the area to secured creditor will efficiently reorganize in this area because for low enough V (or high is pays to wait and reorganize rather than face the immediate loss through liquidation.
To the right of the solid curve in Figure 2(a), the senior creditor will choose to liquidate even though it is efficient to reorganize the firm; in this region that the senior creditor exhibits an
“excess liquidation” bias. For relatively high values of V, the senior creditor’s upside potential from future realizations of V is capped by his claim of S, while his downside is still exposed to liquidate the firm, even when the loss yields an amount less than S.
The curve in Figure 2(a) can also be interpreted in terms of the option exposure faced by creditors’ call option is far out of the money; thus expected transfers to the junior claimants via a reorganization are relatively low. As V increases relative to S, the value of the call held by the
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junior claimants also increases, which reduces the value captured by the senior claimant through reorganization.
3.2. The responses of junior creditors
In the previous section, we assumed that the senior creditor alone made the reorganization versus liquidation decision, and traced out a region of asset coverage values, or ln(V/S), in which senior creditors will choose to liquidate the firm when it is inefficient to so. In this section, we assume that junior creditors can follow bargaining strategies that can mitigate the influence of senior creditors. The Coase Theorem suggests that in the absence of transactions costs, the senior and junior creditors should bargain to reach the efficient outcome. In particular, without such costs, the junior creditors should purchase the senior claim for the lesser of liquidation value or the face value of the senior claim, and then reorganize the firm if it is efficient to do so.
In practice, bargaining in bankruptcy will involve transaction costs. For instance, the legal strategies that junior creditors pursue during a Chapter 11 process, including pushing to reinstate senior creditor claims or cram up a plan on senior creditors, require time and resources to litigate the motions before the court. Alternatively, junior creditors can simply pay off senior creditors in full, taking them out of the bargaining process. But raising capital to fund this payoff could be costly because junior creditors in this position suffer from a “debt overhang” problem
(Myers, 1977); much of the returns to the investment first accrue to senior creditors. value of the senior claim, so that senior claim. This assumption is consistent with the idea that litigation costs are proportional to the scale of the senior claim. These costs may be borne by the junior creditors directly, or if paid by the estate, indirectly, since junior creditors are the residual claimant. A second interpretation, a debt overhang problem between junior creditors and outside investors who finance the purchase of the senior claim.
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Note that a that inefficient liquidation will occur in a positive measure of cases where the benefits of
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The assumption that this cost is proportional to the claim size is consistent with the models of Hennessy (2005) and Hennessy and Whited (2007), among others. For instance, in the dynamic debt model of Hennessy and Whited (2007), equity flotation costs for a levered firm are assumed to be linear quadratic function of the amount raised with a weak convexity parameter.
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efficient reorganization are small. For simplicity, our model rules out bargaining between senior and junior creditors below V/S=1. In later sections, this assumption will help to ensure that our estimates of the frequency and cost of inefficient liquidation are upper bounds.
, and . The most interesting decisions occur in the region where inefficient liquidations would occur if the process was controlled only by the senior creditors. As shown in Figure 2(a), when the senior creditor can fully internalize both the upside and downside of reorganization, the senior creditor makes the efficient decision. There is no role for the junior creditor. But in the region in which j inefficient liquidations are possible, the junior creditor may be willing to purchase the control rights of the firm by paying the senior creditor an amount equal to the face value of the secured claim plus the deadweight
Over the region in which a senior creditor will otherwise inefficiently liquidate a firm, to take over the process and reorganize when
(1 + ) > max (0, ).
(2)
Equation (2) says that junior creditors will take over the process when the value they gain paying the transaction cost and reorganizing, V – S( liquidation after the senior creditor is paid in full, given the absolute priority rule. receive from a
Figure 2(b) shows the influence that junior creditors have on bankruptcy outcomes in our
The impact is to increase substantially the area in which efficient reorganizations occur. Junior creditors will not incur the cost to reorganize a firm for values of
V below S x 1.05, corresponding to the vertical line near 0.05 on the x-axis. For values of V >
S(1.05), junior creditors can pay the transaction cost and the senior claim in full, earning the residual surplus from the efficient reorganization of the firm. This surplus is increasing in V and
Figure 2(b) highlights two important features of our model. First, the role of junior creditors in resolving the incentive conflicts that may otherwise lead to inefficient liquidation.
Intuitively, these conflicts are resolved when the costs of inefficient liquidation are sufficiently high. In these cases, junior creditors have an incentive to overcome any transactions costs and achieve to achieve an efficient outcome. Second, once we account for the influence of junior
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creditors, the likelihood of observing an inefficient liquidation peaks when the senior debt asset coverage ratio, V/S, is just above one.
3.3. Translating the model to the data
The model presented above characterizes a mapping of parameters to bankruptcy outcomes, or
T, (R * , U * /S), where the outcome R * is an indicator of whether the firm is reorganized or liquidated during bankruptcy, and U * is the observed total dollar “ultimate recovery” paid out to creditors at the end of the bankruptcy, conditional on reorganization or liquidation. Unfortunately, neither V/S nor is observed. The observed recovery value for a given firm is:
U * = V
T
if R
= L if R
*
*
= 1
= 0.
(3)
If the firm is liquidated only the product of V/S and is observed, while if it is reorganized, only the terminal reorganization value is observed. This is captured by the T subscript on V
T
. We discuss or empirical proxies for R * and U * in Section 4.1. The model makes predictions about the relationship between R * and U * that should appear in the data. In particular, it suggests that, other things equal, firms with U*/S close to one may have lower reorganization probabilities than firms in surrounding regions because incentives for inefficient liquidation are strongest at this point. We explore this relationship in detail in Section 4.2.
4. Descriptive Evidence
In this section, we provide a simple overview and summary statistics on bankruptcy outcomes from our sample data. We begin by describing our data and the method we use to compute the key outcome variable in our model: the secured debt asset coverage ratio (U * /S). We then present some basic facts on bankruptcy outcomes and the relationship between the secured debt asset coverage ratio and reorganization probabilities. These facts will serve as useful inputs when we formally estimate the model in Section 5.
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4.1. Data
The primary dataset for our study is the Moody’s Default and Recovery Database (DRD), from which we draw detailed information on 834 nonfinancial issuers who filed for bankruptcy in the
U.S. between 1989 and 2011. Figure 3 shows the distribution of filings over time. The data covers three credit default cycles, including the 1990-1991 recession, the 2002-2003 technology and telecom bust, and the 2008-2009 financial crisis.
For each bankruptcy filing, Moody’s collects information about the defaulted issuer, the bankruptcy case, and the debt instruments that were outstanding at the time of filing. Data at the case level include the filing date, the type of filing (e.g., Chapter 7 or Chapter 11), and the outcome. Moody’s classifies outcomes into one of three types: emerged (reorganized), liquidated, or acquired. The outcome of primary interest in our study is whether the firm was reorganized (R * = 1) or liquidated or acquired (R * = 0). For most of our analysis, we group liquidations (via Chapter 7, Chapter 11 conversions, or Chapter 11 liquidating plans) and acquisitions (including through Section 363 sales) together as “not reorganized”. The reason for this is that our theory does not make explicit predictions about the choice to liquidate versus sell the firm, since both yield similar outcomes from the standpoint of senior creditors. Specifically, both piecemeal liquidations and going-concern sales may yield a quicker, less volatile resolution of the case than reorganizing the firm, possibly at the expense of maximizing the value of the firm’s assets.
6
The Moody’s data on the capital structure of the defaulted firms includes a detailed description of each debt instrument in the capital structure at the time of default, including the type of instrument (e.g., revolving credit facility, term loan, or bond) and whether not it is secured, the size of the claim at filing, and the recovery rate. To compute the empirical analog of
S , for each firm i , we define S i
to be the sum of the face values of all secured instruments. To compute the empirical analog of U * , ,we start with Moody’s instrument-level recovery rates from the Ultimate Recovery Database. The Ultimate Recovery Database computes realized recovery rates to each debt instrument class a via the “settlement method”, which uses the value of the cash and securities distributed to instrument holders in satisfaction of their claim at the
6
Grouping liquidations and sales is also useful from a practical standpoint, since many liquidations (including those in Chapter
7) may in fact be sales of substantially all assets to a single buyer. The distinction between the two outcomes does have efficiency implications, which we discuss in Section 5.5.
16

conclusion of the bankruptcy process, discounted back to the default date.
7
We then compute U i
* for each firm in our sample by multiplying the recovery rate on each debt instrument by the face value of that instrument, and summing across all of the firm’s debt instruments at the time of filing.
Table 1 presents summary statistics on the 843 firms in our sample. Of these, 721 firms havea nonzero amount of secured debt outstanding at filing. These firms are the focus of our study. The sample firms carry an average of $692 million face value of interest-bearing at the time that they file for bankruptcy, 62 percent of which is secured debt. Meanwhile, the average
Moody’s total recovery rate among our sample firms is 55%. During our sample period, 72 percent of firms exit bankruptcy via a reorganization, so that 28% end bankruptcy through a liquidation or sale. Taking the ratio of the average family recovery rate to the average proportion of debt that is secured in Table 1 provides a rough indication that secured debt asset coverage ratios in our sample are slightly above one.
Figure 4 reports the actual distribution of secured debt asset coverage values in our sample, graphed in terms of ln(U i
* /S). The figure 4 shows that firms file for bankruptcy when the value of their assets are close to the face value of their secured debt (when the ratio of secured debt coverage is close to one). This evidence is consistent with Carey and Gordy (2007), who argue that the face value of secured debt claims represents an endogenous default point. Our model predicts that filings in which U i
* /S is close to one are also the cases in which distortions from efficient outcomes are most likely to occur. Figure 4 also shows that roughly one-third of the mass of filings lie outside of values of ln(U i
* /S) between -0.5 and +0.5, implying these values lie either below secured debt coverage ratios less than 60% or above ratios greater than 165%.
4.2. Non-parametric Regression Results
We now turn to exploring how observed bankruptcy reorganization probabilities Pr(R * =1) vary as a function of secured debt asset coverage ln(U * /S). Ideally, we would like to allow for the relationship to vary non-linearly (and possibly non-monotonically) over the range of values of
7 Moody’s also reports recovery rates based on the “trading price method,” which estimates the recovery rate for a given instrument as the trading price of the instrument 30 days after default. While trading price method might capture the market’s expectation of the firms’ asset value at a time nearer to the reorganization vs. liquidation decision, it is only calculated on a subset of the firm’s in our sample.
17

ln(U * /S). To accomplish this goal, we run a non-parametric regression. We first rank all 721 observations of ln(U i
* /S) from lowest to highest and divide the ranked observations into 20 equal
5-percentile bins. We then calculate the mean reorganization rate within each category and record the result. Figure 5(a) graphs the result and includes 95% confidence bands, calculated using the within-band standard deviations. The average value of ln(U i
* /S) within bins varies from
-2.25 in Bin 1 to 2.73 for Bin 20. Bins 1 through 10 include firms with U * /S
20 include firms with U * /S >1.
ins 11 through
Two interesting patterns are evident in Figure 5(a). The first is that the probability of reorganization appears to be positively related to secured debt asset coverage over the range of ln(U i
* /S) in our sample. For instance, firms in Bins 19 and 20 have average reorganization probabilties that are above 85 percent and these averages are above the 95% confidence intervals for Bins 1 and 2, which have mean reorganization probabilities of between 51 percent and 57 percent. When we estimate our model in the next section, we show that a positive correlation between asset values and liquidation discounts generates this increasing pattern.
The second pattern to note in Figure 5(a) is the substantial drop in reorganization probabilities from Bin 9 to Bin 10. The Bin 10 mean reorganization probability drops to 45 percent, well below the confidence bands around the Bin 9 average reorganization probability of
83 percent. The lower reorganization probabilities observed in Bin 10 extends to Bins 11 through
12 (and possibly Bin 13) before recovering to reorganization rates observed in Bin 9. Put differently, our data show a statistically significant increase in liquidation frequencies when
U i
* /S is close to one, the region in which our model predicts that incentive conflicts will the biggest impact on inefficient liquidation probabilities.
Figure 5(b) examines the robustness of the patterns observed in Figure 5(a) to regression controls, including year fixed effects and firn-level characteristics. The “No controls” reproduces the plot of mean reorganization probabilities from Figure 5(a). The “Year F.E.” line then regresses the reorganization probabilities on year dummies and ranks and plots the mean withinbin residuals that result from the effect regression. The “Firm controls” line is generated in a similar manner except that the regressions add firm-level characteristics (including industry fixed-effects, firm size, asset tangibility, current ratio, book leverage ratio, EBITDA/Assets, and an indictator for whether EBITDA is postive or negative) are added to the regression to control for firm-specific variation in reorganization probabilities. The take away from Figure 5(b) is that
18

the positive slope and decline in reorganization probabilities around Bin 10 persist from Figure
5(a) after controlling for year and firm effects.
5. Estimating the Model
In this section, we expand on our nonparametric regression results and formally estimate the model presented in Section 3. The benefits of this step are threefold. First, estimating the model allows us to measure the efficiency cost of creditor conflict. In particular, the model allows us to quantify the percentage of firm value that is lost for each firm that is inefficiently liquidated. We estimate this cost to be 4 percent on average across all inefficiently liquidated firms. This quantity would be difficult to estimate from reduced-form analysis alone because counterfactual reorganization values are not observed for liquidated firms. Second, estimating the model allows us to quantify the importance of several factors that may limit the costs of inefficient liquidation in the data, including the distribution of liquidation discounts and the actions of junior claimholders. If liquidation discounts are sufficiently large, then senior claimants’ incentives to liquidate may be muted, and if they are negative, meaning liquidation is the efficient outcome, then the problem is eliminated altogether. Finally, estimating the model allows us to quantify other parameters of interest, in particular the transaction cost parameter that may prevent junior creditors from paying off the senior creditors in full to facilitate efficient reorganization.
It is worth noting at this stage that while estimating a model of bankruptcy decisionmaking is useful for analyzing the efficiency of outcomes, the specifics of the model are not crucially important to our estimates. In fact, many bargaining models that fit the data as well (or better) than our model are likely to yield very similar results on both the frequency and costs of inefficient liquidations. The reason for this is that our main results rely primarily on the data and three key assumptions.
The first assumption is that we observe efficient reorganization decisions when the value of the firm is much less than the face value of the senior claim. For example, consider a firm with a going concern value that is 25 percent of the face value of secured debt. We assume that secured creditors of such a firm would not be concerned about granting “option value” to junior claimants, since the value of the firm would have to increase fourfold before the secured creditor
19

is repaid in full. This is unlikely for reasonable estimates of asset volatility, meaning the secured creditor should have an incentive to reorganize the firm if it is efficient to do so.
A second, closely-related assumption is that we observe efficient reorganization decisions when the value of the firm is much greater than the face value of the senior claim. In this case, consider a firm with a going concern value that is 400 percent (or more) of the face value of secured debt. We assume that for such a firm, either secured creditors have limited control rights, or that junior claimants face negligible transaction costs in their efforts to reinstate, refinance, or repay the secured debt. These assumptions are easily justified by features of the bankruptcy code and the nature of these transaction costs, which we discuss in Section 2.
If efficient outcomes are observed at very high values of V/S and very low values of V/S, a natural next question is what happens in between. Our third assumption is that in the absence of conflicts between senior and junior creditors, the probability of reorganization would increase monotonically with the ratio of the firm’s asset value to secured debt. This would be the case, for example, for any unimodal distribution of V/S and L/S. The idea behind this assumption is that in the absence of creditor conflicts, there is nothing special about the point at which V/S=1, and we should not observe a non-monotonicity in the probability of reorganization around this point.
The assumption that the probability of reorganization would be monotonic with respect to
V/S in the absence of creditor conflicts could be violated if firms that file for bankruptcy with asset values close to the face value of secured debt tend to be those with unusually high liquidation values relative to reorganization values, or vice versa. If firms near V/S=1 have on average higher liquidation values than firms both with slightly higher and slightly lower values of V/S, then it is possible that the probability of reorganization would decline in this region even in the fully efficient case. This might occur, for example, if senior creditors could exert some control over the filing decision and they tended to file firms with high liquidation values whenever they became nearly-secured.
8 This would lead our results to over-state the true impact
of creditor conflict on the frequency and cost of inefficient liquidation.
9
8 We are not aware of a theory that suggests that firms with high reorganization values should be more likely to file near V/S=1, but if this were the case we would under-state, rather than over-state the true effect.
9
This concern is mitigated somewhat by the fact that our results are not greatly affected after controlling for observable firm characteristics. Moreover, in unreported results we have found that the probability of a bankruptcy filing conditional on a missed payment or other “default” defined by Moody’s is monotonic around V/S=1. This suggests that if the selection of firms into the bankruptcy sample is based on unobservables, these do not affect the filing conditional on default decision, making it less likely that this is an important driver of our results.
20

Given the three aforementioned assumptions, there is a large class of models that will give rise to estimates that are similar to (or bounded above by) ours, at least to a first-order approximation. For example, a model could detail a repeated bargaining game, as in Bebchuk and Chang (1992), specify a judicial decision-making process, as in Ayotte and Morrison (2009), formulate a rich specification of transactions costs, or even introduce incomplete information.
Provided that such models fit the data at least as well as ours, all are likely to give similar results.
The reason for this is that the estimates are driven primarily by the data, not the model.
To see this, consider the reorganization probabilities plotted in Figure 7. The solid blue line in the figure labeled “Observed” shows the reorganization probability for each bin of ln(U * /S) observed in the data. The dashed green line labeled “Efficient” shows a hypothetical efficient reorganization line which is fitted to match observed reorganization probabilities when
V/S is much greater than and much less than one. The area between the two lines gives a very rough measure of the fraction of firms that are inefficiently liquidated. (This rough measure is
7.8 percent, nearly identical to our formal estimate.) Any model that fits the “Observed” line well will yield a similar estimate of this frequency.
10
A similar argument holds for the average liquidation discount for inefficiently liquidated firms, although this is slightly more subtle. In this case, the distribution of observed log asset values for reorganized firms and the equivalent distribution for liquidated firms (see Table 4) bound the efficiency costs of inefficient liquidations. For instance, the difference between the means of these distributions is approximately 30 percent. If one simply assumes that the liquidation discount for inefficiently liquidated firms is less than or equal to this the mean, then losses from inefficient liquidation can be no larger than 2.4 percent (or 7.8 percent x 30 percent), independent of any specific bargaining model assumptions. In fact, we estimate the true losses to be much lower.
With all of the above in mind, we next turn to estimating the model presented in Section
3. The estimation proceeds in several steps. In Section 5.1, we describe the stochastic structure of the problem and the Method of Simulated Moments (MSM) approach to estimation. We also motivate our choice of moments and discuss how these moments identify the model parameters.
In Sections 5.2 and 5.3, we present results on model fit and estimated parameter values. In
10
One could write down a model in which the monotonicity assumption was satisfied but the efficient reorganization probability line had a different shape than our line (e.g., suppose it was strictly concave). However, this would make it difficult to match the observed distribution asset values for both reorganized and liquidated firms.
21

Section 5.4, we use the model to compute our key quantities of interest – the frequency and cost of inefficient liquidations. Finally, in Section 5.5 we discuss several reasons why these estimates may be overstated, meaning the actual costs of inefficient liquidation may be lower than we estimate.
5.1. Model Estimation and Identification
As discussed in Section 3.3., our model of creditor decision-making forms a mapping of firm characteristics and objective function parameters (V/S, L/S, ,
* ,
U * /S). To estimate the model, we assume that V/S and L/S are random variables, and that each firm in the sample represents a draw from the joint distribution of V/S and L/S. The moments of this joint distribution, the volatility of the reorganization process, , and the transaction cost are parameters to be estimated. For purposes of estimation, we fix the total volatility of the reorganization process, or , to be 0.30. This value corresponds closely with the estimates of mean U.S. firm asset volatility reported by Correia, Kang, and Richardson (2013) over a oneyear period. We then estimate our model using the Method of Simulated Moments (McFadden,
1989). The goal of the method is to choose parameters that minimize the distance between a vector of observed data moments and a vector of simulated moments generated by the model.
We simplify the implementation of the MSM procedure by making an assumption about the joint distribution of V/S and L/S. We assume that V/S and L/S are both lognormal, so that the joint distribution of ln(V/S) and ln(L/S) is a bivariate normal distribution.
11
Since ln(V/S) + ln(1-
and ln(1bivariate normal distribution; that is:
( / )
( )
~N , (4) to be correlated with the firm’s reorganization value V/S. This correlation is captured by the
11 While an assumption about the distribution of V/S and L/S is necessary to estimate the model, the assumption of bivariate normality is not essential to our results. As discussed above, the most important assumption is that the joint distribution of V/S and L/S is unimodal.
22

un the firm (that is, to not shut down and liquidate) is greater for firms with higher asset values. This would manifest itself in the data as higher reorganization probabilities for firms with higher recovery rates. On the other then the return to continuation is lower for firms with higher asset values, with the opposite empirical implication.
This specification of our model has six estimable parameters: five parameters from the joint distribution of ln(V/S) and ln(1. We econometrically over-identify these six parameters by estimating ten moments in the data. That is we choose ten empirical moments from the data and select the model parameters to match as close as possible the analog moments implied by our model. Estimating the distributional parameters is fairly straightforward, and each of the parameters is identified (loosely speaking) by a specific set of moments in the data.
The mean and standard deviation of ln(V/S) are identified by the observed mean and standard deviation of ln(U * /S) in the data conditional on reorganization (R * = 1). Similarly, the observed mean and standard deviation of ln(1identified by the observed mean and standard deviation of ln(U * /S) in the data conditional on R * = 0.
12
We include six additional moments These moments are based on a regression of the probability of reorganization on ln(U * /S) and four “nearly-secured” dummy variables that correspond to Bins 9-12 in Table 2 and Figure 5. These bins include the 20 percent of filings that are closest to U * /S = 1. This regression yields six moments: an intercept, a slope on ln(U * /S), and four bin coefficients. The parameter determines the correlation between reorganization values ln(V/S) and liquidation discounts ln(1- is identified by the slope coefficient, which captures the rate at which the observed probability of reorganization increases with ln(U * /S). If the slope is positive, then this implies a positive correlation between ln(V/S) and , meaning firms with high reorganization values face greater liquidation discounts and are more likely to be reorganized all else equal.
Once we have defined our moments, estimation is straightforward. For each possible parameter vector, we simulate 100,000 draws of (V/S, L/S) from the joint distribution of ln(V/S) and ln(1- ) and then compute bankruptcy outcomes based on the decision boundaries presented in Section 3. For firms that are reorganized, we also simulate ultimate reorganization values at
12 Note that if we observed only R * and not the underlying asset values, only the difference in means would be identified. As in a typical probit model, neither standard deviation would be identified if only R * were observed.
23

time T by simulating a normal random variable with standard deviation . This gives rise to a simulated joint distribution of R * and ln(U * /S). We compute the ten moments from this simulated joint distribution and calculate the Euclidean distance between these ten simulated moments and the ten corresponding data moments. Parameters are chosen to minimize this distance using the
Nelder-Mead downhill simplex algorithm.
5.2. Model Fit
Table 3 presents the ten moments used to estimate our six parameters (five distributional model fits nearly exactly means of ln(U * /S), conditional on reorganization and liquidation, and explains well the intercept and positive slope estimates from the linear fit model relating the probability of reorganization to ln(U * /S).
Table 3 also shows that the model allows for a drop in reorganization probabilities near
U * /S = 1, consistent with heightened creditor conflicts when asset values just cover secured debt claims. However, the model does not do so well fitting the observed reorganization probabilities in Bins 9 and 10. In Bin 9, the model overestimates substantially the decline in observed probabilities, but in Bin 10 it slightly underestimates the observed decline. In other words, the model is not able to incorporate the sharp drop in reorganization probabilities in Bin 10; the model predicts a much larger and persistent decline that begins in Bin 9 and lasts through Bin 11.
This may be due to the fact that our model rules out bargaining to achieve the efficient outcome when V/S < 1, although such bargaining likely does occur in practice.
Figure 6 provides a more complete picture of model fit by comparing over the range of ln(U * /S), simulated bin estimates of reorganization probabilities implied by the model to the observed probabilities in Figure 5(a). The simulated bin estimates are based on a draw of
100,000 observations from a distribution generated by our fitted model. As a benchmark, we also plot the probabilities implied by a model in which there a no inefficient liquidations. Note that the modeled bin estimates track the observed estimates closely in Bins 1-7 and relatively closely in Bins 14-20, where both modeled and observed reorganization probabilities are near the efficient level predicted by the model. But the modeled estimates deviate from efficient reorganization rates starting around Bin 8, continue the decline through Bin 10, and then slowly
24

recover through Bins 11-13. By contrast, the actual declines in reorganization probabilities do not occur until Bin 10, and then recover quickly to the efficient levels by Bin 14. We return below to a discussion of these differences between observed and modeled outcomes.
5.3. Parameter Estimates
The parameter estimates that minimize the distance between observed moments and moments simulated from the model are presented in Table 4, along with bootstrapped standard error estimates. The first five parameter estimates are those of the joint distribution of ln(V/S) and ln(1-0.08, which implies a ratio of reorganization value to secured debt of 92 percent. This is less than the average observed ratio of reorganization value to secured debt for firms who reorganize, which is 102 percent (see Table 3, row 1), since observed reorganizations tend to occur for firms with higher reorganization values. The estimated mean of ln(1-0.12, which equates to an 11 percent discount from the reorganization value for liquidating early. This estimate falls in between the 7-8 percent discount that Strömberg (2000) reports for expected liquidations costs in his data and the 14 percent estimate reported by Pulvino
(1998) for fire-sale discounts in the airline industry.
The last parameter is the estimate of the transaction costs that junior creditors face to take control of the reorganization process. We estimate this average cost to be 7 percent of the secured claim amount. One interpretation of this cost is that it represents a cost of issuing new equity to pay off the senior claim at par. In this context, the estimate is similar in magnitude to cost that Hennessy and Whited (2007) estimate for large firms raising external junior (equity) financing in the presence of debt overhang. They calculate the cost to equal 5 percent of proceeds raised and note that this amount is similar to the marginal underwriting fees estimated by Altinkilic and Hansen (2000).Our model assumes that this cost is a fixed proportion of the secured claim amount regardless of V/S. If the costs were allowed to decline with V/S, as would be predicted by a model with asymmetric information driven issuance costs, the model would likely fit the data better for V/S > 1.
The implications of these parameter estimates for the distribution of bankrupt firms and the resulting reorganization and liquidation outcomes are illustrated in Figure 7. This figure shows the model illustration Figure 2(b) overlaid with the simulated distribution of bankrupt
25

firms at the estimated parameter values. The figure highlights the fact that many firms fall in the model regions where either reorganization is preferred by senior creditors (to the upper left of the red line), reorganization is undertaken by junior creditors (to the upper right of the purple line), or liquidation is efficient (below the horizontal black line). The remaining firms are inefficiently liquidated. We quantify the fraction of firms in each region in the next section.
5.4. Discussion of Results
Our estimated model allows us to explore the economic importance of creditor conflicts for the probability of reorganization and economic efficiency in the presence of transaction costs. We accomplish this by defining the size of the areas across the range of values of ln(V/S) in Figure 7 that would be classified as reorganizations, efficient liquidations, and inefficient liquidations by our model. Table 6 reports these results. To produce the estimates, we make 100,000 draws from the bivariate normal distribution in equation (4) with parameters for the distribution drawn from the estimates in Table 4. We then use the estimated parameters to demarcate the different regions of Figure 7 by plugging in the estimated parameters to expressions (1) and (2).
Table 6 shows that 73 percent of all of the bankruptcies in our sample end in reorganizations. According to our model estimates, 41 percent of these reorganizations would be initiated by secured creditors, even in the absence of junior claimants, because the valuations were low enough that secured creditors could capture the upside of the reorganizations. This is the area in Figure 7 to the left of the curved line that extends asymptotically towards the L = S boundary. The remaining 33 percent of observed reorganizations derive from junior creditors bankruptcy process. These 33 percent correspond to the area in Figure 7 to the right of the junior creditor’s decision boundary.
We estimate that, of the 27 percent of sample firms that are liquidated or sold during bankruptcy, 19 percentare efficient liquidations – that is, with liquidation values exceeding reorganization values – characterized in Figure 7 as the area under the x-axis. This implies that the remaining 8 percent of the sample firms are liquidated when it would have been more efficient to reorganize these firms, shown in Figure 7 as the area in-between the curve that rises asymptotically and the junior creditor boundary.
26

The table also provides estimates of the average liquidation discounts faced by firms with various outcomes. Firms that are reorganized have an average liquidation discount of 16 percent.
This is higher than the unconditional average of 11 percent because firms with high costs of liquidation are more likely to be reorganized. Firms that are efficiently liquidated (i.e., those with on average 8 percent more if liquidated than if reorganized. Finally, firms that are inefficiently liquidated (i.e., have an average liquidation discount of 4 percent. This is our best measure of the amount of value lost from each firm that is inefficiently liquidated. This average loss of value is lower than the average liquidation discount across all firms because firms that are inefficiently liquidated are disproportionately likely to be firms where the costs of liquidation are low.
To what extent do the inefficient liquidations translate to aggregate valuation losses? The losses will depend on how much value would have been gained through the reorganization of the firms that experienced inefficient liquidations. We estimate this loss to be 0.28 percent of the reorganization value of the firm in expectation across all bankrupt firms, with a 95 percent confidence interval of just over zero to 0.56 percent. Loosely speaking, this value loss can be thought of as the product of (i) the fraction of firms that are inefficiently liquidated, and (ii) the average loss per inefficiently liquidated firm. The first quantity is 8 percent, and we estimate the second to be 4 percent. The actual estimate of 28 basis points is slightly lower than the back of the envelope calculation of 32 basis points (or 8 percent x 4 percent) because firms that are inefficiently liquidated tend to have a slightly smaller values of V than the average firm.
5.5. Estimates Likely Overstate True Costs
We derive our parameter estimates from the simple model introduced in Section 3 and estimated in Sections 5.1-5.3 above. We believe that these parameter estimates likely overstate the true frequency and cost of inefficient liquidations. This is due to the fact that, as shown in Figure 6, our model implies a more prolonged drop in reorganization probabilities around the area in which the secured debt asset coverage ratio (V/S) is close to one. This is most evident by comparing the observed and simulated lines in bins 8, 9, and 13-17 in the figure.
27

There are several potential explanations for why our model gets wrong the persistence of the drop in reorganization probabilities. Perhaps the most important reason is that our model does not allow for Coasian bargaining to occur below the point where it pays for junior claimants to pay off the secured creditors in full. The benefit of ruling out bargaining in our model is that it allows for a sparse set of parameters that can convincingly be estimated from available data without over-fitting, but the cost is that it is restrictive. Because senior creditors in our model are precluded from benefiting from the upside of reorganization as V approaches S, they tend to choose early liquidation. However, a model in which senior claimants could bargain to capture some potential upside from high realizations of V could mitigate the tendency towards inefficient liquidations.
In practice, senior lenders often take equity stakes in reorganizations that allow them to benefit from the upside of a reorganization, and junior claimants often receive consideration when the realized reorganization value U* is less than S. The bargaining that leads to these outcomes is at least one explanation for why we observe fewer liquidations for values of U* that are close to, but less than, S than would be predicted in our model. Also, our model does not allow the costs of reinstating, refinancing, or repaying the senior debt to decline with V/S. If these costs (as a proportion of the size of the senior claim) declined for firms with very high asset values or very little secured debt, our model would predict fewer inefficient liquidations in the over-secured region where V/S > 1.
Even with a more flexible model, our estimates may overstate the true frequency and costs of inefficient liquidation for two additional reasons. First, as discussed at the beginning of this section, if firms that file near V/S=1 tend to have higher liquidation values than firms with slightly higher or lower values of V/S, then it is possible that the probability of reorganization would decline in the nearly-secured region even in the fully efficient case. The result would be a decline in the “Efficient” line in Figure 6 in the middle bins, leading to a smaller distance between “Observed” and “Efficient”, or in other words, fewer inefficient liquidations.
Finally, although our theory does not make a clear distinction between liquidations and sales of substantially all of the firm’s assets, this distinction is important for efficiency considerations. Liquidations involve sales of assets piecemeal and reflect a change in how a firm’s assets are individually deployed, potentially to a variety of different owners. Acquisitions involve sales of firm assets as a whole and reflect the continued deployment of firm assets
28

together, but with a change from one owner to another. This results in an efficiency loss only to the extent that the firm's assets as a whole are worth more when controlled by the firm’s creditors than by the acquirer. If assets are sold for less than their reorganization value this may reflect a transfer of value from the estate (and junior creditors) to the acquirer, but not an efficiency loss.
6. Conclusion
Our paper seeks to investigate and address an important dimension of creditor conflicts: the tendency for a secured creditor to prefer to liquidate a financially distressed firm, even when the expected value from reorganizing the firm exceeds the proceeds from liquidation. We develop a simple model of this conflict, recognizing that U.S. bankruptcy law provides substantial protections to secured creditors, but also confers bargaining rights to junior claimants. In particular, if junior claimants can raise financing to pay the secured creditors in full, they can take over the process and reorganize if it is efficient to do so.
We develop an estimable form of our model that utilizes the observed relationship between secured debt asset coverage ratios and the probability of reorganization to uncover parameters of interest from our model. We use observations from over 700 U.S. bankruptcies during the period 1989 – 2011 to estimate our model. While our results find that the frequency of inefficient liquidations spikes in the area where our model predicts conflicts are the greatest – around the area in which the secured debt asset coverage ratio is close to one – the efficiency costs of these deviations are small relative to other direct and indirect costs of a prolonged bankruptcy process.
29

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31

Table 1: Summary Statistics on Dataset
This table shows summary statistics for the 834 bankruptcy filings that are tracked Moody’s Default and
Recovery Database. All numbers in the table are sample means unless otherwise noted. The probability of reorganization is the fraction of firms in the sample that are classified as “emerged” by Moody’s. Total debt and secured debt correspond to the face values of debt outstanding at filing. The ratios of asset value to total debt and secured debt are computed by summing the ultimate dollar recovery values for each debt instrument outstanding at filing to get a proxy for U * , and then dividing this value by the relevant debt amount. For additional information on variable construction, see Section 4. The table also shows the distribution of the key state variable, U*/S, that will be used in our main empirical tests.
Firms with Secured Debt Firms with No
All Filings Reorganized Liquidated Secured Debt
721
0.72
520
---
201
---
113
0.63
N
Probability of reorganization, Pr(R*=1)
Capital Structure
Total debt at filing ($M)
Secured debt at filing ($M), S
Fraction of total debt secured
Recovery Values
Ratio of asset value to total debt
Ratio of asset value to secured debt, U*/S
- Mean
- Standard deviation
- 10th percentile
- 25th percentile
- 50th percentile
- 75th percentile
- 90th percentile
692
401
0.62
0.55
1.77
5.95
0.32
0.60
1.00
1.41
2.52
783
453
0.60
0.56
1.74
3.53
0.37
0.62
1.02
1.52
3.04
456
268
0.67
0.52
1.85
9.73
0.23
0.50
1.00
1.06
1.79
---
---
---
---
---
---
---
997
---
---
0.39
32

Table 2: Probability of Reorganization by Bin
This table shows the relation between observed secured debt asset coverage ratios and reorganization probabilities, as reported in Figures 5(a) and 5(b). Bins are defined by ranking our 721 observations of ln(U * /S) from lowest to highest and grouping the observations into 20 approximately equal 5-percentile bins. Bins 1 through 10 include firms with U * /S ins 11 through 20 include firms with U * /S >1.
The “No Controls” column is the raw average reorganization rate in each bin. The “Filing Year F.E.” column shows the coefficients on each bin in a regression of a reorganization dummy on 20 bin indicators and filing year fixed effects. The “Firm Controls” column shows the coefficients on each bin in a regression of a reorganization dummy on 20 bin indicators, year fixed effects, and firm-level controls, including industry fixed effects, ln(Assets), Liabilities/Assets, Cash/Assets, PP&E/Assets, Sales/Assets,
EBITDA/Assets, gross margin, EBITDA operating margin, and dummy variables for whether gross margin and EBIDTA are positive. All financial variables are calculated as of the last 10-K or 10-Q before filing for the 390 firms with a reporting period end date was within one year of the bankruptcy filing date.
Percentile Bin
15
16
17
18
11
12
13
14
19
20
7
8
5
6
3
4
1
2
9
10
R 2
N
No Controls
0.57
0.66
0.77
0.86
0.83
0.91
0.89
0.74
0.86
0.86
0.51
0.57
0.80
0.77
0.66
0.63
0.66
0.86
0.83
0.45
(0.07)
(0.07)
(0.07)
(0.07)
(0.07)
(0.07)
(0.07)
(0.07)
(0.07)
(0.09)
(0.07)
(0.07)
(0.07)
(0.07)
(0.07)
(0.07)
(0.07)
(0.07)
(0.07)
(0.05)
0.75
721
Filing Year F.E.
0.60
0.64
0.78
0.86
0.83
0.91
0.89
0.76
0.84
0.89
0.54
0.60
0.83
0.80
0.64
0.63
0.66
0.87
0.83
0.48
(0.17)
(0.17)
(0.17)
(0.17)
(0.17)
(0.17)
(0.17)
(0.17)
(0.17)
(0.17)
(0.17)
(0.17)
(0.17)
(0.17)
(0.17)
(0.16)
(0.17)
(0.17)
(0.17)
(0.16)
0.76
721
Firm Controls
0.60
0.43
0.62
0.78
0.74
0.80
0.75
0.77
0.80
0.77
0.42
0.48
0.75
0.64
0.56
0.58
0.53
0.73
0.70
0.35
(0.27)
(0.26)
(0.27)
(0.26)
(0.26)
(0.26)
(0.26)
(0.26)
(0.25)
(0.26)
(0.27)
(0.26)
(0.26)
(0.27)
(0.26)
(0.26)
(0.27)
(0.26)
(0.26)
(0.25)
0.80
390
33

Table 3: Method of Simulated Moments Model Fit
This table shows the model fit from our MSM estimation of the model of creditor optimization presented in Section 3. The simulated moments are computed by simulating 100,000 draws of (ln(V/S), ln(1-
V V mapping these draws to simulated outcomes ( R , U /S ) using the model in Section 3, and computing simulated analogs to the 10 data moments presented in rows 1 through 10. The first four moments are moments of the observed (log) secured debt asset coverage distributions conditional on reorganization or liquidation. The last six moments are coefficient estimates from a regression of a reorganization dummy variable on observed secured debt asset coverage, ln(U*/S), and four dummy variables for the bins closest to V/S = 1. The simulated moments in Column (2) are computed at the parameter values that minimize the distance between simulated and observed moments.
Moment Observed Simulated
Mean of log(U*/S) if reorganized
Std. dev. of log(U*/S) if reorganized
Mean of log(U*/S) if liquidated
Std. dev. of log(U*/S) if liquidated
Intercept of R* on log(U*/S) regression
Slope of R* on log(U*/S) regression
Coefficient on bin 09 dummy
Coefficient on bin 10 dummy
Coefficient on bin 11 dummy
Coefficient on bin 12 dummy
0.02
0.81
-0.28
0.73
0.77
0.10
0.06
-0.32
-0.20
-0.11
0.02
0.82
-0.29
0.65
0.77
0.09
-0.15
-0.24
-0.18
-0.09
34

Table 4: Method of Simulated Moments Parameter Estimates
This table shows the parameter estimates from our MSM estimation of the model of creditor optimization presented in Section 3. Parameter estimates are computed by minimizing the distance between the simulated and observed moments presented in Table 3. The first five parameters in the table are the moments of the joint distribution of latent random variables ln(V/S) and ln(1ace value of senior debt. The sixth parameter is the total volatility of the reorganization process. This parameter is calibrated from external sources rather than estimated. Standard errors are computed using the bootstrap method with 1,000 resamplings from the observed joint distribution of R * and U * /S.
Parameter Estimate Std. Err.
Mean of log(V/S)
Std. dev. of log(V/S)
Reorganization volatility, T
-0.08
0.76
-0.12
0.14
-0.40
0.07
0.30
(0.026)
(0.063)
(0.024)
(0.033)
(0.062)
(0.030)
---
35

Table 6: Method of Simulated Moments Bankruptcy Outcomes
This table shows simulated bankruptcy outcomes from our estimated model. All values are computed by simulating 100,000 draws of (ln(V/S), ln(1-
V
,
V derived in Section 3. The parameter estimates used in the simulation are those presented in Table 4. See
Table 4 for details on estimation. Average liquidation discounts are computed by taking the sample
simulated firms with a given outcome. Expected efficiency losses are computed by comparing the total simulated value of all firms at the time of filing based on the estimated model to the total simulated value of all firms in the efficient case where reorganization occurs whenever V > L.
Outcome Estimate Std. Err.
Total reorganizations
- By seniors
- By juniors
73%
41%
33%
(2.3%)
(2.5%)
(1.7%)
27%
19%
8%
(2.3%)
(1.8%)
(3.2%)
Total liquidations
- Efficient
- Inefficient
Average liquidation discount
- All bankrupt firms
- Reorganizations
- Efficient liquidations
- Inefficient liquidations
Expected efficiency losses
11%
16%
-8%
4%
0.28%
(1.7%)
(2.8%)
(2.5%)
(0.4%)
(0.14%)
36

Figure 1: Secured Debt in Bankrupt Firms, 1989-2011
This figure shows the use of secured debt by bankrupt firms over the period 1989-2011 for firms in
Moody’s Default and Recovery Database. Each line is a trailing three-year moving average based on the date of filing.
The solid line shows the average fraction of debt outstanding at filing that is secured. This is calculated by dividing total secured debt outstanding at filing by total debt outstanding at filing for each firm and taking the average across firms. The long-dashed line shows the fraction of bankruptcy filers that had only secured debt outstanding at the time of filing. The short-dashed line shows the fraction of filers that had only unsecured debt outstanding at the time of filing.
37

Figure 2(a): Illustration of Senior Creditor Reorganization Decision
This figure shows the decision boundaries for the senior creditor’s decision problem presented in Section
3.1. The x-axis shows the logarithm of the model parameter V/S, and the y-axis shows the liquidation r than its reorganization value, so liquidation is efficient. The dashed line shows the threshold where the liquidation value of the firm exactly equals the face value of the senior claim, or where (1- ior creditor will always liquidate the firm to the right of this line because he gains nothing from reorganization, but faces a potential loss if firm value declines. The solid line shows the decision boundary for L/S < 1, which is given by the condition in Equation (1) of Section 3.1. To the left of the are greater than expected losses from an uncertain reorganization. For further discussion, see Section 3.1.
0.25
L = S asymptote
0.20
0.15
0.10
0.05
Efficient reorganization
Inefficient liquidation
0.00
-1.00
-0.05
-0.80
-0.60
-0.40
-0.20
0.00
0.20
Efficient liquidation
-0.10
0.40
Logarithm of ratio of reorganization value to senior debt (V/S)
0.60
0.80
1.00
38

0.20
0.15
0.10
0.05
Figure 2(b): Illustration of Junior Creditor Response
This figure shows the decision boundaries for the junior creditor’s optimal response presented in Section
3.2. The x-axis shows the logarithm of the model parameter V/S, and the y-axis shows the liquidation
The dashed and solid red lines are the same as in Figure 2(a). The solid blue shows the decision boundary for the junior creditor’s decision to pay off the senior creditor at par, plus transaction costs. The line is vertical at V = S( ), for =0.05. To the left of this point, the firm’s reorganization value is sufficiently low that the junior creditors cannot gain from paying off the senior creditors at par. oundary asymptotically approaches zero, since transaction costs, which are proportional to S, become a vanishingly small fraction of firm value as V/S increases.
Below the boundary, junior creditors choose not to reorganize because the cost of liquidation is smaller than the cost of paying off the seniors at par. For addition discussion, see Sections 2 and 3.2.
0.25
Efficient reorganization
Inefficient
Liquidation
Efficient reorganization
Junior creditors take over process
0.00
-1.00
-0.05
-0.80
-0.60
-0.40
-0.20
0.00
0.20
Efficient liquidation
0.40
-0.10
Logarithm of ratio of reorganization value to senior debt (V/S)
0.60
0.80
1.00
39

Figure 3: Bankruptcy Filings and Reorganization Rate, 1989-2011
This figure shows the number of bankruptcy filings by year in the Moody’s Default and Recovery
Database. The bars (left-axis) show the total number of filings in each year, including both Chapter 7 and
Chapter 11 filings. The line (right-axis) shows the fraction of cases that ended in a reorganization of the firm as a standalone entity. See Section 4 for further discussion.
40

Figure 4: Distribution of Secured Debt Asset Coverage
This figure shows the distribution of the logarithm of secured debt asset coverage, ln(U * /S), for our baseline sample of 721 filings in the Moody’s Default and Recovery Database. Secured debt asset coverage is defined as the ultimate recovery value of the firm’s assets divided by the face value of secured debt outstanding at the time of filing. The face value of secured debt is computed by summing the face value of all secured debt instruments in the firm’s capital structure. The ultimate recovery value of assets is computed by summing the dollar recovery value for each instrument in the firm’s capital structure. The kernel density is estimated using Epanechnikov weights, as implemented by the Stata function ‘kdensity’.
0.80
0.60
0.40
0.20
0.00
-3 -2.5
-2 -1.5
-1 -.5
0 .5
1
Logarithm of secured debt asset coverage
1.5
2 2.5
3
41

Figure 5(a): Probability of Reorganization by Bin
This figure shows the relation between observed secured debt asset coverage ratios and reorganization probabilities. To generate the solid line, we first rank our 721 observations of ln(U * /S) from lowest to highest and group the observations into 20 approximately equal 5-percentile bins. Bins 1 through 10 include firms with U * /S ins 11 through 20 include firms with U * /S >1. For each bin, we calculate the observed reorganization rate, or Pr(R * =1). The 95% confidence bands around each point on the line are calculated based on within-bin variation. Data underlying the figure is presented in Table 2.
42

Figure 5(b): Probability of Reorganization by Bin with Controls
This figure shows the relation between observed secured debt asset coverage ratios, ln(U * /S), and reorganization probabilities, Pr(R*=1). The “No controls” line is the same as reported in Figure 5(a). The
“Year F.E.” line shows the coefficients on each bin in a regression of a reorganization dummy on 20 bin indicators and filing year fixed effects. The “Firm controls” line shows the coefficients on each bin in a regression of a reorganization dummy on 20 bin indicators, year fixed effects, and firm-level controls, including industry fixed effects, log(Assets), Liabilities/Assets, Cash/Assets, PP&E/Assets, Sales/Assets,
EBITDA/Assets, gross margin, EBITDA operating margin, and dummy variables for whether gross margin and EBIDTA are positive. All financial variables are calculated as of the last 10-K or 10-Q before filing for the 390 firms with a reporting period end date was within one year of the bankruptcy filing date.
43

Figure 6: Probability of Reorganization: Model vs. Observed
This figure shows the relation between simulated secured debt asset coverage ratios and reorganization probabilities for the simulated model and for the hypothetical efficient outcome. The “Observed” line is the same as reported in Figure 5(a). The “Simulated” line is the relationship based simulated model data based on the parameter estimates presented in Table 4. The “Efficient” line is the best-fit regression line on simulated model data assuming the efficient allocation rule is followed; that is, reorganize if V > L.
44

Figure 7: Simulated Distribution of Bankrupt Firms
This figure shows the creditor decision boundaries and simulated distribution of bankrupt firms at the estimated parameter values presented in Table 4. The decision boundaries are the same as those in Figures
2(a) and 2(b), except that the junior creditor decision boundary is computed at the estimated transaction cost parameter value = 0.07.
The points in the scatter plot represent individual firms drawn from the joint distribution of ln(V/S) and ln(1- with our estimated distributional parameter values. Firms to the upper left of the red line are “reorganized by senior creditors” for the calculations in Table 6, while firms to the upper right of the purple line are “reorganized by junior creditors”. Firms below the horizontal axis are efficient liquidations, and firms in the remaining region near V/S=1 are inefficient liquidations.
45