Relationship Bank Behavior During Borrower Distress and Bankruptcy

8/20/2019 Relationship Bank Behavior During Borrower Distress and Bankruptcy

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Relationship bank behavior during borrower distress and bankruptcy

ABSTRACT

This paper provides a comprehensive examination of differences between rela-

tionship bank behavior for a set of borrowers that either underwent distress or filed

for bankruptcy relative to normal times. Prior to distress, banks offer preferential

contract terms in the form of lower interest rates and less collateral requirement to

their relationship borrowers. After the onset of distress, banks offer identical loan

contract terms to their relationship borrowers and outside borrowers. Further,

loan availability from relationship lenders (relative to outside lenders) is signifi-

cantly lower after the onset of distress. However, after filing for bankruptcy, banks

again offer preferential terms to their relationship borrowers in terms of collateral

requirement. Further, availability of loans from relationship lenders is comparableto that in normal times. Our findings contribute to both the literature on soft

budget constraints as well as that on DIP financing.

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1. Introduction

Most of the empirical work on relationship lending has focused on banks and borrowerswhen borrowers are performing well financially.1 Little work has focused on the impact

of lending relationships when borrowing firms undergo financial distress.2 This is an

important gap in the literature as the benefits of bank lending arise because of the

(assumed) better screening ability or better refinancing decisions of banks relative to

capital markets during borrower distress.3

This paper seeks to fill this gap in the literature by exploring the behavior of re-

lationship banks when their borrowers undergo financial distress and bankruptcy and

comparing this to relationship bank behavior during normal times. In particular, we

compare loan availability as well as loan rates and collateral to borrowers in normal

conditions, distress and bankruptcy. Further, we also study the impact of maintaining

relationships on the likelihood of distress and bankruptcy.

On one hand, the incentive of a relationship lender to help the borrower is reduced

if a borrower enters distress or bankruptcy. Theoretically, a reduced incentive may arise

because a lender would want to maintain a reputation as being tough to protect its

outstanding loans with other borrowers (Chemmanur and Fulghieri (1994)), because

the borrower becomes more locked in during distress (Sharpe (1990), Rajan (1992)) or

because of the reduced likelihood of future business from a distressed firm.4 A reduced

incentive to help may manifest in less preferential loan contract terms as well a reduced

loan availability.

On the other hand, the borrower may want to continue helping a distressed or

bankrupt firm due to soft budget constraints. Such constraints may arise from a de-

1See for example, Petersen and Rajan (1994) and Berger and Udell (1995).2See Elsas and Krahnen (1998) for evidence from Germany and Peek and Rosengren (2005) for

evidence from Japan. It should be noted that a number of studies study the impact of bank debtduring borrower distress. For example, Gilson, John, and Lang (1990) examine the impact of bankdebt on the decision to enter a formal bankruptcy process versus informal out of court restructuring.Likewise, James (1995) examines the determinants of bank concessions in out-of-court restructurings.However, these studies do not typically study relationship bank versus outside bank finance duringdistress which will be a key focus of this study.

3See, for example, Diamond (1984) and Diamond (1991).4Bharath, Dahiya, Saunders, and Srinivasan (2007) document that an important benefit of relation-

ship banking from the lender’s perspective is the likelihood of repeat business from the same borrower.

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sire of the relationship bank to protect its reputation with other borrowing firms (Boot,

Greenbaum, and Thakor (1993)), to protect its outstanding loans (Dewatripont and

Maskin (1995)) and the borrower’s threat of strategic default (Anderson and Sundare-san (1996)). Thus, a relationship lender may not reduce, in fact, may even increase its

loan availability to its distressed borrower and may continue to offer preferential loan

contract terms.

While many of the above arguments are equally applicable to distress as well as

bankruptcy, the provision for the super priority claims in the form of DIP financing after

bankruptcy may change incentives of relationship lenders. The super priority feature

may give the relationship lender additional incentives to help the relationship borrower

in the DIP financing. Thus, it is interesting to contrast distress to bankruptcy to studyif the possibility of making DIP loans changes relationship bank behavior.

The sample for studying the above hypotheses requires the use of loan data, financial

data on companies as well as bankruptcy data. We use the Dealscan database maintained

by the Loan Pricing Corporation (henceforth, LPC) to obtain loan data. The LPC

database is currently the most comprehensive data source on loans made to publicly

traded companies and as such has been used in several papers that test the impact of

relationships for large publicly traded borrowers.5 Borrowers in the LPC database are

manually matched with the merged CRSP and Compustat database. The base set of the

sample consists of borrowing firms that can be matched to the CRSP and Compustat

databases. We obtain the bankruptcy data information from New Generation Research’s

bankruptcy data.

The empirical methodology uses the above borrowing firms and loan sample in two

ways. First, we construct a firm year sample for all borrowers in the matched LPC-

CRSP-Compustat sample to examine the impact of maintaining a strong prior relation-

ship on the likelihood of distress or bankruptcy of the borrowing firm. Second, we use theloan sample to investigate differences in the loan contract terms (particularly focusing

on the differences in the behavior of relationship lenders) when the firm is in a normal

financial condition, in distress or in bankruptcy. Using both the loan sample and firm

5See Carey, Post, and Sharpe (1998), Drucker and Puri (2005) and Sufi (2007) among others.

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year sample, we construct proxies of the relative importance of relationship lending and

examine the variation in these proxies as a borrowers financial condition deteriorates.

Since a focal point of this paper is documenting the evolution of relationship bank

behavior as the firm undergoes distress and bankruptcy, we provide brief definitions

of relationships, distress and bankruptcy here.6 For the firm year sample, relationship

is computed based on beginning of the year, while for the loan sample, it is based on

the date of the loan. We define a firm as having a relationship with a lender if the

borrowing firm retained the bank as a lead lender in any of its prior loans in the past

five years. At a given point of time, a firm and bank are said to have maintained a strong

lending relationship if the firm had retained the given bank in more than 50% of its past

loans. To classify a firm as being distressed in year t, we use the KMV-Merton model tocompute the expected default frequency of the firm in each month. If a firm falls in the

top 10% of the unconditional EDF distribution for all firm years for at least 6 months

in an year, we classify the firm as distressed in that year. A loan made in a distressed

year is defined as a distressed loan. A loan with a purpose of ”Debtor-in-possession” is

classified as a loan in bankruptcy.

Using the firm year sample, we find that strong prior relationship does not impact the

likelihood of distress or the likelihood of bankruptcy. This is also true if the likelihood

of bankruptcy is examined in the distress sub-sample. This suggests that the positive

wealth effects that have been associated with bank loans (James (1987),Lummer and

McConnell (1989)) principally benefit equity holders of the firm. Default risk does not

appear to be reduced at least in a statistically significant manner.

Next, we examine the relative importance of relationship lending (loan availability

from relationship banks) after the onset of distress. Using four different proxies, that are

based on the presence of a relationship loan, and the relative size of the relationship loans

to the total loans in a given year, we find that the relative importance of relationshiplending is much lower after the onset of distress relative to normal times. The reduction

in magnitude is around 6-10% which is an economically large change as well. Further,

after the onset of distress, loans made by relationship banks and outside banks are

indistinguishable from each other in terms of interest rate or collateral requirement.

6More detailed definitions are provided in section 3.

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In the last stage of analysis, we examine the behavior of relationship lenders after

the borrower has filed for bankruptcy, again in terms of loan contract terms and loan

availability from relationship banks. Using a sub sample of firms that file for bankruptcyand get DIP loans, we find that majority of DIP loans (67%) are from the relationship

lenders, which is consistent with earlier results in Dahiya, John, Puri, and Ramirez

(2003). Using a sample of DIP loans, we find that DIP loans from the relationship

lenders require less collateral. However, the fees from relationship and outside lenders

are comparable as is the case for distress. Thus, there is no benefit in terms of lower fees

for relationship DIP loans. Further, the relative importance of relationship lending after

filing for bankruptcy is comparable to that in normal times. Thus, for the bankruptcy

sub-sample, there is a reversal of results relative to distress. In particular, relationship

banks again appear to help their borrowers, at least in terms of collateral and loan

availability. Finally, we test the impact of DIP loans on the likelihood of emerging from

bankruptcy. Consistent with the results from Dahiya, John, Puri, and Ramirez (2003),

we find that DIP loans reduce the likelihood that a firm is liquidated.

One problem for the tests conducted so far is that the observed lending relationship

could be endogenous. The observed pattern of lending relationship could be determined

by some observable or unobservable characteristics of the borrowing firm, which in turn

may affect the results that we obtain. Two methods have been commonly used to allevi-ate the endogeneity viz. Propensity Score Matching (PSM) and instrumental variables

(IV). We find that our original results continue to hold, after controlling for potential

endogeneity by both of these methods.

This paper is related to several strands of literature. First, in contrast to existing

literature on relationship lending, we examine the impact of relationships when it is

most needed - when the borrower is in distress or bankruptcy. In contrast to the papers

by Elsas and Krahnen (1998) and Peek and Rosengren (2005), who find that German

and Japanese banks tend to help their borrowers in distress, we find that US banks do

not appear to help their borrowers in distress. Institutional differences between the US

versus Germany or Japan may account for this difference. In particular, the German

status as a Hausbank probably imposes a much larger obligation on the bank than

being a lead lender in the US context. Likewise, explicit government pressure on banks

identified in Peek and Rosengren (2005) may play an important role in the decision to

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help distressed borrowers in the Japanese context. Overall, the findings in this study

suggest that soft budget constraints do not play a large role in the lending decision, at

least for loans to public companies in the US. Our results are also consistent with James(1995) who finds that banks give relatively few concessions in distressed restructurings

especially in the absence on large concessions by public debt holders.

Second, by comparing the changes in the behavior of relationship lenders during

distress and bankruptcy, we add to this literature in that DIP financing papers do not

study the financing choice prior to bankruptcy. By showing that relationship banks give

no benefits (price or non-price) to borrowers in distress but some non-price benefits to

borrowers in bankruptcy, we are able to demonstrate that existing lenders would not

lend if the loans were not super-priority as demonstrated by the results in distress priorto bankruptcy.

The paper proceeds as follows. In Section 2, we develop the hypotheses that we

will test in more detail. In Section 3, we describe the construction of the data set and

various variables used for empirical tests. In Section 4, we present summary statistics

and conduct univariate tests of the hypotheses in this paper. In Section 5, we conduct

multivariate tests and analyze possible biases. In Section 6, we conclude with directions

for future research.

2. Hypothesis

This section develops the hypotheses to be tested in this paper. Since a focal point of

this paper lies in documenting the changing nature of relationship bank behavior as the

firm enters distress or bankruptcy, the hypotheses will also be developed in this order.

2.1. Relationships and future likelihood of distress

Theoretically, Diamond (1984) and Diamond (1991) posit that banks have a better

ability to screen borrowers as well as monitor borrowers. Thus, firms that have well

established relationships have been screened by their banks and are subject to continual

monitoring. Empirically, benefits from granting or renewal of a loan by a relationship

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lender has resulted in positive certification effect (James (1987), Lummer and McConnell

(1989), Billett, Flannery, and Garfinkel (1995), Puri (1996)). Further, benefits of rela-

tionship lending in the form of lower interest rates and/or less collateral have been doc-umented in Petersen and Rajan (1994), Berger and Udell (1995) and Bharath, Dahiya,

Saunders, and Srinivasan (2009). Further, the relationship lender may continually mon-

itor the borrower, which would result in lower likelihood of risk shifting.

All of these suggest that maintaining strong lending relationships in normal times

is likely to lower the likelihood of value reducing actions and consequently reduce the

likelihood of distress (that would have resulted from such value reducing actions). This

is formalized in the following hypothesis:

Hypothesis 1 (H1: Future likelihood of distress) A borrowing firm that main-

tains a strong prior relationship with its lender is less likely to enter distress relative to

an equivalent borrower that does not maintain a strong prior relationship with its lender.

2.2. Loan contract terms during distress

The second hypothesis involves contract terms for loans made after a firm enters distress.

Even though the relationship bank does have an incentive to offer preferential terms in

normal times, after a firm has entered distress, it may not have the same incentives.

In particular, the time of distress is one where external financing is not easily avail-

able. This implies that there is a greater ability for the bank to hold up its relationship

borrower (Sharpe (1990), Rajan (1992), Von-Thadden (2004)). Consequently, the in-

centive to offer preferential terms is reduced relative to normal times. 7 Secondly, the

relationship bank may want to develop a reputation for being tough in dealing with

distressed borrowers (Chemmanur and Fulghieri (1994)). Lastly, a reduced likelihood

7The maintained assumption in this paper will be that in normal circumstances, borrowing firmsderive some price or non-price benefits from their lending relationships. Several event studies such asJames (1987) and follow on studies establish this. Direct evidence on the relative unimportance of holdup costs for publicly listed Norwegian firms is also provided in Ongena and Smith (2001). Further,Bharath, Dahiya, Saunders, and Srinivasan (2009) show that lending firms derive overall benefits fromlending relationships. Lastly, publicly listed firms that are likely to be subjected to hold up costs canmitigate this by having multiple bank relationships as documented by Houston and James (1996). Of course, under some circumstances, even publicly listed firms can be subjected to hold up costs such asdocumented in Santos and Winton (2008) and Hale and Santos (2009).

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of repeat business due to the onset of distress may reduce the incentive of the bank to

given preferential terms to its relationship borrower. We will henceforth refer to all of

these potential reasons that banks may not help a borrower in distress as ’hard budget incentives’ .

However, the relationship bank also has incentives to help its distressed borrowers.

This may arise because of soft budget constraints that make it ex-post optimal to help the

distressed borrower. Such constraints may arise because the bank’s desire to protect its

reputation with other borrowers (Boot, Greenbaum, and Thakor (1993)), to protect its

outstanding loans (Dewatripont and Maskin (1995), the borrower’s threat to strategically

default (Anderson and Sundaresan (1996)), governmental pressure to help distressed

borrowers (Peek and Rosengren (2005)) and loan originating officers being reluctant torecognize losses (Hertzberg, Liberti, and Paravisini (2008)).

Thus, the relationship lender may continue to offer the same or even more preferential

terms to its borrowers in distress as in normal times, due to soft budget constraints.

At worst, the relationship lender would charge the same terms as that offered by an

outside lender, due to hard budget incentives. This argument is formalized in the second

hypothesis.

Hypothesis 2 (H2: Loan contract terms during distress) If soft budget con-straints dominate, the bank will continue to give the same level or greater level of prefer-

ential treatment to its relationship borrowers in distress relative in normal times. If hard

budget incentives dominate, the degree of preferential treatment given to a relationship

borrower in distress will reduce relative to normal times. At the extreme, the bank may

treat the relationship borrower at par with the outside borrower.

2.3. Loan Availability: Relative importance of relationship lend-

ing during distress

The above hypothesis focuses on the contract terms offered by a relationship lender.

Another dimension of relationship lending is availability of loans from a relationship

lender. As discussed above, the model by Boot, Greenbaum, and Thakor (1993) implies

that a relationship bank may have an incentive to continue making loans to a borrower

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in distress. Outside lenders have no such incentive. This implies the relative importance

of relationship lending should be greater after a firm enters distress.

On the other hand, the relationship lender may also be better at screening its re-

lationship borrowers relative to outside lenders and make the efficient liquidation or

continuation decisions (Diamond (1991), Chemmanur and Fulghieri (1994)). Screening

would result in the relationship bank rejecting a fraction of its inside borrowers, some

of who may obtain loans from outside lenders due to the lower precision of outside

lenders in evaluating the credit risk of the borrowing firm. This implies a lower relative

importance of relationship lending after the onset of distress.

Likewise, most of the arguments made in section 2.2 are equally applicable for the

availability of relationship loans relative to outside loans. The above discussion (one

of which implies lower likelihood of relationship lending after distress and one of which

implies a higher likelihood of relationship lending after distress) leads to the following

hypothesis.

Hypothesis 3 (H3: Loan availability: Relative importance of relationship

lending during distress) If soft budget constraints dominate, the relative importance

of relationship lending should be comparable to or even greater in distress relative to

normal times. If screening and hard budget incentives dominate, the relative importance of relationship lending should be lower in distress relative to normal times.

2.4. Relationships and bankruptcy

While the above arguments are formulated for distress, the same arguments would be

equally applicable for a firm that has filed for bankruptcy. The first hypothesis (whichcorresponds to H1 in the case of distress) is as follows:

Hypothesis 4 (H4: Future likelihood of bankruptcy) A borrowing firm that

maintains a strong prior relationship with its lender is less likely to enter bankruptcy

relative to an equivalent borrowing firm that does not maintain a strong prior relationship

with its lender.

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However, after filing for bankruptcy, it may be the case that the incentives of re-

lationship lenders change relative to their incentives during distress. After a firm has

filed for bankruptcy, the possibility of making super-priority loans in the form of DIPfinancing may give relationship lenders the incentive to identify the safer borrowers using

their information advantage and extend DIP loans to them. Consistent with this view,

Dahiya, John, Puri, and Ramirez (2003) find that borrowers obtaining DIP loans tend

to have a higher likelihood of recovering from bankruptcy. This would imply that firms

in bankruptcy should get a larger discount in terms of fees and/or collateral requirement

relative to distressed times. This also implies that the relative importance of relationship

lending should increase in bankruptcy relative to distress.

On the other hand, after a firm files for bankruptcy, the chances of recovery andthe likelihood of repeat business, which is one of the important determinants of the

relationship bank offering a discount may be even lower. This implies that any discount

(if given) in bankruptcy should be even smaller relative to that in distress. This also

implies a lower relative importance of relationship lending post bankruptcy.

The above arguments lead to the next two hypotheses (which correspond to H2 and

H3 in the case of distress). These hypotheses incorporate the idea that after filing for

bankruptcy, in addition to soft budget constraints, the possibility of making DIP loans

provides an additional incentive for relationship lenders to offer preferential terms to

their borrowers and increase the relative importance of relationship lending.

Hypothesis 5 (H5: Loan contract terms during bankruptcy) If soft budget

constraints and the possibility of making DIP loans dominate, the bank will continue

to give the same or greater level of preferential treatment in loan contract terms to its

relationship borrowers relative to normal times. If hard budget incentive dominate, the

degree of preferential treatment in loan contract terms to a relationship borrower will

reduce relative to normal times. At the extreme, the bank may treat the relationshipborrower at par with an outside borrower.

Hypothesis 6 (H6: Loan availability: Relative importance of relationship

lending during bankruptcy) If soft budget constraints and/or the possibility of making

DIP loans dominate, the relative importance of relationship lending should be greater than

or comparable to that in normal times. If screening incentives and hard budget incentives

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dominate, the relative importance of relationship lending should be lower in bankruptcy

relative to normal times.

To summarize, our hypotheses imply that if soft budget constraints dominate in

distress, there should be some net benefit of relationship lending to the borrower in

terms of the loan rate and/or collateral and/or availability. If hard budget incentives

dominate, then there should be no benefit to the borrower after the onset of distress.

The only difference between the distress and bankruptcy is that there would be an

additional incentive to lend due in bankruptcy due to the superpriority of the DIP loans

which should at least increase loan availability. It may also induce the lender to share

some of its informational advantage in the form of lower fees and/or collateral.

3. Data Sample Construction

3.1. Data source

The data for firm year sample comes from CRSP/COMPUSTAT Merged Database

and the Dealscan database maintained by the Loan Pricing Corporation (henceforth,

LPC). LPC has been collecting information on loans to large U.S. corporations primarilythrough self-reporting by lenders, SEC filings, and its staff reporters. While the LPC

database provides comprehensive information on loan contract terms (LIBOR spread,

maturity, collateral, etc.), it does not provide much information on borrowers. Borrowers

in the LPC database are manually matched with the merged CRSP and Compustat

database, after excluding financial service companies and real estate companies. The

version of data we have starts in 1986 and ends in 2003.

For several empirical tests, we need accounting information from the Compustat

database. To ensure that only accounting information that is publicly available at the

time of a loan is used, the following procedure is adopted: For those loans made in

calendar year t, if the loan activation date is 6 months or later than the fiscal year ending

month in calendar year t, we use the data of that fiscal year. If the loan activation date

is less than 6 months after the fiscal year ending month, the data from the fiscal year

ending in calendar year t-1 is used. The accounting and stock price information are

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used in the construction of the distress measures, as well as to control for firm level

heterogeneity that may impact variables that we study, such as the loan rate, collateral

requirement as well as the likelihood of distress and bankruptcy.

A second panel data set of firm years is constructed based on the firms in the above

loan sample. For each firm in the loan sample, the first available loan is identified

and that year is used as the starting year for the firm to be included in the firm year

sample. The rationale for such a restriction is as follows: The key focus of this study

is on determining the impact of lending relationships. To determine the identity of

the relationship bank or banks, we need at least one past loan taken by the borrower.

Consequently, we can define the relationship measures (defined later in Section 3.3) only

on or after the firm year when we have the firm’s loan history.

In addition, the last year when any firm can be in the firm year sample would be

either 2003 or 2004. The reason for having two ending dates is as follows: For tests

that link relationships to loan contract terms or the relative importance of relationship

lending during distress or bankruptcy, we need to have contemporaneous measures of

loan contract terms and the relative importance of relationship lending during distress

or bankruptcy. This could be done on the firm year sample till 2003. On the other

hand, when evaluating the impact of relationships on the future likelihood of distress or

bankruptcy, we can use an additional firm year of data that ends in 2004. Of course, if a

firm delists earlier than this date due to any reason including bankruptcy, the last year

that the given firm will be present in the firm year sample will be the year of bankruptcy

or delisting. Based on an ending date of 2004, the total sample size is 28744 firm years

and a loan sample size of 15251. Accounting data for the firm year is constructed along

the same lines as that for the loan sample.

Lastly, a list of public firms that filed for bankruptcy is obtained from the New

Generation Research’s bankruptcy database. This database provides information onthe dates of filing, outcome of the bankruptcy as well as the final date of emergence

or liquidation. Loans made to bankrupt firms are obtained from the LPC database

identified as those with primary purpose being DIP financing.

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3.2. Construction of the distress measure

Our principal measure of distress is based on the option pricing model developedby Merton (1974). This method is being used by the KMV corporation (a subsidiary

of Moody’s) and forms the basis of the market price based measures of bankruptcy

prediction.8 We elaborate on the classification of the firm year sample into distress

and normal years. For each year and each month, we compute the expected default

frequency (EDF, henceforth) as implied by the KMV-Merton model for all firms in the

merged CRSP-Compustat database. Subsequently, for each calender year, we sum up

the months where the EDF of each borrowing firm in LPC database lies in the top

10% of the unconditional EDF distribution for all firms for all years and all months.

9

If this sum is equal to or greater than 6, we classify the given firm year as one where

the borrowing firm is distressed.10 At the end of this process, each firm year when the

firm has sufficient trading and accounting data available is either classified as distressed

(Distress =1) or not distressed (Distress =0).

Using the filing date for bankruptcy, a ”Bankruptcy ” dummy variable is constructed

which takes a value of 1 if the firm filed for bankruptcy in the given year and 0 otherwise.

Note that with the above definitions, a given firm year could be classified both as a

bankruptcy as well as a distress year. Once a firm has filed for bankruptcy in a givenyear, the firm is no longer included in the sample of firm years, i.e., a bankruptcy event

is included only once in the firm year sample, even though the bankruptcy process may

last longer than one firm year. All loans made after the filing of bankruptcy and before

the resolution of bankruptcy are considered to be loans made during bankruptcy firm

year.11

8Shumway (2001), Hillegeist, Keating, Cram, and Lundstedt (2004) and Bharath and Shumway(2008) provide evidence that market based measures of financial distress provide better prediction of

bankruptcy than the earlier accounting based measures such as the Altman score and the Zmijewskiscore. The exact methodology for computation is detailed in Appendix 1.

9Note that the distribution of EDF’s for the entire universe of CRSP-Compustat merged firms isused in computation of this percentile.

10The results are robust to alternate values of the cut off percentile using distress defined by the top20% and top 30%.

11While one could easily include the actual number of bankruptcy firm years as independent obser-vations, firm specific accounting or stock information is rarely, if ever, available after bankruptcy filing.Hence, no data analysis is possible with these additional observations.

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Given the above classification of firm years, the classification of loans into normal

times, distress and bankruptcy is relatively straightforward. A loan facility with starting

date in a normal year is classified as a normal loan, and one made during a distress yearis classified as a distressed loan. However, unlike for the firm year sample, a loan that is

made in a year where the firm is in distress as well as files for bankruptcy is classified as

a distressed loan unless it is explicitly classified as a DIP loan. Thus, in the loan sample,

there is no overlap between the distress and bankruptcy sub-samples.

3.3. Construction of relationship measures

Several loans in the LPC database are syndicated loans where many banks are retained

in several different roles. Hence, before defining the relationship measures, it is important

to identify the banks that are playing an lead role. we follow the methods used in Sufi

(2007) and Bharath, Dahiya, Saunders, and Srinivasan (2009) to classify banks into the

lead role.

In particular, a bank is defined as playing a lead role in a given loan facility if any

one of the following conditions were met. (1) The bank is given a lead arranger credit

for the given loan facility or (2) the bank was retained in any of the following roles: (a)

Agent, (b) Arranger, (3) Administrative Agent, (4) Lead bank, and (5) Sole lender. The

rationale for this selection is that banks retained in these roles typically retained a large

fraction of syndicated loans (over 25%) on average, and for the last role, the given loan

is not syndicated at all. Consequently, it is reasonable to assume that banks retained in

these roles are truly one of the lead lenders in the given loan facility.

All measures of relationship lending are constructed only using lenders retained in a

lead role as defined above. Next, we elaborate on the construction of relationship mea-

sures for the loan sample. For each loan, we have a look back period of 5 years startingon the date of the loan. A given loan is classified as a relationship loan (RELLOAN =1)

if any of the lead lenders retained in the given loan facility was retained as the lead

lender in any loan taken by the same borrower over the last 5 years.

As an additional measure of lending relationships, we define a firm and a lender as

maintaining a strong relationship if more than 50% loans (using the number of loans) in

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the last 5 years came from the same bank.12 A dummy variable (STRONGRELLOAN )

which takes a value of 1 if a strong relationship lender is retained for the current loan

and 0 otherwise.

Thus, the strong relationship measure can also be thought of as capturing the incre-

mental effect of the strength of a relationship (among the set of relationship borrowers)

on loan contract terms, whereas the relationship measure reflects the impact of outside

versus inside lenders. For borrowers where there was no loan in the past 5 years, neither

of these variables are defined.

Along similar lines, we construct relationship measures for the firm year sample.

For each firm year, we identify relationship lenders by searching all the previous loans

(over a 5-year window excluding the current year) of that borrower as recorded in the

LPC database. If at least one loan in the given year comes from a bank which has

extended loans to the firm in the past five years, the given year is classified as one

where a relationship lender made a loan. If none of the loans in the current year were

made by any relationship lender, the given year is classified as one where relationship

banks did not lend to the given firm. Based on the above classification, we construct

a dummy variable (RELLOAN ) that takes a value of 1 for firm years with relationship

loans and 0 for firm years with no relationship loan. One can also define years when a

strong relationship lender was used (STRONGRELLOAN =1) and those where a strong

relationship lender was not used (STRONGRELLOAN =0), conditional on the borrower

taking at least one loan in the given year. For years where the borrowing firm did not

take any loan, or years when the borrowing firm did not take any loans in the past 5

years, neither RELLOAN or STRONGRELLOAN is defined.

We also construct a dummy variable STRONGPRIORELATION along the lines of

Burch, Nanda, and Warther (2005). At the beginning of a firm year, if a given borrowing

firm has retained any bank in more than 50% of its loans over the last 5 years, it isclassified as having a strong prior relationship (STRONGPRIORELATION =1). If not,

it is classified as not having a strong prior relationship. Therefore, borrowing firms that

are very loyal to their lead banks will be classified as having a strong prior relationship

12This is similar to the loyalty measure proposed by Burch, Nanda, and Warther (2005).

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and borrowing firms that split their business across multiple banks will be classified as

not having a strong prior relationship.13

This measure will be critical in examining the impact of relationships on the fu-

ture likelihood of distress or bankruptcy. The reason is that the firm year sample, by

construction, comprise of firm years that have at least one loan in the past 5 years.

Therefore, to evaluate differences in likelihood of distress or bankruptcy, we cannot use

the presence or absence of a lending relationship because the entire sample by construc-

tion consists of firms that have a lending relationship with some bank. For these tests,

the strong prior relationship measure as defined above can be used.14

Construction of relationship measures is complicated by the fact that the sample

period was one where several banks merged with one another. we collect data on such

mergers using the SDC merger database and news searches on the bank mergers in our

sample. In case of a merger, we assume that all the lending relationships of both the

merging banks carry over to the new merged bank. To the extent that this procedure is

classifying related banks as unrelated (for example, in case our search procedure misses

the merger among two banks in our sample), we may be classifying relationship loans

as non-relationship loans. This biases against finding any significant results for our

measures of lending relationships, and consequently, any results obtained here are likely

to underestimate the true impact of relationship lending.

3.4. Measures of relationship benefits to borrowing firms

The hypotheses developed in Section 2 have implications for three sets of variables:

(1) Interest rate charged on the loan, (2) Whether or not it is collateralized, and (3) The

relative importance of relationship lending. we now elaborate on the variables used to

measure each one of these.

Following Drucker and Puri (2005), we use the LPC reported ”All-in-Spread-Drawn”

(hereafter Fee ) as the measure of interest rate for a loan. Fee is the coupon spread over

13As defined, the set of firm years where STRONGRELLOAN takes a value of 1 will be a subset of the observations where STRONGPRIORRELATION is 1.

14More detailed explanations and examples on the construction of relationship measures for the firmyear sample and loan sample are shown in Appendix 2.

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LIBOR assuming the loan is fully drawn plus the annual fee. For collateral, we use a

dummy variable in LPC that indicates whether or not a given loan is secured. As is

evident from the definitions, both of these measures can only be defined for the loansample.

Lastly, to measure loan availability from relationship lenders, we use the relative

importance of relationship lending. We construct four different proxies the relative

importance of relationship lending relative to outside lending, all of which are defined

for the firm year sample. The first measure is simply the RELLOAN for the given firm

year. Recall from the previous subsection that this variable takes a value of 1 if the

given year was one where a relationship lender extended at least one loan to the given

borrower and 0 otherwise. The next three measures look at the fraction of loans andthe average loan size coming from relationship banks relative to outside banks. They

are defined below.

• Relloanratio1 : This is the ratio of the sum of loan facility amounts of all relation-

ship loans taken by a given borrower in a given year to the sum of facility amounts

of all loans taken by the same borrower in the given year.

• Relloanratio2 : This is the ratio of average facility amount of all relationship loans

taken by a given borrower in a given year to average facility amount of all loans

taken by the same borrower in the given year.

• Relloanratio3 : This is the ratio of number of relationship loans taken by a given

borrower in a given year to the total number of loans taken by the same borrower

in the given year.

Similar to the relationship measures for the firm year sample, all these measures are

set to be missing if the given borrower has no loans in the given year. These three ratios

capture the relative importance of relationship lenders and outside lenders in terms of

overall bank lending to the borrowing firm in the given year.

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4. Univariate Analysis

In this section, we present summary statistics on the data sample and perform uni-variate tests of the hypotheses developed in Section 2.

4.1. Summary statistics

Table 1, Panel A reports the summary statistics for total number of observations of

the firm year sample and the loan sample, where both are sub-divided by the finan-

cial condition of the firm (normal times, distress and bankruptcy). The statistics are

presented for firm years till 2004.15

Panel B of Table 1 provides firm characteristics for firms classified as in normal

times, distressed or bankrupt. The differences in firm characteristics across these three

different conditions provide an independent justification for the distress measure, as it

is constructed based solely on the price dynamics of the firm’s stock price and the only

accounting inputs used are the total assets and total debt of the firm. For example,

the coverage ratio (defined as natural log of ratio (1+ EBITDAInterest Expenses

) is 2.68 for the

distressed sample whereas it is 29.96 during normal times. Likewise the profitability of

firms in normal times is 16%, while it is 9% in distressed times. Further, firms classified

as distressed using our measure also have a lower current ratio. This suggests that our

measure of distress is reasonable when evaluated using the firm’s accounting variables

that measure firm performance or liquidity.

Panel C of this table reports differences in loan characteristics (fee, collateral, ma-

turity and size) across firms in these three sub-samples. As expected, there is a large

increase in the fee as a firm goes from normal times to distress and/or bankruptcy. The

mean fee during distress is 338 basis point spread, relative to a mean value of 174 basispoint spread in normal times. Likewise, the percentage of collateralized loans is 43% in

normal times while it is 72% in distressed times. The size of the loan and maturity also

15As mentioned earlier, the total number of firm years will be less than the sum of firm years innormal times, distress and bankruptcy due to some overlap in firm years that are classified as bothdistressed and bankrupt. In particular, there are 44 firm years that are classified as both distressed andbankrupt.

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decrease. Thus, loan contract terms also reflect the onset of distress. Further, in most

cases, the values for fee and collateral in distress lie in between the corresponding values

for normal times and bankruptcy. This provides a further confirmation that the distressmeasure does indeed capture distress and this is reflected in the loan terms.

Table 2 presents summary statistics of the relationship measures, again stratified

into normal, distressed and bankrupt conditions. From panel A, we can see that the

median number of relationship banks is 1, for all three conditions, which indicates that

on average, firms maintain a single lending relationship. Panel B reports relationships

by firm year sample. Out of the 8382 firm years where firms take a loan, 5492 come

from relationship banks. Out of these, firms get loans from strong relationship banks

for 4275 firm years. In percentage terms (relative to the 8382 firm years where firmstake a loan), the likelihood of a relationship loan in normal times/distress/bankruptcy

is 66%/54%/72%. Thus, in distressed times, the likelihood of a relationship loan or

strong relationship loan is much lower, both relative to normal times and relative to

bankruptcy. In panel C, using loan facilities instead of firm years, we find a similar

pattern with the likelihood of a relationship loan in normal times/distress/bankruptcy

being 71%/62%/67% respectively. Both of these findings provide preliminary evidence

that relationship lending is less likely in distress whereas it is more likely in bankruptcy.

However, in terms of relationship loans obtained from strong relationship lenders, thepattern differs somewhat. In particular, using the loan sample, there is a monotonic

decrease in the likelihood of a loan from a strong relationship bank as a firm goes from

a normal condition to distress and then to bankruptcy.

4.2. Univariate tests

In this subsection, we present univariate comparisons of the likelihood of distress,fees charged, percentage of collateralized loans, as well as the relative importance of

relationship lending. These will serve as preliminary tests of the hypotheses in section

2.

To test H1 (strong prior relationship helps to reduce the future likelihood of distress),

we stratify the firm year sample into those where the borrowers maintained a strong prior

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relationship with their bank and those where this was not the case. We compute distress

probabilities for each set of firms one year subsequent to the computation of the strong

prior relationship measure. Thus, if the strong prior relationship measure was computedas of the beginning of year t, the distress measure was computed as of end of year t.

Table 3 Panel A presents the results of this univariate comparison. Firms that maintain

a strong prior relationship in a given year have a distress probability of 8.2% in the next

year while firms that did not maintain a strong prior relationship in a given year had

a distress likelihood of 9.5% in the next year.16 The difference is also significant at the

1% level providing support for H1.

Similarly, the results in Table 3, Panel B suggest that firms that maintain a strong

prior relationship in a given year have a significantly lower probability of filing forbankruptcy in the future. Thus, H4 that suggests that a strong prior relationship

should reduce the likelihood of bankruptcy is supported. However, if the likelihood

of bankruptcy is measured for the subsample of firms that are in distress (Panel C), the

differences in the future likelihood of filing for bankruptcy is not significant for firms with

and firms without a strong prior relationship. Thus, the above univariate tests suggest

that borrowing firms that maintain a strong prior relationship have a lower likelihood of

distress and bankruptcy but not a lower likelihood of bankruptcy when firms are already

in distress.

In table 4, we conduct univariate tests of H2 (loan contract terms in distress versus

normal times) and H5 (loan contract terms during bankruptcy versus normal times). To

test these hypotheses, we examine the differences in fees and percentage of collateralized

loans for relationship and non-relationship loans made in normal times, distress and

bankruptcy. In panel A, we use the relationship loan dummy to stratify the sample,

and in Panel B, we use the strong relationship loan dummy to stratify the sample. In

both cases, relationship loans have much lower fees and collateral requirement, both in

normal times and in bankruptcy. The magnitudes of the differences are quite significant.

For example, a relationship loan in bankruptcy has a 92 basis point lower fee relative to

16Recall that the criterion for being included in the firm year sample is only after a given borrowingfirm has taken their first loan. Therefore, all the firm year sample observations are those where theborrowing firm has at least one relationship bank. Hence, it is not possible to conduct a test of thedifference in distress likelihood of lenders with lending relationships and those without one, as allborrowers in the sample have to have at least one lending relationship to enter the sample.

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a non-relationship loan in bankruptcy. Likewise, the probability that a non-relationship

loan in bankruptcy is collateralized is 94% whereas the probability that a relationship

loan in bankruptcy is collateralized is 68%.

In contrast, for the distress sub-sample, the only statistically significant difference in

loan contract terms between relationship and non-relationship loans is in the collateral

requirement where the difference is around 12%. However, the difference in the collateral

requirement between relationship and non-relationship loans in distress is still lower

than the difference in collateral requirement between relationship and non-relationship

loans in normal times (around 14% difference based on Table 4, Panel A). Thus, the

difference in collateral requirement between the relationship and non-relationship loans

has narrowed during distress. The difference in fees is insignificant. The pattern issimilar if one were to examine differences between loans made by lenders with a strong

relationship and those made by lenders without a strong relationship (Table 4, Panel

B).

Next, in Table 5, we examine the relative importance of relationship lending after

the borrowing firm enters distress or bankruptcy relative to that in normal times (H3

for distress and H6 for bankruptcy). In normal times, we find that the likelihood of a

relationship loan is around 74% whereas in distress, it is around 62%, with the difference

being highly significant (Table 5, Panel A). Other measures of the relative importance

of relationship lending that measure the ratio of relationship loans to total loans in a

given year also show a similar pattern of reduction in distress relative to normal times.

The measures of the relative importance of relationship lending in bankruptcy relative to

normal times shows a similar pattern (Table 5, Panel B). While measures of the relative

importance of relationship lending in bankruptcy appear to be much lower than those

measures in distress, if one were to compare the relative importance of relationship

lending in bankruptcy for the sub-sample of distressed firms, there is no significant

difference (Table 5, Panel C).

The results in Tables 4 provide support for the reduced incentives for banks to help

their borrower in distress in terms of less preferential loan terms. If the true reason for

the reduction in help during distress (as reflected in the univariate tests) were a reduction

in repeat business, then the relationship lenders should be even more reluctant to offer

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any discounts during bankruptcy. In fact, the opposite is true. This suggests that

the possibility of making super-priority DIP loans significantly alters the incentives of

relationship lenders.

Since several of these results could be impacted by differences in firm characteristics

as well as loan characteristics across the sub-sample of relationship and outside borrow-

ers, we investigate whether these results hold after controlling for these differences in

the next section.

5. Multivariate Tests

5.1. Relationships and future likelihood of distress

Hypothesis 1 posits that maintaining a strong prior relationship should lower the future

likelihood of distress (see section 2.1). To test this, we use the firm year sample and run a

regression with distress as the dependent variable, and other firm specific characteristics

and the strong prior relationship dummy as the independent variables. The regression

has the following form:

Distress = β 0 + β 1 × (Strongpriorrelation ) +

β k × (Controlk)

Other control variables include firm characteristics, the Altman Z score and dummy

variables for industry and year. The results of this regression, presented in table 6

panel A (model 1), imply that strong prior relationship does not negatively impact the

likelihood of distress. In model (1), after controlling for all other factors, the coefficient

on strong prior relationship shows no significant effect on the likelihood of distress. Thus,

Hypothesis 1 is not supported by the data.

One concern here is that the decision to form a strong prior relationship may be

endogenous. In particular, banks may select to form strong relationships only with

firms that have lower credit risk. On the other hand, the reverse may also be true.

Firms that form strong relationships could have a higher degree of credit risk. In this

case, the strong prior relationship dummy is simply proxying for a degree of credit risk.

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We use an instrumental variables approach (IV) to control for the potential endogeneity

of the strong prior relationship dummy. In the first stage regression, the likelihood of

forming strong prior relationships is modeled. We use geographic distance between theborrowing firm’s headquarters city and its relationship bank’s headquarters city as an

instrument to predict the likelihood of the strong relationship formation.17 Distance has

been shown to be correlated with the likelihood of relationship formation (Petersen and

Rajan (2002)) but should not affect the probability of distress directly.

In the second stage, the fitted likelihood of strong prior relationship is used in place

of the strong prior relationship dummy for predicting the likelihood of distress. The

insignificant coefficient for the fitted value of the strong prior relationship dummy in

second stage regression shows that hypothesis 1 does not hold, even after controlling forpotential endogeneity. Incidentally, in the first stage regression, the coefficients on some

firm characteristics which are proxies for credit risk (firm size, leverage and tangibility)

suggest that firms with observably higher credit risk are actually more likely to maintain

strong lending relationships. This suggests that accounting for the endogeneity of the

strong prior relationship dummy is important.

Another method often used in literature to deal with the endogeneity of a dummy

variable is Propensity Score Matching (PSM) proposed by Heckman, Ichimura, Todd

(1997, 1998). This methodology has been used by Drucker and Puri (2005) among

others to control for endogeneity of lending relationships. In our case, the relevant

possibly endogenous variable is the strong prior relationship dummy. To find the true

effect of maintaining a strong prior relationship on the likelihood of distress, we construct

a matched sample of borrowers, where each borrower with a strong prior relationship

was matched with another borrower that was equally likely to have had a strong prior

relationship, but in fact did not. By doing this, we account for any possible heterogeneity

in observable firm characteristics that simultaneously affect relationship formation as

well as the likelihood of distress. The difference in the likelihood of distress between the

two sets of borrowers can be attributed solely to the presence of strong prior relationship,

if the underlying assumptions of the PSM method are met. We follow the procedure

17In the case where the relationship lender was a bank headquartered outside of the US, we tried toascertain the headquarters of the bank. In most cases, the headquarters was either New York or SanFrancisco. In cases where we was not able to unambiguously assign the headquarters for a foreign bank,we assumed that the headquarters was New York.

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for the implementation of the PSM method as in Drucker and Puri (2005). The results

in panel B of Table 6 show that the impact of strong prior relationships on distress is

significant using this method. However, in other unreported results, we find that thisresult is not robust to other methods of matching.18

Overall, the results of this sub-section do not provide sufficient support for H1. This

suggests that the earlier univariate results in Section 4.2 (differences in future distress

likelihood of firms with and without a strong prior relationship) were driven by differing

firm characteristics across these two sub-samples.

5.2. Loan contract terms during distress

The previous subsection examined the impact of relationships prior to onset of distress.

This subsection examines the impact of relationships after onset of distress. Recall

from section 2.2 that hypothesis 2 suggests that the loan contract terms offered by

a relationship bank may differ after the onset of distress, either due to soft budget

constraints (such as those in Boot, Greenbaum, and Thakor (1993) or due to hard

budget incentives (such as those in Chemmanur and Fulghieri (1994)). To test this, we

divide the total loan sample into those made in normal times and those made during

distress 19.

The impact of relationship on loan rates and collateral are estimated separately for

borrowers in normal times and borrowers in distress. In table 7, the following two

equations are estimated:

FEE = β 0 + β 1 × (Relationship) +

β k × (Controlk)

Collateral = β 0 + β

1 × (Relationship) +β k × (Controlk)

We use two measures of relationships: based on any prior relationship (RELLOAN )

and based on strong relationship (STRONGRELLOAN ). Several variables are used to

18To be precise, the results in Table 6 use the nearest neighbor matching to match firms. The othermatchings methods used are the Gaussian method and the Epanechnikov method.

19The loans made during bankruptcy are excluded from both samples

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control for the effect of loan and company characteristics on the loan rate. These are

defined below:

• LOG(Facility Amount): The Natural log of loan facility amount adjusted for in-

flation in year 1985 dollars.

• LOG(Maturity): Natural log of maturity of loan facility in months.

• LOG(Total Asset): The natural log of the book value of the assets of the bor-

rower adjusted for inflation in year 1985 dollars. This controls for cross-sectional

variation in borrower size in our sample.

• Market to Book Ratio: Calculated as ratio of (book value of assets-book value of

equity + market value of equity) to book value of assets.

• COVERAGE: Calculated as natural log of ratio (1+ EBITDAInterest Expenses

).

• Leverage: Ratio of book value of total debt to book value of assets.

• Operating Margin: Ratio of EBITDA to Sales.

• Tangibility: Ratio of Property, Plant, and Equipment (PPE) to total assets.

• Current Ratio: Ratio of current assets to current liabilities.

• Loan Concentration: The fraction of the loan size to the sum of existing debt plus

the loan size.20

• Other controls: Other control variables include dummy variables for the year of

the loan facility, loan purpose, loan type, S&P senior unsecured debt rating with

not rated firms considered as a separate group, and the industry of the borrower.

The results of this analysis are presented in table 7. Both RELLOAN and STRON-

GRELLOAN are significantly negative for regressions with the non distress sample,

which indicates that in normal times, lending relationship/strong lending relationship

20This was shown to be a significant determinant in the regression of collateral by Berger and Udell(1990). Hence, it is used only in the collateral equation.

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will benefit the borrower in terms of both lower fees and less collateral. This is consistent

with the earlier study of Bharath, Dahiya, Saunders, and Srinivasan (2009). In contrast,

during distressed times, the effect of relationships on fees and collateral becomes insignif-icant. This holds true for whether the loan came from a relationship bank (Panel A) or

a strong relationship bank (Panel B). Therefore, once firms are in distress, loans made

by relationship banks and outside banks are indistinguishable from each other in terms

of interest rate and collateral requirement.

As before, we investigate the effect of endogeneity of relationships in the above re-

gression. Panel C of table 7 shows results from the IV approach for loans given in

distress. As in Section 5.1, we use the distance between the borrower and the lender as

the instrument, which is correlated with relationship formation, but does not directlyimpact either the fee or the collateral requirement. After controlling for the potential

endogeneity of relationships, the result that outside loans and relationship loans are

identical to each other after the onset of distress continues to hold.

Panel D of table 7 shows results from propensity score matching. For loans given

in distress, each relationship/strong relationship loan is matched with another loan,

that had approximately the same probability of having been a relationship or strong

relationship loan, but in fact was not. Once the matching is done, average fees and

collateral requirement are calculated for each group of loans. The results show the

differences are insignificant for both fee and collateral, which means that when firms are

in distress, relationship loans or strong relationship loans charge statistically similar fees

and collateral as non relationship or non strong relationship loans.

Overall, the results of this sub-section suggest that loan contract terms for relation-

ship and outside loans are similar after the onset of distress, unlike in normal times

where relationship borrowers are given preferential loan contract terms in terms of lower

fee and collateral. Thus, in terms of H2, the findings suggest that the possibility of hold up (Rajan (1992)) and/or the lower likelihood of future business (Bharath, Dahiya,

Saunders, and Srinivasan (2007)) and/or the desire to develop a reputation as a tough

lender (Chemmanur and Fulghieri (1994)) dominate any incentive to help on account of

soft budget constraints.

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5.3. Loan availability: Relative importance of relationship lend-

ing during distress

So far, the results of this subsection indicate that the benefits of relationship lending

in terms of lower fees or collateral, are not present after the onset of distress. However,

as discussed in section 2.2, an alternate benefit of relationship lending during distress

may be increased relative importance of relationship lending. To test this, we estimate

the following regression for the firm year sample:

Relative Importance of Relationship Lending = β 0 + β 1 × (Distress ) +

β k × (Controlk)

The dependent variables in the above equation are the four proxies for the relative

importance of relationship lending in a given firm year (defined in Section 3.4) and

the main independent variable is the distress dummy in the same firm year. Thus, this

regression estimates the contemporaneous relation between the occurrence of distress and

the relative importance of relationship lending. Control variables for firm characteristics

and dummy variables for industry and year are employed.

In addition to the standard control variables used in table 6 and table 7, one needs toaccount for possible incentives for the relationship bank to give loans to their borrowers.

One control variable used here is the sum of outstanding loans by relationship banks to

the given borrower, computed as of the beginning of the firm year. A borrower that has

larger amount of outstanding relationship loans may have a higher likelihood of getting

a loan as the banks (throwing good money after bad as in Dewatripont and Maskin

(1995)). However, for risk management purposes, the relationship banks may also want

to limit their lending in which case the outstanding loan amount may actually reduce

the likelihood of relationship lending.

Another measure we create as a control factor for the likelihood of a relationship loan

is the total number of banks that the given firm has a relationship with, again computed

as of the beginning of the firm year. The larger this number, the higher should the

likelihood of a relationship loan be. In addition, we also compute the sum of market

shares of all relationship banks based on the previous firm year. To the extent that

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relationship banks are large, they are more likely to give a borrower a loan, independent

of any relationship effects.

Table 8 presents the results of the regression model with the relative importance of

relationship lending as the dependent variable. Model 1 uses the RELLOAN dummy

as the dependent variable with Distress and other variables described above as the

independent variables. A negative coefficient on the Distress dummy implies a lower

relative importance of relationship lending in distress and a positive coefficient implies

the opposite. We find a strong negative effect of distress on the likelihood of relationship

loans, suggesting that relationship loans are less likely in distress. The results in model

1 provide a further confirmation of the results from Table 7 that relationship lenders

stop giving preferential treatment to their inside borrowers after the onset of distress.

As the dependent variable in model 1 is a dummy variable, it may not completely

capture the fact that relationship loans may be larger than outside loans after the onset of

distress. To account for potential differences in loan amount given by relationship versus

outside banks, model 2 uses the ratio of the sum of loan amounts given by all relationship

banks relative to the total loan amount given by all banks. The results corroborate those

in model 1, with a 9% reduction in the total loan amount from relationship banks relative

to the total loan amount by all banks. The same pattern continues for model 4 where the

importance of relationship versus outside banks is measured by the number of loans that

they make. Lastly, the average relative loan size of relationship loans to total loans in

distress is lower than the average loan size of relationship loans to total loans in normal

times, by around 6%. Thus, irrespective of the proxy employed, there appears to be an

economically large decrease in the relative importance of relationship lending after the

onset of distress.

Market share is highly significant in all the model specifications in Table 8. Further,

the amount of outstanding loans is also a highly significant predictor of future loans fromrelationship banks. Most of the firm characteristics do not have any consistent impact

on the relative importance of relationship lending across the different measures.

Overall, the results in this subsection indicated that the relative importance of re-

lationship lending is significantly lower during distress relative to normal times. This

provides further support for the notion that relationship lenders behave like outside

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lenders after the onset of distress. Thus, as with the empirical tests of H2, the results

suggest an overall reduced relative importance of relationship lending.

5.4. Relationships and bankruptcy

So far our results show that distressed firms have little advantage of borrowing from

their relationship lenders. Further, any potential soft budget constraints have little effect

during distress in terms of lending from relationship banks. In the next few subsections,

we conduct a similar set of tests for determining the effect of lending relationships on

the likelihood of bankruptcy as well as loan contract terms and the relative importance

of relationship lending during bankruptcy.

5.4.1. Relationships and future likelihood of bankruptcy

We test the effect of maintaining a strong prior relationship on future likelihood of

bankruptcy (test of H4). In table 9 the following equation is estimated with the firm

year sample:

Bankruptcy = β 0 + β 1 × (Strongpriorrelation ) + β 2 × (Distress ) +

β k × (Controlk)

In this equation, the Bankruptcy in year t is predicted by control variables and

the strong prior relationship dummy calculated as of the beginning of year t. Control

variables include the Altman Z score, firm specific accounting variables, and dummy

variables for industry and year. The results are shown in Table 9, Panel A, model

1. The insignificant coefficient of Strongpriorrelation shows that maintaining a strong

prior relationship has no impact on the one year ahead likelihood of bankruptcy. As

before, we evaluate if the endogeneity of this dummy affects these results using the same

instrument for the strong prior relationship (distance). The results (model 2, Table 9,

Panel A) are not impacted by this. In Panel B of Table 9, using the sub-sample of

distressed firms, we evaluate whether the likelihood of filing for bankruptcy conditional

on distress is impacted by the presence of a strong prior relationship. As with Panel A,

the results show no impact. Likewise, a matched sample of firms based on the likelihood

of forming a strong prior relationship using the propensity score matching do not display

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any significant differences in the likelihood of bankruptcy (Table 9, Panel C). Overall,

the results of this sub-section indicate that H4 is not supported in the data. Thus,

maintaining a strong prior relationship does not help in terms of lowering likelihood of distress or likelihood of bankruptcy.

5.4.2. Loan contract terms during bankruptcy

Next, we test whether relationship banks help their borrowers in the DIP financing in

terms of fees charged and collateral required. In panel A of table 10 the following two

equations are estimated for the sub sample of DIP loans:

FEE = β 0 + β 1 × (Relationship) +

β k × (Controlk)

and

Collateral = β 0 + β 1 × (Relationship) +

β k × (Controlk)

As in the case of distress, RELLOAN and STRONGRELLOAN are used as proxies

for the relationship. The results in Table 10, Panel A show that RELLOAN has a signif-

icantly lower impact on collateral requirement; however, the fees charged by relationshiplenders are identical to that charged by outside lenders. Panel B estimates the same

regressions for STRONGRELLOAN . It shows that strong relationship DIP loans have

less stringent collateral requirement. Thus, the multivariate results support the univari-

ate results from Section 4.2 only in case of collateral. However, one important point

to note here is that the sample of observations that have sufficient data to be included

in the multivariate regressions is much smaller than the sample used in the univariate

statistics. This may lower the power of our multivariate tests.

5.4.3. Relative importance of relationship lending during bankruptcy

So far our results indicate that once firms file for bankruptcy, relationship lenders may

have incentives to help in the DIP financing in terms of lower collateral requirement.

Those incentives come from both the super priority of DIP loans and or from soft budget

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constraints discussed earlier. The incentives could also come from the informational

advantage of inside lenders. Given that relationship banks do not appear to help their

distressed (but not bankrupt) borrowers, the super-priority nature of the DIP loansappears to be the main difference between distress and bankruptcy that can account

for the difference in relationship bank behavior. All other reasons discussed that would

give a relationship bank an incentive to help are equally applicable to distress as well as

bankruptcy.

To test whether the relative importance of relationship lending is different after

borrowing firms file for bankruptcy relative to normal times, we estimate the following

equation using a firm year sample:

Relative Importance of Relationship Lending = β 0 + β 1 × (Bankruptcy ) +

β k × (Controlk)

Both the Relative Importance of Relationship Lending and the control variables are

identical to those in Section 5.3. The result in Table 11, model 1 shows that Bankruptcy

has an insignificant coefficient in all the models, which indicates that compared to normal

times, firms during bankruptcy are equally likely to get loans from their relationship

lenders. This is also true with the other three measures of the relative importance of

relationship lending. In unreported results, we find that identical results are obtainedif the firm year sample excludes the distress observations and includes only the normal

and bankruptcy observations. Likewise, if the base sample is limited to firm years

with distress and bankruptcy alone, bankruptcy has a significantly positive effect on

the relative importance of relationship lending for two of the four proxies, indicating

that the relative importance of relationship lending in bankruptcy is larger than that in

distress.

5.4.4. Recovery from bankruptcy

Some scholars have argued that DIP financing may encourage risk shifting (Bebchuk

(1996)), while Dahiya, John, Puri, and Ramirez (2003) empirically document that DIP

financing typically benefits bankrupt firms in terms of emergence from bankruptcy. Al-

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though not a focal point of this paper, we test for the same effect in our sample. Following

Dahiya, John, Puri, and Ramirez (2003), we estimate the following equation:

Liquidation = β 0 + β 1 × (DIP ) +

β k × (Controlk)

where Liquidation is a dummy to indicate whether a bankruptcy firm finally has been

liquidated (Liquidation =1) or emerged (Liquidation =0). For each filing event, if there

is a DIP loan, then DIP=1, else DIP=0. The results show that the coefficients of DIP

is significantly negative, which indicates that firms that get DIP loans have a higher

likelihood of emergence. The results provide further support of the findings by Dahiya,

John, Puri, and Ramirez (2003), who also document that DIP financed firms are more

likely to emerge from Chapter 11 than non DIP financed firms.

5.5. Summary of empirical analysis

Overall, the results in Sections 4 and 5 suggest that loan contract terms provided

by relationship and outside lenders are comparable during distress. After the onset of

bankruptcy, relationship lenders require lower collateral (but not fees). This is worse

than in normal times where borrowers get lower fees and collateral, but better than

distress where borrowers get no discount, either in fees or in collateral. Further, the

relative importance of relationship lending is reduced in distress relative to normal times,

but not reduced in bankruptcy.

While it is not possible to rule out reputation or informational effects or the expected

reduction of repeat business as a potential cause for the lack preferential loan terms

and reduced relative importance of relationship lending in distress, these arguments are

equally if not more applicable to firms that are in bankruptcy. The fact that we find noeffect of relationship in distress but an effect in bankruptcy strongly suggests that the

critical factor in the determination of preferential loan terms is DIP financing.

Further, the lack of any help by relationship lenders during distress is consistent with

the notion that soft budget constraints do not appear to have any importance in the

lending process, at least in the US context.

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6. Conclusion

Previous literature has already documented the benefits of lending relationship tothe borrowers. But few studies consider the case when borrowers are in distress or

bankruptcy. In the event of distress, relationship banks may not want to continue to

help their borrowers due to the uncertainty about the borrowers’ viability. They could

even charge higher fees or require more collateral with their information monopoly. Our

results show that when firms enter distress, their relationship banks behave as outside

banks in that they charge fees and collateral that are comparable to what outside banks

charge. However, once firms file for bankruptcy, they are more likely to get DIP loans

from relationship banks than from outside banks. Relationship DIP loans require lesscollateral compared to outside DIP loans. Finally, DIP loans help bankrupt firms to

avoid liquidation.

Our results show that relationship lending in the US is significantly different from

that in Germany in Japan. In particular, the type of inefficiencies identified by Peek

and Rosengren (2005) in the Japanese context do not appear to have much economic

impact in the US. US bank’s lending during distress and bankruptcy is consistent with

the notion that informational advantages are priced, but at the same time, soft budget

constraints appear to play a relatively unimportant role in the lending process.

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References

Anderson, R.W., and S. Sundaresan, 1996, The design and valuation of debt contracts,Review of Financial Studies 9, 37–68.

Bebchuk, L., 1996, The uneasy case for the priority of secured claims in bankruptcy,

Yale Law Review 105, 857–891.

Berger, A.N., and G. Udell, 1990, Collateral, loan quality and bank risk, Journal of

Monetary Economics 25, 21–42.

Berger, A.N., and G.F. Udell, 1995, Relationship lending and lines of credit in smallfirm finance, Journal of Business 68, 351–381.

Bharath, S., S. Dahiya, A. Saunders, and A. Srinivasan, 2007, So what do I get? The

bank’s view of lending relationships, Journal of Financial Economics 85, 368–419.

Bharath, S., S. Dahiya, A. Saunders, and A. Srinivasan, 2009, Lending relationships and

loan contract terms, Review of Financial Studies, Forthcoming.

Bharath, S.T., and T. Shumway, 2008, Forecasting default with the Merton distance to

default model, Review of Financial Studies 21, 1339–1369.

Billett, M.T., M.A. Flannery, and J.A. Garfinkel, 1995, The effect of lender identity on

a borrowing firm’s equity return, Journal of Finance 50, 699–718.

Boot, A.W.A., S.I. Greenbaum, and A.V. Thakor, 1993, Reputation and discretion in

financial contracting, American Economic Review 83, 1165–1183.

Burch, T., V. Nanda, and V. Warther, 2005, Does it pay to be loyal? An empirical

analysis of underwriting relationships and fees, Journal of Financial Economics 77,

673–699.

Carey, M., M. Post, and S.A. Sharpe, 1998, Does Corporate Lending by Banks and

Finance Companies Differ? Evidence on Specialization in Private Debt Contracting,

Journal of Finance 53, 845–878.

35


36/57

Chemmanur, T. J., and P. Fulghieri, 1994, Reputation, renegotiation, and the choice

between bank loans and publicly traded debt, The Review of Financial Studies 7,

475–506.

Dahiya, S., K. John, M. Puri, and G. Ramirez, 2003, Debtor-in-possession financing

and bankruptcy resolution: Empirical evidence, Journal of Financial Economics 69,

259–280.

Dewatripont, M., and E. Maskin, 1995, Credit and efficiency in centralized and decen-

tralized economies, Review of Economic Studies 52, 541–555.

Diamond, D., 1984, Financial Intermediation and Delegated monitoring, Review of Eco-nomic Studies 51, 393–414.

Diamond, D., 1991, Monitoring and reputation: The choice between bank loans and

directly placed debt, Journal of Political Economy 99, 689–721.

Drucker, S., and M. Puri, 2005, On the benefits of concurrent lending and underwriting,

Journal of Finance 60, 2763–2799.

Elsas, R., and J.P. Krahnen, 1998, Is relationship lending special? Evidence from credit-

file data in Germany, Journal of Banking and Finance 22, 1283–1316.

Gilson, S., K. John, and L. Lang, 1990, Troubled debt restructuring: an empirical study

of private reorganizations of firms in default, Journal of Financial Economics 27,

315–353.

Hale, G., and J.A. Santos, 2009, Do Banks Price their Informational Monopoly?, Journal

of Financial Economics, Forthcoming.

Hertzberg, A., J.M. Liberti, and D. Paravisini, 2008, Information and incentives

inside the firm: Evidence from loan officer rotation, SSRN Working Paper,

http://ssrn.com/abstract=971179.

Hillegeist, S.A., E.K. Keating, D.P. Cram, and K.G. Lundstedt, 2004, Assessing the

probability of bankruptcy, Review of Accounting Studies 9, 5–34.

36


37/57

Houston, J., and C. James, 1996, Bank information monopolies and the mix of private

and public debt claims, Journal of Finance 51, 1863–1889.

James, C., 1987, Some evidence on the uniqueness of bank loans, Journal of Financial

Economics 19, 217–235.

James, C., 1995, When do banks take equity in debt restructurings?, The Review of

Financial Studies 8, 1209–1234.

Lummer, S.L., and J.J. McConnell, 1989, Further evidence on the bank lending process

and the reaction of the capital market to bank loan agreements, Journal of Financial


Merton, R.C., 1974, On the pricing of corporate debt: The risk structure of interest

rates, Journal of Finance 29, 449–470.

Ongena, S., and D. Smith, 2001, The duration of bank relationships, Journal of Financial


Peek, J., and E.S. Rosengren, 2005, Unnatural selection: perverse incentives and the

misallocation of credit in Japan, American Economic Review 95, 1144–1166.

Petersen, M.A., and R.G. Rajan, 1994, The benefits of lending relationships: Evidencefr

Documents

Relationship Bank Behavior During Borrower Distress and Bankruptcy