WORKING PAPER SERIES

Bank Specialization and the Design of Loan Contracts

Marco Giometti

Universidad Carlos III de Madrid

Stefano Pietrosanti

Financial Stability Directorate, Bank of Italy

November 2022

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revision.
Bank Specialization and the Design of Loan Contracts

Marco Giometti†and Stefano Pietrosanti‡

†

Universidad Carlos III de Madrid

‡Financial Stability Directorate, Bank of Italy

First version: December 2019

This version: November 2022

Abstract. We study bank specialization in lending in the U.S. corporate loan market. We

document that banks specialize in lending to specific industries. Specialization is persistent

over time and common across industries. Using detailed information on syndicated loans,

we show that the typical loan contract between a bank specialized in an industry and a

firm in the same industry has less restrictive financial covenants and no higher spreads.

These results are not explained by relationship lending, high industry market shares, or

geographical proximity, and are robust to using default shocks on lenders’ loan portfolios as

a source of variation in banks’ self-assessment of screening abilities. Overall, our evidence

suggests that banks specialize in lending because of information advantages in monitoring

specific industries. Furthermore, the laxer contract terms offered by specialized banks could

provide an explanation for recent evidence that firms cannot easily substitute credit granted

by specialized banks.

Keywords: Bank Specialization, Security design, Covenants, Monitoring, Screening.

JEL Classification: L15, L22, G21, G30, G32.

We are grateful to David Musto, Guillermo Ordóñez, and Michael Roberts for their advice and feedback. We also thank Edoardo Acabbi,

Mitchell Berlin, Emilia Bonaccorsi di Patti, Elena Carletti, Alessandro Dovis, Marc Flandreau, Erik Gilje, Itay Goldstein, Richard Herring, Lorena

Keller, Tong Liu, David Martinez-Miera, Christian Opp, Francesco Palazzo, Dominik Supera, and Petra Todd for their feedback and comments.

We thank participants in the Philadelphia Fed and Wharton brown-bag seminars, the reading group on Financial Intermediation at Bocconi

University, the 20th FDIC Annual Bank Research Conference, the VPDE 14th PhD Workshop in Economics, the 2022 SGF Conference, the 2022

FMCG Conference, the 2022 FMA European Conference, the Summer Workshop on Money, Banking, Payments, and Finance, the 8th Bank of

Italy Annual Banking Research Network Workshop, and the MadBar Workshop on Corporate Finance and Banking for their comments. We

thank Juan Gorostiaga, Marco Pelosi, Olga Briukhova, Lorenzo Schönleber, and Carlos Ramirez for the insightful discussions. All errors are our

own. The opinions expressed do not necessarily reflect those of the Bank of Italy, the Eurosystem, and their staff.

† Marco Giometti: marco.giometti@uc3m.es

‡ Stefano Pietrosanti: stefano.pietrosanti@bancaditalia.it
1. Introduction

Diversification of risk plays a central role in many theories of financial intermediation (e.g.

Boyd & Prescott, 1986; Diamond, 1984). However, empirical evidence shows that banks often

concentrate their lending across multiple dimensions, including geography, scale, and industry.1

There has been extensive work showing how portfolio concentration can have important

implications for banks’ economic performance and risk, as well as for their borrowers via the

transmission of shocks through the banking sector.2

What is less well understood are the implications of bank specialization for security design.

In particular, there is no or little evidence on the role of specialization in lending for loan

contract terms, such as covenants or loan spreads.3 We believe it is important to fill this gap

for three reasons. First, contracts reflect the preferences of and the information available to

the contracting parties and, as such, they provide insight into the objectives of those parties.

They might inform a better understanding of the lending advantages associated with bank

specialization and, ultimately, of how the structure of credit markets interacts with financial

contracting. Second, clauses allocating control rights within the lending relationship – covenants

– represent a key channel for the transmission of financial shocks to the real economy.4 Third,

contract terms make differences in funding sources explicit, thereby helping identify the degree

to which bank loans are substitutable across different lenders.

The goal of this paper is to address the question of how specialization in bank lending affects

the design of loan contracts, in the context of the $2 trillion, corporate syndicated loan market.5

1. Berger and DeYoung (2001); Carey, Post, and Sharpe (1998); Hughes, Lang, Mester, and Moon (1996);

Paravisini, Rappoport, and Schnabl (2022).

2. On the relation between bank portfolio concentration and related performances, see Acharya, Hasan, and

Saunders (2006); Beck, De Jonghe, and Mulier (2022); Boeve, Duellmann, and Pfingsten (2010); Hayden, Porath,

and Westernhagen (2007); Tabak, Fazio, and Cajueiro (2011). On the real effects of bank specialization, see

De Jonghe, Dewachter, Mulier, Ongena, and Schepens (2020); Gopal (2021); Paravisini et al. (2022); Schwert

(2018).

3. Two exceptions are Daniels and Ramirez (2008), who document that banks specialize in lending towards large

firms and non-banks towards small firms, with banks demanding a lower loan spread, and Blickle, Parlatore, and

Saunders (2021), who show that specialized banks offer lower loan spreads, longer maturity, and greater amounts

using confidential FR Y-14 data from 2012 to 2020.

4. Specifically, covenant violations imply a shift in firm control rights from the borrower to the lender, which

matters for firm investment (Chava & Roberts, 2008) and the transmission of bank-level shocks to firms’ credit

availability (Chodorow-Reich & Falato, 2022).

5. U.S. Syndicated Lending Topples Records in 2017, Reuters, December 2017.

1
First, we document that the average bank’s loan portfolio has a higher industry concentration

than the market; bank specialization is common across industries and persistent over time.

Then, we show that loan contracts display less restrictive covenants when the borrower belongs

to an industry in which the bank is specialized, with no higher spreads or fees. To interpret

this finding, we build on theoretical works such as Gârleanu and Zwiebel (2009), which argues

that covenant strictness reflects the degree of information frictions between borrowers and

lenders. In this sense, we suggest that the evidence we bring supports an explanation of bank

specialization based on information advantages in screening and monitoring specific types of

projects.

In order to perform our analysis, we obtain data on the syndicated loans from LPC DealScan,

and we merge it with Compustat. The resulting dataset is a loan-level panel with bank, firm,

and loan characteristics, from 1996 to 2016.6 We use this data to estimate the degree of

diversification of bank loan portfolios. We then analyze the extent to which banks specialize

their lending towards different sectors adapting the approach in Paravisini et al. (2022) to our

setting. A bank is defined as specialized in a sector if it has an abnormally large portfolio share

of loans in a sector, relative to other banks. Intuitively, this measure captures the extent to

which corporate lending on banks’ balance sheets deviates from a value-weighted portfolio. In

doing so, the measure accounts for heterogeneity in the size of sectors in the economy and in

the size of bank sectoral lending relative to the bank’s overall corporate lending.

We find clear evidence of bank specialization. First, we show that the average bank displays

more concentration in lending than what would be implied by the overall distribution of credit

in the market. Second, we document that certain banks specialize in lending by holding a

disproportionately large share of loans in certain sectors. In particular, each sector consistently

displays at least one specialized bank. Furthermore, specialization is persistent: a bank that is

specialized in a given year has a 25% probability of being specialized 10 years after.

We then explore the implications of bank specialization for the design of loan contracts.

In particular, we focus on the allocation of control rights and cash flow rights between the

lender and the borrower. To proxy for the degree of ex-ante control rights allocated to the

6. We choose this sample period because coverage of the syndicated loan market sharply improves in DealScan

after 1995 (Chava & Roberts, 2008).

2
lender, we employ the measure of covenant strictness developed by Demerjian and Owens

(2016).7Intuitively, this measure captures the ex-ante probability of violating at least one of

the covenants embedded in the contract. For cash flow rights, we use the All-In Drawn Spread

(AISD).8Looking at both is important as these contract terms are jointly determined, and there

might be a trade-off between them (Bradley & Roberts, 2015).

We find that the average loan contract between a bank specialized in a sector and a borrower

from that sector includes covenants that are 24 percentage points less restrictive and an all-indrawn spread that is 30 basis points lower—compared to a loan contract granted by the same

bank, in the same year, to a firm in another sector. The observed effects are economically and

statistically significant. For covenant strictness it amounts to 60% of the empirical standard

deviation; for the AISD, it amounts to 25% of the empirical standard deviation.

Comparing loans made by the same bank in the same year rules out the argument that our

finding is driven by unobserved, time-varying lender heterogeneity. However, the observed

variation in contracts might be simply driven by specialized banks matching systematically with

different firms. We take several steps to mitigate this concern. First, we control for observable

proxies of borrower risk, such as expected default probability, size, leverage, liquidity, ability to

provide collateral, profitability, and age. Second, we restrict our analysis to firms that borrow

from more than one bank over the duration of our sample, employing a within-firm approach.

Third, we restrict our comparison to loans made to firms that have the same credit rating. The

main finding does not change: the average loan contract between a bank specialized in a sector

and a borrower from that sector includes a covenant structure that is less restrictive, and it does

not display higher spreads.

We then ask whether our findings can improve our understanding of the lending advantages

associated with bank specialization. Theory suggests that the degree of allocation of ex-ante

control rights to the lender should be directly proportional to the level of asymmetric information

that exists between a borrower and a lender over potential future transfers from debt to equity

(Gârleanu & Zwiebel, 2009). In this view, the strictness of the covenant structure embedded in

a loan contract captures the information distance between a borrower and a lender. Therefore,

7. This measure is similar to the one developed by Murfin (2012).

8. The AISD is a fee paid over the base rate (usually LIBOR) for each dollar of credit drawn.

3
a plausible interpretation of our results implies the existence of an industry-specific information

advantage for banks specializing their lending towards a specific industry. The fact that a less

restrictive covenant structure is not compensated by a higher spread provides further support

to this interpretation.

We rule out a number of alternative explanations for our findings. First, we show that

specialization in lending toward an industry does not simply reflect a pattern of relationship

lending with borrowers in that industry. While it is indeed true that the longer the relationship

with a given borrower the lower the cost of credit – consistent with the empirical results of

Bharath, Dahiya, Saunders, and Srinivasan (2011) and Schenone (2010) – this appears to be

uncorrelated with bank specialization. Moreover, we do not find any linear effect between

relationship lending and covenant strictness.9 Second, our results are not driven by geographical

specialization, which confirms the notion of an industry-specific information advantage. This

is consistent with the recent evidence provided by Di and Pattison (2022) and Duquerroy,

Mazet-Sonilhac, Mésonnier, and Paravisini (2022) for small business lending.

Third, specialized banks might have a high market share in an industry. Recent work by

Giannetti and Saidi (2019) suggests that lenders with high market shares in an industry have a

high propensity to internalize the spillovers of their credit decision. This might involve writing

less strict contracts to avoid triggering potentially costly defaults or renegotiations and could

represent a different economic mechanism that would explain our results.10 We show that this

is not the case: Larger industry market shares do not have an effect on covenant strictness

and are associated with higher spreads, possibly implying a higher bargaining power in the

contracting process. These findings also alleviate the possible concern that specialized banks

might offer favorable contract terms to capture a higher industry market share and not as a

result of monitoring advantages.

Finally, to further validate our interpretation, we use defaults on lenders’ loan portfolios as a

plausible source of exogenous variation in lenders’ perception of their own screening ability

(Murfin, 2012). We look at the extent to which defaults of firms in each bank’s loan portfolio

9. Prilmeier (2017) finds a non-linear, quadratic relationship between the intensity of the credit relationship and

covenant strictness.

10. There is a large literature documenting the negative consequences of debt covenant violations on investment,

employment, and other firm-level outcomes. See Chava and Roberts (2008) and Chodorow-Reich (2014).

4
affects the covenant strictness of subsequent loans arranged by a particular bank. We show that

a bank is more sensitive to the default of a firm whenever such a firm belongs to a sector in

which the bank is specialized, as it is expected under an interpretation of specialization patterns

as stemming from information advantage.

With this paper, we contribute to the growing literature on different forms of specialization in

the credit market. A first strand of papers, such as Carey et al. (1998) and Daniels and Ramirez

(2008), studies how different kinds of intermediaries specialize in lending to different types of

firms and the consequences of such choices. Black, Krainer, and Nichols (2020) show that in the

commercial real estate mortgage market banks systematically fund riskier collateral compared

to arm’s length investors. Liberti, Sturgess, and Sutherland (2017) and Gopal (2021) document

how different lenders focus on demanding specific types of collateral to back the loans they

provide, and how this matters for lending decisions in new markets and in the presence of

lender constraints. Acharya et al. (2006), Beck et al. (2022), and Tabak et al. (2011) focus

instead on the overall risk effect of banks and financial intermediaries’ portfolio specialization,

finding null or negative effects.

A further strand, closer to our paper, focuses on banks only. Blickle et al. (2021), Di and

Pattison (2022), Duquerroy et al. (2022), De Jonghe et al. (2020), Jiang and Li (2022), and

Paravisini et al. (2022) show that even within a single class of intermediaries, i.e. banks, there

is specialization in lending towards specific firms, with positive effects on credit supply. Among

these, Jiang and Li (2022) and Paravisini et al. (2022) focus on the heterogeneity in credit

supply responses to shocks by specialized and non-specialized banks. Both papers document

the presence of a comparative advantage in lending, respectively towards specific industries

and specific export markets. Overall, they suggest a degree of non-substitutability between

credit provided by specialized banks and non-specialized banks.

To the best of our knowledge, we are the first to look at the implications of lender specialization for financial contracting and security design. Similarly to Blickle et al. (2021),

we show that industry specialization in lending drives loan spreads. Uniquely, we document

that specialization is a key determinant of loan covenants too, which represent a key channel

determining the impact of firm-level and aggregate shocks on investment, as shown by Chava

and Roberts (2008) and Chodorow-Reich and Falato (2022) respectively. Furthermore, by

5
leveraging detailed contract information and focusing on covenant strictness, we present a

possible explanation for why credit obtained from specialized lenders is difficult to substitute:

specialized lenders offer overall more generous terms that non-specialized lenders might not

be willing to offer. Finally, we provide an alternative test to the one proposed by Paravisini

et al. (2022) to identify the presence of information advantages in lending associated with

specialization, based on a measure of information distance between borrowers and lenders.

Our findings regarding the importance of bank specialization for contract features also

contribute more generally to the study of financial contracting and its determinants. Several

works highlight the role of borrower or lender characteristics for the determination of loan

covenants (e.g. Abuzov, Herpfer, & Steri, 2020; Berlin & Mester, 1992; Billett, King, & Mauer,

2007; Bradley & Roberts, 2015; Demerjian, Owens, & Sokolowski, 2018; Demiroglu & James,

2010; Murfin, 2012), or pricing (e.g. Cai, Eidam, Saunders, & Steffen, 2018; Ivashina, 2009).

Closer to our paper, a smaller set of studies stresses the importance of jointly taking into

account borrowers’ and lenders’ characteristics when looking at the determinants of contract

features. Prilmeier (2017) shows that relationship lending affects the design of financial

covenants. Gorostiaga (2022) shows that banks with higher market shares in an industry offer

loans with stricter covenants to firms in that industry, supporting an explanation based on banks’

incentives to limit competition among similar customer firms.11 Hubbard, Kuttner, and Palia

(2002) and Santos and Winton (2019) document that the interaction of bank capital and firm

profitability matters for the determination of loan spreads. Bao (2019) finds that peer effects in

loan portfolios affect the cost of credit. With respect to these studies, we provide an additional

joint dimension—lender’s industry specialization and borrower’s industry—that is relevant for

the determination of price and non-price terms.

The paper proceeds as follows. In Section 2 we describe the sample construction, discuss

how we measure specialization, and provide evidence on bank specialization in the syndicated

loan market. In Section 3 we present our empirical strategy, our findings, and discuss several

alternative explanations. In Section 4 we provide concluding remarks.

11. Note that we find the opposite effect for banks’ portfolio shares. In Gorostiaga (2022) total credit to the

industry is the denominator, in our work, total credit from the bank. The two results are not at all in contrast.

6
2. Data and Measurement

To characterize specialization and to study its implications, we construct a sample of syndicated loans matched with bank and firm characteristics. Below we describe the sample

construction, introduce and discuss the way we measure bank specialization, present the other

economic variables we employ in our analysis, and summarize the sample characteristics.

2.1. Sample Construction

Our two main sources of data for this paper are LPC DealScan and Compustat. LPC DealScan

contains detailed information on syndicated loans, including loan amounts, covenants, pricing,

and maturity. Compustat provides balance-sheet information for both banks and firms. We

merge the loan data with borrower characteristics thanks to the linking table provided by Chava

and Roberts (2008), which matches firms in Compustat to borrowers in DealScan from 1987

to 2017.12 We also merge firm characteristics in Compustat with the industry classification

developed by Hoberg and Phillips (2010, 2016), which is available for most public companies

present in Compustat starting from 1987.

We obtain information on banks by matching lenders in DealScan with bank characteristics,

thanks to the linking table provided by Schwert (2018), which identifies the Bank Holding

Company (BHC) of all DealScan lenders with at least 50 loans, or $10 billion loan volume in

the matched DealScan-Compustat sample. We define a bank to be the BHC, not the individual

DealScan lender. As most loans in DealScan are syndicated, the same loans will be associated

with one or more lead arrangers, and several other participant banks. Consistently with other

studies, we focus only on the lead arranger(s), and we attribute the whole loan amount to the

lead arranger(s) of the syndicate.13 This choice stems from observing that a lead arranger is

the bank in charge of the active management of the loan, even if it does not retain the entirety

of its amount on their balance sheets (Ivashina, 2009).14 We identify a lead arranger following

12. The linking table is constantly being updated; as of April 2022 this is the most recent and comprehensive

version.

13. See, for example, Chakraborty, Goldstein, and MacKinlay (2018); Prilmeier (2017); Schwert (2018).

14. If there are multiple lead arrangers, we split the loan amount equally among them.

7
the procedure outlined in Chakraborty et al. (2018).15

We restrict the sample to loans originated between 1996 to 2016 since the coverage of the

syndicated lending activity and contract terms in Dealscan is sparse before 1996 (Chava &

Roberts, 2008). We further restrict the sample to loans that have borrowers headquartered in

the US. We also drop from our sample all loans to financial corporations (Compustat SIC codes

from 6000 to 6999).16 All variables, except the measures of covenant strictness and of expected

default probability that are naturally bounded between 0 and 100, are winsorized at the 1st

and 99th percentile.

The unit of observation in DealScan is a loan facility. However, information on loan covenants

is available only at the package, or deal, level. Since in our analysis the main dependent variable

is covenant strictness, we conduct our analysis at the package level, aggregating facility-level

information by weighting the facility characteristics – such as the spreads and maturity – by the

respective facility amounts. Therefore the observation level in the dataset is the package-bankfirm triplet at a quarterly frequency. Following Murfin (2012), we also report the contracting

date of a package as 90 days prior to the DealScan reported start date, to account for the time

lag between the effective moment in which banks and firms commit to loan contract terms and

the legal start date reported by DealScan.

2.2. Two Measures of Bank Specialization

We are interested in understanding whether banks specialize in lending to specific sectors of

the economy. To address this issue, we employ two approaches. The first consists in comparing

15. Specifically, DealScan has two fields that can be used to determine the lead arranger, a text variable that defines

the lender role in the syndicate and a yes/no lead arranger credit variable, both are employed to define which

bank has a lead role. Chakraborty et al. (2018), who in turn follow Bharath, Dahiya, Saunders, and Srinivasan

(2007); Bharath et al. (2011), defines as lead arranger, within each syndicate, the bank that “scores” highest in the

following ten-part raking: “1) lender is denoted as “Admin Agent”, 2) lender is denoted as “Lead bank”, 3) lender is

denoted as “Lead arranger”, 4) lender is denoted as “Mandated lead arranger”, 5) lender is denoted as “Mandated

arranger”, 6) lender is denoted as either “Arranger” or “Agent” and has a “yes” for the lead arranger credit, 7)

lender is denoted as either “Arrange” or “Agent” and has a “no” for the lead arranger credit, 8) lender has a “yes”

for the lead arranger credit but has a role other than those previously listed (“Participant” and “Secondary investor”

are also excluded), 9) lender has a “no” for the lead arranger credit but has a role other than those previously

listed (“Participant” and “Secondary investor” are also excluded), and 10) lender is denoted as a “Participant” or

“Secondary investor”." (Chakraborty et al. 2018, Online Appendix, p.1)

16. However, to compute the measures of specialization we retain every loan from 1987 to 2016 for which we can

identify a borrower in Compustat and for which the TFIC classification is available, regardless of headquarter or

SIC codes. Computing the measures of specialization from 1996 does not affect our results.

8
how concentrated the commercial lending portfolio of an average bank is relative to the whole

syndicated-loan market portfolio. Intuitively, if banks are more concentrated than the market, it

means at the very least that they prefer to focus their lending towards some, but not all, sectors

of the economy – implying a certain degree of specialization. The second involves identifying

those banks that are abnormally exposed to a given industrial sector with respect to the other

banks active in that sector, that is, specialized in that industry.

The goal of the first approach is to present a stylized fact about the syndicated loan market

and understand whether banks, on average, display more or less portfolio concentration than

what would be implied by the overall distribution of credit to the US economy. The goal of the

second approach is instead to classify each bank as specialized – or not – in lending to a given

industry. In the rest of our empirical analysis we will use this second approach to classify banks

as specialized or not.

2.2.1. Methodology

In the first approach, we employ the Herfindahl-Hirschman Index (HHI), commonly used to

measure the degree of market concentration. Specifically, we use it to characterize the level of

concentration of the market portfolio and of the average bank, with respect to the different

industries in the economy.17 The HHI of the commercial lending portfolio of a given bank is

defined as follows:

HHIb,t =

X

I

i=1

L

2

i,b,t

(1)

in which Li,b,t denotes the portfolio share of loans from bank b, towards industry i, at time t.

HHIb,treaches its maximum – which is equal to 1 – in presence of a perfectly concentrated

portfolio, i.e. Li,b,t = 1 for only one industry i, and 0 for all the others, and its minimum – equal

to 1/I in presence of a perfectly diversified portfolio, i.e. Li,b,t = 1/I ∀i ∈ I.

18

We can then compute the HHI for the average bank by simply taking a weighted average of

17. There are various ways to characterize portfolio concentration/diversification. See Avila, Flores, Lopez-Gallo,

and Marquez (2013) for a comparison of the various approaches employed in banking and finance.

18. We define portfolio shares as decimal values between 0 and 1, i.e. they are not in percentage terms. Therefore

the HHI is bounded between 0 and 1. In other applications in which percentage terms are used the HHI varies

between 0 and 10000.

9
the HHI of all banks, in which the weights are represented by a bank’s share of total credit:

HHI t =

X

B

b=1

Lb,t

Lt

X

I

i=1

L

2

i,b,t



(2)

in which Lb,t =

PI

i=1

Li,b,tis the total amount of credit issued by bank b still outstanding at

time t and Lt =

PB

b=1

Lb,tis the total amount of credit outstanding at time t.

Similarly, we can define the HHI for the market portfolio. If we think of all the credit

exposures of all the banks, summed together at a given time, as the “market” portfolio for the

syndicated loan market at that time, we can define the HHI for the “market” portfolio as follows:

HHIM,t =

X

I

i=1

L

2

i,t

(3)

in which Li,t =

PB

b=1

Li,b,t denotes the share of credit – from all banks – towards industry i, in

the whole syndicated loan market.

In the second approach, we adapt the methodology developed by Paravisini, Rappoport, and

Schnabl (2017) to capture bank specialization at the industry level. According to Paravisini

et al. (2017), a bank is specialized in lending towards a given industry if its portfolio share of

loans outstanding in that industry is abnormally large, relative to other banks. More formally,

specialization is a dummy variable, defined as follows:

Speci,b,t =







1 if Li,b,t ≥ L

∗

it

0 otherwise

(4)

where L

∗

it = L

75-th pctile

i,b,t + 1.5 ∗



L

75-th pctile

i,b,t − L

25-th pctile

i,b,t



in which Li,b,tis, as above, the share of credit issued bank b to industry i outstanding at time

t, and L

∗

it is an extreme value defined as the sum of two items: i) the 75th percentile of the

distribution of bank portfolio shares in industry i at time t, and ii) 1.5 times the inter-quartile

range of the same distribution. In other words, according to this approach, a bank is specialized

in an industry if it is a right-tail outlier in the distribution of portfolio shares of lending by all

10
banks towards that industry.

To help understand this approach and highlight its advantages over measures that capture

the overall concentration of the loan portfolio, such as the HHI, Figure 1 presents some simple

examples involving two banks and an economy with only two sectors. In panel (a) neither

bank is specialized as each bank’s balance sheet is split in half between the two sectors, and the

pattern is equal across banks. Panel (c) is similar to the first case. Although one bank is larger

and the other smaller, and they are both mostly exposed to sector A, the pattern of exposure is

the same. Thus, large exposures to sector A might simply reflect a different demand of credit

from sector A with respect to sector B in that particular economy, and we cannot detect evidence

that one particular bank is specialized.

In panel (b), instead, we have an example of specialization. In this case, Bank 1 is specialized

in sector A, and Bank 2 in sector B. Each bank may lend to both sectors – and they do – but each

of them is abnormally exposed to one sector, indicating a bank-level pattern that is coherent

with comparative advantage in lending towards that sector. This does not depend simply on

the amount of credit that goes from each bank to each sector. In fact, in panel (d), Bank 1 is

specialized in sector A, and bank 2 is specialized in sector B. Bank 1 provides overall more

credit to sector B than Bank 2, but its portfolio share is really small compared to Bank 2, which

only lends to sector B.

2.2.2. Specialization in Lending in the US Syndicated Loan Market

To compute these measures of specialization, we need granular information on banks’

commercial lending portfolios. For this purpose, we rely on DealScan, which allows us to obtain

data on bank-firm credit relationships. The focus is on syndicated lending, which represents a

sizable portion of the corporate loan market in the US. Since DealScan only provides information

on loan originations, we create a panel akin to a credit registry by aggregating DealScan loanlevel data at the bank-firm relationship level over time, similarly to Chakraborty et al. (2018),

Gomez, Landier, Sraer, and Thesmar (2021), and Lin and Paravisini (2012).

We assume each loan is outstanding until the original end date, or, if the information

is available on DealScan, until the amended end date.19 In this way we obtain a dynamic

19. To track loan amendments, we exploit the information present in the "facilityamendment" table present in the

11
representation of the commercial lending portfolio for each bank in our sample, which we then

use to compute time-varying portfolio shares in each industry by aggregating loan amounts for

each bank-firm relationship at each given point in time.

Since a bank portfolio share towards a given industry is a proxy to capture comparative

advantage in lending towards specific types of projects in the economy, we use the Text-based

Fixed Industry Classifications (TFIC) developed by Hoberg and Phillips (2010, 2016), which

better measures similarities across firms with respect to a standard SIC or NAICS classification,

and is updated annually. Specifically, TFIC uses textual data to track the products (types of

projects) that characterize each firm’s core business activity. Then, it classifies firms as belonging

to a specific cluster (industry) based on the similarity of the firm’s core activity. This classification

follows the evolution of the firm’s core business over time, and thus it is closer in spirit to what

we aim to measure than a static NAICS or SIC industry definition.

We employ the 25-industry version of their classification, as this ensures a good balance

between the number of firms present in DealScan in each industry and sufficient precision in

the characterization of the different sets of projects in the economy. We apply the methodology

described in the previous subsection and compute the two measures of specialization for all the

banks in the sample of syndicated loans granted to firms that have a TFIC classification, from

1987 to 2016.

First, we look at the measure of loan portfolio diversification. In Figure 2, we plot the HHI

of the commercial lending portfolio for the average bank computed for each quarter as in

Equation (2), and the same measure computed for the market portfolio as in Equation (3).

Given that a larger value of this measure implies larger concentration of exposure, a comparison

of the two reveals that the loan portfolio of the average bank is more concentrated than the

market. Comparing the average HHI of the market portfolio (∼ 0.07) and that of the average

bank (∼ 0.105) over time, we see that the average bank is significantly more concentrated

than the market. This implies that not every bank is lending to every industry in the same way,

legacy version of Dealscan in WRDS. One potential caveat is that renegotiated/amended loans could appear as

new loans in DealScan; if loan renegotiations are not identically and independently distributed across bank-firm

pairs, this could imply an imperfect measurement of a bank’s lending activity. To partially address this issue, we

perform our analysis dropping from our sample all the loans that have a description such as "This loan amends

and restates..." in the various "comment" fields available in Dealscan. All the results of the paper are robust to not

dropping these loans.

12
providing suggestive evidence of specialization in lending.

Second, we look at specialization by industry. Specifically, we are interested in understanding

whether we can observe abnormally large loan portfolio shares towards certain industries,

similarly to what Paravisini et al. (2017) do for countries of destination for Peruvian exporting

firms. Figure 3 shows, at four different moments in time, the box-and-whisker plots of the

distribution of Li,b,t −  ̄Lit, that is of bank portfolio shares towards each industry i demeaned

by the average share of lending in that industry. We can see that across time almost every

industry display at least one or more right-tail outliers; that is, one or more specialized lenders.

Moreover, specialization is persistent. In Figure 4 we plot the autocorrelation of Speci,b,t defined

in Equation (4), and we can see that a bank specialized in lending towards an industry in a

given year is 25% more likely to be specialized in lending towards the same industry 10 years

later, with respect to a bank that was not specialized.

Overall, the evidence presented in this section points to bank specialization in lending as a

defining feature of the US syndicated loan market.

2.3. Measurement of Economic Variables

2.3.1. Dependent Variables: Loan Covenant Strictness and Loan Spreads

Our goal is to understand whether specialization is associated with information advantages

in lending towards specific sectors of the economy. We, therefore, need an empirical proxy to

capture the notion of information advantage when a bank is lending to firms in a given industry.

To achieve this, we build upon the theoretical work by Gârleanu and Zwiebel (2009), and

consider the covenant structure embedded in a loan contract as capturing the information

“distance” between a bank and a firm. The more restrictive the contract – in terms of what

the firm can or cannot do in order not to trigger a technical default by violating a covenant –

the less information a bank has about a borrower, according to the theory. However, when a

contract includes more than one covenant it is not obvious how to assess the overall strictness

of the covenant package. Therefore, we are going to rely on the measure of covenant strictness

developed and made available by Demerjian and Owens (2016).20

20. The measure developed by Demerjian and Owens (2016) can be downloaded on Edward L. Owens’ personal

13
Covenant strictness is defined as the ex-ante probability of violating at least one financial

covenant during the lifetime of the loan, ranging from 0 to 100. This measure is characterized

by four properties, all valid on an “all else equal” basis. First, it increases in the number of

covenants; second, for a fixed number of covenants, it decreases in the initial slack of a covenant,

defined as the distance between the level of the covenant threshold and the starting level of

the corresponding financial ratio; third, it increases in the volatility of the ratios targeted

by covenants; fourth, it decreases in the correlation between covenants—intuitively, since a

technical default is triggered even if a single covenant is violated, contracting on independent

financial ratios increases the probability of violating at least one.

In order to draw conclusions it is also important to track the cost of credit, since there is a

trade-off between covenants and the cost of credit—stricter contracts might be associated with

lower costs and vice versa (Bradley & Roberts, 2015; Matvos, 2013; Reisel, 2014). Therefore

we also collect information on loan pricing available on DealScan. In particular, we focus on the

All-in Drawn Spread (AISD), which is the sum of two terms: i) the spread over LIBOR, which a

borrower needs to pay for every dollar of credit drawn down, and ii) the facility fee, which a

borrower needs to pay annually.

2.3.2. Bank, Firm, and Relationship Level Variables

We obtain bank- and firm-level variables from Compustat, and information on loan quantities and characteristics from Dealscan. Using this merged dataset, we construct proxies for

relationship lending and banks’ industry market share.

We create different proxies to capture the strength of a bank-firm credit relationship. Specifically, we define four measures, following Bharath et al. (2007, 2011), Schenone (2010), and

Prilmeier (2017). Previous Rel.f ,b,tcaptures the presence of an existing credit relationship

between firm f and bank b at the extensive margin. It is a dummy variable that takes value 1 if

bank b granted a loan to firm f in the 3 years prior to a loan at time t. Rel. Intensity (Amt)f ,b,t

and Rel. Intensity (Num)f ,b,tcapture the strength of the credit relationship at the intensive

margin. They are defined, respectively, as the fraction of credit (loans) that firm f obtained

website https://sites.google.com/site/edowensphd/researchdata. We thank Demerjian and Owens (2016) for

making the measure available.

14
from bank b over the total amount of credit (number of loans) firm f took out over the 3 years

prior to a loan at time t. Finally, we compute the length of an outstanding bank-firm relationship.

Rel. Lengthf ,b,tis defined as the time elapsed between period t and the first interaction between

firm f and bank b in DealScan.

We also collect information on the geographic distance between the borrower and the lender,

to proxy for “arms-length” credit relationships. In particular, we construct a dummy variable,

Same Statef ,b,t, which takes value 1 if bank b and firm f are in the same state at time t, and 0

otherwise.21 Finally, we compute each bank’s industry Market Shareb,f ,t. This is the fraction of

credit that a bank b provides to the industry of firm f over the total credit that the industry

receives at period t − 1. Taking into account a bank’s industry market share is important since

bank specialization in lending to an industry could be correlated with a high industry market

share. All other variables are defined in Table 1.

2.4. Sample Characteristics

Table 2 reports summary statistics for the samples we use in our empirical analysis. In particular, we distinguish two samples. The first one, “Matched Sample" is the full DealScan-Compustat

matched sample obtained from the sample selection procedure described in Section 2.1. The second one, “Strictness Sample", is the subsample of loans for which both the All-In Drawn Spread

and covenant strictness measure developed by Demerjian and Owens (2016) are non-missing.

We conduct our main empirical analysis over this subsample.

The top panel of Table 2 reports information on the characteristics of loan-level variables

in our samples. The Strictness Sample includes 11,684 distinct loans. On average, a loan

agreement contains more than two financial covenants and displays a level of strictness such

that the borrower has 36% probability to violate at least one covenant as well as an All-In-Drawn

Spread of 188 basis points. The average loan package has a maturity of almost 4 years, amounts

to $567 million, and the average syndicate size (number of lenders) is 9. These statistics are

similar to the larger Matched sample, which displays on average a smaller number of covenants,

21. We use the historical data on firm and bank locations collected from the SEC filings by Bai, Fairhurst, and

Serfling (2020) and Gao, Leung, and Qiu (2021), supplementing them with Compustat header information when

missing.

15
a larger average loan amount, and a slightly smaller number of syndicate members.

The mid panel of Table 2 reports information on the borrowers in our samples. The Strictness

Sample includes 11,231 firm-quarter observations for 3,634 firms. These are public firms,

large – on average $1 billion in total assets – and mature – on average 20 years since IPO.

55% do not have a long-term issuer credit rating, and for those that have a rating, the average

rating is BBB-/BB+.

22 Over our sample period (1996-2016), they enter, on average, into 9

syndicated loan agreements. Overall, there are no major differences between the Strictness and

the Matched Sample.

Finally, the bottom panel of Table 2 reports information on the lenders in our samples. The

Strictness Sample includes 2, 093 bank-quarter observations for 95 banks. The average bank is

large, with $200 billion in total assets, a deposit-to-asset ratio of 60%, with book equity capital

amounting to 7%.

3. Bank Specialization and Loan Contract Terms

In this section, we explore the effect of bank specialization in lending on loan covenant

strictness and the cost of credit. We first perform a simple univariate analysis, which highlights

potential non-randomness in the matching between banks and firms. Employing different multivariate specifications aimed at mitigating this concern, we then show that bank specialization

is associated with significantly lower covenant strictness and no higher spreads.

We interpret this evidence as support for explanations of bank specialization based on

lending advantages, and we suggest that part of this advantage is an information advantage,

which is sector-specific. Finally, using default on lenders’ loan portfolios as a possible source

of exogenous variation in banks’ perception of their own expertise in dealing with a certain

industrial sector, we show that specialized banks are more sensitive to defaults of firms in their

sector of specialization, further substantiating our interpretation.

22. Rating is a categorical variable. We assign value 1 to AAA ratings, 2 to AA, and so on. The largest value is 9,

assigned to “D" or “SD" indicating default in the Capital IQ Long-Term Issuer Credit Rating.

16
3.1. Univariate Analysis

We begin by comparing the characteristics of loans arranged by a bank specialized in lending

towards the industry a given borrower belongs to, with all other loans. To make things clear,

a loan to a firm f starting at time t is considered to be arranged by a specialized bank b if

Speci,b,t−1, defined in Equation (4), is equal to 1 and the firm f belongs to industry i. The top

panel of Table 3 reports the results of these basic univariate t-tests.

Loans arranged by specialized banks in their industries of specialization display several

different features compared to loans arranged to other industries and/or non-specialized banks,

even though they are similar in their amount. In particular, “specialized loans” display stricter

covenants, higher spreads, shorter maturities, a more concentrated syndicate, and a lower

fraction of revolving credit, compared to “non-specialized loans”. Even though this is suggestive

of a relationship between bank specialization and contract features, this evidence may simply

arise from the different characteristics of specialized banks and their borrowers. By performing

t-tests on bank and firm characteristics, we aim to understand whether this is the case.

The mid panel of Table 3 displays the results of the t-tests for firm characteristics.23 The

estimates confirm that firms obtaining loans from banks specialized in the industry they belong

to are generally different from other firms. They are smaller, younger, less likely to have a

long-term issuer credit rating – even though if they do have a credit rating, it is on average

similar to other firms. This implies that a firm borrowing from banks specialized in its own

industry is less likely to have access to public debt/equity markets and thus subject to more

severe information frictions. These firms also appear to perform slightly better in terms of

liquidity, tangibility, and leverage.

The bottom panel of Table 3 shows the estimate of t-tests on bank characteristics.24 To

be clear, a bank can appear both in the “specialized” and “non-specialized” sample at a given

moment in time. With this caveat in mind, what emerges is that banks specialized in lending

towards a given sector are different compared to other banks. Specifically, they are smaller,

have a larger reliance on deposits, appear to be better capitalized, and are more profitable, with

23. We split all firm-quarter observations into those that are associated with a loan arranged to any sector any

bank is specialized in, and those that are not. We do the same for bank-quarter observations.

24. Since the same bank issue more than one loan, the standard errors for the t statistics in Table 3 have been

adjusted for clustering at the bank level.

17
a similar ratio of non-performing assets.

Overall, the evidence in Table 3 suggests that bank specialization might play a role in

determining loan characteristics, but any conclusion based on simple univariate analysis would

be distorted by the pervasive selection in the matching between borrowers and lenders. In the

next Section, we analyze this in a multivariate regression framework with fixed effects.

3.2. Empirical Strategy: A Within-Bank Approach

To retrieve the effect of bank specialization on loan covenant strictness, ideally, we would

like to observe identical firms borrowing from two different banks, one specialized in lending

towards the firm’s industry and one not specialized. In particular, the firms should be randomly

assigned to the banks, and each bank should differ from each other only for its specialization

status. However, matching between banks and firms is rarely random and loan contract terms

are an outcome of this matching process. If, as Table 3 suggests, specialized banks are small

banks that in general tap a pool of borrowers that are smaller, more opaque, and riskier, any

observed variation in the loan covenant strictness might just be the direct consequence of the

systematically different characteristics of the firms and banks involved.25

To mitigate these concerns, we proceed in the following way. We start from a within-bank

approach, akin to the one proposed at the firm level by Khwaja and Mian (2008). Underlying

our empirical strategy there is the idea of comparing two loans arranged by the same bank in

the same year-quarter, one issued to a borrower in an industry in which the bank is specialized

in lending to, and one issued to a borrower in another industry. This, however, does not fully

account for borrower selection problems. Even after absorbing all bank-specific, time-varying

characteristics, it may be the case that within each bank’s borrower pool, firms that fall within

the industries in which the bank is specialized are systematically different. To address this,

we first include firm balance sheet controls, which absorb variation due to observable and

time-varying firm characteristics. Furthermore, we add firm fixed effects, which account for all

firm-specific, observable, and unobservable characteristics that are fixed in time.26

25. This systematic difference can regard both observable and unobservable characteristics. It is in fact well known

in the literature that covenant strictness reflects borrower riskiness (Demiroglu & James, 2010), and ex-ante bank

confidence in the underwritten loans (Murfin, 2012).

26. Ideally, we would rather to have a within bank-time and within firm-time specification. Unfortunately, as we

18
Formally, we employ the following specification:

Loan Contract Termf ,b,t = θb,t + Other Fixed Effects + β · Specializationf ,b,t−1

+ γF· Firm Controlsf ,t + γL· Loan Controlsf ,b,t + εf ,b,t

(5)

in which Loan Contract Termf ,b,tstands for loan covenant strictness or the AISD for a loan

originated in quarter t by bank b to firm f . θb,trepresents bank×year-quarter fixed effects; the

term Other Fixed Effects includes borrower fixed effects and separate intercepts for each S&P

long-term issuer credit rating, with the omitted dummy variable capturing unrated firms. The

main explanatory variable of interest is Specialization, a lagged 12-quarters rolling average of

the specialization dummy Speci,b,t(defined in Equation (4)). Firm Controls includes firm-level

proxies of time-varying risk. Specifically, it includes the expected default probability (EDF),

based on the Merton model of credit risk (Merton, 1974) and computed implementing the

“naive” approach proposed by Bharath and Shumway (2008), as well as log of total assets,

debt to asset ratio, current ratio, tangible net worth to asset ratio, interest coverage ratio, and

years since IPO. These controls account for repayment risk (especially for non-rated firms), size,

leverage, liquidity, the ability to provide collateral, firm profitability, and firm age.27 Finally,

Loan Controls includes also loan-level controls such as log of maturity, log of loan amount, log

of number of syndicate participants, the fraction of revolving credit over the total package

amount, and separate intercepts for different loan purposes.

We make the choice to average the specialization dummy over 12 quarters to put less weight

on banks that are only sporadically specialized in a sector. This might be simply the result of a

single large loan at a time of relatively low lending activity in that industry, or measurement

error due to the limitations of our dataset. We chose 12 quarters as this length ensures a

good balance between capturing persistence and avoiding that our measure simply mimics the

origination of new loans—the average maturity of a loan in DealScan is around 4 years.28

work on a sample of very large loans, we do not see many firms doing multiple deals in the same year-quarter.

This makes the adoption of such a strategy infeasible.

27. Similar controls are used in similar studies focusing on the determinants of loan covenant strictness, such as

Murfin (2012) or Prilmeier (2017).

28. However, we stress that performing the analysis with a measure of specialization averaged over rolling

windows of different lengths does not change the main results of the paper—see the robustness checks presented

in Section 3.6. Finally, we note that the same choice of rolling window length, performed for similar reasons, can

also be found in Paravisini et al. (2017).

19
3.3. The Effect of Specialization on Loan Covenant Strictness and Pricing

We now introduce the baseline results of our analysis. Table 4 reports the regression estimates

of the specification in Equation (5) over the Strictness Sample, for two of the main different loan

contract characteristics: Covenant Strictness and the All-In Drawn Spread (AISD). Looking at

covenant strictness first, the estimate on the specialization variable is negative and statistically

significant, indicating that banks specializing in lending towards a given industry write less

strict contracts when entering loan agreements with firms in that industry. The estimates on

AISD are also negative across specifications.

In particular, a simple regression of covenant strictness on bank-time fixed effects shows that

a loan contract with a firm in the bank’s area of specialization displays less strict covenants

by 12.4 percentage points compared to firms in other industries (column 1). This estimate is

economically significant, as it amounts to 33% of the mean value and 30% of the standard

deviation of the distribution of covenant strictness in our sample. Note that this is not associated

with higher loan spreads: the point estimate on the specialization variable in relation to the

AISD is negative (column 4), even though not statistically significant.

When we account for borrower selection and borrower risk by including firm fixed effects

and firm controls, the results are even stronger. The point estimate of the coefficient on bank

specialization doubles for both covenant strictness and AISD. Banks specialized in an industry

provide credit to firms in that industry with covenants looser by 24 p.p. relative to firms in

other industries, with no higher cost of credit (columns 2 and 5). Alternatively, a 1 standard

deviation increase in bank specialization implies a decrease in covenant strictness by 4 p.p. These

results reduce concerns that the effect of specialization is entirely a byproduct of unobserved

heterogeneity in borrower types and riskiness.

However, banks specializing in lending towards certain industries might provide credit with

characteristics that are systematically different compared to non-specialized banks; e.g. suppose

that specialized banks only agree to provide credit in the form of term loans, whereas nonspecialized banks only in the form of revolving credit. To address this concern we include

loan controls to the baseline specification, and as shown in columns 3 and 6, results are

virtually unchanged, while the effect on AISD becomes slightly larger and marginally statistically

20
significant.

The negative, economically and statistically significant effect of the specialization variable

on covenant strictness suggests less information asymmetry, or “distance”, between a bank

specialized in lending towards a given industry and firms in that industry, in line with the

theoretical framework developed by Gârleanu and Zwiebel (2009). The negative estimates

on the same specialization variable when loan spreads are the dependent variables support

this interpretation, ruling out that lower strictness is compensated with a higher cost of credit,

which would weaken the notion of lending advantage. It appears that specialized banks not

only leave more leeway to their borrowers, but they appear not to see this as a risk for which

they must be properly compensated. This suggests less restrictive covenants actually reflect

better ex-ante knowledge of the projects/capacity to screen them, and this is consistent with

explanations of bank specialization based on the existence of lending advantages, specifically

an industry-specific information advantage.

3.4. Assessing Alternative Explanations

There might be alternative explanations for the results presented in Table 4. In particular,

the results presented so far are consistent with at least three other economic mechanisms: the

presence of borrower-specific knowledge (relationship lending), insurance incentives stemming

from a high industry market share, and local knowledge spillovers implied by geographical,

rather than industry, specialization.

Relationship Lending

First, we could argue that the industry-specific information advantage could originate from

an information advantage that is borrower-specific. This would be consistent with widespread

“relationship lending” (Berger & Udell, 1995; Petersen & Rajan, 1994, 1995). For example,

Bharath et al. (2011) and Prilmeier (2017) specifically show that relationship lending matters

for the determination of covenants and other contract terms in syndicated loan agreements.

To explore the role that borrower-specific information might have on the determination of loan

covenant strictness, we include in our specification the various empirical proxies we described in

21
Section 2, which are meant to capture different aspects of relationship lending. Table 5 reports

the results for these regressions. Across all specifications, for both covenant strictness and loan

spreads, the estimated coefficient on the specialization variable is virtually unchanged and still

statistically significant, validating the hypothesis that banks have an information advantage

that stems from an industry-specific expertise and not only from borrower-specific information.

In conclusion, we see that an explanation based only on relationship lending does not

seem appropriate to rationalize the observed relationship between bank specialization and the

existence of an information advantage relative to that industry.

High Industry Market Share

Second, banks that are specialized in lending towards a given industry might also provide

a relatively large share of credit to that industry, i.e. have a high industry market share. This

would point to at least two other potential explanations for our results. On the one hand, the

observed effect of bank specialization on contract terms might in fact simply reflect a bank’s

willingness to gain market share in an industry. Banks could offer favorable credit terms to

crowd out other lenders from a given industry, thereby increasing both their industry market

share and their industry portfolio share. If this is true, the observed effect on contract terms

should be driven by the bank’s industry market share and not by specialization.

On the other hand, banks with a high market share might have incentives to offer better

contract terms to borrowers for reasons unrelated to an information advantage. Specifically,

Giannetti and Saidi (2019) show that banks with a high market share in an industry are more

likely to internalize negative spillovers and possible systemic effects of tougher credit conditions

in that industry – as well as upstream and downstream the related supply chain – in periods

of distress. For analogous reasons, they might have incentives to write less strict contracts to

avoid triggering covenant violations that might potentially be costly not only for the specific

firm – in terms of investment, for example – but also for the entire industry the firm is part of.

To address both these issues, we include in our specifications the variable Market Share,

defined in Section 2, which is the share of credit outstanding that a bank has in one industry

relative to the total credit supplied to the industry by all banks. Table 6 reports the results

for these regressions. When looking at covenant strictness, the estimates on the specialization

22
variable are slightly larger and still highly significant. For loan spreads, we still observe a

negative effect on the specialization variable, confirming the results of the main analysis.

Turning to the effect of a high market share, we can see that the estimated coefficient for

the market share variable on covenant strictness is not significant (columns 1-3), whereas it is

positive and significant on AISD (columns 5 and 6). We have two possible explanations for this

relation. First, it may be a result of the fact that banks with high market share in an industry

have a larger pool of loans in that industry by construction. As a consequence, their marginal

borrower is of lower quality, has a higher information distance from its lender, and receives

higher loan spreads. Second, it may be the case that these banks have overall higher market

power, which increases their charter value, making them less willing to take risks (Keeley,

1990).

Geographical Proximity

Third, the literature points to the role of geographic distance as an important proxy for

the degree of asymmetric information between borrowers and lenders. Loan terms are more

favorable when borrowers are geographically closer to lenders (Agarwal & Hauswald, 2010;

Alessandrini, Presbitero, & Zazzaro, 2008; Degryse & Ongena, 2005), even in presence of large

corporations (Hollander & Verriest, 2016).

We are thus concerned that banks specialized in lending towards a given industry have

an abnormal exposure to that industry because they are lending to specific locations that

feature business concentration in that industry and that are geographically close to these banks’

headquarters. This geographical proximity between banks and firms in specific industries might

in turn explain our results. If this is the case, we would still interpret our result in light of

an information advantage of these banks. However, this advantage would not stem from an

industry-specific expertise, but from the acquisition of soft information based on geographical

proximity. To address this issue, we construct a dummy variable, Same State, which takes value

1 if the bank and the firm headquarters are located in the same state, and we include it in our

specifications.

Table 7 presents the results for these regressions. Consistent with the notion that geographical

proximity between borrowers and lenders reflects a lower level of asymmetric information,

23
the estimates on the same-state dummy are negative for both covenant strictness and loan

spreads. However, they are not significant. On the other hand, the estimated coefficients on the

specialization variable are essentially the same as the baseline specifications.

3.5. Specialization and Defaults on Lender Portfolios

To provide further evidence in support of our proposed interpretation, we employ defaults

on lender loan portfolios as a relatively exogenous source of variation to the lenders’ perception

of their own screening ability (Murfin, 2012). We examine whether defaults of firms in industry

i that have outstanding loans with bank b differentially affect the contracting behavior of banks

that are specialized in lending to i and banks that are not. In particular, we focus on how

covenant strictness changes for loans underwritten by specialized and non-specialized banks

following the default shocks.

We compute the number of defaults each bank experiences in its loan portfolio by counting

instances in which borrowers with an outstanding loan with a given bank have a credit rating of

“D” or “SD” over a period of 90 days, following Murfin (2012). Suppose that banks specialized

in lending towards one sector have an information advantage in screening or monitoring

specific projects in that sector. We posit that, for a given number of borrowers in an industry

defaulting while having outstanding loans with a bank, banks specialized in lending towards

that industry would revise more the perception of their own ability of screening borrowers in

that industry, compared to banks not specialized in lending towards that industry. Indeed, a

default in a given industry should be relatively more informative for those banks who have an

information advantage for that given industry. If defaults occur in industries out of a bank’s

area of specialization, on the other hand, we should observe a smaller or null revision of a

bank’s own screening ability.

We empirically test this implication by employing a specification similar to the one in

Equation (5), with the inclusion of interaction terms between the specialization variable and

24
the number of defaults on lender portfolio, as follows:

Loan Termf ,b,t = θb,t + θf + ρ · Specializationf ,b,t−1 × Defaultsb,t−1(6)

+ β · Specializationf ,b,t−1 + γD· Defaultsb,t−1 + γ · X f ,b,t + εf ,b,t

The coefficient of interest is ρ, which measures the differential effect on loan contract terms

of a specialized bank in response to one more default with respect to a non-specialized bank. In

particular, given a loan agreement between a bank b and a firm f that starts at time t, we are

going to consider two different types of Defaults variable: Defaults (same), which denotes only

the defaults that have occurred in the same industry as f , and Defaults (other), which considers

the total number of defaults occurred in all other industries. Crucially, we expect a positive and

significant effect only for the interaction term of the specialization variable and Defaults (same),

but not for the interaction term with Defaults (other). This amount to saying the following: a

bank that is specialized in lending towards industry i, when lending to a borrower in industry i,

is going to be more responsive in making covenants stricter– relative to a bank that is lending

to the same industry – only when it experiences borrower defaults in industry i, and not when

the defaults occur in other industries.

Table 8 shows the results of regressions as in Equation (6), and the evidence is consistent

with our hypotheses. From this table, we can observe that the coefficient on the specialization

variable is very similar to the baseline regression, and still highly significant. Two patterns also

emerge. When specialized banks incur defaults in their loan portfolios, they increase covenant

strictness relatively more than non-specialized banks, but only when these defaults occur to

borrowers in the industry the bank specializes in. When defaults occur outside of the industry

of specialization of the bank, there is no differential response in terms of covenant strictness. In

fact, the coefficient on the interaction term between the specialization variable and the number

of defaults is positive and highly statistically significant only when the defaults occur in the

industry of specialization of the bank

In terms of the economic interpretation of the coefficients, a specialized bank in an industry

that suffers from one default in a quarter, and this default concerns a borrower in its industry

of specialization, will respond by increasing covenant strictness by approximately 30 p.p.

25
relative to a specialized bank that does not experience default, and by approximately 6 p.p

(−24.36 + 30.28 × 1) relative to a non-specialized bank that suffers from 1 default in the same

industry.

3.6. Robustness Checks

The results presented so far stand to a series of robustness checks. First, restricting the

analysis only to loans with a single lead arranger confirms the baseline results, as shown in

Table 9. Second, computing the specialization measures starting from 1996 instead of 1987

leaves the results virtually unchanged; the estimates are presented in Table 10. Third, repeating

the analysis focusing on the pre-2008 sample period also confirms the main results, and if

anything the estimates are even stronger, as displayed in Table 11. This alleviates concerns

that our results are driven by the post-financial crisis period, in which the share of leveraged,

covenant-light loans increased dramatically, and relatedly the coverage of covenants offered by

Dealscan appears to have decreased in quality (Bräuning, Ivashina, & Ozdagli, 2022).

Finally, averaging the specialization dummy defined in Equation (5) over different time

horizons does not change the main message of the paper. As can be seen in Table 12, the

effect of the specialization variable on covenant strictness is very similar in both economic

magnitude and statistical significance when averaging over 3, 4 or 5 years, in particular for

covenant strictness (columns 3, 4 and 5). The estimate of specialization on covenant strictness

is attenuated both economically and statistically when averaging over 4 or 8 quarters (1 or 2

years), but on the other hand, it is larger and statistically significant when looking at the AISD.

The lower and mostly non-statistically significant estimates that we obtain when averaging

the specialization dummy over a period of 1 and 2 years could actually represent an indirect

validation of our proposed mechanism. It takes time to build expertise that is industry-specific,

and therefore estimates on covenant strictness are larger and less noisy once the average of the

specialization dummy is taken over longer periods.

26
4. Conclusion

In this paper we provide evidence that banks specialize in lending toward specific industries

even in a credit market for large borrowers, such as the U.S. syndicated loan market. We

show that loan contracts between borrowers in an industry and banks specialized in lending

towards that industry display a less restrictive covenant structure and no higher spreads. This,

comparing two loans made by the same bank in the same year-quarter, one towards the industry

of specialization and one to any other industry. Our results cannot be fully explained by borrower

risk, relationship lending, a high industry market share, or geographical proximity.

We look at our results in light of financial contracting theory, and interpret the restrictiveness

of the covenant structure as the degree of information asymmetry between a borrower and a

lender (Gârleanu & Zwiebel, 2009). Thus, we conclude that specialized banks have a comparative advantage in monitoring specific industries. This carries implications for the understanding

of competition and monopoly power in credit markets, and thus for the transmission mechanism

of monetary policy and potential heterogeneous effects of regulation (see Corbae & D’Erasmo,

2021). Moreover, documenting implications on the non-price conditions of credit, we propose

a possible mechanism that makes credit by specialized banks difficult to substitute (Paravisini

et al., 2022).

27
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32
Appendix A. Tables

Table 1. Variable Definitions

Variable Name Definition Data Source Unit

Specialization Rolling 12-qtr sum of specialization dummy Dealscan float

as defined in Equation (4) (0-1)

Specialization (nY) Rolling 4n-qtr sum of specialization dummy Dealscan float

as defined in Equation (4) (0-1)

EDF See Bharath and Shumway (2008), pp. 1247-48 CRSP/ float (%)

Compustat

Assets atq Compustat USD Mil

Tangibility (atq - intanq - ltq)/atq Compustat float

Leverage (dlttq + dlcq)/atq Compustat float

Current Ratio actq/lctq Compustat float

Int. Cover. Ratio Rolling 4-qtr sum of oibdq/Rolling 4-qtr sum of xintq Compustat float

Years since IPO Current date minus first date in Compustat Compustat float

Rated Dummy variable equal to 1 if firm-quarter Capital IQ int

has a long-term issuer credit rating, 0 otherwise (0/1)

Rating Categorical variable equal to 1 for credit rating "AAA", Capital IQ int

to 2 for "AA", ... , to 9 for "D"/"SD" (indicating default)

N. Loans Number of packages per borrower over sample period Dealscan

Covenant Strictness Ex-ante prob of violating a financial covenant. Ed. Owens’ float (%)

See Demerjian and Owens (2016) Website

N. Covenants Number of financial covenants in package Dealscan int

All-In Drawn Spread Average of each facility’s allindrawn Dealscan basis

in package weighted by facilityamt points

Loan Amount Ln(dealamount) Dealscan USD

Maturity Ln(average of each facility’s maturity Dealscan months

in package weighted by facilityamt)

Lenders Ln(N. syndicate members) Dealscan int

Revolver Fraction Revolver credit amount in package / dealamount Dealscan float

(0-1)

Previous Rel. Described in Section 2.3.2 Dealscan int

(0/1)

33
Table 1. Variable Definitions

Rel. Intensity (Amt) Described in Section 2.3.2 Dealscan float

(0-1)

Rel. Intensity (Num) Described in Section 2.3.2 Dealscan float

(0-1)

Rel. Length Described in Section 2.3.2 Dealscan years

Market Share Described in Section 2.3.2 Dealscan float (%)

Same State Described in Section 2.3.2 Compustat/ int

SEC (0/1)

Defaults N. outstanding loans to firms with credit rating Dealscan/ int

changed to "D"/"SD" over prev. qtr Capital IQ

Defaults (Same) N. defaults in bank’s portfolio in same industry Dealscan/ int

as the borrower’s Capital IQ

Defaults (Other) N. defaults in bank’s portfolio in different industry Dealscan/ int

from the borrower’s Capital IQ

Bank Assets atq Compustat USD Mil

Deposits (dptcq/atq)×100 Compustat float (%)

Book Equity (ceqq/atq)×100 Compustat float (%)

Market Equity (prccq×cshoq)/(atq − ceqq + prccq×cshoq) Compustat float

Tier 1 Capital capr1q Compustat float (%)

Non-Performing Assets (npatq/atq)×100 Compustat float (%)

Profitability (niq/atq)×100 Compustat float (%)

34
Table 2. Descriptive Statistics

This table reports the descriptive statistics for the full matched Dealscan-Compustat sample obtained after applying

the selection criteria described in Section 2.1, and for the Strictness sample, which further restricts the sample to

observations with non-missing covenant strictness and all-in drawn spread. All variables are described in Table 1.

MATCHED SAMPLE STRICTNESS SAMPLE

Mean Std. Dev. Obs. Mean Std. Dev. Obs.

Loan Characteristics

Covenant Strictness 35.86 41.19 12, 124 35.85 41.17 11, 684

N. Covenants 2.46 1.13 14, 483 2.47 1.14 11, 684

All-In Drawn Spread 190.93 128.36 22, 724 188.22 118.43 11, 684

Loan Amount ($M) 620.64 1, 444.92 23, 164 567.59 1, 201.17 11, 684

Maturity (Months) 45.45 22.25 22, 266 46.02 19.99 11, 628

N. Lenders 7.98 8.72 23, 164 9.04 9.36 11, 684

Revolver Fraction 0.74 0.38 23, 164 0.79 0.34 11, 684

Previous Rel. 0.69 0.46 18, 167 0.70 0.46 9, 127

mean sd Obs mean sd Obs

Firm Characteristics

Ln(Assets) 7.02 1.92 21, 416 6.80 1.82 11, 231

EDF 0.05 0.17 19, 275 0.06 0.17 10, 209

Tangibility 0.20 0.30 13, 857 0.20 0.29 7, 200

Leverage 0.30 0.21 20, 656 0.31 0.20 10, 879

Current Ratio 1.91 1.25 20, 626 1.87 1.15 10, 885

Ln(1+Int. Cover. Ratio) 2.23 1.14 16, 432 2.20 1.05 8, 999

Years since IPO 20.18 16.79 20, 732 19.62 16.11 10, 932

Rated 0.46 0.50 21, 417 0.45 0.50 11, 231

Rating 4.50 1.18 9, 829 4.62 1.05 5, 064

N. Loans 9.03 6.89 21, 417 8.81 6.41 11, 231

mean sd Obs mean sd Obs

Bank Characteristics

Ln(Bank Assets) 12.39 1.58 2, 723 12.39 1.57 2, 093

Deposits 61.93 12.84 2, 069 61.47 12.66 1, 651

Book Equity 7.26 2.89 2, 673 7.35 2.75 2, 062

Market Equity 12.10 6.52 2, 503 12.58 6.52 1, 944

Tier 1 Capital 9.71 2.17 1, 935 9.50 2.13 1, 574

Non-Performing Assets 0.65 0.52 1, 754 0.63 0.49 1, 441

Profitability 0.25 0.17 2, 072 0.26 0.18 1, 654

35
Table 3. Univariate Evidence on Loan Contracts and Bank-Firm Selection

This table reports the results of univariate t-tests, meant to document systematic differences in loan, firm, and banklevel observables between loans made by a specialized bank within its sector of specialization and out of its sector

of specialization. For each variable X listed in the table, H0is that E[X(Specialized) − X(Non-Specialized)] = 0.

Spec. Non Spec. Diff. t-Stat N (Spec.) N (Non Spec.)

Loan Characteristics

Covenant Strictness 40.23 35.06 5.17 3.30 733 11, 484

N. Covenants 2.53 2.44 0.09 2.44 938 13, 616

All-In Drawn Spread 231.28 189.62 41.66 11.72 1, 416 21, 813

Loan Amount ($M) 666.63 668.65 −2.03 −0.05 1, 448 22, 237

Maturity (Months) 40.35 46.16 −5.80 −9.34 1, 391 21, 394

N. Lenders 6.25 8.24 −1.99 −8.38 1, 448 22, 237

Revolver Fraction 0.69 0.74 −0.04 −4.11 1, 448 22, 237

Previus Rel. 0.67 0.68 −0.02 −1.15 1, 028 17, 668

Firm Characteristics

Ln(Assets) 6.37 7.24 −0.88 −16.65 1, 448 22, 236

EDF 0.07 0.05 0.02 3.24 1, 266 20, 107

Tangibility 0.21 0.19 0.03 2.56 833 14, 856

Leverage 0.28 0.31 −0.03 −5.14 1, 372 21, 505

Current Ratio 2.23 1.85 0.38 11.13 1, 403 21, 397

Ln(1+Int. Cover. Ratio) 2.19 2.21 −0.02 −0.64 888 17, 452

Years since IPO 16.88 21.09 −4.21 −8.92 1, 404 21, 512

Rated 0.36 0.49 −0.13 −9.61 1, 448 22, 237

Rating 4.58 4.49 0.09 1.75 524 10, 938

Bank Characteristics

Ln(Assets) 11.3 13.3 −1.96 −3.33 1, 375 21, 548

Deposits 68.1 55.3 12.7 3.13 1, 105 18, 391

Book Equity 8.10 7.65 0.45 1.01 1, 352 21, 357

Market Equity 14.9 12.1 2.83 2.44 1, 268 20, 743

Tier 1 Capital 10.0 9.36 0.69 0.92 1, 059 18, 005

Non-Performing Asset 0.63 0.62 0.012 0.15 997 17, 151

Profitability 0.29 0.25 0.039 2.29 1, 104 18, 351

36
Table 4. The Effect of Bank Specialization on Covenant Strictness and Loan Spread

This table reports the estimates of the coefficients from the following regression using our baseline sample, which

includes loans for which the loan covenant strictness and all-in drawn spread are available:

Loan Termf ,b,t = θb,t + FEs + β · Specializationf ,b,t−1 + γ · X f ,b,t + εf ,b,t

in which Loan Termf ,b,tis either the covenant strictness (columns 1 to 3) or the all-in drawn spread (columns 4

to 6) for a loan originated in year-quarter t by bank b to firm f . θb,trepresents bank×year-quarter fixed effects,

and FEs include, depending on the specification, firm fixed effects, firm rating fixed effects, loan purpose fixed

effects. X f ,b,trepresents firm and loan controls. All the variables are defined in Table 1. In parentheses, t statistics

obtained from two-way clustering at the bank and borrower level. ∗∗∗,

∗∗

,

∗

indicate statistical significance at the

1%, 5% and 10%, respectively.

COVENANT STRICTNESS ALL-IN DRAWN SPREAD

(1) (2) (3) (4) (5) (6)

Specialization −12.4∗∗ −23.56∗∗∗ −24.35∗∗∗ −14.19 −28.45 −31.77∗

(−2.51) (−3.16) (−3.38) (−1.25) (−1.45) (−1.78)

Ln(Assets) 3.498∗∗ 4.313∗∗ −19.48∗∗∗ −14.71∗∗∗

(2.11) (2.46) (−4.79) (−3.37)

EDF 18.7∗∗∗ 19.7∗∗∗ 134.9∗∗∗ 127∗∗∗

(3.72) (4.23) (5.32) (4.99)

Tangibility 24.69∗∗∗ 27.05∗∗∗ −37∗∗ −33.23∗∗

(4.25) (4.64) (−2.26) (−2.04)

Leverage 32.16∗∗∗ 34.02∗∗∗ 30.91∗∗ 15.18

(4.41) (4.77) (2.17) (1.03)

Current Ratio −3.688∗∗∗ −3.713∗∗∗ 3.055∗ 1.015

(−2.77) (−2.99) (1.70) (0.66)

Ln(1+Int. Cover. Ratio) −13.62∗∗∗ −13.53∗∗∗ −13.13∗∗∗ −15.03∗∗∗

(−9.32) (−9.47) (−5.86) (−8.57)

Years since IPO −29.36 −10.48 132.4∗∗ 138.5∗∗

(−0.57) (−0.18) (2.37) (2.17)

Ln(Loan Maturity) .6531 11.44∗∗∗

(0.63) (3.75)

Ln(Lenders) −.735 −6.604∗∗∗

(−0.85) (−3.08)

Ln(Loan Amount) −1.098∗ 1.049

(−1.75) (0.35)

Revolver Fraction 1.818∗ −52.63∗∗∗

(1.93) (−6.95)

Bank×YearQtr FE Yes Yes Yes Yes Yes Yes

Firm FE − Yes Yes − Yes Yes

Rating FE − Yes Yes − Yes Yes

Loan Purpose FE − − Yes − − Yes

Adj. R

2

.074 .565 .57 .278 .749 .779

Observations 9, 834 4, 653 4, 643 9, 834 4, 653 4, 643

37
Table 5. Bank Specialization and Loan Terms, Accounting for Bank-Firm Lending Relationships

This table reports the estimates of the coefficients from the following regression using our baseline sample, which

includes loans for which the loan covenant strictness and all-in drawn spread are available:

Loan Termf ,b,t = θb,t + FEs + β · Specializationf ,b,t−1 + βR· RELf ,b,t−1 + γ · X f ,b,t + εf ,b,t

in which Loan Termf ,b,tis either the covenant strictness (Panel A) or the all-in drawn spread (Panel B) for a loan

originated in year-quarter t by bank b to firm f . θb,trepresents bank×year-quarter fixed effects, and FEs include,

depending on the specification, firm fixed effects, firm rating fixed effects, loan purpose fixed effects. REL represent

the relationship lending variable(s), and X f ,b,trepresents firm and loan controls. All the variables are defined in

Table 1. In parentheses, t statistics obtained from two-way clustering at the bank and borrower level. ∗∗∗,

∗∗

,

∗

indicate statistical significance at the 1%, 5% and 10%, respectively.

(1) (2) (3) (4) (5) (6) (7)

Panel A: COVENANT STRICTNESS

Specialization −24.46∗∗∗ −24.39∗∗∗ −24.31∗∗∗ −24.29∗∗∗ −24.47∗∗∗ −24.41∗∗∗ −24.37∗∗∗

(−3.41) (−3.38) (−3.37) (−3.37) (−3.40) (−3.39) (−3.38)

Rel. Length .107 .104 .123 .126

(0.73) (0.70) (0.79) (0.81)

Previous Rel. .259 .088

(0.26) (0.089)

Rel. Intensity (Amt) −.306 −.523

(−0.30) (−0.48)

Rel. Intensity (Num) −.401 −.620

(−0.37) (−0.56)

Panel B: ALL-IN DRAWN SPREAD

Specialization −31.22∗ −31.28∗ −31.38∗ −31.3∗ −30.92∗ −31∗ −30.96∗

(−1.76) (−1.75) (−1.75) (−1.74) (−1.73) (−1.74) (−1.73)

Rel. Length −.581∗ −.496 −.509∗ −.523∗

(−1.98) (−1.63) (−1.69) (−1.70)

Previous Rel. −3.40∗∗ −2.58

(−2.20) (−1.65)

Rel. Intensity (Amt) −3.12 −2.23

(−1.65) (−1.19)

Rel. Intensity (Num) −2.80 −1.89

(−1.43) (−0.95)

Firm Controls Yes Yes Yes Yes Yes Yes Yes

Loan Controls Yes Yes Yes Yes Yes Yes Yes

Bank×YearQtr FE Yes Yes Yes Yes Yes Yes Yes

Firm FE Yes Yes Yes Yes Yes Yes Yes

Rating FE Yes Yes Yes Yes Yes Yes Yes

Loan Purpose FE Yes Yes Yes Yes Yes Yes Yes

Observations 4, 643 4, 643 4, 643 4, 643 4, 643 4, 643 4, 643

38
Table 6. Bank Specialization and Loan Terms, Accounting for Bank Industry Market Share

This table reports the estimates of the coefficients from the following regression using our baseline sample, which

includes loans for which the loan covenant strictness and all-in drawn spread are available:

Loan Termf ,b,t = θb,t + FEs + β · Specializationf ,b,t−1 + βM · Mkt Sharef ,b,t−1 + γ · X f ,b,t + εf ,b,t

in which Loan Termf ,b,tis either the covenant strictness (columns 1 to 3) or the all-in drawn spread (columns 4

to 6) for a loan originated in year-quarter t by bank b to firm f . θb,trepresents bank×year-quarter fixed effects,

and FEs include, depending on the specification, firm fixed effects, firm rating fixed effects, loan purpose fixed

effects. X f ,b,trepresents firm and loan controls. All the variables are defined in Table 1. In parentheses, t statistics

obtained from two-way clustering at the bank and borrower level. ∗∗∗,

∗∗

,

∗

indicate statistical significance at the

1%, 5% and 10%, respectively.

COVENANT STRICTNESS ALL-IN DRAWN SPREAD

(1) (2) (3) (4) (5) (6)

Specialization −11.39∗∗ −24.34∗∗∗ −25.1∗∗∗ −8.503 −30.11 −34.16∗

(−2.20) (−3.30) (−3.52) (−0.78) (−1.52) (−1.90)

Market Share −8.375 8.45 8.337 −47.17 18.04∗ 26.6∗∗∗

(−0.87) (1.19) (1.25) (−1.29) (1.73) (2.74)

Bank×YearQtr FE Yes Yes Yes Yes Yes Yes

Firm FE − Yes Yes − Yes Yes

Rating FE − Yes Yes − Yes Yes

Firm Controls − Yes Yes − Yes Yes

Loan Purpose FE − − Yes − − Yes

Loan Controls − − Yes − − Yes

Adj. R

2

.074 .565 .57 .279 .749 .779

Observations 9, 834 4, 653 4, 643 9, 834 4, 653 4, 643

39
Table 7. Bank Specialization and Loan Terms, Accounting for Bank-Firm Geographical Proximity

This table reports the estimates of the coefficients from the following regression using our baseline sample, which

includes loans for which the loan covenant strictness and all-in drawn spread are available:

Loan Termf ,b,t = θb,t + FEs + β · Specializationf ,b,t−1 + βS· Same Statef ,b,t−1 + γ · X f ,b,t + εf ,b,t

in which Loan Termf ,b,tis either the covenant strictness (columns 1 to 3) or the all-in drawn spread (columns 4

to 6) for a loan originated in year-quarter t by bank b to firm f . θb,trepresents bank×year-quarter fixed effects,

and FEs include, depending on the specification, firm fixed effects, firm rating fixed effects, loan purpose fixed

effects. X f ,b,trepresents firm and loan controls. All the variables are defined in Table 1. In parentheses, t statistics

obtained from two-way clustering at the bank and borrower level. ∗∗∗,

∗∗

,

∗

indicate statistical significance at the

1%, 5% and 10%, respectively.

COVENANT STRICTNESS ALL-IN DRAWN SPREAD

(1) (2) (3) (4) (5) (6)

Specialization −13.72∗∗ −23.35∗∗ −24.16∗∗ −8.307 −37.37∗ −38.8∗

(−2.32) (−2.43) (−2.76) (−0.67) (−1.73) (−1.85)

Same State −.0431 −.6604 −.8592 −8.508 −7.705 −7.132

(−0.021) (−0.20) (−0.25) (−1.13) (−1.08) (−1.07)

Bank×YearQtr FE Yes Yes Yes Yes Yes Yes

Firm FE − Yes Yes − Yes Yes

Rating FE − Yes Yes − Yes Yes

Firm Controls − Yes Yes − Yes Yes

Loan Purpose FE − − Yes − − Yes

Loan Controls − − Yes − − Yes

Adj. R

2

.072 .576 .58 .268 .755 .781

Observations 9, 072 4, 267 4, 259 9, 072 4, 267 4, 259

40
Table 8. Bank Specialization and Loan Terms After Defaults on Banks’ Loan Portfolio

This table reports the estimates of the coefficients from the following regression using our baseline sample, which

includes loans for which the loan covenant strictness and all-in drawn spread are available:

Loan Termf ,b,t = θb,t +FEs+βSpecializationf ,b,t−1 +βD DEFb,t−1 +δSpecializationf ,b,t−1∗DEFb,t−1 +γ·X f ,b,t +εf ,b,t

in which Loan Termf ,b,tis the covenant strictness for a loan originated in year-quarter t by bank b to firm f . θb,t

represents bank×year-quarter fixed effects, and FEs include, depending on the specification, firm fixed effects,

firm rating fixed effects, loan purpose fixed effects. X f ,b,trepresents firm and loan controls. All the variables are

defined in Table 1. In parentheses, t statistics obtained from two-way clustering at the bank and borrower level.

∗∗∗

,

∗∗

,

∗

indicate statistical significance at the 1%, 5% and 10%, respectively.

COVENANT STRICTNESS

(1) (2) (3) (4) (5) (6)

Specialization −24.36∗∗∗ −25.3∗∗∗ −25.29∗∗∗ −22.73∗∗∗ −23.6∗∗∗ −23.59∗∗∗

(−3.38) (−3.25) (−3.24) (−2.98) (−2.79) (−2.79)

Specialization * Defaults (Same) 30.28∗∗ 30.35∗∗ 30.71∗∗ 30.76∗∗

(2.28) (2.28) (2.34) (2.34)

Specialization * Defaults (Other) 2.073 2.075 1.837 1.839

(0.40) (0.40) (0.34) (0.34)

Defaults (Same) .8011 .8048 .7885 .7921

(1.02) (1.02) (1.01) (1.01)

Defaults (Other) −.824 −.8116

(−1.05) (−1.04)

Industry Portfolio Share −9.782 −9.547 −9.562

(−0.94) (−0.89) (−0.89)

Firm Controls Yes Yes Yes Yes Yes Yes

Loan Controls Yes Yes Yes Yes Yes Yes

Bank×YearQtr FE Yes Yes Yes Yes Yes Yes

Firm FE Yes Yes Yes Yes Yes Yes

Rating FE Yes Yes Yes Yes Yes Yes

Loan Purpose FE Yes Yes Yes Yes Yes Yes

Adj. R

2

.57 .57 .57 .57 .57 .57

Observations 4, 643 4, 643 4, 643 4, 643 4, 643 4, 643

41
Table 9. Robustness: Sample Restricted to Loans with Single Lead Arranger

This table reports the estimates of the coefficients from the following regression using the baseline sample, further

restricted to include only loans with a single-lead arranger:

Loan Termf ,b,t = θb,t + FEs + β · Specializationf ,b,t−1 + γ · X f ,b,t + εf ,b,t

in which Loan Termf ,b,tis either the covenant strictness (columns 1 to 3) or the all-in drawn spread (columns 4

to 6) for a loan originated in year-quarter t by bank b to firm f .θb,trepresents bank×year-quarter fixed effects,

and FEs include, depending on the specification, firm fixed effects, firm rating fixed effects, loan purpose fixed

effects. X f ,b,trepresents firm and loan controls. All the variables are defined in Table 1. In parentheses, t statistics

obtained from two-way clustering at the bank and borrower level. ∗∗∗,

∗∗

,

∗

indicate statistical significance at the

1%, 5% and 10%, respectively.

COVENANT STRICTNESS ALL-IN DRAWN SPREAD

(1) (2) (3) (4) (5) (6)

Specialization −11.67∗∗ −17.32∗∗ −18.59∗∗ −15.53 −34.28 −37.66∗

(−2.23) (−2.05) (−2.30) (−1.28) (−1.46) (−1.76)

Bank×YearQtr FE Yes Yes Yes Yes Yes Yes

Firm FE − Yes Yes − Yes Yes

Rating FE − Yes Yes − Yes Yes

Firm Controls − Yes Yes − Yes Yes

Loan Purpose FE − − Yes − − Yes

Loan Controls − − Yes − − Yes

Adj. R

2

.071 .564 .569 .28 .751 .778

Observations 9, 370 4, 359 4, 348 9, 370 4, 359 4, 348

42
Table 10. Robustness: Bank Specialization Measure Calculated Using Data from 1996

This table reports the estimates of the coefficients from the following regression using our baseline sample:

Loan Termf ,b,t = θb,t + FEs + β · Specialization (96)f ,b,t−1 + γ · X f ,b,t + εf ,b,t

in which Loan Termf ,b,tis either the covenant strictness (columns 1 to 3) or the all-in drawn spread (columns 4

to 6) for a loan originated in year-quarter t by bank b to firm f . θb,trepresents bank×year-quarter fixed effects,

and FEs include, depending on the specification, firm fixed effects, firm rating fixed effects, loan purpose fixed

effects. X f ,b,trepresents firm and loan controls. All the variables are defined in Table 1. In parentheses, t statistics

obtained from two-way clustering at the bank and borrower level. ∗∗∗,

∗∗

,

∗

indicate statistical significance at the

1%, 5% and 10%, respectively.

COVENANT STRICTNESS ALL-IN DRAWN SPREAD

(1) (2) (3) (4) (5) (6)

Specialization (96) −11.9∗∗ −23.56∗∗∗ −24.33∗∗∗ −8.043 −28.45 −31.76∗

(−2.52) (−3.16) (−3.38) (−0.59) (−1.45) (−1.78)

Bank×YearQtr FE Yes Yes Yes Yes Yes Yes

Firm FE − Yes Yes − Yes Yes

Rating FE − Yes Yes − Yes Yes

Firm Controls − Yes Yes − Yes Yes

Loan Purpose FE − − Yes − − Yes

Loan Controls − − Yes − − Yes

Adj. R

2

.081 .566 .571 .281 .75 .779

Observations 8, 990 4, 648 4, 638 8, 990 4, 648 4, 638

43
Table 11. Robustness: Sample Restricted to Loans Originated Before 2008

This table reports the estimates of the coefficients from the following regression using the baseline sample, further

restricted to include only loans originated before (excluding) 2008:

Loan Termf ,b,t = θb,t + FEs + β · Specializationf ,b,t−1 + γ · X f ,b,t + εf ,b,t

in which Loan Termf ,b,tis either the covenant strictness (Panel A) or the all-in drawn spread (Panel B) for a

loan originated in year-quarter t by bank b to firm f . θb,trepresents bank×year-quarter fixed effects, and FEs

include, depending on the specification, firm fixed effects, firm rating fixed effects, loan purpose fixed effects. X f ,b,t

represents firm and loan controls. All the variables are defined in Table 1. In parentheses, t statistics obtained

from two-way clustering at the bank and borrower level. ∗∗∗,

∗∗

,

∗

indicate statistical significance at the 1%, 5%

and 10%, respectively.

COVENANT STRICTNESS ALL-IN DRAWN SPREAD

(1) (2) (3) (4) (5) (6)

Specialization −14.6∗∗ −30.15∗∗∗ −32.83∗∗∗ −17.29 −16.57 −17.77

(−2.54) (−2.86) (−2.87) (−1.33) (−0.48) (−0.59)

Bank×YearQtr FE Yes Yes Yes Yes Yes Yes

Firm FE − Yes Yes − Yes Yes

Rating FE − Yes Yes − Yes Yes

Firm Controls − Yes Yes − Yes Yes

Loan Purpose FE − − Yes − − Yes

Loan Controls − − Yes − − Yes

Adj. R

2

.051 .536 .537 .234 .739 .773

Observations 6, 832 2, 089 2, 085 6, 832 2, 089 2, 085

44
Table 12. Robustness: Bank Specialization Measure Computed Over Different Time Windo

This table reports the estimates of the coefficients on the Specialization variable—averaged over different time windows—from the 

our baseline sample:

Loan Termf ,b,t = θb,t + FEs + β · Specialization (nY)f ,b,t−1 + γ · X f ,b,t + εf ,b,t

in which Loan Termf ,b,t is either the covenant strictness (columns 1 to 3) or the all-in drawn spread (columns 4 to 6) for a loan origibank b to firm f . θb,t represents bank×year-quarter fixed effects, and FEs include, depending on the specification, firm fixed effectloan purpose fixed effects. X f ,b,t represents firm and loan controls. All the variables are defined in Table 1. In parentheses, t statisticlustering at the bank and borrower level. ∗∗∗, ∗∗, ∗ indicate statistical significance at the 1%, 5% and 10%, respectively.

COVENANT STRICTNESS ALL-IN DRAWN S

(1) (2) (3) (4) (5) (6) (7) (8) 

Specialization (1Y) −8.303 −33.57∗∗

(−1.14) (−2.64)

Specialization (2Y) −15.93∗ −37.91∗∗

(−1.92) (−2.39)

Specialization (3Y) −24.35∗∗∗ −31.77∗

(−3.38) (−1.78)

Specialization (4Y) −23.04∗∗∗ 

(−2.76) 

Specialization (5Y) −20.32∗∗ 

(−2.25) 

Firm Controls Yes Yes Yes Yes Yes Yes Yes Yes Loan Controls Yes Yes Yes Yes Yes Yes Yes Yes Bank×YearQtr FE Yes Yes Yes Yes Yes Yes Yes Yes Firm FE Yes Yes Yes Yes Yes Yes Yes Yes Rating FE Yes Yes Yes Yes Yes Yes Yes Yes Loan Purpose FE Yes Yes Yes Yes Yes Yes Yes Yes 

Adj. R2 .569 .568 .57 .572 .57 .779 .779 .779 Observations 4, 767 4, 701 4, 643 4, 576 4, 455 4, 767 4, 701 4, 643 

45
Appendix B. Figures

Figure 1. Simple Examples to Understand Bank Specialization at the Industry Level

This figure illustrates the notion of bank specialization in an industry according to the definition by Paravisini et al.

(2022) with simple examples from two-bank, two-sector lending markets. From the top left, we can see: (a) an

example of no specialized banks; (b) an example of specialized banks – Bank 1 in sector A and Bank 2 in sector B;

(c) a case in which no bank is specialized because both banks allocate the same portfolio shares to both sectors;

(d) a case in which both banks are specialized – Bank 1 in lending to sector A and Bank 2 in lending to sector B.

(a) Neither bank is specialized (b) Both banks are specialized (1-A, 2-B)

(c) Neither bank is specialized (d) Both banks are specialized (1-A, 2-B)

46
Figure 2. Comparison Between Portfolio Concentration of the Average Bank and the “Market”

This figure plots on the y-axis the HHI measure of loan portfolio concentration, and on the x-axis the year at which

it is recorded. HHI is computed for the Market (blue) and Average Bank (green) portfolios per each year-quarter.

A higher value of HHI implies that lending to sectors is more concentrated in the market/average bank’s portfolio.

The fact that the average bank is systematically characterized by a higher HHI compared to the market shows

graphically that the average lender in the syndicated loan market remained overall more concentrated than the

whole syndicated market over 1996-2016.

47
Figure 3. Specialization Is Common Across Industries and Time

This figure presents evidence of specialization in lending towards specific industries in four different moments

from our sample: 2000q2, 2005q2, 2010q2, 2015q2. Each subfigure reports the box-plot graph, for each of the

25 TFIC industries, of the distribution of banks’ demeaned loan portfolio shares in a given industry. Each dot

represents an outlier, and therefore, a banks specialized in that industry.

2000

-.5

0 .5 1

(Centered) Banks' Industry Portfolio Shares

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

TFIC Industry

2005

-.5

0 .5 1

(Centered) Banks' Industry Portfolio Shares

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

TFIC Industry

2010

-.5

0 .5 1

(Centered) Banks' Industry Portfolio Shares

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

TFIC Industry

2015

-.5

0 .5 1

(Centered) Banks' Industry Portfolio Shares

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

TFIC Industry

48
Figure 4. Specialization Is Persistent Over Time

This figure plots the n-year autocorrelation of the specialization dummy, averaged at the bank-year-sector level,

where n takes value from 1 to 10.

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

1 2 3 4 5 6 7 8 9 10

Autocorrelation

Years

49