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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 going-

concern 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 speculative-

grade 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.1

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.

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.

When V is much larger than S, i.e., when the secured claim is unimpaired and “over-

secured”, 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.2

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.3

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.

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.

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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.

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.

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 ı. We refer to expectation of V as the firm’s reorganization value. If the

firm is liquidated, its value is L = (1-į�9�ZLWK�FHUWDLQW\��:H�UHIHU�WR�/�DV�WKH�ILUP¶V�OLTXLGDWLRQ�

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value. We assume that į is realized at the time the company files for bankruptcy. If į�! 0, then

UHRUJDQL]LQJ�WKH�ILUP�LV�HIILFLHQW��DQG�LI�į�< 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, ı, 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 - &�6��ı��7�. 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 combinatioQV�RI�9��6�� į�� DQG�ı��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 FKDUDFWHUL]H� UHJLRQV� RI� WKH� SDUDPHWHU� VSDFH� �9�6�� į�� ı, 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:

െ ,)ܥ (,ߪ, > min(ܮ, ), (1)

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where L = (1-į�9��7KH�reorganization decision depends importantly on the parameter values V,

6�� ı, and į� 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) DQG�WKH�OLTXLGDWLRQ�GLVFRXQW��į���setting the total volatility

of the reorganization process, ߪξ, equal to 0.30. The senior creditor will always liquidate a firm

ZKHQ� LW� LV� HIILFLHQW� WR�GR� VR�� L�H���ZKHQ�į������ VKRZQ�DV� WKH� DUHD�XQGHU� WKH�[-axis. The senior

creditor will also efficiently reorganize a firm when V is low relative to S, and when liquidation

GLVFRXQWV�RQ�UHRUJDQL]DWLRQ�YDOXH�DUH�UHODWLYHO\�ODUJH��į�!�����VKRZQ�in Figure 2(a) as the area to

WKH� OHIW�RI� WKH�VROLG�FXUYHG� OLQH� WKDW�DSSURDFKHV� WKH�/� �6�DV\PSWRWH�DV�9�DQG�į� LQFUHDVH��7KH�

secured creditor will efficiently reorganize in this area because for low enough V (or high

HQRXJK�į���WKH�VHFXUHG�FUHGLWRU�FDSWXUHV�HQRXJK�RI�WKH�XSVLGH�YDOXH�IURP�WKH�UHRUJDQL]DWLRQ�WKDW�

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

GHFOLQHV� LQ� 9�� 7KHUHIRUH�� WKH� VHQLRU� FUHGLWRU� RSWLPDOO\� FKRRVHV� WR� LQWHUQDOL]H� WKH� ORVV� į� DQG�

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

WKH� VHQLRU� FUHGLWRU�� $VVXPLQJ� į� !� ��� IRU� YDOXHV� RI� 9� WKDW� DUH� ORZ� UHODWLYH� WR� 6�� WKH� MXQLRU�

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.

)ROORZLQJ�WKLV�OLWHUDWXUH��ZH�LQWURGXFH�D�WUDQVDFWLRQ�FRVW�ș�WKDW�PXVW�EH�SDLG�E\�WKH�MXQLRU�

FUHGLWRUV�WR�WDNH�FRQWURO�RI�WKH�EDQNUXSWF\�SURFHVV��:H�DVVXPH�WKDW�ș�LV�SURSRUWLRQDO�WR�WKH�IDFH�

value of the senior claim, so that MXQLRU�FUHGLWRUV�PXVW�SD\�D�WRWDO�DPRXQW�RI�6���ș��WR�UHWLUH�WKH�

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,

ZKLFK�ZH�SUHIHU��LV�WKDW�WKH�FRVW�ș�UHVXOWV�IURP�a debt overhang problem between junior creditors

and outside investors who finance the purchase of the senior claim.5

5 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.

Note that aQ\�ș�!���LPSOLHV�

that inefficient liquidation will occur in a positive measure of cases where the benefits of

14

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.

7KH�MXQLRU�FUHGLWRU¶V�RSWLPDO�GHFLVLRQ�GHSHQGV�RQ�WKH�SDUDPHWHUV�9��6��ı��į, and ș. 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

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 WUDQVDFWLRQ�FRVW�ș��

Over the region in which a senior creditor will otherwise inefficiently liquidate a firm,

jXQLRU�FUHGLWRUV�ZLOO�SD\�6���ș��to take over the process and reorganize when

െ (1 + (ߠ > max (0, ܮ െ ). (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(����ș��H[FHHGV�WKH�YDOXH�WKH\�ZRXOG�receive from a

liquidation after the senior creditor is paid in full, given the absolute priority rule.

Figure 2(b) shows the influence that junior creditors have on bankruptcy outcomes in our

PRGHO� DVVXPLQJ� ș� � ����� 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

GHFUHDVLQJ�LQ�į�

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

15

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

0���9�6��į��ı��T, ș��Æ (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 į is observed. The observed recovery value for a

given firm is:

U* = VT if R* = 1 (3)

= L if R* = 0.

If the firm is liquidated only the product of V/S and į is observed, while if it is reorganized, only

the terminal reorganization value is observed. This is captured by the T subscript on VT. 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.

16

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 Si 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.

17

conclusion of the bankruptcy process, discounted back to the default date.7

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.

We then compute Ui*

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.

Figure 4 reports the actual distribution of secured debt asset coverage values in our

sample, graphed in terms of ln(Ui*/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 Ui*/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(Ui*/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.

18

ln(U*/S). To accomplish this goal, we run a non-parametric regression. We first rank all 721

observations of ln(Ui*/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(Ui*/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 �����DQG�Eins 11 through

20 include firms with U*/S >1.

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(Ui*/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

Ui*/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 within-

bin 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

19

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 decision-

making 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

20

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.

21

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.

22

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, ߪξ, ș��WR�REVHUYDEOH�RXWFRPHV��5*,

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, ߪξ, and the transaction cost ș�

are parameters to be estimated. For purposes of estimation, we fix the total volatility of the

reorganization process, or ߪξ, 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 one-

year 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-

į�� � OQ�/�6��� WKLV�DVVXPSWLRQ� LPSOLHV� WKDW� WKH� MRLQW�GLVWULEXWLRQ�RI� OQ�9�6� and ln(1-į�� LV�DOVR�D�

bivariate normal distribution; that is:

ቀ୪୬ ( / ௌ)୪୬ (ଵఋ)ቁ~Nቆȝȝį ൨ , ቈ ıଶ ȡııį

ȡııį ıįଶቇ (4)

$V�LQ�+DUW�DQG�0RRUH���������ZH�DOORZ�WKH�UHWXUQV�WR�FRQWLQXDWLRQ��ZKLFK�LV�FKDUDFWHUL]HG�E\�į��

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.

23

SDUDPHWHU�ȡ�� ,I� ȡ� LV� SRVLWLYH�� WKHQ� WKH� UHWXUQ� WR� FRQWLQXLQJ� WR� Uun 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

KDQG��LI�ȡ�LV�QHJDWLYH�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-į��� SOXV� ș. 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(1-į�� DUH� identified by the observed mean and standard

deviation of ln(U*/S) in the data conditional on R*= 0.12

We include six additional moments WR� LGHQWLI\�ȡ�DQG�ș��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 ȡ�� ZKLFK� determines the correlation

between reorganization values ln(V/S) and liquidation discounts ln(1-į� 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 į, 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- į) 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.

24

time T by simulating a normal random variable with standard deviation ߪξ. 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

SDUDPHWHUV�� SOXV� ș��� 7KH�PRGHO� ILWV�ZHOO�� %DVHG� RQ� D� VDPSOH� RI� �������� VLPXODWHG� ILUPV�� WKH�

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

25

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-į���7KH�HVWLPDWHG�PHDQ�RI�OQ�9�6��LV�-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-į�� LV� -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

26

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

LQFXUULQJ� WKH� WUDQVDFWLRQ� FRVW� ș� DQG� SD\LQJ� RII� WKH� VHFXUHG� FUHGLWRUV� LQ� IXOO� WR� WDNH� RYHU� WKH�

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.

27

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

į������KDYH�DQ�DYHUDJH�OLTXLGDWLRQ�SUHPLXP�RI���SHUFHQW��PHDQLQJ�WKHVH�ILUPV¶�DVVHWV�DUH�ZRUWK�

on average 8 percent more if liquidated than if reorganized. Finally, firms that are inefficiently

liquidated (i.e., WKRVH�ZLWK�į�!����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.

28

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

29

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.

30

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31

Gilson, Stuart, Kose John, and Larry Lang, 1990, Troubled debt restructurings: An empirical study of private reorganization of firms in default, Journal of Financial Economics 27, 315-353. Hart Oliver and John Moore, 1998, 1998, Default and renegotiation: A dynamic model of debt, Quarterly Journal of Economics 103, Hennessy, Christopher A., 2004, Tobin’s Q, debt overhang, and investment, Journal of Finance 59, 1717–1742. Hennessy, Christopher A. and Toni Whited, 2007, How costly is external financing? Evidence from a structural estimation, Journal of Finance 67, 513-559. Hotchkiss, Edith S., and Robert M. Mooradian, 1997, Vulture investors and the market for control of distressed firms, Journal of Financial Economics 43, 401–432. Hotchkiss, Edith, David C. Smith, and Per Strömberg, 2014, Private equity and the resolution of financial distress, University of Virginia working paper. Ivashina Victoria, Benjamin Iverson, and David C. Smith, 2014, The ownership and trading of Chapter 11 restructurings, Harvard University working paper. James, Christopher, 1996, Bank debt restructurings and the composition of exchange offers in financial distress, Journal of Finance 51, 711-727. Jiang, Wei, Kai Li and Wei Wang, 2012, Hedge funds in Chapter 11, Journal of Finance 67, 513-560. Myers, Stewart C., 1977, Determinants of corporate borrowing. Journal of Financial Economics 5, 147–175. Nini, Greg and David C. Smith, 2013, Some facts and figures on secured lending, Loan Market Chronicle, Loan Syndication and Trading Association Annual Review, 80-82. Pulvino, Todd C., 1998, Do asset fire sales exist? An empirical investigation of commercial aircraft transactions, Journal of Finance 53, 939-978. Rajan, Raghuram G., 1992, Insiders and outsiders: The choice between informed and arm’s-length debt, Journal of Finance 47, 1367-1400. Skeel, David A., 2003, Creditors’ ball: The “new” new corporate governance in Chapter 11, University of Pennsylvania Law Review 152, 917–951. Strömberg, Per, 2000, Conflicts of interests and market liquidity in bankruptcy auctions, Journal of Finance 55, 2641–2692. White, Michelle, J., 1980, Public policy toward bankruptcy: Me-first and other priority rules, The Bell Journal of Economics 11, 550-564. White, Michelle, J., 1983, Bankruptcy Costs and the new bankruptcy code,” Journal of Finance 38, 477-487.

32

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 NoAll Filings Reorganized Liquidated Secured Debt

N 721 520 201 113Probability of reorganization, Pr(R*=1) 0.72 --- --- 0.63

Capital StructureTotal debt at filing ($M) 692 783 456 997Secured debt at filing ($M), S 401 453 268 ---Fraction of total debt secured 0.62 0.60 0.67 ---

Recovery ValuesRatio of asset value to total debt 0.55 0.56 0.52 0.39Ratio of asset value to secured debt, U*/S - Mean 1.77 1.74 1.85 --- - Standard deviation 5.95 3.53 9.73 --- - 10th percentile 0.32 0.37 0.23 --- - 25th percentile 0.60 0.62 0.50 --- - 50th percentile 1.00 1.02 1.00 --- - 75th percentile 1.41 1.52 1.06 --- - 90th percentile 2.52 3.04 1.79 ---

33

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 �����DQG�Eins 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 No Controls Filing Year F.E. Firm Controls1 0.51 (0.07) 0.54 (0.17) 0.42 (0.27)2 0.57 (0.07) 0.60 (0.17) 0.48 (0.26)3 0.80 (0.07) 0.83 (0.17) 0.75 (0.26)4 0.77 (0.07) 0.80 (0.17) 0.64 (0.27)5 0.66 (0.07) 0.64 (0.17) 0.56 (0.26)6 0.63 (0.07) 0.63 (0.16) 0.58 (0.26)7 0.66 (0.07) 0.66 (0.17) 0.53 (0.27)8 0.86 (0.07) 0.87 (0.17) 0.73 (0.26)9 0.83 (0.07) 0.83 (0.17) 0.70 (0.26)

10 0.45 (0.05) 0.48 (0.16) 0.35 (0.25)11 0.57 (0.07) 0.60 (0.17) 0.60 (0.27)12 0.66 (0.07) 0.64 (0.17) 0.43 (0.26)13 0.77 (0.07) 0.78 (0.17) 0.62 (0.27)14 0.86 (0.07) 0.86 (0.17) 0.78 (0.26)15 0.83 (0.07) 0.83 (0.17) 0.74 (0.26)16 0.91 (0.07) 0.91 (0.17) 0.80 (0.26)17 0.89 (0.07) 0.89 (0.17) 0.75 (0.26)18 0.74 (0.07) 0.76 (0.17) 0.77 (0.26)19 0.86 (0.07) 0.84 (0.17) 0.80 (0.25)20 0.86 (0.09) 0.89 (0.17) 0.77 (0.26)

R2 0.75 0.76 0.80N 721 721 390

34

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-į���IURP� D� ELYDULDWH� QRUPDO� GLVWULEXWLRQ� ZLWK� PHDQ� YHFWRU� �ȝV�� ȝį�� DQG� FRYDULDQFH� PDWUL[� Ȉ�ıV�� ıį�� ȡ���mapping these draws to simulated outcomes (R, Uכ/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 0.02 0.02Std. dev. of log(U*/S) if reorganized 0.81 0.82Mean of log(U*/S) if liquidated -0.28 -0.29Std. dev. of log(U*/S) if liquidated 0.73 0.65Intercept of R* on log(U*/S) regression 0.77 0.77Slope of R* on log(U*/S) regression 0.10 0.09Coefficient on bin 09 dummy 0.06 -0.15Coefficient on bin 10 dummy -0.32 -0.24Coefficient on bin 11 dummy -0.20 -0.18Coefficient on bin 12 dummy -0.11 -0.09

35

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(1-į���7KH�ILIWK�PRPHQW�LV�WKH�WUDQVDFWLRQ�FRVW�ș�WKDW�PXVW�EH�SDLG�E\�WKH�MXQLRU�FUHGLWRUV�WR�UHRUJDQL]H�WKH�ILUP��DV�D�IUDFWLRQ�RI�WKH�Iace value of senior debt. The sixth parameter ߪξ 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) -0.08 (0.026)Std. dev. of log(V/S) 0.76 (0.063)0HDQ�RI�ORJ���į� -0.12 (0.024)6WG��GHY��RI�ORJ���į� 0.14 (0.033)&RUU�ORJ�9�6���ORJ���į�� -0.40 (0.062)-XQLRUV�WUDQVDFWLRQ�FRVW��ș 0.07 (0.030)Reorganization volatility, 0.30 ---TV

36

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-į���IURP�D�ELYDULDWH�QRUPDO�GLVWULEXWLRQ�ZLWK�PHDQ�YHFWRU��ȝV, ȝį�� DQG� FRYDULDQFH� PDWUL[� Ȉ�ıV�� ıį�� ȡ�� DQG� FRPSXWLQJ� HDFK� RXWFRPH� EDVHG� RQ� WKH� UHJLRQ� ERXQGDULHV�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 DYHUDJH� į� IRU� DOO 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 73% (2.3%) - By seniors 41% (2.5%) - By juniors 33% (1.7%)

Total liquidations 27% (2.3%) - Efficient 19% (1.8%) - Inefficient 8% (3.2%)

Average liquidation discount - All bankrupt firms 11% (1.7%) - Reorganizations 16% (2.8%) - Efficient liquidations -8% (2.5%) - Inefficient liquidations 4% (0.4%)

Expected efficiency losses 0.28% (0.14%)

37

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.

38

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 GLVFRXQW�į��,I�į������WKHQ�WKH�ILUP¶V�OLTXLGDWLRQ�YDOXH�LV�JUHDWHr 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- į�9�6� �/�6� ���� ,Q� WKH� DEVHQFH�RI�EDUJDLQLQJ�� WKH� VHQior 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 OLQH��WKH�VHQLRU�FUHGLWRU�ZLOO�FKRRVH�WR�UHRUJDQL]H�WKH�ILUP�EHFDXVH�ORVVHV�IURP�OLTXLGDWLRQ��VLQFH�į�!����are greater than expected losses from an uncertain reorganization. For further discussion, see Section 3.1.

-0.10

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Efficientreorganization

Efficient liquidation

Inefficient liquidation

L = S asymptote

39

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 GLVFRXQW� į��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(��ș), for ș=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. $V�9�6�LQFUHDVHV�IURP�6���ș��� WKH�GHFLVLRQ�Eoundary 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 �VLQFH�į�!����is smaller than the cost of paying off the seniors at par. For addition discussion, see Sections 2 and 3.2.

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Efficientreorganization

Efficient liquidation

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40

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.

41

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’.

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Figure 4: Distribution of Secured Debt Asset Coverage

42

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 �� ��� DQG� E 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.

43

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.

44

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.

45

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 Ʌ = 0.07. The points in the scatter plot represent individual firms drawn from the joint distribution of ln(V/S) and ln(1-į� 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.