WORKING PAPER SERIES

Global Banks and Syndicated Loan Spreads: 

Evidence from U.S. Banks 

Edith X. Liu 

Federal Reserve Board of Governors 

Jonathan Pogach 

Federal Deposit Insurance Corporation 

November 2016 

FDIC CFR WP 2017-01 

fdic.gov/cfr 

NOTE: Staff working papers are preliminary materials circulated to stimulate discussion and critical 

comment. The analysis, conclusions, and opinions set forth here are those of the author(s) alone and do not 

necessarily reflect the views of the Federal Deposit Insurance Corporation. References in publications to this 

paper (other than acknowledgement) should be cleared with the author(s) to protect the tentative character 

of these papers. 
Global Banks and Syndicated Loan Spreads: Evidence from US

Banks1

Edith X. Liu2 Jonathan Pogach3

November 16, 2016

1The views and opinions expressed here are those of the authors and do not necessarily reflect the views

of the Federal Reserve or Federal Deposit Insurance Corporation. This draft is preliminary and incomplete.

Please do not quote or distribute without permission. We thank Ricardo Correa, Linda Goldberg, Manju Puri,

Jack Reidhill for insightful discussions, as well as the seminar participants at the Federal Deposit Insurance

Corporation and Federal Reserve Board of Governors. We also thank Arthur Michaeli for excellent research

assistance. All errors and omissions are our own.

2Federal Reserve Board of Governors. edith.x.liu@frb.gov

3Federal Deposit Insurance Corporation. jpogach@fdic.gov
Abstract

This paper explores the relationship between bank global exposure and their syndicated loan spreads.

Linking syndicated loan information from Dealscan with confidential US bank foreign exposure data

and borrower characteristics, we find that more bank global exposure is associated with a higher

loan spread that is statistically significant. To analyze this relationship between global banks and

loan spreads, we develop a theoretical framework where, in equilibrium, riskier borrowers are more

likely to work with global banks. Using a Heckman selection model, we confirm that borrower risk

characteristics indeed predict a higher likelihood of having a loan arranged by a global bank. However,

after controlling for the bank-borrower selection effect, we continue to find that global banks charge on

average an 12.7 bps higher spread on their syndicated loans, as compared with domestically focused

banks. Finally, we explore a non-risk based alternative where firms with multinational operations are

more likely to work with global banks for international banking services.
1 Introduction

Do global banks price loans differently than non-global banks? The answer to this question is essential

for understanding the ever changing role of banks and evaluating the fundamental economic consequences of a globalizing financial sector. When banks operate in a perfectly competitive environment,

borrower risk characteristics alone should inform loan pricing. However, when frictions exist, it is possible that banks lend to customers with spreads that reflect both borrower risk and bank characteristics.

Yet, little is known about the effect of a lender’s international exposure, as a bank characteristic, on

loan terms to its customers.

In this paper, we examine how a bank’s global exposure might affect cost of corporate funding

through the spreads charged on syndicated loan. On one hand, if an extensive global bank network

leads to larger loan capacity and lower bank funding costs, banks may can pass down these savings

to their borrowers as lower loan rates or spreads. On the other hand, if global banks deliver premium

international banking services and can better withstand a domestic funding cycles, then they may

charge higher loan spread. While these channels, and potentially many more, are likely to be working

simultaneously, we find that on net, more bank globalization tend to be associated with higher loan

costs for firms.

We construct a new dataset that matches bank characteristics (Y9C) and borrower characteristics

(Compustat), for syndicated loans led by US banks in DealScan. In a simple regression framework, we

find evidence that loans organized by US banks with larger foreign exposure is correlated with higher

spread, even after controlling for borrower riskiness, loan size, and other bank characteristics. This

positive relationship between a bank’s global exposure and loan pricing structure is true for spreads,

fees, and all-in-spreads that include both. When we break down the purpose of the loans, we find that

this relationship is largest in magnitude for term loans, but more statistically significant for lines of

credit and real working capital loans. Further, we find that our results are stronger for the period prior

1
to the global financial crisis (GFC)1

.

While our initial empirical results suggest that global banks are associated with higher loan spreads,

this finding may be the result of an unobserved selection where global banks are more likely to work

with riskier borrowers. However, unlike the usual self-selection endogeneity bias, banks cannot simply

choose the riskiness of the clientele they want to lend. Instead, the bank-borrower relationship that

links global banks with riskier borrowers must be an equilibrium outcome of both bank selection by

borrowers and borrower selection by banks. To analyze how this bank-borrower selection can arise

in equilibrium, we develop a model with asymmetric information where global and non-global banks

face differential costs. Banks then design various loan contracts (loan spread and collateral) such that

different borrower types choose the appropriate bank (global versus non-global) and contract that

maximizes their expected utility. In equilibrium, we show that a larger fraction of bad type borrowers

will work with global banks and that the average loan rate will be higher at the global bank to reflect

the distribution of borrowers they face.

Given our theoretical predictions, we refine our empirical analysis by using a Heckman selection

model where borrower risk characteristics are used in a first-stage Probit to identify the relationship

between borrower type and their selection into global versus non-global banks. In the second stage,

we run the pooled OLS regression specification with the inverse mills ratio from the Probit to control

for this borrower-bank selection bias. We find that borrower characteristics of leverage ratio, size, and

Altman Z score are all strongly predictive of working with a global bank. Moreover, the direction

of association supports our hypothesis that firms with riskier characteristics (high leverage and low

Altman Z score) have a significantly higher probability of having a loan arranged by a global bank.

Despite these results, our second stage regressions show that this bank-borrower selection does not

account for the higher premium on loan spreads from global banks.

Finally, we investigate the relationship between multinational borrowers and global banks, as a

1

In a previous version, we also find that syndicated loans with a lead bank arranger with larger global exposure tend

to include more foreign bank participation, which is associated with slightly lower spreads.

2
potential explanation for the positive relationship between bank foreign exposure and higher loan

spreads. If global banks use their foreign operations to offer premium international financial services,

firms with more multinational operations are more likely to pay higher loan spreads to access these

services. Under this view, the premium can be interpreted as the equilibrium value of borrowers

willingness-to-pay to work with a US global bank, as compared with other financial institutions.

To examine whether global banks offer specialized services to borrowers at a premium, we analyze

the relationship between the foreign taxes paid by borrowers and the global banks. Including borrower

foreign taxes as a measure of the borrower’s global operations in the Heckman selection model, we

find that indeed borrowers that pay higher foreign taxes are more likely to work with global banks.

Then in the second stage regression, we find that the inclusion of borrower foreign tax increases the

statistical significance of the coefficient on the inverse mills ratio, but still fails to explain the higher

loans spreads from global banks2.

Most academic research on global banks and bank international exposure has come on the heels

of the recent global financial crisis. Papers in this line of research focus on shock transmission, where

global operations of a bank can both serve to propagate shocks from abroad (contagion) and to alleviate

the effects of domestic negative shocks (internal capital markets). Goldberg and Ceterelli (2014) show

that global banks actively used their internal capital markets and tapped into these funding channels

to offset liquidity shocks during the crisis. By contrast, Kalemli-Ozcan et al (2013) find that financial

crises spread in a contagious way through banking linkages. Finally, Giannetti and Laeven (2012) find

that global banks tend to pull back capital to their home country during a crisis. Not surprisingly,

conclusions differ depending on whether the source of the negative shock is foreign or domestic. Beyond

shock transmission, global banks play a role in altering the competition in foreign financial markets.

Claessen, Demirguc-Kunt, and Huizinga (2001) finds that when foreign banks enter new markets, they

2Unlike the positive correlation between the riskiness of borrowers and loan spreads, there’s no theoretical reason to

assume that borrowers who are more global should have higher spreads. Therefore, unlike the upward selection bias with

borrower riskiness, the same empirical effect should hold for borrower international operations.

3
earn profits at the expense of domestic banks in emerging countries, but not in developed markets.

While the shock transmission and effects on the foreign economy are clearly interesting topics related

to global banks, our paper does not directly address these issues. Perhaps closest to our paper is the

line of research that examines the link between global banks and exports. Niepmann and SchmidtEisenlohr (2014) show that global banks play a large role in financing trade through the use of letters

of credit. Similarly, Paravisini, Rapporport, and Schnabl (2014) find that banks specialize and develop

comparative advantages in funding exports to different destination countries. Similar to these findings,

our paper provides evidence that banking functions are specialized and that global banks provide

value beyond simply a source of funding. Moreover, our paper quantifies this value as the borrower’s

willingness to accept higher loan spreads to access global banking services.

This paper is also distinct from the bank lending channel literature, where a fundamental identification problem exists because supply of funds from banks and the demand of funds by borrowers

move together with the underlying state of the economy. Therefore, any identification of a supply side

effect (or bank channel) on loan terms requires isolating demand factors. We argue that our variable of

interest, bank foreign exposure, is less susceptible to this simultaneity issue. First, foreign operations

of banks are generally associated with large fixed costs and therefore variations in global exposure

cannot be easily reversed in response to business cycle fluctuations. This is certainly true for banking

institutions that take in foreign deposits, but even for those banks that make loans globally, there are

still fixed costs to operate and lend in foreign markets. Second, we focus on the loans of US global

banks to US domestic borrowers. Foreign operations are likely to increase in response to increased

foreign demand, which is not perfectly correlated with US borrower loan demand.

The paper is organized as follows. Section 2 provides an overview of the data and outlines our basic

empirical specification. In addition, Section 2 summarizes the data and reports our empirical finding.

Given our initial empirical findings, Section 3.1 outlines a bank selection relationship model, and derives

an equilibrium relationship between global banks, borrower types, and loan spreads. Section 3.2 then

4
provides empirical evidence in support of the theory by employing a Heckman selection model. Section

4 examines the international exposure of the borrower in connection with the prior empirical analysis.

Section 5 concludes.

2 Empirical Analysis

This section provides some basic empirical evidence on the relationship between bank global exposure

and the spreads on the syndicated loans that the banks arrange. We construct a new dataset that

encompasses borrower financial information, bank balance sheet information, and loan terms. Below,

we describe the construction of our dataset, summarize key features of the data, and characterize the

relationship between bank foreign exposure and loan terms accepted by borrowers in a simple pooled

regression framework.

2.1 Data Construction

We begin with the syndicated loan data from Dealscan as the base for our data construction. This

data features deal level information such as spreads, fees, purposes, covenants, and the banks involved

in the deal from 1985 - 2013. These syndicated loans can be lines of credit as well as term loans3.

Therefore, the spread on a syndicated loan can be both actual borrowing costs for new funds, or

potential borrowing costs to committed funds from the bank. In order to associate the transactional

data from Dealscan with quarterly financial reports on banks and firms, we use reported bank and

firm financial data as of the end of the quarter prior to the deal date.

First, we identify all deals in which a US bank organized or participated in a syndicated loan and

hand-match every US bank by their name to their bank holding company (BHC)4. This match is done

3

It is important to note here that syndicated loans can often be lines of credit rather than received funds. For instance,

Apple has a $1.5 billion US dollar credit facility open, but has only draw down a small fraction of the line.

4We include subsidiaries of banks such as Bank of America Capital Markets, under Bank of America bank holding

company. We do not include Investment Banks and other financial institutions that are not a part of a bank holding

company.

5
at the bank year observation level because there are significant changes to the association of banks to

bank holding companies over time, through mergers, spinoffs, and corporate re-organizations5. Because

we have in-depth regulatory filings on US banks, our current study confines to only US banks, although

there is a clear increase in the participation by foreign banks both as lead managers and participants

over time. After matching banks in Dealscan to their bank holding companies that report a Y9C, we

collect individual bank financial information from Y9C (bank total assets, total deposits, capitalization

ratio).

Next, we eliminate all deals that where the lead bank was not a part of a US BHC. Following

Ivanshina (2009), we define lead bank as administrative agent, if identified. If the syndicated deal

does not have an administrative agent, we use book runner, lead arranger, lead bank, lead manager,

agent, or arranger to identify lead banks. Moreover, following Ivanshina (2009), we eliminate any deal

with more than two lead banks. In the cases where there are two lead banks, we average the financial

variables across the two banks and identify the average as the lead bank statistics.

Finally, we collect borrower information from CRSP/Compustat using the data link provided by

Michael Roberts through WRDS. Again, this restricts our sample to pubically-traded US borrowers.

Using only Compustat firms also eliminates deals with non-listed firms. However, the benefit of focusing

on public firms is being able to collect financial information before and after the loan is structured.

Using Compustat data, we collect the following borrower information: long term debt, total assets,

total liabilities, current assets, current liabilities, market value of equity, book value of equity, working

capital, retained earnings, total sales, earnings before interest and tax (EBIT), foreign tax, net property

plant and equipment (PPE), income before extraordinary items, depreciation and amortization.

5We were able to match over 85% of all US banks deals. Banks not included in the match often have less than 5 deals

in Dealscan across all sample periods.

6
2.2 Data Overview

This section provides a brief summary of the data, broken down by deal, borrower, and bank statistics.

Table 1 characterizes the average loan statistics, as well as the tenth percentile and ninetieth percentile.

We compute these statistics for the full sample period of 1984 - 2013, for select individual years, and

for years surrounding the global financial crisis (2007, 2008, 2009)6.

First, we find a dramatic increase in the number of deals leading up to the global financial crisis, and

a dramatic decline during the crisis. In 1990, there were 677 syndicated loans made to US borrowers

and organized US banks. By 2000, this number had increased to 1,552 deals, a 229% increase. During

the global financial crisis, we find the number of syndicated loan deals decreased from 1,067 in 2007 to

a mere 448 in 2009. Since then, the number of loans has recovered somewhat to 741 and 776 in 2010

and 2013 respectively. Interestingly, while the number of deals has not returned to the levels in 2000,

loan amounts exceed pre-crisis levels. Prior to the crisis in 2007, the average loan size in our sample

is $648 million, which decreased to $447 million during 2008. Most recently, in 2013, the average loan

amount of $900 million is notably higher than in 2007. This pattern not only holds for the average

loan size statistic, but also for the 10th and 90th percentile.

Second, Table 1 shows the time variation in spreads and various fee types such as annual fees,

upfront fees, commitment fees, and standby line of credit fees. For loan spreads, we find that the

average spread over LIBOR over the entire sample period was 155.5 basis points (bps), with the lower

spreads earlier in the sample period. During the global financial crisis, spreads jumped from 124.1 bps

in 2007 to 323.2 bps in 2013. Loan spreads remained high throughout 2010 at an average of 271.1 bps,

and came down to 185.0 bps in 2013. This trend is particularly apparent in the lowest 10 percentile

of spreads, which increased four-fold during the financial crisis and in 2013 remains twice as high as

2007. We see a similar pattern across all fee types.

6For a complete analysis of how banks changed their loan pricing in response to the global financial crisis, see Santos

(2009)

7
To understand the types of participants in the syndicated loan market, Table 2 reports financial

information of lead US banks and US borrowers. We begin with a summary of the number of lead US

banks who organized the loans described in Table 1. We find that overall the number of US banks

that participate in the syndicated loan market has declined. This is due to the heavy consolidation of

banks during the late 1990s and 2000s, detailed in Karolyi and Taboada (2015). For example, in 1990,

89 distinct lead US banks organized 677 deals. By 2007, as few as 41 US banks organized 1,067 deals,

and more recently in 2013, only 27 US banks lead 776 deals.

For bank statistics, Table 2 shows that the average ratio of foreign CI (commercial and industrial)

lending as a ratio of total CI lending, ForeignToTotCI, has increased slightly over time. However, for

US banks in the tails of the distribution at the 10th and 90th percentile, foreign exposure experienced

a larger increase over time. Interestingly, US banks involved in syndicated loans varied dramatically

on their ratio of foreign to total CI lending during the financial crisis, going from 70.7% in 2007 to

30% in 2009. This is due to the fact that large global US banks pulled back their lending activities

during the financial crisis. For other bank financial variables, we find that the sample of US banks

that operate in the syndicated loan market appear to be shifting out of the commercial and industrial

lending business, as evidenced by the decline in average total CI over assets, Lead TotCItoAssets, over

time. Moreover, we find that bank capitalization ratio, equity to assets, and bank size (log asset),

appears to increasing steadily over the sample period.

Finally, the last three variables of Table 2 describes the evolution of average borrower financial

characteristics over time. Unlike bank characteristics, we find that borrower characteristics have largely

stayed the same throughout the sample period, with a slight increase in firm size over time. For the

two measures of borrower risk, firm leverage and Altman Z score, we find a slight decline in the average

leverage ratio and Z score during the financial crisis. However, by 2010, both statistics mostly recovered

to pre-crisis levels.

8
2.3 Pooled Regressions

Our empirical analysis begins with a basic pooled ordinary least squares (OLS) analysis of loan spreads

on bank characteristics, while controlling for borrower risk characteristics. Given our loan-borrower

level observations, we apply the following regression specification on a loan from bank, i, to borrower,

j:

Spreadi,j,t = α + βBankF oreignExpi,t−1 + ΘBankChari,t−1 + ΩBorrowerCharj,t−1 + ui,j,t (1)

where Spreadi,j,t is the spread over LIBOR of the loan7, and BankF oreignExpi,t−1 is the measure

of bank foreign exposure. BankChari,t−1 and BorrowerCharj,t−1 are the bank and borrower characteristics respectively in the quarter prior to the loan. For bank characteristics, BankChari,t−1, we use

the following variables: Total CI lending to Total Assets, Equity to Asset ratio (Capitalization), and

log Total Assets (size)8. For borrower characteristics, BorrowerCharj,t−1, we compute the following

variables: Leverage ratio = LT Debt / Total Assets, Cashflow ratio = (income before extraordinary

items + depreciation and amortization )/net property plant and equipment, and Altman Z score =

[(3.3*PreTax Income + Total Sales + 1.4*Retained Earnings + 1.2*(Current Assets - Current Liabilities)]/ Total Assets. Finally, we also include borrower industry fixed effects, year fixed effects, and

cluster standard errors by bank.

For the purpose of our empirical analysis, we define bank foreign exposure, BankForeignExp, in

two ways. First, for the asset side, we use foreign CI to total CI as a measure of a bank’s international

exposure. This is a more general exposure measure as foreign CI as cross-border loans do not necessitate

the existence of a foreign office. By contrast, our second liability side global exposure measure is foreign

7

In a previous version, we have also used All-In spreads, which include fees. Results are quantitatively similar, and

available upon request.

8We have also included other characteristics such as liquid assets to total assets. However, these characteristics appear

to be insignificant over the entire sample, and matter only during the recent financial crisis.

9
deposits to total deposits. The foreign deposit variable is defined by the amount of deposits taken by

the foreign office. Therefore, foreign deposits is related to physical operations in another country, as

well as subject to deposit and banking laws of the host country.9

The importance of global operations can affect bank operations on several dimensions. On one

hand, banks can use international markets to diversify risk. Diversification benefits can be realized

from either lending to a more diverse set of borrowers or capitalizing from a larger funding base. Global

banks can pass on these funding benefits to borrowers in the form of lower loan spreads. Alternatively,

global bank may bundle loans with premium international financial services to customers. Given the

additional access to these services, global banks can charge higher loan spreads to the customers that

require these services. While both channels are likely to affect global banks in their loan pricing,

our hypothesis examines the net effect and provides a possible explanation for the dominant effect.

Therefore, our basic hypothesis on the relationship between bank foreign exposure and loan spreads

are as follows:

H0: In a competitive market, borrower risk characteristics alone price loan spreads and bank

characteristics do not matter. Controlling for borrower risk and other bank characteristics, we expect

NO association between foreign exposure of banks and loan spreads.

H1a: Global exposure allow banks to provide international banking services that require a premium

and therefore can charge HIGHER spreads on loans, as compared with less global banks. Controlling for borrower risk and other bank characteristics, foreign exposure of banks is POSITIVELY

associated with loan spreads.

H1b: Global exposure allow banks to realize diversification benefits, lower funding costs, therefore

price their loans with LOWER spreads than less global banks. Controlling for borrower risk and other

bank characteristics, foreign exposure of banks is NEGATIVELY associated with loan spreads.

Table 3 reports the results for the pooled OLS specification and hypothesis outlined above. For our

9Note that foreign lending of the bank is lagged one period to avoid simultaneity issues of the bank co-determining

foreign lending and loan spreads.

10
variable of interest, foreign CI lending to total CI lending, we find a positive (28.004) and statistically

significant (tstat = 4.87) slope coefficient. Therefore, a 10 percentage point change of foreign CI to

total CI would imply a 2.8 basis point higher average spread. To put this in perspective, note that a one

standard deviation in the ratio of Foreign CI to Total CI across banks is 20.65 percentage points, which

translates to a 5.6 basis point increase. Table 3 also shows that among other bank characteristics, only

size (log assets) has a negative (-1.978) and marginally significant (tstat = -1.65) relationship with

loan spreads. Our findings suggest that our bank foreign exposure measure is not simply a proxy for

bank size, and that size and global exposure play distinct roles in affecting loan spreads. Moreover,

the opposite signs of the slope coefficients show that size and global exposure may be countervailing

effects on loan spreads.

Not surprisingly, we also find that borrower characteristics have a large and significant effect on

the price of loan spreads. For example, the slope coefficient on the borrower’s leverage ratio across all

loans is 152.168 basis points and highly statistically significant (tstat = 21.05). This implies that a 1%

increase in the leverage ratio correlates with a 1.52 bps higher spreads. Moreover, the slope coefficient

on the borrower characteristics intuitively follows the direction of higher risk and higher spreads. We

confirm that higher loan spreads are associated with borrowers who are more leveraged, smaller in size,

and have lower Altman Z scores.

One motivation for this paper is to better understand global banks and their loan pricing during

non-crisis times. Therefore, Table 1 also reports the results of an identical pooled regressions applied

on pre-crisis data, 1985 - 2007. Eliminating the disruptive effects from the global financial crisis, we

find that the magnitude of the coefficients on All Loans, Credit Lines, and Real Loans are larger

and have stronger statistical significance than the full sample estimates. While one may expect that

bank characteristics matter only during crisis times, our results suggest that a bank’s global exposure

matters for loan pricing even during normal times.

While Table 3 uses the ratio of foreign CI lending to domestic CI lending as a measure of global

11
exposure for the bank, foreign liabilities can also be an indication of a bank’s global operations. Using

foreign deposits to total deposits as an alternative measure of a bank’s global exposure, 4 confirms

the positive correlation between a bank’s global exposure and the spreads charged on their syndicated

loans. Using the ratio of foreign deposits to total deposits as a measure of a bank’s global exposure, we

find that the slope coefficient is 21.421 and highly statistically significant (tstat = 3.55) for all loans.

This implies that a 1% increase in a bank’s foreign deposit to total deposit yields a 0.214 basis point

increase in loan spreads.

Interestingly, when we use liability side to measure global exposure, other bank characteristics also

become statistically significant correlated with loan spreads. In particular, a higher total deposits

to total liability ratio is associated with a higher spread on all loans, and significantly so (38.041,

tstat = 3.31). This is perhaps a bit surprising as some studies have shown that higher ratio of core

deposit funding on the bank’s balance sheet leads to more lending volumes (e.g. Cornett et al (2011)).

Alternatively, this result may be viewed in the context of the relationship lending literature. Although

the banks in our sample are generally larger than is usually considered in the relationship lending

literature (e.g. Berger and Udell (1995)), there may still be relative differences in how banks resolve

information asymmetries with borrowers. If deposit-taking banks in our sample are more inclined to

lend to borrowers with lesser quality information then this will be reflected in higher loan rates earlier

in the relationship (see Petersen and Rajan (1995)). In addition, our result is similar to Berlin and

Mester (1999), who also find a positive relationship between core deposits and loan spreads. Finally,

Table 2 suggests that higher bank capitalization ratio, equity over assets, is associated with lower loan

spreads for their customers.

Last, we explore a specification where we include both our asset side (foreign CI to total CI) global

exposure measure and our liability side (foreign deposits to total deposits) global exposure measure

in the pooled OLS specification. Table 5 confirms our previous findings with the slope coefficient on

foreign CI to total CI loans again positive (55.522) and statistically significant (tstat = 4.99). By

12
contrast, the slope coefficient on the foreign deposit to total deposit turns negative and statistically

significant (-29.692, tstat = -2.56) when both measures of foreign exposure are included. Consistent

with the view that internal capital markets allow global banks to diversify sources of funding, US

banks with high fraction of foreign deposit offer lower loan spreads. This suggests that the relationship

between bank foreign exposure and loans pricing differ depending on the type of global activities the

bank chooses to pursue.

2.4 Additional Considerations

One potential concern with the above analysis is that we are not perfectly controlling for the riskiness

of the syndicated loans. Therefore, the higher observed loans spreads organized by banks with higher

global exposure simply reflect riskier borrowers (low credit rating) or riskier deals (larger loan amounts).

This section evaluates these two alternatives by including credit ratings and loan amounts into the

pooled OLS specification. First, we evaluate our prior analysis on a subsample of firms with credit

rating information. The addition of credit rating reduces our sample of loans by almost 50%, with

qualitatively similar results. Second, we include log loan amount as a control for deal size. This

specification evaluates the possibility that banks can trade off loan rate and the loan amount in order

to manage risk.

Table 6 shows that the inclusion of these two variables does not change our initial findings that

bank global exposure increases loan spreads charged to their customers. When we evaluate only firms

with credit ratings, we find that the slope coefficient on the bank’s global exposure characteristic

is attenuated from 28.004 to 12.54, but nevertheless statistically significant with tstat of 2.09. Not

surprisingly, borrower credit rating has a large effect on loan spreads (21.112, tstat = 35.76), and

reduces the importance of firm size, as credit rating and firm size are correlated. With the inclusion

of credit ratings, we continue to find a positive and statistically significant slope coefficient on our

foreign exposure measure, foreign C&I to total C&I, even though the sample size with credit ratings

13
is only about half of the full sample of all borrowers. Table 3 also reports the results of the pooled

OLS that includes log loan amount. We find that there is a negative association between loan amounts

and loan spreads. Since loan amount and borrower size are strongly correlated, including loan amount

attenuates the effect of borrower size and like borrower size, has a negative slope coefficient. Again,

we find that the slope coefficient on our global exposure remains largely unaffected (26.008, tstat =

4.62) even when we control for loan size.

3 Bank-Borrower Selection

The above analysis shows that banks with more global exposure are associated with higher loan spreads.

While there may be valid reasons for why loans from global banks command a premium, it may also be

the case that riskier borrowers work with global banks. In the case where riskier firms systematically

choose to work with global banks, the higher loan spread found above may be a product of selection

bias.

At first glance, the above problem appears to be a standard self-selection problem. However, the

relationship between a bank’s global exposure and the loan spreads is the result of an endogenous

matching problem involving both banks and borrowers rather than simply the result of selection decisions. If global banks’ borrowers are riskier on average, but this risk is observable, then pricing

differentials should vanish with the appropriate borrower level controls. If, on the other hand, a bank

does not perfectly observe a borrower’s riskiness then loan spreads would vary with a bank’s unobservable inference of risk given borrower characteristics. That is, we argue that this matching problem

gives rise to a non-zero correlation between borrower characteristics and the error term in equation

1, BorrowerCharj,t and ui,j,t respectively. Since our parameter of interest is the slope coefficient, β,

on BankF oreignExpi,t−1, this correlation is not generally a concern if BorrowerCharj,t is unrelated

to BankF oreignExpi,t−1. However, if riskier firms tend to work with global banks, then borrower

14
characteristics will be related to bank foreign exposure, and our β estimate will be biased.

The following section develops a simple model to examine this relationship between riskier borrowers

and global banks. We consider a model with two bank types and two borrower types. We assume that

borrower type is not observed by banks and that global banks are less efficient than domestic banks

in loan servicing. The model equilibrium features the selection of risky borrowers with global banks

and, consequently, higher equilibrium loan prices for global banks driven endogenously by its pool of

borrowers.

3.1 A Model of Bank Selection

This section outlines a model that generates, as an equilibrium outcome, the self-selection of riskier

borrowers to more globally exposed banks. The basic intuition of our model goes as follows. For

simplicity, we can think of there being two bank types, global and domestic, and two borrower types,

high and low. Global and domestic banks differ in their production technologies. Both bank types

compete for good and bad borrowers, characterized by their probability of achieving a high return.

Because banks are unable to distinguish between borrower types, banks offer a menu of interest rates

and collateral requirements to screen borrower types, in the spirit of Rothschild and Stiglitz (1976).

Higher fixed costs for global banks along with a zero profit condition imply that global banks’ all-in

pricing to borrowers will be higher than that of domestic banks. On the other hand, their higher

monitoring costs imply that they are less efficient at screening and will therefore have a comparative

advantage in lending to bad borrowers. Consequently, their contracting terms to bad relative to good

borrowers is better than that of domestic banks. Because global banks’ contract terms give a relative

preference to bad borrowers when contrasted with domestic banks, global banks will disproportionably

attract bad borrowers.

Global banks are modeled as less efficient at monitoring than their domestic counterparts, in the

15
sense of costly state verification in the spirit of Townsend (1979).10 There are at least two of each bank

type. Borrowers, indexed by i ∈ {H, L}, invest in projects that have return R > 0 with probability pi

and return 0 with probability (1 − pi). We assume that the high type borrower has higher pi than the

low type borrower, so that p

H > pL. In addition, borrowers are endowed with a preference parameter

η ∼ Φ([η, η ̄]) that is realized in the borrower’s utility should the borrower chooses a global bank.

Banks, indexed by b ∈ {G, D}, cannot observe the borrower’s type.11 To screen borrowers, banks

offer loan contracts, (r

i

b

, ci

b

), specifying an interest rate and amount of collateral to be posted. In the

event that the bank seizes the collateral, the bank is only able to recover a fraction mb ∈ (0, 1) of the

collateral. The quantity (1 − mb) ∗ cb can be interpreted a cost of liquidating collateral and mb ∗ cb as

the recovery value. Assume mG < mD, so that global banks recover a lower fraction of the collateral

in liquidation. Finally, global banks and domestic banks differ in their fixed costs, Kb. We assume

that the fixed costs of being global are greater than that of being domestic, so that KG ≥ KD.

The bank’s profits can be written as:

Πb = max

(r

i

b

,ci

b

),i∈{H,L}

μb



p

HrH

b + (1 − p

H)mbcH

b

) − Kb



(2)

+(1 − μb)



p

L

r

L

b + (1 − p

L

)mbc

L

b

) − Kb



(3)

where μb is the proportion of H borrowers at bank type b.

Borrower i must choose both a bank and a contract within a bank. A borrower i’s utility from a

contract (r, c) from bank b is given by: p

i

(R − r) + (1 − p

i

)(−c) + η1{b=G}, where the last term reflects

the borrowers utility from choosing a global bank. Borrower preferences for a global bank may include

considerations such as a branch network or global banking services, discussed in the introduction. The

incentive compatibility constraints dictate that a borrower of type i does not prefer the contract of

the other type, for both bank types. This implies that for any bank type, b, we have the following

10Using only two borrower outcomes, this reduces trivially to monitoring conditional on the low state.

11Alternatively, the model can be thought of as being conditional upon any public signals of type.

16
incentive compatibility constraint for both borrower types i ∈ {H, L}

12:

p

i

(R − r

i

b

) + (1 − p

i

)(−c

i

b

) ≥ p

i

(R − r

−i

b

) + (1 − p

i

)(−c

−i

b

) (4)

Given the borrower’s selection equation across contracts within a bank, we next turn to the borrower’s selection decision across bank types. First, assume that η is sufficiently small that a borrower

with this realization of η will always prefer a D bank and  ̄η is sufficiently high that borrowers with this

η realization will always prefer a G type bank. By continuity, this implies that there will exist some

threshold value of η for each borrower type, (η

∗

H, η∗L

), such that a borrower of type i is indifferent between banks at η

∗

i

. Moreover, borrower i chooses a G type bank if and only if ηi > η∗

i

. The indifference

condition at this threshold is written as:

p

i

(R − r

i

G) + (1 − p

i

)(−c

i

G) + η

∗

i = p

i

(R − r

i

D) + (1 − p

i

)(−c

i

D) (5)

Consequently, for any given thresholds (η

∗

H, η∗

L

) the proportion of high type borrowers μb facing

each bank type b will be:

μ

∗

G =

1 − Φ(η

∗

H)

(1 − Φ(η

∗

H)) + (1 − Φ(η

∗

L

)) (6)

μ

∗

D =

Φ(η

∗

H)

Φ(η

∗

H) + Φ(η

∗

L

)

(7)

where the numerator is the mass of high type borrowers that choose bank b and the denominator is

the total mass of borrowers that choose bank b.

Due to the competition within the group of global banks, G, and within the group of domestic

12Note that η does not appear in the IC constraint, as it affects the selection between global and domestic banks but

not the selection of contracts within a bank type.

17
banks, D, banks in equilibrium will have a zero profit condition. In fact, the zero profit condition must

hold for each bank-borrower combination (see Mas-Colell, Whinston & Green Lemma 13.D.3).13

The zero profit conditions for both borrower types for both bank types can be written as:

p

HrH

b + (1 − p

H)mbcH

b = Kb (8)

p

L

r

L

b + (1 − p

L

)mbc

L

b = Kb (9)

First, we show that for the low types, both global and domestic banks specify a contract with a

zero collateral requirement. The reason for this is that since banks only recover a fraction, mb, of the

collateral in the liquidation, there is a dead-weight loss associated with using collateral as a contracting

tool. Therefore, banks want to limit the use of collateral only as a device to screen bad borrowers from

good. Moreover, banks set interest rates sufficiently high to cover the fixed costs. Proofs appear in the

Appendix.

Claim 1 The Global banks offer the low (L)type borrower a contract of (

KG

pL , 0), while the Domestic

banks offer the low (L) type borrower a contract of (

KD

pL , 0)

Since the low borrower type is offered either ( KG

pL , 0) or ( KDpL , 0), depending on bank type, the

expected cost for the low type is higher at Global banks G than at Domestic banks D. This comes

from the fact that fixed costs KG is higher than KB. From this result, we rewrite the incentive

compatibility constraints (Inequalities 4) as:

13The reason for this is as follows. Suppose that a bank earns positive profits on one borrower type and negative profits

on another borrower type. Then, the bank could deviate by increasing the fees or rates to the negative-profit borrower.

Doing so makes this contract less attractive to all types. So, the positive-profit borrower type’s incentives are unchanged.

Meanwhile, the negative-profit borrower would leave for a competing bank. As the borrower yielded negative profits, this

is a profitable deviation for the bank, a contradiction.

18
p

H(R − rH

b

) + (1 − p

H)(−cH

b

) ≥ p

H(R −

Kb

pL

) (10)

p

L

(R −

Kb

pL

) ≥ p

L

(R − r

H

b

) + (1 − p

L

)(−c

H

b

) (11)

which simplifies to:

p

HrH

b + (1 − p

H)cH

b ≤ p

H Kb

pL

(12)

Kb ≤ p

L

r

H

b + (1 − p

L

)c

H

b

(13)

From inequality 13 there is a lower bound on c

H

b

. This is the minimum collateral required to

prevent the L type borrower from deviating to the H type contract. However, because collateral

impose a deadweight loss, banks will minimize the extent to which collateral is used so long as borrower

incentives are met.

The contract for the H type borrower is then solved in two parts. First, it is argued that the

expression 13 holds with equality. Then we combine the incentive compatible condition in the equality

of expression 13 with the zero profit condition on the high type borrowers in equation 8. With these

two equations, we have a system of two equations and two unknowns, with which we can solve for the

contract of the high type.

Claim 2 The contract offered to the H type borrower is

(r

H∗

b

, cH∗

b

) = 

Kb



(1 − p

L) − mb(1 − pH)



pH(1 − p

L) − mbpL(1 − pH)

,

Kb(p

H − pL)

pH(1 − p

L) − mbpL(1 − pH)

!

.

Finally, we examine the proportions of borrower types at each bank type. In order to show that

proportionally more low type L borrowers go to global banks, it is sufficient to show that η

H∗ > ηL∗

,

19
where η

i∗

is the threshold above which borrowers of type i prefer bank G and are defined in equations

6. From the bank selection equations of 5, we have the following equalities for the two borrower types:

η

∗

H = (p

HrH

G + (1 − p

H)cH

H) − (p

HrH

D + (1 − p

H)cH

D) (14)

η

∗

L = p

L

r

L

G − p

L

r

L

D = KG − KD (15)

where we have substituted in the the solution for the contract into the expression for η

∗

i

in Equation

5. Since η

∗

L = KG − KD, to show that the proportion of L types is higher at G banks than D banks,

it is sufficient that η

∗

H > KG − KD = η

∗

L

.

Claim 3 The relative proportion of L borrowers to G banks is higher than that for D banks, μ

∗

G < μ∗

D.

Together, our results demonstrate how differences in fixed costs and monitoring costs between

global and domestic banks may affect loan terms. We showed that this will then affect the equilibrium

distribution of borrowers facing each bank type. Therefore, the next sections use empirical analysis to

account for the equilibrium selection decisions and loan bundle pricing considerations laid out above.

3.2 Heckman Selection Model

The model above outlined how information asymmetries between banks and borrowers can generate

an equilibrium where global banks may face a set of borrowers that are risker than domestic banks. To

address this endogenous selection of borrowers to bank type, we augment a Heckman two stage model

to account for the effect of the equilibrium matching between banks and borrowers. In the first stage

of the Heckman model, we model the probability of risky borrowers choosing to work with a global

bank in a probit model. We define a global bank dummy to be any bank-quarter in the top 25% of

foreign CI to total CI. Then, in the second stage, we include the inverse mills ratio from the first stage

probit in the second stage regression of global bank exposure on spreads.

20
Table 7 begins with a simple pooled OLS regression with the Global Bank Dummy. Similar to Table

3, we find that the slope coefficient on Global Bank Dummy is positive and statistically significant. On

average, banks with the highest 25% of foreign CI to total CI are associated with 11.535 basis points

higher spreads on their syndicated loans than all other banks (tstat = 4.60). These higher spreads

are realized across loan types, with 7.255 bps premium on credit lines (tstat = 3.79) and 24.007 bps

premium on term loans (tstat = 4.23). These results in Table 7 show that the Global Bank Dummy

generates quantitatively similar results to using foreign CI to total CI.

The last two columns of Table 7 reports the results of the Heckman selection model. The first-stage

Probit regression shows that higher borrower leverage and lower borrower Altman Z-score increases

the probability of a global bank leading the syndicated loan deal14. These results support our model

prediction that riskier borrowers tend to work with global banks. In addition to borrower riskiness,

we also find that firm size is positively associated with the probability of having a syndicated loan

organized by a global bank, and highly statistically significant (tstat = 21.56). In contrast to leverage

and Altman Z, firm size does not have an immediate relationship to borrower riskiness. In later

analysis, we explore the link between multinational firms, which is associated with size, and global

banking.

Finally, we evaluate the second stage regression of loan spreads on bank characteristics, borrower

characteristics, and the inverse mills ratio (λ) from the first stage probit. First, we find that even

after controlling for the selection of risky borrowers with global banks, global banks have on average a

12.716 bps (tstat = 2.77) higher loan spread than non-global banks. In addition, we continue to find

that higher leverage and lower Altman Z scores are associated with statistically higher loan spreads.

Interestingly, the coefficient on λ is positive but statistically insignificantly (tstat = 0.80). This suggests

that while risky borrowers may select to borrow from global banks, this selection does not fully explain

the higher observed loan spread of global banks.

14We choose the Altman Z-score to provide a continuous measure of borrower riskiness, as compared to discrete measures

such as credit ratings.

21
4 Global Banking and Multinational Firms

This section examines the relationship between global banks and multinational firms. Recent papers

by Niepmann and Schmidt-Eisenlohr (2014) and Paravisini, Rappoport, and Schnabl (2014) showed

that global banks provide specialized financial services to export firms. Examples of these premium

financial services that may rely heavily on the international exposure of global banks might be trade

finance, foreign exchange, and local banking services. Anecdotal evidence suggests that global financial

firms offer these premium services only to regular customers, which may be bundled with loans and

lines of credit with them. Moreover, the firms that demand these banking services are likely to be

large, multinational firms with significant foreign or international banking needs.

Using the foreign tax data item in Compustat for the borrower, and scaling by total sales, we

proxy for the borrower’s potential demand for multinational banking services15. To understand the

relationship between borrower international sales and global bank exposure, we augment the pooled

OLS and Heckman analysis of Table 7 with our foreign tax measure. Table 9 shows that multinational

borrowers have a higher probability of working with a global bank on their syndicated loan deals, that is

statistically significant. Moreover, we find that the inclusion of the borrower’s foreign tax increases the

magnitude and statistical significance on the inverse mills ratio in the second stage Heckman regression.

Nevertheless, we cannot reject the hypothesis that the richer selection of multinational risky firms with

global banks has no affect on observed loan spreads. However, we continue to find that global banks

are associated with a 12.43 bp higher loan spread that is statistically significant (tstat = 2.45).

As another piece of empirical evidence on the relationship between borrower characteristics and

bank global exposure on loan spreads, we augment our pooled OLS specification in Table 3 to include

interaction terms, with and without foreign taxes. The interaction term captures the joint effect on

global bank exposure and borrower characteristics. As a benchmark, we evaluate the pooled OLS with

15We have also used foreign pre-tax income over total pre-tax income as an alternative proxy. The results are qualitative

similar. However, given the large number of missing observations for foreign pre-tax income, we lose statistical significance

due to low power.

22
interaction terms, but without the foreign sales tax of the borrower. Table 8 shows that including the

interaction terms between global bank exposure and borrower characteristics lowers the magnitude and

statistical significance of the slope coefficient on the bank foreign exposure variable, ForeignToTotalCI.

This implies that global bank exposure alone does not necessarily imply higher loan spreads. Rather,

when global banks work with specific types of borrowers, loan spreads tend to be larger. For the

interaction terms, we find that when banks with high global exposure lend to firms that are less likely

to default (high Z score), loan spreads tend to be lower (-16.919, tstat =-2.77) than from banks with

more domestically oriented US banks. Similar results hold for the slope coefficient (-2.761, tstat =

-1.69) on the interaction term between global bank exposure and high cashflow ratio. This suggests

that US global banks price discriminate and offer better loan terms than US banks with less global

exposure. Interestingly, we find that the cross term between global bank exposure and borrower size

is positive (5.137) and statistically significant (tstat = 2.20). This suggests that when global banks

lend to larger size firms, the borrower is charged a higher rate than from a less global US bank. This

evidence supports the above argument that global banks provide specialized banking services that is

positively valued by large multinational borrowers.

Next, we repeat the pooled OLS analysis with interaction terms and include the borrower’s foreign

sales to total sales variable to proxy for multinational sales firms. First, the slope coefficient on the

foreign tax to total sales (-396.954, tstat = -3.00) suggest that more multinational borrowers tend to

have lower loan rates. However, the slope coefficient on the interaction term between global banks

and the foreign sales tax of the borrower shows a positive relationship with loan spreads. The positive

coefficient on CrossCIForeignTax loosely supports the argument that loans with global banks and

multinational firms are priced higher to include premium banking services. However, we find that this

relationship is only statistically significant for Term Loans which has the smallest sample size.

23
5 Conclusion

This paper examines the relationship between a bank’s global exposure and the loan terms realized by

their corporate borrowers. After adjusting for borrower risk characteristics, we find that on average

spreads tend to be higher for banks with more global exposure, and statistically significantly so across

loan types. This result is stronger for the sample period prior to the global financial crisis of 2008, and

remains even after controlling for loan amount or for credit ratings for those borrowers that are rated.

Motivated by results, we investigate an alternative hypothesis that higher spreads from global

banks are driven by an endogeneity in which they face a riskier pool of borrowers. Such a problem

would imply that the bank itself does not observe the riskiness of borrowers, and equilibrium loan

rates from global banks are higher due to this asymmetric information.16 To examine the equilibrium

relationship between global banks, borrower type, and loan spreads, we model banks as offering different

contracts, spreads and collateral, to screen borrowers and resolve the information asymmetry problem.

Differentiated by their cost structures, global and non-global banks offer contracts such that a larger

proportion of risky borrowers will borrow from global banks. In equilibrium, loan rates from global

banks will then be higher than loan rates from non-global banks.

We empirically evaluate our theoretical finding that riskier borrowers are more likely to choose

global banks, and drive up loan rates, by using a variation of the Heckman selection model. Rather

than self-selection by the bank, we use borrower risk characteristics in the first stage selection equation.

We find strong evidence that riskier borrowers have a higher probability of working with global banks.

However, using the inverse mills ratio in the second valuation equation, we do not find a significant

affect of this selection on the higher loan spread found previously.

Alternative to a risk based explanation to the higher spread charged by global banks, we examine

the hypothesis that global banks provide premium banking services such as trade financing, foreign

16This is in contrast to the case where the information asymmetry lies only between the econometrician and the bank.

In that case the problem would be resolved if the econometrician could condition on the bank’s variables.

24
exchange, and cross-border lending. If global banks offer these services only to customers that also

hold lines of credit or debt facilities with the bank, then spreads may be higher as an access fee for

these premium services. We conjecture that these international financing services are most needed by

firms with high foreign sales. Using data on the borrowers foreign tax paid, scaled by total tax, we

find evidence that borrowers with higher foreign taxes prefer to work with global banks, but that this

selection does not explain the global bank premium.

In this paper, we examined the role that global operation play in financing to customers. However,

valuing the social costs and benefits of global banks must account for all stakeholders. For example, how

do global banks affect firm productivity (TFP) and shareholder value (Tobin’s Q), or other measures

of firm productivity post loans. In addition, we want to more closely examine the set of firms that do

multiple syndicated loans and use the switch between banks with more or less global exposure.

25
6 References

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[3] Cetorelli, Nicola, and Linda S. Goldberg. ”Liquidity management of US global banks: Internal

capital markets in the great recession.” Journal of International Economics. vol 88, no. 2 (2012):

299-311.

[4] Claessens, Stijn, and Neeltje van Horen. ”Foreign Banks: Trends, Impact, and Financial Stability.”

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[5] Claessens, Stijn, A. Demirg-Kunt, and Harry Huizinga. ”How does foreign entry affect domestic

banking markets?.” Journal of Banking & Finance 25, no. 5 (2001): 891-911.

[6] Cornett, M., J. McNutt, P. Strahan and H. Tehranian. ”Liquidity risk management and credit

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[8] Ivashina, Victoria. ”Asymmetric information effects on loan spreads.” Journal of Financial Economics. vol 92, no. 2, (2009): 300-319.

[6] Ivashina, Victoria, David S. Scharfstein, and Jeremy C. Stein. ”Dollar Funding and the Lending

Behavior of Global Banks.” Quarterly Journal of Economics. vol 130, no. 3 (August 2015): 1241-1281.

[9] Kalemli-Ozcan, Sebnem, Elias Papaioannou, and Fabrizio Perri. ”Global banks and crisis transmission.” Journal of International Economics. vol 89, no. 2 (2013): 495-510.

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[11] Niepmann, Friederike and Tim Schmidt-Eisenlohr, ”International Trade, Risk and the Role of

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[12] Petersen, M. A. and R. G. Rajan. ”The Effect of Credit Market Competition on Lending Relationships.” Quarterly Journal of Economics 110 (1995): 407-443.

[13] Paravisini, Daniel, Veronica Rappoport, and Philipp Schnabl. ”Comparative Advantage and Specialization in Bank Lending.” Discussion paper, London School of Economics and New York University,

2014.

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the economics of imperfect information” Quarterly Journal of Economics. vol 80 (1976): 629-49.

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of Economic Theory. vol 21, no. 2, (1979): 265-293.

27
Table 1: Syndicated Loan Statistics

Deal term summary statistics pooled and across selected years. Variables reported in means and the 10th and 90th percentiles.

1984 - 2013 1987 1990 2000 2010 2013 2007 2008 2009

Number of Deals 28,591 303 677 1,552 741 776 1,067 638 448

Spread (BaseRate = LIBOR) 155.5 110.2 118.8 156.8 271.1 185.0 124.1 195.0 323.2

p10 27.5 37.5 27.5 22.5 150.0 100.0 20.0 50.0 200.0

p90 300.0 225.0 250.0 312.5 425.0 300.0 250.0 375.0 450.0

shareForeignBank 18.0% 8.8% 12.1% 18.1% 23.9% 24.6% 20.5% 17.3% 18.3%

p10 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

p90 50.0% 40.0% 50.0% 50.0% 52.5% 50.0% 50.0% 50.0% 50.0%

AnnualFee (bps) 30 33 30 27 53 35 16 33 65

p10 8 1 4 10 25 20 6 8 15

p90 57 92 50 50 75 50 26 75 131

UpfrontFee (bps) 96 161 115 73 119 63 80 170 190

p10 13 25 15 15 30 0 5 11 23

p90 200 345 260 200 200 200 150 300 300

CommitmentFee (bps) 62 77 70 70 70 52 42 51 77

p10 25 38 25 25 30 12 15 19 38

p90 100 100 100 100 113 100 75 75 113

StandbyLCFee (bps) 275 239 252 347 426 363 182 265 464

p10 83 120 100 120 250 225 53 90 255

p90 500 350 400 600 638 550 338 431 644

LoanToAssets 18.4% 20.4% 18.1% 16.9% 16.6% 15.3% 18.6% 18.0% 14.4%

p10 2.6% 1.5% 2.8% 2.3% 3.0% 2.0% 2.9% 2.2% 1.8%

p90 38.4% 43.8% 39.2% 37.7% 36.6% 33.3% 38.1% 38.1% 34.3%

LoanAmt (in Mil$) 346 126 114 290 568 900 648 447 542

p10 8 4 3 10 30 75 40 25 25

p90 835 250 300 700 1300 2000 1500 1000 1000

28
Table 2: Firm and Lead Bank Statistics

Firm and lead bank summary statistics pooled and across selected years. Variables reported in means and the 10th and 90th

percentiles. When there is more than one lead bank, the lead bank variables are averaged. Bank variables are taken from Y-9C

reports. Firm variables are taken from Compustat. Lead ForeignToTotCI is foreign CI lending as a proportion of total CI

lending. TotCItoAssets is total CI lending as a proporiton of assets. Firm Size is measured in log terms.

1984 - 2013 1987 1990 2000 2010 2013 2007 2008 2009

Number of Deals 28,591 303 677 1,552 741 776 1,067 638 448

Number of Lead Banks 344 57 89 81 34 27 41 34 37

Lead ForeignToTotCI 22.4% 21.8% 21.1% 21.9% 20.8% 29.0% 25.1% 22.0% 22.0%

p10 0.6% 0.6% 0.1% 0.8% 2.2% 3.9% 2.3% 2.1% 2.4%

p90 49.3% 48.6% 56.2% 43.9% 22.9% 75.7% 70.7% 49.0% 30.0%

Lead TotCItoAssets 16.3% 25.3% 23.0% 19.4% 8.0% 8.6% 9.9% 12.0% 10.2%

p10 7.1% 19.2% 17.2% 13.5% 4.6% 5.3% 7.6% 8.5% 6.0%

p90 26.1% 37.6% 30.1% 26.0% 13.5% 11.0% 13.3% 16.3% 15.7%

Lead Cap Ratio 7.8% 4.9% 5.5% 7.2% 9.3% 10.1% 8.4% 8.3% 9.3%

p10 5.8% 3.6% 4.5% 6.1% 7.7% 8.4% 6.0% 6.5% 7.6%

p90 9.9% 6.2% 6.5% 8.4% 10.6% 11.3% 9.8% 9.8% 11.4%

Lead Log Assets 19.36 17.54 17.72 19.44 21.03 21.26 20.74 20.68 20.89

p10 17.32 16.51 16.32 18.12 19.38 21.08 19.04 18.80 19.13

p90 21.39 18.43 19.24 20.34 21.58 21.62 21.43 21.47 21.54

Firm Leverage 30.1% 31.0% 30.2% 30.7% 29.1% 32.9% 30.5% 27.7% 28.5%

p10 2.9% 2.9% 2.3% 3.5% 1.1% 11.5% 2.9% 0.3% 1.3%

p90 55.6% 61.3% 55.0% 58.1% 55.2% 55.6% 56.8% 54.6% 59.8%

Firms Size 6.8 5.6 5.4 7.0 7.7 8.3 7.8 7.3 7.6

p10 4.2 3.5 3.3 4.5 5.5 6.4 5.6 5.1 5.2

p90 9.5 7.7 7.9 9.6 10.0 10.7 10.1 9.9 10.3

Firm Altman Z 1.5 1.8 1.8 1.4 1.5 1.6 1.6 1.4 1.3

p10 0.2 0.5 0.3 -0.1 0.1 0.3 0.2 -0.3 0.1

p90 3.2 3.1 3.2 3.2 3.1 3.0 3.2 3.3 2.9

29
Table 3: Loan Spreads: Foreign Lending as Global Exposure

OLS Regression results of base rate over LIBOR spreads on bank and firm characteristics. The first five columns are results from the entire sampleperiod. The last five columns are pre-2008 only. In both sample periods, the first columns report the pooled sample. The second columns reportsthe subsample of Term Loans only. This is contrasted to the third columns that report the results from Credit Lines only. The fourth columnsreports the results from Real Loans only. The fourth columns are contrasted with the fifth columns that report the results from RestructuringLoans only. In this table, global operations are measured with regard to foreign loans, captured in the ForeignToTotCI variable. t-Statistics arereported below the estimated coefficients. Significance at the 5% level are highlighted in bold.

Base Rate = LIBOR Full Sample (1985 - 2013) Pre 2008

All Term Credit Real Restruct All Term Credit Real Restruct

Loans Loans Lines Loans Loans Loans Loans Lines Loans Loans

ForeignToTotalCI 28.004 42.013 22.564 40.672 -8.387 30.879 41.741 27.475 47.681 -9.808

4.87 2.95 5.16 4.89 -0.36 5.12 2.87 5.81 4.97 -0.40

TotCItoAssets 17.123 -10.648 31.852 20.555 -16.339 12.811 -20.736 33.516 4.107 -6.313

1.04 -0.27 2.23 0.91 -0.26 0.78 -0.54 2.32 0.19 -0.10

Cap Ratio -92.154 38.076 -104.002 -56.680 -611.570 -16.245 25.678 -46.196 123.051 -775.021

-1.01 0.17 -1.48 -0.45 -1.79 -0.16 0.10 -0.58 0.80 -1.95

Log Assets -1.978 -5.678 -1.798 -1.552 -3.547 -2.597 -7.706 -2.538 -2.490 -4.751

-1.65 -1.99 -1.69 -1.04 -0.75 -2.01 -2.50 -2.16 -1.45 -0.92

Firm Leverage 152.168 94.944 127.012 159.054 72.599 142.193 92.876 118.811 151.580 67.729

21.05 9.34 19.49 15.45 3.74 20.66 8.95 18.98 14.24 3.42

Firm Cashflow 0.026 -0.529 0.045 0.003 -4.342 0.082 -0.403 0.083 0.049 -4.038

0.35 -0.84 0.76 0.04 -2.06 1.55 -0.66 1.70 1.23 -1.22

Firms Size -26.530 -13.751 -27.392 -23.968 -21.875 -28.059 -13.423 -28.983 -25.248 -22.202

-43.65 -8.55 -57.11 -29.99 -6.85 -43.07 -7.86 -56.96 -27.89 -6.48

Firm Altman Z -13.135 -17.780 -10.174 -9.304 -22.833 -13.548 -14.158 -11.384 -8.535 -21.395

-6.30 -5.23 -5.72 -4.24 -4.97 -6.19 -4.13 -6.12 -3.34 -4.42

Constant 248.0 331.2 240.0 197.7 400.6 269.225 362.070 265.134 212.449 429.183

7.28 5.18 8.36 4.37 3.95 7.63 5.32 8.83 4.50 3.86

Adj R-sq 0.42 0.29 0.48 0.43 0.30 0.39 0.23 0.43 0.37 0.27

Obs 20004 4093 15003 10894 1270 16255 3228 12268 7835 1111

30
Table 4: Loan Spreads: Foreign Deposit as Global Exposure

OLS Regression results of base rate over LIBOR spreads on bank and firm characteristics. The first five columns are results from the entire sampleperiod. The last five columns are pre-2008 only. In both sample periods, the first columns report the pooled sample. The second columns reportsthe subsample of Term Loans only. This is contrasted to the third columns that report the results from Credit Lines only. The fourth columnsreports the results from Real Loans only. The fourth columns are contrasted with the fifth columns that report the results from RestructuringLoans only. In this table, global operations are measured with regard to foreign deposits, captured in the ForeignToTotDep variable. t-Statisticsare reported below the estimated coefficients. Significance at the 5% level are highlighted in bold.

Base Rate = LIBOR Full Sample (1985 - 2013) Pre 2008

All Term Credit Real Restruct All Term Credit Real Restruct

Loans Loans Lines Loans Loans Loans Loans Lines Loans Loans

ForeignToTotDep 21.421 45.151 16.839 38.954 -17.844 28.416 46.928 24.176 46.304 -9.613

3.55 3.11 3.71 4.36 -0.77 4.58 3.21 5.03 4.73 -0.40

TotDepToLiab 38.041 15.604 35.344 35.727 -8.357 69.817 37.763 58.879 68.818 25.107

3.31 0.54 3.80 2.35 -0.16 5.40 1.19 5.57 3.69 0.42

Cap Ratio -196.797 57.272 -193.362 -96.733 -658.885 -245.035 -75.614 -224.683 -79.570 -877.560

-2.00 0.23 -2.46 -0.73 -1.77 -2.19 -0.28 -2.51 -0.50 -2.01

Log Assets 1.074 -2.984 0.415 1.545 -3.757 1.771 -4.112 0.650 2.581 -3.758

0.88 -1.04 0.40 0.97 -0.75 1.40 -1.32 0.58 1.56 -0.70

Firm Leverage 152.370 94.450 127.129 159.342 72.712 141.837 92.168 118.432 151.292 67.675

21.04 9.33 19.44 15.45 3.75 20.67 8.91 18.92 14.23 3.42

Firm Cashflow 0.022 -0.530 0.042 -0.003 -4.364 0.076 -0.404 0.078 0.042 -4.061

0.31 -0.85 0.72 -0.04 -2.06 1.49 -0.67 1.67 1.14 -1.22

Firms Size -26.253 -13.956 -27.133 -23.850 -21.578 -27.791 -13.666 -28.731 -25.037 -22.000

-43.01 -8.69 -56.27 -29.74 -6.76 -42.53 -8.01 -56.03 -27.59 -6.42

Firm Altman Z -13.217 -17.787 -10.254 -9.359 -22.905 -13.661 -14.140 -11.517 -8.666 -21.358

-6.32 -5.25 -5.74 -4.25 -5.01 -6.23 -4.14 -6.18 -3.39 -4.42

Constant 178.72 265.61 190.62 126.47 409.62 159.04 266.77 187.19 88.80 397.41

4.91 3.68 6.46 2.53 3.50 4.26 3.46 6.15 1.74 3.11

Adj R-sq 0.42 0.29 0.48 0.43 0.30 0.39 0.23 0.43 0.37 0.27

Obs 20004 4093 15003 10894 1270 16255 3228 12268 7835 1111

31
Table 5: Loan Spreads: Foreign Lending and Deposit as Global Exposure

OLS Regression results of base rate over LIBOR spreads on bank and firm characteristics. The first five columns are results from the entire sampleperiod. The last five columns are pre-2008 only. In both sample periods, the first columns report the pooled sample. The second columns reportsthe subsample of Term Loans only. This is contrasted to the third columns that report the results from Credit Lines only. The fourth columnsreports the results from Real Loans only. The fourth columns are contrasted with the fifth columns that report the results from RestructuringLoans only. In this table, global operations are measured with regard to both foreign loans and foreign deposits, captured in the ForeignToTotCIand ForeignToTotDep variables, respectively. t-Statistics are reported below the estimated coefficients. Significance at the 5% level are highlightedin bold.

Base Rate = LIBOR Full Sample (1985 - 2013) Pre 2008

All Term Credit Real Restruct All Term Credit Real Restruct

Loans Loans Lines Loans Loans Loans Loans Lines Loans Loans

ForeignToTotalCI 55.522 10.761 47.640 44.538 28.773 38.177 -16.557 42.205 44.279 -1.446

4.99 0.38 5.36 3.35 0.64 2.92 -0.51 3.92 2.74 -0.03

TotCItoAssets 2.338 -7.607 18.128 9.826 -19.894 -1.757 -16.987 20.227 -7.127 -9.068

0.14 -0.19 1.26 0.44 -0.31 -0.11 -0.44 1.39 -0.34 -0.14

ForeignToTotDep -29.692 35.402 -27.129 -1.573 -42.149 -7.191 62.561 -15.491 5.377 -8.220

-2.56 1.21 -2.97 -0.11 -0.93 -0.53 1.92 -1.42 0.33 -0.16

TotDepToLiab 29.071 15.379 25.053 32.557 -11.753 54.982 45.679 39.210 51.260 25.992

2.49 0.53 2.69 2.14 -0.22 3.98 1.35 3.55 2.75 0.41

Cap Ratio -234.968 52.855 -230.858 -152.877 -642.211 -220.837 -79.923 -206.861 -48.101 -873.347

-2.38 0.21 -2.90 -1.14 -1.72 -1.97 -0.29 -2.31 -0.30 -1.98

Log Assets -1.701 -3.681 -1.647 -0.226 -5.623 -0.530 -3.505 -1.455 -0.244 -3.804

-1.23 -1.10 -1.36 -0.13 -1.00 -0.35 -0.98 -1.06 -0.12 -0.61

Firm Leverage 152.668 94.544 127.424 159.465 72.751 142.449 91.923 119.141 151.834 67.635

21.11 9.32 19.52 15.48 3.73 20.70 8.91 18.98 14.26 3.41

Firm Cashflow 0.021 -0.533 0.041 -0.002 -4.416 0.075 -0.416 0.078 0.042 -4.052

0.29 -0.85 0.70 -0.04 -2.07 1.45 -0.69 1.64 1.12 -1.22

Firms Size -26.254 -13.950 -27.148 -23.864 -21.596 -27.785 -13.729 -28.723 -25.047 -22.026

-43.00 -8.69 -56.28 -29.73 -6.74 -42.48 -8.03 -56.06 -27.55 -6.39

Firm Altman Z -13.160 -17.778 -10.201 -9.308 -22.804 -13.600 -14.204 -11.427 -8.593 -21.369

-6.30 -5.25 -5.73 -4.23 -4.96 -6.20 -4.18 -6.14 -3.36 -4.41

Constant 233.293 280.603 229.651 158.089 451.059 207.619 254.663 229.893 147.715 400.792

5.94 3.37 7.11 3.02 3.44 4.95 2.87 6.60 2.62 2.67

Adj R-sq 0.42 0.29 0.48 0.43 0.30 0.39 0.23 0.43 0.37 0.26

Obs 20004 4093 15003 10894 1270 16255 3228 12268 7835 1111

32
Table 6: Loan Spreads: Global Exposure, Credit Ratings, and Loan Amounts

OLS Regression results of base rate over LIBOR spreads on bank and firm characteristics. The regression mimic those reported in Table 3, thoughcredit ratings and log loan amount are added as explanatory variables. The first five columns are results from the entire sample period. The lastfive columns are pre-2008 only. In both sample periods, the first columns report the pooled sample. The second columns reports the subsample ofTerm Loans only. This is contrasted to the third columns that report the results from Credit Lines only. The fourth columns reports the resultsfrom Real Loans only. The fourth columns are contrasted with the fifth columns that report the results from Restructuring Loans only. In thistable, global operations are measured with regard to foreign loans, captured in the ForeignToTotCI variable. t-Statistics are reported below theestimated coefficients. Significance at the 5% level are highlighted in bold.

Base Rate = LIBOR Full Sample (1985 - 2013) Full Sample (1985 - 2013)

All Term Credit Real Restruct All Term Credit Real Restruct

Loans Loans Lines Loans Loans Loans Loans Lines Loans Loans

ForeignToTotalCI 12.549 19.037 8.779 16.818 -7.513 26.008 41.589 20.935 40.398 -6.415

2.09 1.12 2.14 2.05 -0.20 4.62 2.94 4.88 4.96 -0.28

TotCItoAssets 14.129 -3.909 20.469 -7.529 2.206 22.527 -9.412 35.669 27.609 -13.011

0.58 -0.05 1.05 -0.26 0.02 1.38 -0.24 2.52 1.25 -0.21

Cap Ratio 13.909 371.177 -70.211 37.275 -11.952 -135.234 32.436 -144.087 -104.404 -579.461

0.13 1.25 -0.92 0.25 -0.02 -1.51 0.14 -2.08 -0.85 -1.73

Log Assets -0.572 -4.718 -1.165 0.952 -0.197 -0.739 -5.392 -0.872 -0.198 -2.844

-0.31 -0.81 -0.85 0.46 -0.02 -0.62 -1.88 -0.83 -0.13 -0.61

Firm Leverage 67.944 38.595 55.368 63.140 58.009 157.429 97.256 131.037 164.073 76.273

8.31 2.87 7.73 5.29 2.12 22.05 9.05 20.50 16.26 3.99

Firm Cashflow -1.826 -6.222 -0.858 -0.757 -11.892 -0.002 -0.519 0.021 -0.037 -4.789

-1.51 -2.10 -1.06 -0.62 -2.30 -0.03 -0.83 0.39 -0.62 -2.38

Firms Size 0.109 6.146 -3.050 2.136 2.601 -16.906 -12.181 -18.371 -13.266 -13.230

0.09 1.97 -3.43 1.36 0.40 -19.18 -5.27 -24.63 -12.09 -3.04

Firm Altman Z -8.595 -13.156 -6.605 -7.575 0.952 -12.427 -17.725 -9.548 -8.572 -21.449

-4.57 -3.24 -4.09 -3.17 0.09 -6.17 -5.22 -5.59 -4.08 -4.67

Firm Credit Rating 21.112 24.174 17.690 21.693 18.632

35.76 13.74 36.50 27.19 6.23

Log Loan Amt -14.102 -2.166 -13.528 -16.429 -12.535

-16.27 -1.08 -16.18 -15.12 -3.14

Constant -121.117 -176.092 -57.546 -203.011 -66.312 235.427 324.510 235.828 186.583 370.055

-2.53 -1.40 -1.53 -3.68 -0.27 6.80 5.04 7.96 4.29 3.65

Adj R-sq 0.63 0.46 0.68 0.65 0.49 0.43 0.29 0.49 0.44 0.31

Obs 10746 2127 8074 5897 517 20004 4093 15003 10894 1270

33
Table 7: Global Bank Dummy and Heckman Selection

OLS Regression results of base rate over LIBOR spreads on bank and firm characteristics using a Global

Bank Dummy and results of a Heckman Selection model used to address firm selecting to borrow from global

banks. Global banks are defined as those in the top 25% of foreign CI to total CI. The first columns reports

the pooled sample. The second column reports the subsample of Term Loans only. The third column reports

the results from Credit Lines only. The fourth column reports the results from Real Loans only. The fifth

column reports results from a first-stage Probit regression of firm characteristics on the bank global dummy.

The sixth column reports the second stage results of the Heckman Selection model, with Lambda being the

coefficient on the Inverse Mills Ratio. t-Statistics are reported below the estimated coefficients. Significance

at the 5% level are highlighted in bold.

Base Rate = LIBOR Pooled Regression Probit Heckit

All Term Credit Real Bank Global Rate over

Loans Loans Lines Loans Dummy Libor

BankGlobalDummy 11.535 24.007 7.255 16.915 12.716

4.60 4.23 3.79 4.80 2.77

TotCItoAssets 32.305 15.886 42.600 41.395 30.714

1.93 0.41 2.94 1.80 0.72

Cap Ratio -62.523 168.102 -100.329 -49.745 -162.193

-0.67 0.73 -1.38 -0.40 -1.13

Log Assets 0.048 -2.959 -0.036 1.322 1.462

0.04 -1.12 -0.04 0.96 0.96

Firm Leverage 152.037 94.420 126.866 159.142 0.467 137.213

21.04 9.28 19.47 15.48 5.33 3.42

Firm Cashflow 0.027 -0.489 0.047 0.004 -0.944

0.38 -0.78 0.79 0.06 -1.02

Firms Size -26.291 -13.738 -27.079 -23.644 0.327 -20.159

-44.33 -8.57 -58.22 -30.49 21.56 -1.47

Firm Altman Z -13.146 -17.840 -10.192 -9.331 -0.035 -13.454

-6.32 -5.29 -5.73 -4.27 -3.89 -4.39

Lambda 44.193

0.80

Constant 211.422 274.044 209.595 151.148 -3.334 253.528

6.26 4.40 7.41 3.30 -13.86 1.24

Adj. R-sq 0.42 0.29 0.48 0.43

Obs 20004 4093 15003 10894

Avg yearly R-sq 0.16 0.35

Avg yearly Obs 1043 804

34
Table 8: Loan Spreads: Cross Effects and Multinational Firms

OLS Regression results of base rate over LIBOR spreads on bank and firm characteristics, including interaction terms

between bank foreign lending and borrower firm characterisitcs. The first four columns mimic those reported in Table

3, for the full sample, with each of the borrower characteristic interacted with the bank foreign CI to total CI variable.

The first column is pooled over all loan types. The second column is term loans only. The third column is credit lines

only. The third column is real loans only. The final four columns repeat the analysis, but add in a ForeignTaxToSales

variable representing the global operation of the firm along with an interaction term between this variable and bank

foreign CI to total CI. t-Statistics are reported below the estimated coefficients. Significance at the 5% level are

highlighted in bold.

Base Rate = LIBOR Full Sample (1985 - 2013) Full Sample (1985 - 2013)

All Term Credit Real All Term Credit Real

Loans Loans Loans Loans Loans Loans Lines Loans

ForeignToTotalCI 3.620 33.319 -23.438 -51.395 41.335 103.001 1.453 -8.668

0.13 0.56 -0.98 -1.28 1.41 1.62 0.06 -0.21

TotCItoAssets 31.287 5.410 45.659 39.512 14.405 10.328 36.274 24.840

1.85 0.14 3.13 1.73 0.80 0.24 2.30 1.04

Cap Ratio -104.329 22.971 -122.174 -88.905 -183.540 -157.457 -167.951 -215.225

-1.17 0.10 -1.73 -0.74 -1.93 -0.67 -2.18 -1.65

Log Assets -0.500 -4.087 -0.242 1.162 -1.493 -4.463 -1.230 -0.247

-0.40 -1.37 -0.22 0.75 -1.11 -1.42 -1.03 -0.15

Firm Leverage 143.952 98.250 122.436 127.011 141.472 100.406 116.469 129.395

15.01 6.20 14.24 8.94 13.78 6.08 12.48 8.37

Firm Cashflow 0.130 0.233 0.122 0.143 0.355 0.193 0.392 0.574

2.14 0.53 2.09 1.98 0.80 0.35 0.86 1.18

Firm Size -27.679 -15.109 -29.183 -25.863 -26.716 -11.812 -28.766 -24.980

-34.13 -7.21 -43.62 -24.27 -28.79 -4.94 -37.94 -20.36

Firm Altman Z -9.621 -13.496 -7.732 -6.325 -9.052 -12.507 -7.755 -5.949

-5.08 -4.05 -4.63 -3.31 -4.68 -3.76 -4.33 -3.19

CrossCILeverage 30.433 -13.513 21.702 133.609 16.728 -31.652 16.499 97.066

1.03 -0.28 0.93 2.85 0.53 -0.64 0.66 1.90

CrossCICashflow -2.761 -9.594 -2.047 -3.897 -4.179 -9.632 -3.346 -7.371

-1.69 -2.34 -1.33 -1.67 -1.95 -2.24 -1.44 -2.08

CrossCISize 5.137 5.445 7.358 9.289 -0.394 -7.972 3.469 3.639

2.20 0.83 3.85 2.81 -0.15 -1.06 1.62 0.93

CrossCIAltmanZ -16.919 -19.763 -11.631 -14.909 -16.101 -19.935 -9.806 -13.327

-2.77 -1.76 -2.24 -1.64 -2.54 -1.78 -1.74 -1.41

ForeignTaxToSales -396.954 -1901.558 -297.002 -509.368

-3.00 -3.95 -2.99 -3.46

CrossCIForeignTax 625.678 4880.003 447.493 762.905

1.37 2.79 1.30 1.27

Constant 229.598 302.885 224.148 175.458 255.514 295.030 251.025 214.588

6.55 4.67 7.70 3.71 6.94 4.37 7.95 4.61

Obs 20004 4093 15003 10894 17030 3562 12700 9274

Adj R-sq 0.43 0.29 0.48 0.43 0.43 0.30 0.48 0.43

35
Table 9: Multinational Firms and Heckman Selection

OLS Regression results of base rate over LIBOR spreads on bank and firm characteristics using a Global

Bank Dummy and results of a Heckman Selection model used to address firm selecting to borrow from global

banks. Global banks are defined as those in the top 25% of foreign CI to total CI. This table mimics Table

7, the only difference being the addition of a ForeignTaxToSales variable, intended to capture the extent

of international firm operations. The first columns reports the pooled sample. The second column reports

the subsample of Term Loans only. The third column reports the results from Credit Lines only. The

fourth column reports the results from Real Loans only. The fifth column reports results from a first-stage

Probit regression of firm characteristics on the bank global dummy. The sixth column reports the second

stage results of the Heckman Selection model, with Lambda being the coefficient on the Inverse Mills Ratio.

t-Statistics are reported below the estimated coefficients. Significance at the 5% level are highlighted in

bold.

Base Rate = LIBOR Pooled Regression Probit Heckit

All Term Credit Real Bank Global Rate over

Loans Loans Lines Loans Dummy Libor

BankGlobalDummy 6.842 16.783 4.306 11.106 12.434

2.52 2.70 2.07 2.94 2.45

TotCItoAssets 15.288 20.525 33.796 23.718 14.826

0.87 0.49 2.17 1.01 0.34

Cap Ratio -178.674 -75.923 -168.163 -195.542 -212.441

-1.81 -0.31 -2.13 -1.46 -1.30

Log Assets -0.732 -3.436 -0.801 -0.059 0.241

-0.62 -1.22 -0.74 -0.04 0.17

Firm Leverage 146.956 94.238 120.137 153.638 0.508 164.386

19.14 8.65 17.10 13.92 4.99 6.92

Firm Cashflow -0.201 -0.768 -0.086 -0.340 -0.980

-0.56 -1.02 -0.26 -0.68 -0.93

Firms Size -26.583 -14.065 -27.506 -23.993 0.318 -15.729

-39.59 -7.82 -52.28 -27.06 20.90 -1.48

Firm Altman Z -12.349 -16.906 -9.782 -8.573 -0.036 -15.996

-5.72 -4.93 -5.16 -3.82 -3.73 -6.96

ForeignTaxToSales -243.635 -521.868 -194.155 -326.124 5.402 -182.376

-2.58 -2.08 -2.62 -3.31 2.10 -0.58

Lambda 63.945

1.46

Constant 244.796 294.458 239.398 201.278 -3.276 219.912

6.89 4.54 7.78 4.48 -14.28 1.34

Adj. R-sq 0.43 0.30 0.48 0.43

Obs 17030 3562 12700 9274

Avg yearly R-sq 0.16 0.36

Avg yearly Obs 891 685

36
A Appendix

Claim 1 Proof. Suppose instead that the contract was (r, c) with c > 0. Then, the bank could offer

a contract (r +

(1−p

L)

pL , c − ). This would leave the L type’s utility (and thus, incentives) unchanged.

In addition, this would reduce the H type’s incentives to deviate to the contract (since the H type

prefers collateral to interest in comparison to the L type). In addition, this new contract changes the

banks profits by p

L (1−p

L)

pL − (1 − p

L)mb = (1 − pL)(1 − mb) > 0. This implies that there would be a

profitable deviation for any low type contract with c

L

b > 0. Therefore c

L

b = 0 for all b. The zero profit

condition then dictates that r

L

b =

Kb

pL

.

Claim 2

Proof. The logic for the proof is outlined here (it follows Mas-Colell, Whinston, and Green Lemma

13.D.5). The idea is that Inequality 13 places a lower bound on the amount of collateral required for any

given r

H

b

. Indeed, it must hold with equality, otherwise the bank could increase profits by decreasing

c

H

b

and increasing r

H

b much in the same way as the proof of Claim 1. The proof then follows from

solving the system of equations given from the binding constraint 13 and the zero profit condition for

the high type, Equation 8.

Kb = p

L

r

H

b + (1 − p

L

)c

H

b

(16)

Kb = p

HrH

b + (1 − p

H)mbcH

b

(17)

Claim 3

Proof. It is sufficient to show that η

H∗ > ηL∗

. And as shown in the text, this implies that it is

sufficient to show η

∗

H > KG − KD. Note that adding (1 − p

H)(1 − mb)cH

b

to Equation 8 yields:

p

HrH

b + (1 − p

H)cH

b = Kb + (1 − p

H)(1 − mb)cH

b

(18)

37
Consequently:

η

∗

H = p

HrH

G + (1 − p

H)cH

G − p

HrH

D − (1 − p

H)cH

D (19)

= KG − KD + (1 − p

H)(1 − mG)cH

D − (1 − p

H)(1 − mD)cH

D (20)

Therefore, to show that η

∗

H > KG − KD it is enough to show that (1 − p

H)(1 − mb)cH

G > (1 − p

H)(1 −

mb)c

H

D. Using the expression for collateral in the H contract from Claim 2:

(1 − p

H)(1 − mG)cH

G > (1 − p

H)(1 − mD)cH

D (21)

⇔

KG(1 − mG)

(1 − p

L)pH − (1 − pH)pLmG

>

KD(1 − mD)

(1 − p

L)pH − (1 − pH)pLmD

(22)

We have that KG ≥ KD by assumption. Therefore, it is sufficient to show that Ω ≡

(1−m)

(1−pL)pH−(1−pH)pLm

is decreasing in m. Differentiating, we get:

∂Ω

∂m =

p

L − pH

[(1 − p

L)pH − (1 − pH)pLm]2

< 0 (23)

38