Global Financial Crisis and Foreign Currency Borrowing1

Philippe Bacchetta2 Ouarda Merrouche University of Lausanne University of Lausanne

Swiss Finance Institute CEPR CEPR

First draft: March 2015

Abstract Despite international financial disintegration, we document a dramatic increase in foreign currency borrowing among leveraged Eurozone corporates since the financial crisis. Using firm-level borrowing data, we trace this increase to two main symptoms of the global financial crisis: (1) a domestic credit crunch causing leveraged corporates to switch to foreign banks; and (2) a higher funding cost in the borrower home currency causing foreign banks to increasingly transfer currency risk to the borrower. Further, we show that disruptions in swap markets led exporters to increasingly shift from currency swaps to foreign currency bank credit. While large high-credit quality corporates could tap the bond market during the credit crunch, lower-credit quality borrowers turned to foreign banks. Although global bank lending is often reported to amplify the international credit cycle, we show that foreign banking acted as a shock absorber that weathered the real consequences of the credit crunch for Eurozone corporates that suffered most from the credit crunch. JEL classification numbers: G21, G30, E44

Keywords: Money market, swaps, credit crunch, corporate debt, foreign banks, currency risk

1 Financial support from the ERC Advanced Grant #269573 is gratefully acknowledged. This paper was earlier circulated with the title “Money Market Freeze and Foreign Currency Borrowing”. 2 Authors’ emails: philippe.bacchetta@unil.ch and ouarda.merrouche@eui.eu

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1. Introduction

Global banking flows have been a major victim of the financial crisis. While gross capital flows

declined sharply in general (e.g., Broner et al., 2013), the decline has been particularly steep for

banking flows among developed economies (Milesi-Ferretti and Tille, 2011). The literature

shows evidence of a flight home effect in syndicated bank loans (Giannetti and Laeven, 2012a)

and of financial protectionism in bank lending (Rose and Wieladek, 2014). We also observe that

global banks have increased the use of their local currency in their lending (e.g., Ivashina et al.,

2012). More generally, the evidence indicates that global banking flows amplify international

credit cycles (Giannetti and Laeven, 2012b).

In this context of substantial financial disintegration, it is surprising that foreign currency

borrowing by leveraged Eurozone non-financial corporates increased dramatically. While the

proportion of dollar borrowing by non-investment grade firms was about 30% in 2003-2006, it

increased to 90% in the second half of 2008.3 Importantly, this persistent increase in foreign

currency debt comes mainly from newly issued syndicated loans by leveraged corporates as

illustrated in Figure 1. In contrast, the increase in foreign currency bond issuance was transitory

(it lasted on average 1 quarter before Lehman) and much smaller (4 percentage points for

investment grade firms and 8 percentage points for non-investment grade firms).

The purpose of this paper is to document this surprising aspect of international banking flows and

identify the factors that led to that development. Moreover, we show that foreign banking could

mitigate the transmission of the credit crunch to employment and investment.

We argue that the increase in foreign currency borrowing by Eurozone leveraged4 firms is a

consequence of two main (and related) symptoms of the global financial crisis: the domestic

credit crunch and the drying up of the Euro interbank market. Figure 2 plots the evolution of

3 This increase cannot be attributed to a valuation effect: the Euro appreciated against the dollar by about 20% during the period when the increase was strongest, i.e., Q2-2007 and Q3-2008. 4 Throughout the paper we use the terms leveraged, non-investment grade, and low-credit quality interchangeably. Non-investment grade firms in our sample have a leverage ratio (or a ratio of long-term debt over total debt) of 19.5% against 5.7% for investment grade firms.

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dollar lending together with the Euro interbank risk premium measured by Euribor-OIS 3-month

spread. The two series are highly positively correlated: a higher cost of bank funding in home

currency is associated with an increase in foreign currency borrowing. Foreign currency

borrowing is also highly correlated with the contraction of domestic banks’ credit supply (partly

resulting from the money market freeze).5,6

All in all, we argue that the transmission of the financial crisis to the currency composition of

corporate debt operates through two channels: a domestic credit crunch channel and a currency

risk transfer channel.

Figure 1. Syndicated loan issuance of Eurozone leveraged

corporates by currency of denomination in billion US dollars

Sources: Reuters Dealscan, SDC platinum, and Datastream,

authors’calculations

Figure 2. Fraction of leveraged coporate debt (left axis) and

money market risk premium (right axis)

The relevance of the first channel of transmission (the credit crunch channel) chiefly rests on the

assumption that foreign lenders are less capital constrained than domestic lenders. This

5 An abundant literature has already established the negative consequences of money market freezes on bank lending. See, for example, Buch and Suarez (2010) for a theoretical contribution and Acharya and Mora (2015) for empirical evidence from the 2007-2008 freeze. 6 We will measure credit supply contraction by the net percentage of banks surveyed by the European Central Bank (ECB) that report tightening in credit standards to large firms in the past 3 months.

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assumption is consistent with the fact that on the eve of the crisis, Eurozone banks looked much

more vulnerable than US banks (the most important foreign lenders to Eurozone non-financial

corporates in volume and value). Eurozone banks were less capitalized and relied less on stable

forms of funding.7 According to Merrouche and Mariathasan (2012) Eurozone banks also

required much more public capital injections compared with US banks. Relatedly, Laeven and

Valencia (2013) find that a much bigger fraction of the banking system failed or was intervened

in the Eurozone than in the US, reaching 80% of total banking sector assets in some countries

like Greece, Belgium, or France.

Another fundamental difference between US and Eurozone banks is the fact that US banks are

not subject to Basel II; until now their capital requirement is still fixed under the Basel I

framework. Under the Basel I framework, the risk weight on risky and safe corporate debt is the

same. This means that US banks have greater incentive than Eurozone banks to load onto risky

corporate debt.8

The second channel means that lenders have been less willing to bear currency risk. Foreign

lenders found it more convenient to get their funding in their own currency, mainly dollars, and

avoid a currency mismatch by lending in the same currency. The main reason is that they found it

more costly to get their funding in euros or to hedge foreign currency exposure through FX swaps

given the violations of covered interest parity (CIP) during this period.

Using quarterly bond and syndicated loans issuance data by currency of denomination and lender

nationality at the firm level (from Thomson-Reuters SDC platinum for the period Q1-2003 to Q3-

2013), we find evidence for both channels being at work. Looking at the composition of newly

issued debt by instrument we find that when the domestic supply of bank credit contracts,

investment grade firms shift to bonds while non-investment grade firms shift to foreign bank

loans.9 We find that the shift to bonds is stronger for large investment grade firms and for firms

that are located in non-GIIPS countries. On the other hand, the shift to foreign bank loans is

7 For example see The Economist “Reshaping banking: the retreat from everywhere”, April 2012. According to Bankscope data in 2007 Eurozone banks had a Tier1 leverage ratio of 5% and a ratio of retail deposits to total assets of 44% against 9% and 72% respectively for US banks. 8 Relatedly, Duchin and Sosyura (2013) show that after receiving government support, US bank rebalance toward riskier assets and that this shift in risk occurs mostly within the same asset class and therefore remains undetected by regulatory capital ratios. 9 In our setting we define foreign banks as banks headquartered outside the Eurozone. Moreover, we define a foreign bank loan as a syndicated loan with at least one lead foreign bank.

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stronger for risky firms, irrespective of their size, and for firms that have a natural hedge against

currency risk (exporters). Moreover, foreign banks manage exchange rate risk by lending more in

their home currency as funding conditions deteriorate in the borrowers’ home currency. And

exporters increasingly shift from the swap market to directly borrowing in dollar from foreign

banks as a consequence of the violation of CIP. These combined effects explain why the increase

in foreign currency borrowing is dramatic during the period 2007-2009 and significant only for

non-investment grade firms.

In addition, we find that foreign banking alleviates the financial constraints of risky firms: risky

firms cut employment and investment less when they have a relationship with a foreign lender

and when they have a natural hedge against foreign currency risk.

Last, we analyze stock returns to assess the valuation effects due to exchange rate movements

and associated with the increase in dollar bank credit. Consistent with the fact that the observed

increase in dollar bank credit is mostly concentrated among exporters, we find that the exchange

rate exposure of borrowers has not increased significantly during the crisis.

We specify a linear probability model with firm fixed effects to model the borrower choice

among different sources of finance and among different currencies. We examine whether this

choice is a function of a firm exposure to the two transmission channels mentioned above.

Exposure to the credit crunch channel is captured by the interaction between home country credit

supply conditions and a borrower credit quality. Exposure to the currency risk transfer channel is

captured by the interaction between home currency risk premium and a dummy for whether

lending is by foreign banks.

The fact that we focus on within-firm time variations means that our results cannot be driven by

changes in the composition of firms tapping different forms of finance, by changes in the

aggregate demand for debt, or by changes in the demand for a particular currency over time.

Further, the fact that we focus on Eurozone countries means that our results cannot be driven by

differences in the stance of monetary policy or exchange rate expectations across countries.

Our findings on the role of foreign banks extends earlier work by Haselmann and Watchel (2011)

and Bruno and Hauswald (2012). Haselmann and Watchel (2011) document that foreign banks

(banks headquartered outside the borrower home country) play a prominent role in the syndicated

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loan market and that they lend more to riskier borrowers in developed markets. They however do

not study the role of foreign banks during a crisis. Using country-level data, Bruno and Hauswald

(2012) find a positive effect of foreign banks’ presence on real growth and this effect is stronger

during banking crises and in contexts where informational and legal frictions loom larger,

hindering firms’ access to credit. Our firm-level data allow for a better identification of the

channel through which the presence of foreign banks alters firm performance during a crisis.

Our paper extends two other strands of literature on the reshaping of corporate financing during

the credit crunch and on the real effects of the credit crunch. We confirm the result of Ivashina

and Becker (2014) that Eurozone corporates increased their reliance on the bond market but we

show that the shift to bond markets does not concern debt raised for real investment finance

purposes (which matters more for real outcomes) and we do not find supporting evidence that

firms that tapped the bond market increasingly did so because they faced a reduction in bank

lending rather than because bond markets became more attractive for them due to flight to

quality.10

A theoretical literature studies the choice between bank finance and bond finance. Diamond

(1991) explains this choice by the interaction between borrower reputation and monitoring.

Reputation effects eliminate the need for monitoring (when interest rates are low and expected

future profitability is high) so that borrowers with higher credit ratings choose to raise debt

directly from the market rather than via financial intermediaries. Holmstrom and Tirole (1997)

use a model with firms that are heterogeneous in their net worth to study the effect of a credit

contraction on the forms of financing. In line with our findings they obtain that a contraction in

credit induces an increase in bank finance for firms with low net worth. Fiorella de Fiore and

Uhlig (2013) develop a general equilibrium model with firms that differ in their risk of default

that can replicate the aggregate shift from bank finance to bond finance witnessed in Europe since

2009.

10 Our samples are however quite different. We do include credit lines as well as term loans, but do not cover small loans because we are also interested in studying the changing patterns of corporate financing towards foreign banks. Ivashina and Becker (2014) collect additional debt data from CapitalIQ which unfortunately are not sufficiently granular to know whether the loans are from domestic banks or from foreign banks. And we do not cover three countries due to the unavailability of the credit supply contraction index.

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The literature on foreign currency borrowing or lending focuses on emerging markets in the

context of financial crises11, but there is little work on advanced economies. The empirical

evidence shows that firms are more likely to borrow in foreign currency when they are exporters

or with large cross-currency interest differentials (e.g., Keloharju and Niskanen, 2001 or

McCauley et al., 2015). For the recent financial crisis, Ivashina et al. (2012) find that Eurozone

banks reduced their dollar lending.

The growing literature on the real consequences of the 2008-2009 credit crunch includes

Chodorow-Reich (2013); Bentolila et al. (2013), and Haltenhof et al. (2014) who study the

impact on employment; and Acharya et al (2014) and Cingano et al. (2014) who also analyse the

effect on investment. Like our paper, all these papers find a significant effect on both

employment and investment exploiting micro (firm or industry) level data. But none of these

papers studies the mitigating role of foreign banks which is a main focus of this paper. Our

findings also contrast with some papers, using US data, showing that bond market access

mitigates the real effect of the credit crunch. Using Eurozone data we show instead that the

increase in bond market activity concerned only non-real investment purpose debt and did not

concern firms that suffered most from the credit crunch.

The remainder of the paper is organized as follows. The next section develops the theoretical

hypotheses that guide our empirical analysis and interpretation of the data. Section 3 describes

the methodology and the data. Section 4 discusses the results and Section 5 concludes with policy

lessons.

2. Theoretical Predictions

Consider an economy where three categories of agents co-exist: firms, banks, and bond investors.

Firms differ along two dimensions: credit quality and transparency (or size). And banks are split

into domestic and foreign banks. Loans are denominated in domestic currency or in foreign

currency. Banks borrow from other banks in either the domestic or the foreign interbank market

11 For theoretical papers, see Aghion et al. (2004), Burnside et al. (2004), Jeanne (2005), or Schneider and Tornell (2004).

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to fund their loans. Following Holmstrom and Tirole (1997) and Diamond (1992) we make the

following key assumptions:

(i) Bonds are contracts that depend only on public information. As a consequence, they

are used primarily by high-quality and transparent borrowers. Nonetheless financing

through bonds is a risky choice for firms because a situation of financial distress can

only be resolved with liquidation and the total loss of the firm’s net worth.

(ii) Bank loans are an information intensive source of finance. Banks spend resources to

acquire information and monitor borrowers hence they reduce the amount of required

collateral. Therefore low-credit quality (non-investment grade) firms demand more

information intensive finance. However, the fact that banks spend resources to acquire

information implies that bond finance is less costly than bank finance.

And we add assumption (iii):

(iii) Foreign loans are more costly than domestic loans and bonds because foreign banks

are newer in the market and unlike domestic banks cannot capitalize on past

information production. In other words, domestic banks have an informational

advantage over foreign banks.

The currency denomination of loans can be determined by several factors (see Shapiro, 1985, for

an early analysis). First, assume that banks prefer to fully hedge currency risk. This can be done

by lending and borrowing in the same currency. For example, a foreign bank can lend in

domestic currency borrowing from the domestic interbank market. Alternatively, a foreign bank

may borrow in foreign currency and lend in domestic currency using FX swaps. In these cases, a

foreign bank can offer a domestic currency loan to a domestic firm at not risk. However, if the

domestic funding market or the FX swap market are not functioning efficiently or are costly to

use, a foreign bank may prefer borrowing and lending in foreign currency. In that case, the

domestic firm bears the exchange rate risk (which may become a credit risk for the bank).

However, exporting firms should be less sensitive to a change in currency denomination.

When domestic currency depreciations are anticipated, it may be that domestic firms prefer

borrowing in foreign currency because they find the domestic interest rate “too high”. The reason

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is that domestic firms are less affected by a depreciation than lenders: in case of a large

depreciation firms may default (Aghion et al., 2004).12 Then domestic firms may prefer foreign

currency borrowing with high income and currency risk.

We are interested in the effect of the financial crisis on the choice between different sources of

finance, the currency denomination of newly issued loans, employment, and investment. We

discuss two channels of transmission: a credit crunch channel which operates through domestic

banks and a currency risk transfer channel which operates through foreign banks.

i. Credit crunch channel

A financial crisis causes domestic banks to cut credit to firms. Following from assumptions (i) to

(iii) we can derive the following hypotheses regarding the impact of a domestic credit crunch on

the forms of financing for different categories of firms.

Hypothesis 1. In response to a contraction in the supply of domestic credit, investment

grade (and large or transparent) firms shift to bonds while non-investment grade firms (of

all size) shift to foreign loans.

Holmstrom and Tirole (1997) show that a credit crunch hits more severely non-investment grade

firms. If investment grade firms are unaffected by the credit crunch we ought to observe that they

do not shift to bonds or that any shift to bonds is not attributable to the fact that these firms are

financially constrained. A simultaneous flight to quality in the bond market is a plausible

confounding factor; it renders bond finance more attractive than bank finance for investment

grade firms.

Now consider what happens to small investment-grade firms. Since large investment-grade firms

shift to bonds, that may crowd-in domestic bank credit for small investment-grade firms.

Hypothesis 2. During a domestic credit-crunch, small investment-grade firms shift from

foreign to domestic loans. This follows directly from the assumption that foreign bank loans

are more onerous than domestic bank loans (foreign premium).

12 Firms may also receive a government subsidy in case of large depreciation (Burnside et al., 2004, Schneider and Tornell, 2004).

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This should hold if the cost of domestic loans remains below the cost of foreign loans for this

category of borrowers.

ii. Currency risk transfer channel

Foreign banks react to the increase in the cost of funding in host currency by lending less in host

currency and more in foreign currency.13

Hypothesis 3. When host country interbank markets dry up, foreign banks increasingly

transfer the currency risk to borrowers: they lend less in the borrower home currency and

more in their home currency.

We may also expect that low-credit quality firms are more willing to assume the currency risk as

the cost of borrowing in domestic currency increases relative to the cost of borrowing in foreign

currency (Aghion et al., 2004).

The spillover of interbank markets turbulence has also consequences on the use of currency

swaps for funding in foreign currency and this in turn has potential consequences on the demand

and supply of bank credit in foreign currency. Ivashina et al. (2012) and Baba et al. (2008) have

shown that the reduced access to interbank dollar funding led to an increased use of synthetic

dollar funding. The subsequent increase in the cost of dollar funding through swaps should cause

a reduced supply of dollar loans by domestic banks and an increase in the demand for dollar loans

by exporters which is more likely to be met by foreign banks.

Hypothesis 4. An increase in the cost of synthetic foreign currency borrowing leads to a

decline in the supply of foreign currency loans by domestic banks and an increase in the

supply of foreign currency loans by foreign banks.

Typically when the swap market is well-functioning a Eurozone exporter would find it attractive

to raise dollar through swapping euro against dollar with a US exporter. The dollar interest rate

paid to the US exporter would tend to be lower than the dollar interest rate she would pay to a

bank. However when liquidity in the swap market evaporates, as it did since 2007, synthetic

dollar borrowing becomes more onerous. One should then observe an increase in dollar lending

from foreign banks and this effect should be larger for exporters.

13 Ivashina et al. (2012) show that the disruption in the FX swap markets led Eurozone banks to lend less in dollars.

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iii. Real effect of the credit crunch

In light of Hypotheses 1 and 2, what will be the consequence of the credit crunch on employment

and investment and the mitigating role of foreign banking?

Hypothesis 5. The real effects of the credit crunch are mitigated for non-investment grade

firms that have a relationship with a foreign bank.

Firms and banks form relationships and cannot costlessly or swiftly switch to borrowing from

less capital constrained foreign banks (Chodorow-Reich, 2013). However the benefit of using a

previous foreign bank relationship, or conversely the lemons cost to switching to a foreign lender,

should decline with the transparency (size) of the borrower (Sufi, 2007; Williamson, 1987).

Following from Hypothesis 3, if the chief purpose of foreign banks is to limit their exposure to

risk, they will refrain from transactions that rather than transferring the currency risk transform it

into credit risk. This happens the more they extend foreign currency loans to borrowers who do

not earn foreign currency revenues.

Hypothesis 6. The real benefit of foreign banking during a credit crunch is stronger for

firms with a natural hedge against currency risk

In the next section we present the methodology and data that we use to test these hypotheses.

3. Methodology and Data

First, we investigate the relevance of the credit crunch channel: we relate a direct country-level

time-varying measure of domestic credit supply contraction (CCI) to the choice between different

sources of finance.

Our prior interpretation is that firms switch to alternative sources of finance when domestic credit

is tight because they are financially constrained. For this interpretation to be valid we must also

show that domestic credit supply conditions are not negatively correlated with bond market

liquidity or with changes in the supply of foreign banks’ credit.

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Second, we document the currency risk transfer channel using a specification that relates the

currency denomination of newly issued loans with the lender cost of funding in the borrower

home currency.

a. Methodology

i. Credit crunch channel

Hypothesis 1 posits that more risky firms tend to borrow with foreign loans. We test this

hypothesis in two steps using the following baseline regression:

(1) 𝐹𝐹𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖 + 𝛽𝛽1𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 ∗ 𝑁𝑁𝑁𝑁𝑁𝑁 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 + 𝛽𝛽2𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 ∗ 𝑅𝑅𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 + 𝛽𝛽3𝑅𝑅𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖

In the first regression, the left-hand side variable is a dummy that takes value one if firm i

headquartered in country j issues a bond at time t and 0 if it issues a loan. In the second

regression, 𝐹𝐹𝑖𝑖𝑖𝑖𝑖𝑖 is a dummy that takes value one if the firm issues a syndicated loan at least partly

subscribed by a foreign (extra-Eurozone) lead bank and zero if it issues a (fully) domestic loan or

a bond. Hence all the firms in our analysis have a positive demand for debt.

𝛼𝛼𝑖𝑖 is a firm fixed effect and 𝛾𝛾𝑖𝑖 a time fixed effect.14 Risky is a dummy that indicates whether the

firm is rated investment grade and Not risky whether it is rated below investment grade or not

rated. 𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 is a country level credit contraction index, i.e. a measure of the decline in the supply

of domestic bank credit which varies over time as well as across countries. The inclusion of firm

fixed effects is key to our analysis: it rules out the possibility that our results could be driven by

changes over time in the composition of firms raising debt. And the fact that we focus on changes

in the debt composition rather than the debt level means that we abstract from changes in the

demand for debt.

The coefficients of interest are 𝛽𝛽1 and 𝛽𝛽2 estimated by OLS.15 They are interpreted as average

effects on the probability that a firm issues a bond or borrows from a foreign bank. If Hypothesis

14 All our results are also robust to the inclusion of country*time fixed effects. 15 We specify a linear probability model in order to include firms fixed effects. Our analysis would not benefit from using non-linear models such as probit or logit (see Angrist and Pischke, 2009). All predicted probabilities from our model range between 0 and 1. Horrace and Oaxaca (2006) show that, as the relative proportion of linear probability models (LPM)’ predicted probabilities that fall outside the unit interval increases, the potential

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1 is verified we should obtain 𝛽𝛽1 > 0 and 𝛽𝛽2 < 0. We estimate equation (1) for the full sample

and for a sub-sample of debt issued for real investment purposes (general corporate purpose and

working capital).

To test Hypothesis 2, we run an augmented version of equation (1) allowing 𝛽𝛽1 and 𝛽𝛽2 to vary

across firm size bins.

ii. Competing Interpretation

A competing interpretation for 𝛽𝛽1 being positive is that bonds or foreign loans become more

attractive independently of the deterioration of the domestic supply of bank credit. A flight to

quality or liquidity in bond markets may render bonds more attractive for investment-grade

borrowers. Figure 3 shows a high correlation between CCI and bond investors risk aversion.

Foreign loans may become more attractive if the cost of funding of foreign banks falls relative to

the cost of funding of domestic banks due to looser monetary policy abroad. To test the relevance

of these alternative interpretations we proceed as follows:

1. In the case of the shift to bonds we let the coefficients 𝛽𝛽1 and 𝛽𝛽2 differ between firms

headquartered in countries that experienced a severe disruption in bond markets due to

heightened sovereign risk (GIIPS countries) and firms headquartered in other countries

(Not-GIIPS countries). If the shift to bonds is more significant for GIIPS corporates that

gives more credibility to the prior interpretation that the shift is due to the contraction in

bank credit rather than to the flight to quality in the bond market.

2. In the case of the shift to foreign loans we test the robustness of our estimates to adding as

a control variable the interaction between 𝐶𝐶𝐶𝐶𝐶𝐶 and the monetary policy rate abroad.

3. In addition, we test whether the difference in the spread between safe and risky borrowers

varies positively with CCI for bonds (indicating a positive correlation with a flight to

quality in the bond market) and negatively with CCI for foreign loans (indicating a

positive correlation with heightened search for yield among foreign lenders). For this we

estimate the following regression:

bias of the LPM increases. Conversely if no (or very few) predicted probabilities lie outside the unit interval then the LPM is expected to be unbiased and consistent (or largely so).

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(2) 𝑟𝑟𝑠𝑠𝑟𝑟𝑠𝑠𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖 + 𝛾𝛾𝑐𝑐𝑖𝑖 + ∑ 𝜎𝜎𝑠𝑠𝑠𝑠 + [∑ 𝜎𝜎𝑠𝑠𝑠𝑠 ∗ 𝑅𝑅𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖]𝛽𝛽1𝑠𝑠 + �∑ 𝜎𝜎𝑠𝑠𝑠𝑠 ∗ 𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖�𝛽𝛽2𝑠𝑠 + 𝑋𝑋𝑖𝑖𝑖𝑖𝛽𝛽3 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖

Where spread is the cost of debt issued, 𝛾𝛾𝑐𝑐𝑖𝑖 are country*time fixed effects, ∑ 𝜎𝜎𝑠𝑠𝑠𝑠 are issue type

fixed effects (bond, foreign loan, or domestic loan), and X is a vector of control variables including the

issue size, maturity, and issue purpose dummy (real investment purpose dummy). The other

variables are as previously defined (see Equation 1). Here too the inclusion of firm fixed effects

is important since otherwise, because of flight to quality or search for yield interest rate, spreads

across periods would not be comparable.

If 𝛽𝛽2𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵>0, that indicates that the difference in the cost of bond debt between good and bad

borrowers widens during the credit crunch which is suggestive of CCI being positively correlated

with a flight to quality or liquidity in the bond market. That would cast doubt on the validity of

our prior interpretation.

If 𝛽𝛽2𝐹𝐹𝐵𝐵𝐹𝐹𝐹𝐹𝑖𝑖𝐹𝐹𝐵𝐵 𝐿𝐿𝐵𝐵𝐿𝐿𝐵𝐵<0, that indicates that the difference in the cost of foreign debt between good and

bad borrowers narrows during the credit crunch, which is suggestive of CCI being positively

correlated with heightened search for yield among foreign lenders. Again that would cast doubt

on the validity of our prior interpretation.

iii. Currency risk transfer channel

Hypothesis 3 is tested using the following specification:

(3) 𝐹𝐹𝐶𝐶𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖 + 𝛾𝛾𝑖𝑖 + 𝑅𝑅𝑅𝑅𝑖𝑖 ∗ 𝐹𝐹𝑁𝑁𝑟𝑟𝑠𝑠𝑟𝑟𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 ∗ 𝜌𝜌1 + 𝐹𝐹𝑁𝑁𝑟𝑟𝑠𝑠𝑟𝑟𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 ∗ 𝜌𝜌2 + 𝑋𝑋𝑖𝑖𝑖𝑖𝜌𝜌3 + 𝜀𝜀𝑖𝑖𝑖𝑖

Where the left-hand side variable is a dummy that indicates if the loan issued is not in the

borrower home currency; RP is a vector of domestic (Euro) and foreign (Dollar) interbank risk

premium; Foreign indicates whether the lender is a foreign bank; and 𝑋𝑋𝑖𝑖𝑖𝑖 is a vector of control

variables including the borrower credit-quality, a dummy for whether the borrower has a natural

hedge against currency risk16, and the Euro-Dollar interest rate differential. The coefficients of

interest are 𝜌𝜌1=[𝜌𝜌1𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸,𝜌𝜌1𝐷𝐷𝐸𝐸𝐷𝐷𝐷𝐷𝐷𝐷𝐸𝐸]. A positive coefficient 𝜌𝜌1𝐸𝐸𝐸𝐸𝐹𝐹𝐵𝐵 indicates that an increase in the cost

16 We measure this by whether she belongs to an export intensive sector (a sector with higher than median export sale to total sales)

14

of funding in the borrower home currency reduces the probability that foreign banks lend in the

borrower home currency.

We run alternative specifications where we include triple interactions to test whether the

willingness of the borrower (or lender) to assume exchange rate risk vary with her natural hedge

against currency risk. We also run a specification where the main explanatory variable is the cost

of hedging against currency risk, the Euro basis, to assess how the spillover of money market

disturbance to cross-currency swap markets affects the supply and demand of bank credit in

foreign currency (Hypothesis 4).

The specification reads as follows:

(4) 𝐹𝐹𝐶𝐶𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖 + 𝛾𝛾𝑖𝑖 + 𝑏𝑏𝑠𝑠𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖 ∗ 𝐻𝐻𝑠𝑠𝑠𝑠𝐹𝐹𝑠𝑠𝑠𝑠𝑖𝑖 ∗ 𝜌𝜌4 + 𝑏𝑏𝑠𝑠𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖 ∗ 𝐹𝐹𝑁𝑁𝑟𝑟𝑠𝑠𝑟𝑟𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖 ∗ 𝐻𝐻𝑠𝑠𝑠𝑠𝐹𝐹𝑠𝑠𝑠𝑠𝑖𝑖 ∗ 𝜌𝜌5+ 𝑋𝑋𝑖𝑖𝑖𝑖𝜌𝜌6 + 𝜀𝜀𝑖𝑖𝑖𝑖

Where now 𝑋𝑋𝑖𝑖𝑖𝑖 also includes relevant partial terms and Hedged is a dummy that indicates

whether the firm belongs to an export intensive industry. If Hypothesis 4 is verified we should

obtain that 𝜌𝜌4 < 0 and 𝜌𝜌5 > 0.

iv. Real Effects of the Credit Crunch

To test Hypotheses 5 & 6 we estimate the difference in the change in employment and investment

during the credit-crunch for low-credit quality firms, between firms that have a relationship with

a foreign bank and firms that do not have such a relationship. The regression for the change in

real outcome R reads:

(5) ∆𝑅𝑅𝑖𝑖𝑖𝑖 = 𝛿𝛿𝑖𝑖 + 𝜃𝜃𝑠𝑠 + 𝜇𝜇1∆−1𝑅𝑅𝑖𝑖𝑖𝑖 + 𝜇𝜇2𝑅𝑅𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖 ∗ 𝐻𝐻𝑠𝑠𝑠𝑠𝐹𝐹𝑠𝑠𝑠𝑠𝑖𝑖𝑠𝑠 + 𝜇𝜇3𝑅𝑅𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖 ∗ 𝑈𝑈𝐹𝐹𝑠𝑠𝑠𝑠𝐹𝐹𝑠𝑠𝑠𝑠𝑖𝑖𝑠𝑠 + 𝑋𝑋𝑖𝑖 ∗ 𝜇𝜇4+ 𝜀𝜀𝑖𝑖𝑖𝑖

where ∆−1𝑅𝑅𝑖𝑖𝑖𝑖 is the lagged dependent variable; 𝛿𝛿𝑖𝑖 and 𝜃𝜃𝑠𝑠 are country and industry-sector fixed

effects; 𝑅𝑅𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖 indicates whether the firm was rated below investment grade before the credit

crunch and captures the firm’s exposure to the credit crunch; 𝑋𝑋𝑖𝑖 is a vector of variables which

control for credit demand (cash holdings before the credit crunch, whether the last pre-crisis debt

issued was a credit line rather than a term loan or a bond, and whether the firm has a debt

maturing during the credit crunch), access to the bond market, and other relevant firm

15

characteristics (total assets and age). 𝐻𝐻𝑠𝑠𝑠𝑠𝐹𝐹𝑠𝑠𝑠𝑠𝑖𝑖𝑠𝑠 (𝑈𝑈𝐹𝐹𝑠𝑠𝑠𝑠𝐹𝐹𝑠𝑠𝑠𝑠𝑖𝑖𝑠𝑠) is a dummy that indicates whether

the firm belongs to a sector with higher (lower) than median export sales over total sales.

If foreign banking alleviates the financial constraint of firms we should expect 𝜇𝜇2 to be

significant and negative only in the sample of firms that do not have a relationship with a foreign

bank. A firm with a foreign bank relationship is defined as a firm that has borrowed from a bank

headquartered outside the Eurozone at least once between 2003 and 2012 (our sample period).

Further if Hypothesis 6 is verified we should find that 𝜇𝜇3 is not significantly different between

the sample of firms with and without a foreign bank relationship.

b. Data

Our benchmark sample covers the quarterly debt issuance of Eurozone non-financial corporates

during the period Q1-2003 to Q3-2013. The data source for bonds and syndicated loans is

Thomson-Reuters SDC platinum and Dealscan. We are able to distinguish between three sources

of finance: bonds, domestic syndicated loans, and foreign syndicated loans. For each form of debt

we observe the amount in US dollars, the currency denomination, and can separate real

investment purpose debt and debt raised for other purposes such as mainly refinancing and

restructuring purposes (leveraged-buyouts, mergers and acquisitions). We also obtain the

(partially populated) spread to benchmark at issuance and maturity of the debt. We include only

non-convertible bonds and exclude mortgage backed-securities, asset-backed securities, and

preference shares which are listed as bonds. And include both term loans and credit lines.

2537 firms borrow from foreign banks and 511 firms issue foreign currency loans. Foreign loans

are defined as syndicated loans underwritten by at least one lead bank headquartered outside the

Eurozone. As is common in the literature, we consider loans to be issued by the lead banks.17

Syndicated loans are often subscribed by more than one lead bank, but we do not observe the

contribution of each lead bank. In order to get a proxy for the amount extended by foreign banks

we prorate the total amount by the number of lead banks in a syndicate. US banks participate in

17 A syndicated loan is jointly extended by a group of banks, including one or several lead banks and other participant banks. Lead banks assess the quality of the borrowers and negotiate terms and conditions. Once the main terms are in place, lead banks invite other banks to acquire a stake of the loan.

16

about 9% of all syndicates and other foreign (i.e. extra-Eurozone) banks 20% on average over the

sample period with peaks at 20% and 30% respectively after 2008-Q3.

The data are organized as a panel of firm-quarter observations with positive debt issuance. As our

models include firm fixed effects we eliminate from our regressions firms that tap only one

source of finance and borrow in only one currency. Indeed the 𝛽𝛽 coefficients in equation (1) can

be identified only for switchers, i.e. firms that switch from one source of finance to another and

from one currency to another.18

We match the firm-level data with macroeconomic variables used in the analysis. The credit

contraction index (CCI) is from the ECB bank lending survey and measures the net percentage of

banks that report having tightened their lending standard for large firms in the past 3 months.

Although the method of calculation of this index is not harmonized across countries that does not

affect our analysis because our regressions include firms fixed effects. Three countries for which

the index is not available, Greece, Finland, and Belgium, are excluded from the sample. From the

same survey we also collected a measure of credit demand, the net percentage of banks reporting

an increase in the demand for credit by marge firms.19 We calculate an orthogonalized CCI form

which we have subtracted correlated variations in the supply and demand for credit in the US. We

downloaded the US credit supply and demand indices from the Fed website. We use the Fed fund

target from Datastream to proxy for the stance of monetary policy in the US.

Table 1 reports summary statistics for the variables issued in regressions (1), (2), and (3) for the

sample of firms that issue both bonds and loans (Panel A); the sample of firms that issue both

domestic and foreign loans (Panel B); and the sample of firms that issue both foreign and

domestic currency denominated syndicated loans (Panel C). Non-investment grade firms

represent about one third of the observations in both samples. Bond spreads at issuance are

calculated as yields above the equal maturity ECB yield curve spot rate. Loan spreads at issuance

are all in drawn spreads above Libor. In Panel A, bonds represent 63 per cent of total debt issued,

the average spread to benchmark is about 137 basis points for bonds and 112 basis points for

loans, and maturity on average about 9 years for bonds (value weighted average by quarter) and 5

18 Hence our analysis will deliver lower band estimates of the real effect of the credit crunch because it misses firms that are excluded from all sources of finance. 19 Our results are robust to use the overall credit supply and demand indicators. There is a 95% correlation between the overall indicators and the indicators for large firms only.

17

years for loans. The average spread for non-investment grade borrowers (all instruments

confounded) is 212 basis points and the average maturity 9 years indicating that low-credit

quality borrowers are charged higher spreads but borrow at longer maturities compared to high-

credit quality borrowers. Average issue size is above 1 billion USD. The characteristics of debt

instruments in the second sample are about the same. Here the share of bank debt subscribed by

at least one foreign bank is above 60 per cent.

The credit contraction index experienced important variations over the sample period from -7% at

the 10th percentile to 39.2% at the 90th percentile. Variations across countries are important with

an earlier, more persistent, and deeper contraction of credit in southern countries. The period

covered also witnessed important fluctuations in the stance of US monetary policy with the

average quarterly fed funds target varying between 1.76% and 5.22%. Hence the importance of

controlling for US monetary policy, as loose monetary policy could trigger higher risk taking

among US banks and therefore would contribute also to explain changes in the pattern of

corporate financing across different types of Eurozone corporates.

In Panel B, foreign loans represent more than 60% of the sample. The sample is comparable to

the sample used in specification 1 as regards the characteristics of the debt issued, the

characteristic of the average borrower, and macroeconomic variables. In Panel C we report the

average interbank market risk premium for the domestic market and the US market and the

currency basis (the cost of hedging against EUR/USD exchange rate risk). The US premium

reached significantly higher values following the Lehman default and significantly lower values

since the break out of the Eurozone sovereign debt crisis. The currency basis is the difference

between the FX-swap implied dollar rate and the USD Libor 3 month, a measure of the cost of

hedging against EUR/USD exchange rate risk. The FX-swap implied dollar rate is calculated as 𝑥𝑥𝐹𝐹

𝑥𝑥𝑆𝑆(1 + 𝐸𝐸𝐸𝐸𝑟𝑟𝑟𝑟𝑏𝑏𝑁𝑁𝑟𝑟 3 𝑚𝑚𝑁𝑁𝐹𝐹𝑁𝑁ℎ) . 𝑥𝑥

𝐹𝐹

𝑥𝑥𝑆𝑆 is the ratio between the EUR/USD 3 month forward exchange rate

and the spot rate. Rates and exchange rate data are downloaded from Reuters. The currency basis

experienced two peaks in Q4-2008 (Lehman collapse) and Q3-Q4 2011 (Greek crisis).

Table 2 summarizes the composition of our sample by country. Columns I and III show the

number of observations for the sample of firms with access to the bond market and for the sample

of firms with a foreign bank relationship by country. In column II the share of bond debt seems

18

high for Portugal and Austria which is attributable to the fact that we do not cover small loans. In

column IV the percentage of debt that are subscribed by foreign banks is generally high at around

60% on average (74% for Spain and Portugal), and the average prorated contribution of foreign

banks (column V) is about 35%. When we consider the full sample of firms with and without

foreign bank borrowing, the participation of foreign banks is much higher in foreign currency

loans than in domestic currency loans, 75% and 42%, respectively.

Figure 4 depicts the sharp decline in syndicated lending since 2007 by loan purpose, with a more

immediate decline in real investment purpose loans.

To estimate regressions (4) and (5) we hand-matched the SDC data with the Bureau van Djink

Amadeus data which contains the number of employees by firm and firms’ balance sheet data.

We could exactly match 691 firms and after eliminating firms that reported zero total assets in

2007 we were left with a sample of 506 firms and nine countries. Of these 506 firms about half

are non-investment grade firms or leveraged firms, 116 have issued a bond over the SDC sample

period and 268 firms had a relationship with a foreign bank, which means that they borrowed at

least once from a foreign lead bank between 2003 and 2012.

Table 3 reports the descriptive statistics of the variables used in the regressions for the sample of

firms at the intersection of SDC and Amadeus. The sample is about equally split between firms

with and without a foreign bank relationship and risky and safe firms. We notice that about 20%

of the firms have debt maturing during the credit crunch and the same proportion had contracted

a credit line rather than a term loan or a bond pre-crisis initially due to mature after 2009.20 Both

proportions are significantly higher for the sample of firms that have a relationship with a foreign

bank. Another variable we obtain to capture the demand for credit is the cash to total assets ratio

in 2007. Cash includes bank accounts and cash equivalents such as marketable securities and

short-term government bonds.

The growth rate in the number of employees per firm (winsorized at the 1% and 99% level to

remove outliers) is on average -12% between 2007 and 2009, and twice higher in the sample of

firms with a foreign bank relationship, suggesting that the fact that these firms had greater

reliance on credit lines, were slightly less liquid, and were more likely to have debt maturing

20 This is meant to capture whether the firm raised an instable or a stable form of debt to cover its liquidity needs during the credit crunch.

19

during the credit crunch, caused them to lay off more. Because firms with and without a foreign

bank relationship differ in all these dimensions which matter for employment it is important to

control for all these factors to be able to properly identify the role of foreign banking as a cushion

against domestic credit shocks.

Figure 5 shows that the drop in the number of employees per firm between 2005 and 2012

coincides with the timing of the credit crunch. We observe a decline already starting in 2006 but

this is due to an increase in the number of firms reporting to Amadeus in 2007, for this reason we

focus our analysis on the change between 2007 and 2009.

We also calculate investment growth as the ratio of the change in fixed assets between 2007 and

2009 scaled by tangible assets in 2007 (also winsorized at the 1% and 99% level to remove

outliers). We set negative values to zero.

We next turn to the multivariate regression analysis of our data testing one hypothesis at a time.

This will allow accounting for confounding factors which a simple comparison of means cannot

do.

4. Results

This section is organized as follows. First, we document the switch to alternative forms of finance

in reaction to the credit crunch and discuss estimates of equation (1). We discuss estimates of

equation (2) which we use to assess the validity of our prior interpretation that borrowers

increasingly tap alternative sources of finance because they are credit constrained rather than

because these alternative sources of finance become more attractive due to (simultaneous) flight

to quality or search for yield. Then we assess the relevance of the currency risk transfer channel

using equation (3). Finally, we turn to the estimation of equation (4) which we use to study the

real cost of the credit crunch and the mitigating role of foreign banking.

a. Safe borrowers shift to bonds

In Table 4 we report estimates of equation (1) when the dependent variable is a dummy that takes

value 1 if the firms issues a bond and 0 if it issues a loan. Hence the estimates are interpreted as

average effects on the probability of issuing a bond. In column I, 𝛽𝛽1� is positive and statistically

significant at the 5 per cent level and 𝛽𝛽2� is negative and statistically significant at the 10 per cent

20

level, which confirms hypotheses 1: as domestic bank credit contracts investment grade firms

shift to bonds and non-investment grade firms shift to loans. The effect is also economically

significant: a contraction of credit from the 10th percentile to the 90th percentile is associated with

an increase 7.5 percentage point increase in the probability of issuing a bond for investment grade

firms and an 8 percentage point increase in the probability of issuing a loan for non-investment

grade firms.

Next we split the sample of firms into three size bins (large, medium, and small) and re-estimate

the coefficients 𝛽𝛽1� and 𝛽𝛽2� for firms falling in each bin. The results reported in column II confirm

that larger or more transparent firms have a better access to the bond market. For the investment

grade medium size firm a contraction of credit from the 10th percentile to the 90th percentile is

associated with a 9.7 per cent increase in the probability of issuing a bond rather than a loan. In

contrast the effect is insignificant for small issuers irrespective of their credit quality and

significantly different statistically from the effect for large and medium size issuers.

The fact that investment grade firms shift to bonds may be interpreted as evidence that they

experience a decline in bank lending and that the bond market acts as a shock absorber.

However, this interpretation fails if simultaneously to the contraction of credit the bond market

experiences a flight to quality. Indeed if that is the case one could suppose that investment grade

firms shift to bonds because bond finance becomes more attractive for them (see Figure 3). In

column III we report a first test for this competing interpretation. We estimate 𝛽𝛽1� and 𝛽𝛽2� for

firms headquartered in GIIPS countries and Not-GIIPS countries. There is evidence that bond

markets in GIIPS countries have witnessed important disruptions due to heightened sovereign

risk (Almeida et al, 2014). We find that 𝛽𝛽1� is not significant for firms in the GIIPS countries but

is significant for firms in the countries that benefited from a flight to quality which casts doubt on

the validity of our initial interpretation and is suggestive of the fact that high-credit quality firms

shift to bonds not because they are financially constrained but because they benefit from a flight

to quality in the bond market. In addition, in columns IV the shift to bonds is not significant when

we restrict the sample to real investment purpose debt, which are expected to matter more for real

outcomes like employment and investment.

21

All in all, at this stage we are not able to rule out the possibility that firms that increasingly tap

the bond market during the credit crunch do so because they benefit from a flight to quality and

not to compensate for a decline in bank lending.

b. Risky borrowers shift to foreign loans

In Table 5 we report estimates of equation (1) when the dependent variable is a dummy that

indicates whether a firm borrows from a foreign bank in a given quarter. In column I we confirm

that high credit quality firms shift away from loans (𝛽𝛽1� < 0), and that 𝛽𝛽2� is positive but not

statistically significant. In column II we report the estimates for different issuer size bins. Here

interestingly and in line with Hypothesis 2 we find that 𝛽𝛽1� is bigger in magnitude, negative and

statistically significant for small investment grade issuers. Indeed this result, combined with the

result reported in Table 4, indicates that this category of borrowers switches from foreign loans to

domestic loans.

𝛽𝛽2� is positive and statistically significant at the 5 per cent level for small non-investment grade

firms. In other words, the switch to foreign loans is economically and statistically significant for

low-credit quality and small firms, i.e., those firms that are most likely to be credit constrained

and have limited access to bond finance.

In column III the results are robust if we control for credit demand interacted with risky. And in

column IV the shift to foreign loans is significant economically and statistically at the 5 percent

level if we restrict the sample to real investment purpose loans. A point estimate of 0.269 implies

that an increase in CCI from the 10th percentile to the 90th percentile is associated with a 10.5

percent increase in the probability of borrowing from a foreign bank.

Focusing on real investment purpose loans in Table 6 we run several additional tests. First, we

verify whether the shift to foreign banks is driven by the fact that US banks are subject to Basel I.

Basel I does not attribute a risk-weight higher for risky corporates than for non-risky corporates

potentially incentivizing US banks to load onto risky corporate debt. To do that we first exclude

loans extended by US banks from the sample and then loans extended by non-US foreign banks

for comparison. The results are reported in columns I and II, respectively. We find the shift to

non-US foreign banks to be significant, but not the shift to US banks which indicates that the

effects documented so far are not driven by US banks.

22

In column III we compare hedged and unhedged firms and find the shift to foreign banks to be

significant only for risky and hedged firms. In column IV we find no significant difference

between GIIPS and non-GIIPS firms in their probability to shift to foreign banks indicating that

banks discriminate less against GIIPS firms than bond investors.

In columns V to VIII we use the orthogonalized CCI from which we have subtracted correlated

variations in the US credit supply and demand (Orthog CCI is the residual from a regression of

CCI on the US credit supply and demand indices). This is in order to account for the possibility

that risky Eurozone corporate could borrow more from foreign banks due to a decline in credit

supply and demand in the US. In the full sample (column V) we do not find a significant effect of

orthog CCI on the probability of foreign bank borrowing. However if we either exclude large

issuers (column VI) or US banks (column VII) or both (column VIII) from the sample the effect

is significant economically and statistically. An increase in Orthog CCI from the 10th percentile

to the 90th percentile is associated with a 16 percent increase in the probability of borrowing from

a non-US foreign bank. This is additional evidence that the shift to foreign banks benefits

financially constrained firms and does not reflect solely an increase in the supply of foreign

credit. This is particularly true for real investment purpose loans.

In order to further strengthen our interpretation of the results we next turn to the estimation of

equation (2).

c. Competing interpretation: flight to quality or search for yield

The results are reported in Table 7. The dependent variable is the cost of new debt issued in a

given quarter (spread to benchmark) and for three samples: the full sample, the sample of real

investment purpose debt, and the sample of non-real investment purpose debt.

In columns I and II we report the estimates for the spread on the full sample of debt and for the

sample of real investment purpose debt issued. As predicted �̂�𝛽2𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 is positive and �̂�𝛽2𝐹𝐹𝐵𝐵𝐹𝐹𝐹𝐹𝑖𝑖𝐹𝐹𝐵𝐵 𝐿𝐿𝐵𝐵𝐿𝐿𝐵𝐵

is negative but none of these estimates are significant statistically. Now if we focus on the sample

of non-real investment purpose debt issued (column III) for which we found the shift to bonds to

be most significant, we find that �̂�𝛽2𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 is positive, large, and statistically significant. And it is

even larger if we remove small issuers from the sample (column IV).

23

Further we find that both �̂�𝛽2𝐹𝐹𝐵𝐵𝐹𝐹𝐹𝐹𝑖𝑖𝐹𝐹𝐵𝐵 𝐿𝐿𝐵𝐵𝐿𝐿𝐵𝐵 and �̂�𝛽2𝐷𝐷𝐵𝐵𝐷𝐷𝐹𝐹𝑠𝑠𝑖𝑖𝑖𝑖𝑐𝑐 𝐿𝐿𝐵𝐵𝐿𝐿𝐵𝐵 are statistically insignificant

consistent with Holmstrom and Tirole (1997) who predict that credit crunches increase the

interest rate spread between intermediated debt and market debt. They further demonstrate that

when both intermediary capital and firm capital contract, the sign of the change in the interest

rate depends crucially on the change in the relative amounts of capital. Our estimates indicate that

bonds become cheaper than loans for investment grade borrowers and the contrary for non-

investment grade borrowers.

Importantly, with this additional tests we are not able to exclude the hypothesis that investment-

grade borrowers switch to bonds due to a flight to quality in bonds rather than due to a decline in

bank credit. Instead, we can more firmly state that non-investment grade borrowers switch to

foreign loans because they are financially constrained rather than because foreign loans become

more attractive due to heightened search for yield.

Next, we provide evidence through the estimation of equation (3) that foreign banks lend less in

the borrower home currency especially when the cost of funding in the borrower home currency

increases relative to the cost of funding in the lender home currency. And that borrowers

(exporters in particular) shift from synthetic dollar borrowing to dollar bank credit as the cost of

synthetic dollar borrowing (borrowing dollar through swaps) rises.

d. Currency risk transfer channel

Estimates of equation (3) are reported in Table 8. In column 1, we find that foreign banks

increasingly lend in their home currency (rather than the borrower home currency) when the

borrower home currency risk premium increases. Consistent with Hypothesis 3, an increase in the

Euro risk premium from the 10th to the 90th percentile is associated with a 26 percentage point

higher probability that a foreign bank loan is denominated in foreign currency. In column II we

confirm that borrowers that have a natural hedge against currency risk are more willing to assume

the exchange rate risk and borrow in foreign currency. In column III the currency risk transfer

channel remains significant if we control for the interest differential between Eurozone and US.

Here interestingly we confirm the prediction of Aghion et al. (2004) and find that risky borrowers

who deal with a foreign bank increase their borrowing in dollar when the interest differential rises

but not safe borrowers. In column IV these results are robust to controlling for exchange rate

24

variations. A depreciation of the dollar triggers an increase in dollar borrowing across the board:

it is associated with a higher increase in dollar borrowing by unhedged firms and safe firms,

usually reluctant to borrow in foreign currency.

In column V we test Hypothesis 4 using the currency basis instead of the interbank premium.

Here we find as expected that domestic banks reduce their supply of dollars to exporters in

response to an increase in the cost of synthetic dollar funding. In other words, a domestic bank is

less likely to lend in dollar to a borrower that has a natural hedge against currency risk (an

exporter) when the cost of synthetic dollar funding increases. Instead this effect is positive and

significant if the lender is a foreign bank. Further, we find that exporters (hedged issuers) tap the

currency swap market less (they borrow more in dollar from foreign banks) as the currency basis

rises. A 100 basis point increase in the Euro basis is associated with a 56 percent increase in the

probability that an exporter borrows from a foreign bank in dollars rather than in euro. When the

currency basis is zero instead they prefer borrowing from foreign banks in Euro and exchanging

the euro loan against dollars in the swap market presumably to benefit from a lower interest rate

(Foreign bank*Hedged enters negatively). This result is robust to controlling for the exchange

rate interacted with the Risky and Hedged dummies (column VI).

Next, since most (95%) of the foreign currency lending is in dollar, we check whether the

currency risk transfer effect is driven by US banks; non-US foreign banks and domestic banks

being equally vulnerable to the dollar shortage. The results are reported in Table 9 where we

separate out the effect of the increase in the Euro-risk premium and the currency basis for US and

non-US foreign banks. In column I, we find that an increase in the Euro-risk premium leads to a

higher and more significant increase in the probability that a foreign bank lends in foreign

currency (mostly dollar) when the foreign bank is a US bank than when it is a non-US foreign

bank. In column II we confirm that an increase in the cost of synthetic dollar funding triggers a

similar reaction by non-US foreign banks and domestic banks; both group of banks reduce dollar

lending when the basis rises (albeit not to exporters for the non-US foreign banks). We also

checked for differences between domestic banks (not reported) in particular between French

banks and other domestic banks and found that given their lower initial dollar funding gap

(according to BIS data) French banks reduce dollar lending less in response to an increase in the

basis. Another reason why French banks are less reactive to an increase in the Euro basis may be

25

also attributable to anecdotal evidence that they had abundant access to Yen funding and could

raise dollar in the Yen swap market at a lower cost than in the Euro swap market (in particular

during the Eurozone debt crisis).

In sum, the currency risk transfer effect is mostly attributable to US banks’ lending more in

dollar. All in all, we find supporting evidence that foreign banks lend less in the borrower home

currency as the costs of funding in the borrower home currency increase so that a reliance on

foreign banks is increasingly associated with a higher probability of borrowing in foreign

currency.

Next, irrespective of the currency in which foreign banks lend, we want to assess whether the

increased reliance of Eurozone corporates on foreign banks has real effects i.e. whether it has

contributed to mitigate the consequence of the credit crunch on employment and investment.

e. Foreign banking alleviates firms’ financial constraints

In Table 10 we report estimates of equation (5) when the dependent variable is either

employment growth (columns I and II) or investment growth (columns III and IV) between 2007

and 2009. In columns I and III, we report estimates for the sample of firms without a foreign

bank relationship and in columns II and IV for the sample of firms with a foreign bank

relationship. In line with Hypothesis 5, low-credit quality firms do not downsize and do not cut

investment more than high credit quality firms in the sample of firms with a foreign bank

relationship, but they do cut investment more in the sample of firms without a foreign bank

relationship. And the difference between low and high-credit quality firms is economically

significant if we compare the estimates with the sample mean investment growth (-1.452).

Interestingly, we find that in the sample of firms with a foreign bank relationship, firms downsize

more if they contracted a credit line rather than a term loan or a bond to cover their liquidity

needs during the credit crunch, but not in the sample of firms without a foreign bank relationship

(the estimate in column I is statistically significantly lower than in column II). This suggests that

26

foreign banks are more likely than domestic banks to revoke credit lines in a crisis.21 Other

results (unreported) indicate that in the full sample firms which have a higher cash buffer in 2007

downsize less. Also, firms with access to the bond market reduce investment less (column IV).

This effect is not statistically different between the samples of firms with and without a foreign

bank relationship.

Importantly, we allow the transmission of the credit crunch to employment and investment to

vary for firms with and without a natural hedge against exchange rate risk. Consistent with

Hypothesis 6 we find that financially constrained firms (i.e. low-credit quality firms) downsize

more than other firms during the credit crunch when they have no natural hedge against currency

risk (column I). The difference in the coefficients between hedged and unhedged firms is

statistically significant. This may be attributable to the fact that exporters have a larger (global)

client base and are therefore less vulnerable to local demand and credit shocks. What is of more

interest is therefore the difference between firms with and without a foreign bank relationship.

When the dependent variable is employment, the difference in 𝜇𝜇2 (equation 5) between columns I

and II is not statistically significant. However when we consider investment growth as the

dependent variable instead, the difference in 𝜇𝜇2 between columns III and IV is insignificant for

unhedged firms but significant for hedged firms. Since foreign banks lend more in foreign

currency than domestic banks, the real benefit of having a relationship with a foreign bank in the

crisis is more important for firms that have a natural hedge against currency risk. Arguably,

employment is a less flexible adjustment variable than investment due to labor market protection

laws and hiring and firing costs which may explain why this result holds only for investment

growth.

In sum, the real consequences of the domestic credit crunch are weaker for low-credit quality

(exposed) firms that have a relationship with a foreign bank. And for firms belonging to export

intensive sectors that would supposedly have a weaker mismatch between foreign-currency debt

and revenues and be therefore more likely to access foreign currency loans. This is consistent

with the theoretical prediction that risk-averse banks would cut credit in foreign currency (or at

21 Acharya et al (2013) argue that credit lines are a less reliable form of finance in a crisis since banks retain the right

to revoke them when a firm is in financial distress.

27

least not augment it) when currency risk is transformed into credit risk rather than transferred to

borrowers who can assume it (Shapiro, 1985). Further, the additional cost of having to borrow in

foreign currency is larger for firms without a natural hedge against currency risk and this can

eventually weigh on employment and investment growth. Cowan (2006) predicts that if a bank

knows that a firm is mismatched, it will pass on the corresponding expected default costs

immediately.

In the next section we provide concrete evidence that the increase in foreign currency credit

witnessed since 2007 is not associated with an economically significant increase in the currency

risk exposure of firms.

f. Dollar credit and exchange rate exposure

In order to measure the exchange rate exposure of firms, i.e. the effect of exchange rate changes

on the value of the firm, we collected quarterly stock returns for the listed firms in our sample

and estimated the following corss-sectional regression:

(6) 𝑟𝑟𝑖𝑖𝑖𝑖 = 𝑐𝑐 + 𝛽𝛽1𝑥𝑥𝑟𝑟𝑥𝑥𝑖𝑖 + 𝛽𝛽2𝑥𝑥𝑟𝑟𝑥𝑥𝑖𝑖 ∗ $𝑏𝑏𝑁𝑁𝐹𝐹𝑠𝑠𝑖𝑖𝑖𝑖 + 𝛽𝛽3𝑥𝑥𝑟𝑟𝑥𝑥𝑖𝑖 ∗ $𝑙𝑙𝑁𝑁𝑠𝑠𝐹𝐹𝑖𝑖𝑖𝑖 + 𝑋𝑋𝑖𝑖𝑖𝑖−1𝜌𝜌 + 𝜖𝜖𝑖𝑖𝑖𝑖

Where 𝑟𝑟𝑖𝑖𝑖𝑖 is the rate of return on the i-th firm’s stock, 𝑟𝑟𝑥𝑥 is the exchange rate expressed in dollar

per one euro, $bond ($loan) is a dummy that indicates whether the firms has a dollar denominated

bond (loan) outstanding, and X is a vector of 1-quarter lagged controls (beta, liquidity, log

market capitalization, log market to book ratio).

𝛽𝛽1𝑥𝑥 measures the exchange rate exposure and is negative (positive) for net exporters (importers).

𝛽𝛽2𝑥𝑥 and 𝛽𝛽3𝑥𝑥 measure the (positive) effect on the exchange rate exposure of dollar bond and loan

debt, respectively. Eurozone firms that are indebted in dollar should be on average positively

affected by a depreciation of the dollar.

Table 11 reports the estimates. In column I we find that 𝛽𝛽1𝑥𝑥� is negative suggesting that our

sample is dominated by net exporters. A 10 percent depreciation of the dollar above the mean is

associated with an average 15 percent decline in firm value.

Having a dollar bond or loan outstanding does not augment the currency risk exposure of firms

significantly (columns II). While 𝛽𝛽3𝑥𝑥� is positive and statistically significant, it is not

28

economically significant, including during the crisis (column III). Finally, in column IV we

checked whether loans extended by foreign banks would carry a higher currency risk exposure

(perhaps because they are less informed than domestic banks or monitor less) but we find no

evidence supporting this conjecture.

5. Conclusion

We have uncovered new mechanisms that explain cross-sectional and time-series variations in

foreign currency borrowing at the firm level. The existing literature emphasizes the role of

demand side factors in determining the currency denomination of debt, mainly the borrower’s

export intensity or foreign currency income and the interest rate differential between domestic

and foreign currency loans. In this paper we have shown that during liquidity crises supply side

factors matter more for low-credit quality firms: the lenders’ cost of funding in domestic currency

which alters both the supply of credit by domestic lenders and the currency risk management

strategy of foreign lenders (more precisely their willingness to assume currency risk or transfer it

to the borrower) is key in explaining higher foreign currency borrowing. By shifting attention to

funding issues as the constraint on bank lending, we have been able to explain changes in the

currency composition of debt. This could not be explained by a shock to bank capital alone as

such shock would tend to cause a reduction in credit across the board.

Our analysis also delivers new results on the stabilizing role of foreign banks during crises. We

have shown that the real benefit of foreign banking is significant for foreign currency earners

(exporters). This suggests enhanced international risk sharing through global entrepreneurship

combined with global banking. While Eurozone banks have been the principal contributor to the

observed aggregate decline in international banking flows, non-Eurozone banks have experienced

the opposite trend taking advantage of the opportunity offered by the retreat of Eurozone banks to

increase their share in cross-border credit including credit to Eurozone firms.

Finally, by showing that low-credit quality firms increasingly rely on foreign banks, we confirm

the theoretical prediction that the category of borrowers who suffer most from a credit crunch are

29

better served by financial intermediaries than by markets. Bond markets did not act as shock

absorbers for financially constrained corporates. The depth and duration of the great recession in

Europe is often explained by the over reliance of European corporates on bank finance and the

“underdevelopment” of the capital market. Multiple calls have been voiced to create better

conditions for firms to turn to markets when banks are distressed which culminated with

Commissioner Jean-Claude Junker’s idea to establish a so called capital markets union. 22 Our

analysis demonstrates that the bond market served firms that were least in need of finance, which

explains its timid role in mitigating the transmission of the credit crunch to the real economy. In

contrast, foreign banks proved to be more flexible due to monitoring, ease of renegotiation, and

their ability to diversify risk internationally. Foreign banking effectively contributed to alleviate

the financial constraint of poorly capitalized firms.

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33

Table 1. Summary statistics SDC Platinum sample

This table reports the number of firm-quarter observations with positive debt issuance (column I), means (column II) and percentiles (columns III-V) for the variables used in our regressions reported in Tables 4, 5, and 6. In column VI we report the mean for debt issued for real investment purposes only. Panel A covers the sample of firms that have issued at least one bond in the sample period 2003-Q1 to 2013-Q3 and Panel B the sample of firms that have issued syndicated loans at least once with a foreign bank (headquartered outside the Eurozone) as lead bank. Panel C covers the sample of firms that issue both domestic and foreign currency denominated loans. Risky is a dummy that indicates whether a firm is rated non-investment grade. The Credit Contraction Index (CCI) is from the ECB bank lending survey and gives the net percentage of banks that report having tightened credit standards to large firms in the previous 3 months. Credit demand is the net percentage of banks reporting an increase in the demand for credit by large firms. Orthogonalized CCI abstracts from variations in CCI correlated with US CCI and US credit demand. Share of bond debt is the percentage of debt issued in the form of bonds. Share of foreign bank debt is the percentage of debt that is issued in the form of a syndicated loan for which at least one of the lead bank is a foreign bank. The Euro (Dollar) premium is the spread between the 3 month Euribor (Libor USD) and equal maturity Euro (Dollar) OIS. The Basis is the difference between the FX-swap implied dollar rate and the USD Libor 3 month, a measure of the cost of hedging against EUR/USD exchange rate risk. The FX-swap implied

dollar rate is calculated as 𝑥𝑥𝐹𝐹

𝑥𝑥𝑆𝑆(1 + 𝐸𝐸𝐸𝐸𝑟𝑟𝑟𝑟𝑏𝑏𝑁𝑁𝑟𝑟 3 𝑚𝑚𝑁𝑁𝐹𝐹𝑁𝑁ℎ). 𝑥𝑥

𝐹𝐹

𝑥𝑥𝑆𝑆 is the ratio between the EUR/USD 3 month forward exchange

rate and the spot rate. Rates and exchange rate data are downloaded from Reuters.

A. Sample of Firms with Bond Market Access

I II III IV V VI VARIABLES N Mean

All debt p10 p50 p90 Mean

Real Investment purpose debt

Risky 5,378 0.317 0 0 1 0.231 Credit Contraction Index (CCI) 396 11.40 -7 6 38 Credit Demand 396 -7.671 -30 -6 13 Orthogonalized CCI 396 -0 -0.998 -0.148 1.165 Fed funds target 43 1.76 0.23 1 5.22 Share of bond debt 5,378 19.77 0 0 100 46.96 Spread to benchmark 4,572 150.2 0 40 392.0 106.8 Maturity in months 4,227 87.54 36 70.13 132 89.07 Share of foreign bank debt 5,378 61.41 0 100 100 41.12 Issue size in USD billion 5,378 1.095 0.085 0.478 2.328 0.910

34

B. Sample of Firms with a Foreign Bank Relationship

C. Sample of Firms which issue both domestic and foreign currency denominated loans

(1) (2) (3) (4) (5) VARIABLES N Mean p10 p50 p90 Risky 1,118 0.264 0 0 1 Share of foreign bank debt 1,118 0.742 0 1 1 Euro premium 43 0.303 0.055 0.126 0.727 Dollar premium 40 0.312 0.073 0.135 0.750 Basis 43 0.284 0.039 0.086 0.683 Dollar/Euro exchange rate 43 1.313 1.192 1.308 1.448 US-EU interest differential 43 -0.088 -1.311 -0.384 1.889 Share of foreign currency loans 1,118 0.670 0 1 1

I II III IV V VI VARIABLES N Mean

All debt p10 p50 p90 Mean

Real Investment

purpose debt

Risky 3,666 0.333 0 0 1 0.338 Credit Contraction Index (CCI) 387 11.610 -7 6 39.2 Fed funds target 43 1.76 0.23 1 5.22 Share of bond debt 3,666 63.08 0 100 100 80.21 Spread to benchmark 2,886 130.4 0 41.06 365 118.9 Maturity in months 2,792 94.24 39 72 131 104.7 Share of foreign bank debt 3,666 26.98 0 0 100 13.80 Issue size in USD billion 3,666 1.392 0.118 0.654 3 0.944

35

Table 2. Sample composition by country

This table reports the composition of our SDC platinum sample by country. The sample period is 2003-Q1 to 2013-Q3. Loans include both credit lines and term loans. Bonds are non-convertible bonds. We exclude also mortgage backed securities and preference shares which are listed as bonds in SDC. In column I we report the number of firm-quarters with positive debt issuance including only firms that issued at least one bond during the sample period; column II gives the number of bonds as a percentage of the total number of debt issuance; column III the number of firm-quarters with positive debt issuance including only firms that have a foreign bank relationship during the sample period (i.e. they borrowed at least once from a foreign lead bank); column IV the number of loans subscribed by a least one foreign lead bank as a percentage of the total number of debt issuance; and column V the amount of foreign bank loans prorated by the number of banks in a syndicate as a percentage of the total debt issued.

Country

Firms with bond market

access Bond debt share

Firms with foreign bank relationship

Foreign bank share

Foreign bank share prorated

I II III IV V Austria 156 64% 89 59% 23% Belgium 213 38% 205 74% 31% Finland 188 34% 290 72% 46% France 1197 51% 1605 63% 25% Germany 683 28% 1216 78% 36% Greece 108 57% 141 67% 38% Ireland 118 44% 147 67% 41% Italy 267 39% 394 71% 27% Luxembourg 131 61% 156 64% 34% Netherlands 646 68% 727 76% 44% Portugal 92 80% 70 77% 28% Spain 376 60% 975 90% 34%

36

Table 3. Summary statistics matched SDC Platinum- Bureau van Dijk Amadeus sample

This table reports descriptive statistics for variables used in Table 9. The sample includes firms at the intersection of the SDC Platinum database and the Bureau van Dijk Amadeus database. Further we eliminated firms that reported zero total assets. Of the 506 firms that we successfully matched 117 had issued a bond during the period Q1-2003-Q3-2013 (Bond access=1, and 0 otherwise) and 268 had a relationship with a foreign bank (Foreign bank=1, and 0 otherwise). Maturing debt is a dummy which indicates whether the firm has a debt maturing during the period 2007-2009; credit line is a dummy that indicates whether the firm has contracted a credit line, rather than a term loan or a bond, before 2007 initially due to mature after 2009; and risky indicates whether the firm is rated non-investment grade in 2007. Employment growth is the growth rate of the number of employees for a given firm between 2007 and 2009. Investment growth is the change in fixed assets between 2007 and 2009 scaled by tangible assets in 2007. (a) sample of banks without a foreign bank relationship; (b) sample of banks with a foreign bank relationship.

(1) (2) (3) (4) (5) (6) (7) (8) VARIABLES N Mean p10 p50 p90 Mean

(a) Mean

(b) Test p-value

(7)=(8) Maturing debt 506 0.193 0 0 1 0.109 0.267 0.000 Cash/Total assets 506 0.069 0.001 0.031 0.148 0.076 0.063 0.276 Log total assets 506 19.93 17.81 20.00 22.40 19.56 20.26 0.001 Log age +1 506 3.330 2.398 3.258 4.407 3.364 3.300 0.325 Bond access 506 0.230 0 0 1 0.193 0.263 0.061 Foreign bank 506 0.531 0 1 1 0 1 Credit line 506 0.201 0 0 1 0.079 0.307 0.000 Risky 506 0.514 0 1 1 0.525 0.504 0.629 Employment growth 506 -0.120 -2.094 0 0.980 -0.089 -0.146 0.426 Investment growth 506 -1.452 -12.127 -0.044 2.424 -1.447 -1.456 0.981

37

Table 4. The shift to bonds

The dependent variable is a dummy which takes value 100 if the firm issues a bond and 0 if it issues a loan. Risky indicates whether a firm is rated non-investment grade. CCI is the credit supply contraction index i.e. the net percentage of banks reporting having tightened lending standards to large firms in the previous 3 months. A firm is classified as Large if its average issue size over the sample period is at least 1 billion; Medium if it is below 1 billion and above 500 million; Small if it is 500 million or below. GIIPS indicates whether the firm is headquartered in Italy, Ireland, Spain or Portugal. Standard errors reported in parentheses are heteroskedasticity-robust and clustered by country*year. All columns include firm fixed effects and year-quarter fixed effects. We exclude firm-quarters with zero debt issuance and firms that never issued a bond during the sample period. + p<0.1; * p<0.05; ** p<0.01

I II III IV

Real purpose debt

Risky 41.991 41.813 42.201 36.248 (2.429)** (2.426)** (2.437)** (2.507)** CCI*Not Risky 0.163 0.070 (0.064)* (0.088) CCI*Risky -0.171 -0.267 (0.100)+ (0.102)* CCI*Not Risky*Large 0.177 (0.087)* CCI*Not Risky*Medium 0.211 (0.078)** CCI*Not Risky*Small -0.052 (0.099) CCI*Risky*Large -0.318 (0.182)+ CCI*Risky*Medium -0.123 (0.124) CCI*Risky*Small -0.090 (0.157) CCI*Not Risky*Not GIIPS 0.230 (0.069)** CCI*Risky*Not GIIPS -0.104 (0.108) CCI*Not Risky*GIIPS -0.033 (0.118) CCI*Risky*GIIPS -0.398 (0.199)* R2 0.16 0.16 0.16 0.19 N 3,666 3,666 3,666 2,015

38

Table 5. The shift to foreign bank loans

The dependent variable is a dummy which takes value 100 if the firm issues a syndicated loan which is at least partly subscribed by a foreign lead bank and 0 if it issues a domestic loan or a bond. Risky indicates whether a firm is rated non-investment grade. CCI is the credit supply contraction index i.e. the net percentage of banks reporting having tightened lending standards to large firms in the previous 3 months. A firm is classified as Large if its average issue size over the sample period is at least 1 billion; Medium if it is below 1 billion and above 500 million; Small if it is 500 million or below. Standard errors reported in parentheses are heteroskedasticity-robust and clustered by country*year. All columns include firm fixed effects and year-quarter fixed effects. We exclude firm-quarters with zero debt issuance and firms that do not have a foreign bank relationship (i.e. never borrow from a foreign lead bank). + p<0.1; * p<0.05; ** p<0.01

I II III IV

Real purpose debt

Risky -89.328 -86.861 -103.735 (23.171)** (23.973)** (32.458)** CCI*Not Risky -0.187 -0.190 0.003 (0.087)* (0.088)* (0.103) CCI*Risky 0.165 0.193 0.269 (0.093)+ (0.099)+ (0.124)* CCI*Not Risky*Large -0.140 (0.121) CCI*Not Risky*Medium -0.065 (0.102) CCI*Not Risky*Small -0.463 (0.145)** CCI*Risky*Large 0.177 (0.134) CCI*Risky*Medium 0.009 (0.136) CCI*Risky*Small 0.357 (0.153)* Risky*Fed Funds target 1.495 1.522 1.257 0.606 (0.939) (0.945) (1.009) (1.763) Risky*Exchange rate 43.368 40.783 42.033 52.134 (17.810)* (17.812)* (18.244)* (24.393)* Credit demand -0.011 0.107 (0.062) (0.099) Risky*Credit demand 0.074 -0.119 (0.120) (0.144) R2 0.08 0.08 0.08 0.17 N 5,378 5,378 5,378 1,818

39

Table 6. Foreign bank borrowing: Additional tests

The dependent variable is a dummy that takes value 100 if the firm issues a foreign loan rather than a domestic loan or a bond. CCI is the credit supply contraction index. Risky indicates whether a firm is rated non-investment grade or not rated. Credit demand is the net percentage of banks reporting an increase in credit demand by large firms. In column I we include only foreign loans by non-US banks and in column II by US banks. Orthog CCI used in columns V to VIII abstracts from correlated variations in US credit demand and supply. Standard errors are heteroskedasticity-robust and clustered by country*year. All columns include firm fixed effects and quarter fixed effects. Controls include credit demand, and Risky interacted with the dollar/euro exchange rate, the Fed Funds target, and credit demand. + p<0.1; * p<0.05; ** p<0.01

I II III IV V VI VII VIII

Non-US foreign bank

loans

US bank loans X=Hedged firm dummy

X=GIIPS firm dummy

Full sample Excluding large issuers

Excluding US banl loans

Excluding large issuers & US bank loans

Orthog CCI Risky -46.129 -95.245 -106.473 -100.496 -106.888 -56.160 -132.748 -94.679 (27.848) (35.098)** (32.702)** (3.08)** (34.959)** (31.423)+ (46.156)** (38.001)* CCI*No risky -0.014 0.048 -0.083 -0.052 0.729 0.211 0.780 0.774 (0.105) (0.109) (0.115) (0.46) (1.544) (1.625) (2.352) (2.346) CCI*Risky 0.329 0.019 0.197 0.227 2.404 4.066 5.815 7.780 (0.127)* (0.109) (0.145) (1.78)+ (2.584) (2.158)+ (3.210)+ (2.547)** CCI*No risky*X 0.312 0.171 (0.142)* (0.92) CCI*Risky*X 0.217 0.236 (0.252) (0.74) R2 0.11 0.18 0.17 0.17 0.16 0.10 0.23 0.17 N 1,515 1,374 1,818 1,818 1,818 1,515 1,269 1,069

40

Table 7. Flight to quality or search for yield

The dependent variable is either the cost of debt measured by the spread to benchmark. Risky indicates whether a firm is rated non-investment grade. CCI is the credit supply contraction index i.e. the net percentage of banks reporting having tightened lending standards to large firms in the previous 3 months. Bond indicates whether the firm issued a bond (rather than a loan) in a given quarter and Foreign (Domestic) loan indicates whether it issued a loan at least partly (fully) subscribed by (domestic) foreign lead bank(s). Standard errors reported in parentheses are heteroskedasticity-robust and clustered by country*year. All columns include firm fixed effects and country*year-quarter fixed effects, and control for issue type fixed effects, issue size, maturity in months, and issue purpose (columns I only). We exclude firm-quarters with zero debt issuance. + p<0.1; * p<0.05; ** p<0.01

I II III IV

All debt Real purpose debt

Non-Real purpose debt

Non-Real purpose debt & Excluding Small Issuers

Risky*Bond 55.113 3.874 137.025 74.330 (16.029)** (13.408) (47.394)** (47.667) Risky*Foreign Loan 180.842 64.269 204.003 113.419 (29.015)** (63.781) (28.549)** (40.041)** Risky*Domestic Loan 100.033 237.185 84.498 32.852 (30.905)** (83.059)** (45.747)+ (90.711) CCI*Risky*Bond 0.721 0.612 8.784 12.660 (0.732) (0.713) (5.063)+ (7.308)+ CCI*Risky*Foreign Loan -0.559 0.341 0.608 1.476 (1.125) (3.346) (1.126) (1.535) CCI*Risky*Domestic Loan 1.288 -7.704 3.331 -1.958 (1.861) (4.084)+ (2.656) (4.294) R2 0.15 0.49 0.26 0.40 N 7,789 3,069 4,720 1,482

41

Table 8. Currency risk transfer

The dependent variable is a dummy that takes value 1 for foreign currency loans and 0 for domestic currency loans. Risky indicates whether a firm is rated non-investment grade. Foreign bank is a dummy which indicates whether one of the lead bank is a foreign bank. ERP is the Euro risk premium (the difference between 3 month Euribor and equal maturity OIS Euro) and DRP is the Dollar risk premium (the difference between 3 month Libor USD and equal maturity OIS USD). Idiff is the difference between Euribor 3 month and USD Libor 3 month. Basis is the Euro-Dollar currency basis. Hedged (Unhedged) indicates whether the borrower (does not) belong(s) to an export intensive sector. Standard errors (not reported) are heteroskedasticity-robust and clustered by country*year. All columns include firm fixed effects and quarter fixed effects. We exclude firm-quarters with zero issuance and firms that never borrow in foreign currency. + p<0.1; * p<0.05; ** p<0.01

I II III IV V VI Risky -0.002 -0.002 -0.171+ 1.633** -0.122 0.428 Foreign Bank 0.031 0.021 0.025 0.024 0.183+ 0.165+ ERP*Foreign Bank 0.391+ DRP*Foreign Bank -0.378** ERP*Foreign Bank*Hedged 0.565* 0.980** 1.030** ERP*Foreign Bank*Unhedged 0.252 0.555* 0.579* DRP*Foreign Bank*Hedged -0.467** -0.607** -0.630** DRP*Foreign Bank*Unhedged -0.302* -0.420** -0.431** Foreign Bank*Hedged -0.238+ -0.251+ -0.318* -0.295* Foreign Bank*Risky 0.218+ 0.208+ 0.210+ 0.206+ Idiff*Risky -0.215** -0.173* -0.237** -0.227** Idiff*Foreign Bank*Risky 0.228* 0.217* 0.261** 0.252** Idiff*Foreign Bank -0.148** -0.149** -0.106* -0.106* Basis*Foreign Bank*Hedged 0.561** 0.541** Basis*Hedged -0.313+ -0.234 Basis*Foreign Bank -0.313* -0.286* Exchange rate*Risky -1.354** -0.418 Exchange rate*Hedged -0.177 -0.269 R2 0.10 0.11 0.13 0.14 0.13 0.13 N 1,048 1,048 1,048 1,048 1,118 1,118

42

Table 9. Foreign currency borrowing: Additional tests

The dependent variable is a dummy that takes value 1 for foreign currency loans and 0 for domestic currency loans. US bank (Otherf) is a dummy which indicates whether one of the lead banks is a US (non-US foreign) bank. ERP is the Euro risk premium and DRP is the Dollar risk premium. Basis is the Euro currency basis. Standard errors (not reported) are heteroskedasticity-robust and clustered by country*year. All columns include firm fixed effects and quarter fixed effects. Other controls include Risky, Risky and Hedged interacted with the exchange rate, the interest rate differential interacted with Risky, US bank, Otherf, US bank*Risky, and Otherf*Risky. + p<0.1; * p<0.05; ** p<0.01

I II

US bank -0.143 -0.010 Otherf -0.023 0.281** ERP*US bank 0.916** ERP*Otherf 0.573* DRP*US bank -0.618** DRP*Otherf -0.423** Basis*US bank -0.146 Basis*Otherf -0.546** Basis*US bank*Hedged 0.489** Basis*Otherf*Hedged 0.777** Basis*Hedged -0.224 US bank*Hedged -0.126 Otherf*Hedged -0.417** R2 0.13 0.14 N 1,048 1,118

43

Table 10. Employment and investment growth during the credit-crunch, foreign banking, and currency risk exposure

The dependent variable in columns I & II (III & IV) is employment growth (investment growth) for a given firm between 2007 and 2009. This table reports separate regressions for the sample of firms with and without a foreign bank relationship. Hedged (Unhedged) indicates firms belonging to sector with higher (lower) than medium export sales to total sales. Of the 506 firms 117 had issued a bond during the period Q1-2003-Q3-2013 (Bond access=1, and 0 otherwise). Maturing Debt is a dummy which indicates whether the firm has a debt maturing during the period 2007-2009; Credit Line is a dummy that indicates whether the firm has contracted a credit line, rather than a term loan or a bond, before 2007 initially due to mature after 2009; Risky indicates whether the firm is rated non-investment grade in 2007. All regressions include 1-digit SIC code fixed effects and country fixed effects. Errors are clustered by country. + p<0.1; * p<0.05; ** p<0.

Without a foreign bank relationship

With a foreign bank relationship

Without a foreign bank relationship

With a foreign bank relationship

I II III IV Lagged dependent variable -0.128 -0.105 -0.900 -0.889 (0.060)+ (0.018)** (0.083)** (0.131)** Risky*Hedged -0.003 0.005 -0.933 0.788 (0.074) (0.140) (0.217)** (0.316)* Risky*Unhedged -0.321 -0.286 -0.562 0.187 (0.073)** (0.286) (0.202)* (0.521) Bond Access -0.022 -0.008 1.264 0.257 (0.116) (0.105) (0.579)+ (0.449) Cash 0.509 0.813 -1.090 1.111 (0.320) (0.462) (1.048) (0.962) Credit Line -0.049 -0.202 0.891 0.110 (0.109) (0.080)* (0.471)+ (0.792) Maturing Debt 0.101 -0.039 0.732 -0.616 (0.155) (0.132) (0.321)+ (0.353) Log Total Assets -0.001 0.005 -0.210 -0.067 (0.027) (0.018) (0.104)+ (0.054) Log Age +1 -0.162 -0.010 0.065 0.364 (0.063)* (0.078) (0.197) (0.237) R2 0.18 0.12 0.43 0.35 N 238 268 238 268

44

Table 11. Dollar credit and exchange rate risk exposure

The dependent variable is the quarterly stock return at time t. ER is the exchange rate, $bond ($loan) is a dummy that indicates whether the firm has a dollar bond (loan) outstanding at time t, Crisis (NoCrisis) is a post (pre) Q4-2007 dummy, and Foreign bank indicates whether any part of the outstanding loans has been extended by a foreign bank. The regression controls for two digits SIC code, and stock illiquidity, log market to book ratio, beta, market return, and log market capitalization, all measured at time t-1.

I II III IV ER -0.587 -0.588 -0.592 -0.580 (0.065)** (0.065)** (0.066)** (0.066)** ER*$bond -0.003 -0.006 (0.004) (0.005) ER*$loan 0.014 0.024 (0.007)* (0.011)* ER*$bond*Crisis 0.001 (0.006) ER*$loan*Crisis 0.014 (0.007)+ ER*$bond*NoCrisis -0.014 (0.007)+ ER*$loan*NoCrisis 0.014 (0.014) ER*$loan*Foreign bank -0.009 (0.014) R2 0.07 0.07 0.07 0.07 N 19,660 19,660 19,660 17,351

+ p<0.1; * p<0.05; ** p<0.01

45

Figure 3. ECB global investors’ risk aversion index and credit contraction index

The index is calculated as a principal component of five available risk aversion indicators. The credit contraction index gives the net percentage of surveyed banks that report tightening of credit standard to large firms in the previous 3 months.

46

Figure 4. Syndicated lending by purpose

The figure shows the amount of syndicated loans issued by purpose in billion USD. Real investment loans (right scale) include general corporate purpose loans and working capital. Non-real investment (left scale) purpose loans include mainly refinancing and restructuring purpose loans.

47

Figure 5. Number of employees per firm in logarithm

The data are from Bureau van Djink Amadeus for the sample of Eurozone firms with matched SDC platinum data.

48

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