Bank Capital and Lending: Evidence from Syndicated

Loans

Yongqiang Chu, Donghang Zhang, and Yijia Zhao∗

This Version: June, 2014

Abstract

Using a large sample of bank-loan-borrower matched dataset of individual loans, we

find that, conditional on loan demand, the lead bank of a syndicated loan contributes

more to the loan if it has a higher capital ratio. The result holds if within-loan estima-

tions are employed to further remove the impact of demand side factors. Additionally,

using the Troubled Asset Relief Program (TARP) as a quasi-natural experiment, we

find that TARP recipients increase their contributions to syndicated loans after receiv-

ing TARP capital injections. Taken together, we provide new evidence on the causal

effect bank capital on lending.

Keywords: Bank Capital, Syndicated Loans, Lending, TARP

∗All authors are from Moore School of Business at the University of South Carolina, 1705 CollegeStreet, Columbia, SC 29208. Chu can be reached at yongqiang.chu@moore.sc.edu, Zhang can be reached atzhang@moore.sc.edu, and Zhao can be reached at zhao45@email.sc.edu. The authors thank Allen Berger forhis comments and Allen Berger and Raluca Roman for sharing their data on the TARP program.

Both regulators and academics are interested in how bank capital affects bank lending.

This issue is particularly acute in light of debates over the potential economic consequences of

the strengthened bank capital requirements proposed in the aftermath of the recent financial

crisis. Both Basel III and the Dodd-Frank Act propose to substantially increase bank capital

requirements on the premise that more capital can provide the safety net and thus increase

financial stability and efficiency. However, bankers have been arguing that raising capital

requirements could impede lending. Although the academic literature has devoted much

attention to understanding this important issue, a clear identification of a causal effect from

bank capital to bank lending has proven to be difficult, especially with only aggregate or

bank balance sheet data. The main obstacle is to separate the effect of bank capital on

lending from (often unobservable) demand side factors because demand side factors can be

correlated with bank capital. In this paper, we make new attempts to uncover how capital

affects lending using a dataset that matches individual loan-level data (from LPC DealScan)

and borrower financial statement data (from Compustat) with bank’s balance sheet data

(from the Consolidated Report of Condition and Income or the “Call Report”). Specifically,

we study how a bank’s capital level affects its lending measured by its allocation share in

syndicated loans.

We examine all banks that participate in a syndicated loan, including both the lead bank

that arranges the loan and the participant banks that often just provide funding for the

loan. A bank’s allocation share for a syndicated loan, which is also referred to as a bank’s

allocation or a bank’s share, is the ratio of the funds contributed by the bank over the total

loan amount. Our empirical approach enables us to better separate the effect of bank capital

on lending from the impact of demand side factors, therefore providing a clean identification

of the causal effect of bank capital on credit supply. We rely on detailed loan level data

rather than aggregate or bank balance sheet data to identify bank lending behavior. This

renders us two advantages compared with previous studies. First, we are able to observe a

bank’s allocation, or share, within a loan, which has been conditioned on the total amount

of credit demanded by the borrower (Duchin and Sosyura (2014)). So any effect of capital

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on the bank’s allocation decision within a loan is more likely to be driven by lender (bank)

side factors. Second, because we link each loan to its borrower, we are also able to control

for a broad set of borrower-specific variables. This can further mitigate the potential bias

caused by the correlation between omitted demand side factors with bank capital.

Using a large sample of 4,356 (5,634) syndicated loan packages (facilities) made to U.S.

firms between 1996 and 2012, we find that lead banks’ allocation shares within loans are

positively related to the lead banks’ capital ratios. Specifically, in our baseline OLS regres-

sions that control for a variety of loan, borrower and lead bank characteristics, we show that

one standard deviation increase of the lead bank’s total capital ratio causes an increase of

the lead bank share by 3.2 to 3.5 percent. This result persists at both the package and the

facility levels. The positive effect of lead bank capital on lead bank share remains intact if

we include bank fixed effects to control for unobserved bank characteristics. It is also robust

to the inclusion of state-year fixed effects that account for the possible impact of borrower

local economic conditions.

We then employ two additional identification strategies to further establish the causal

effect of bank capital on lending. First, we examine how the differences of capital levels of

banks participated in the same loan affect their contributions to the loan, which we call the

within-loan estimation. Empirically, we regress a bank’s share in a package/facility on the

bank’s capital ratio and other characteristics with a dummy variable for the package/facility.

Note that the estimation is executed by first washing out the package/facility fixed effects,

which also differences out any possible confounding firm and loan-level factors that are

otherwise unobservable. That is, by estimating the effect of bank capital on the within-loan

difference in lender shares, we further remove the impacts of demand side factors. The point

estimates of the impact of a bank’s capital ratio on the bank’s share at the package level

range from 0.51 to 0.63, and at the facility level, such estimates range from 0.58 to 0.80.

These point estimates are about half of those for across different loans and are generally

statistically significant. These results provide further support that a higher capital ratio of

a bank leads to more lending.

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In the second identification strategy, we exploit plausibly exogenous variations in bank

capital levels generated by the Troubled Asset Relief Program (TARP). Using the within-

loan estimation, we estimate the effect of TARP capital injection in a difference-in-difference

framework and find that TARP recipient banks contribute more to syndicated loans than

non-TARP banks after they receive TARP funding. More specifically, one percentage in-

crease in a bank’s capital due to TARP capital injection would result into 2.29/2.17 percent

(at the facility level) or 3.69/3.60 percent (at the package level) higher share of lending from

the recipient bank. This finding indicates that, conditioning on the overall loan amount, an

exogenous increase in capital is associated with an increase in a bank’s contribution to a

loan. This result again confirms a casual effect of bank capital on lending. Note that the

exercise using TARP helps us deal with the more general endogeneity problem, which can

be caused not only by demand side factors but also by any other omitted variables, or other

problems.

With our main findings described above and others that will be discussed later, our

paper contributes to the large literature on how bank capital affects bank lending. Many

theories suggest that bank capital is positively related to bank lending. First, low bank

capital increases the risk premium demanded by depositors and therefore increases the cost

of bank loans (Jayaratne and Morgan (2000), Kishan and Opiela (2000), and Van den Heuvel

(2002)). Second, banks with lower capital may cut back lending to shrink their balance

sheets. Third, under risk-based capital regulations, banks may simply substitute loans,

which usually have higher risk weights, for safer assets, especially when bank capitals are

low. However, Diamond and Rajan (2000) make an opposite argument that low levels of

bank capital create the incentive for banks to monitor and therefore should increase lending.

Early empirical literature on the relationship between bank capital and lending has mostly

focused on the causes and consequences of the “credit crunch” in the late 1980’s and early

1990’s and the U.S. adoption of the Basel Accord in the early 1990’s. Most early literature

finds a significant impact of bank capital on lending. For example, Bernanke et al. (1991) use

state-level data and find that one percentage point increase of the capital ratio leads to an

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increase of the loan growth rate by 2.6 percentage points. Hancock and Wilcox (1993) find

that one dollar bank capital shortage (relative to regulatory capital) leads to a reduction of

bank credit by three dollars. Brinkmann and Horvitz (1995) find that banks with a higher

risk-based capital ratio have substantially higher loan growth rates during the early 1990’s

“credit crunch”. One exception is Berger and Udell (1994), who find very limited effects

of the risk-based capital ratio on loan growth rates using bank-level data from the Call

Report. While the early empirical literature provides important insights into the effects of

bank capital on bank lending, most papers have difficulties in separating the effect of bank

capital from that of demand side factors.

More recent literature has devoted much attention to separating the effect of bank capital

on bank lending from that of the demand side factors. For example, Peek and Rosengren

(1997) use the dramatic decline of the Japanese stock market in the late 1980s and early 1990s

as an exogenous shock to capitals of Japanese banks and study how decreased capital levels

of U.S. branches of Japanese banks affect their lending. They find that these subsidiaries

substantially reduced their commercial lending in the U.S. Peek and Rosengren (2000) use the

same exogenous shock and further demonstrate that loan supply shortage due to constrained

bank capital has real effects on economic activities. Puri et al. (2011) use German banks’

exposure to the U.S. subprime market as an exogenous shock to capitals of German banks

and find that German banks exposed to the U.S. financial crisis substantially reduce lending.

Most recently, Rice and Rose (2012) use the bailout of the Government Sponsored Enterprises

(GSEs, or specifically Fannie Mae and Freddie Mac) as a natural experiment, because many

U.S. banks held a substantial amount of preferred equity of the GSEs, which was wiped out

during the bailout. They find that banks more exposed to the GSEs experienced substantial

decreases of capital and reduced lending after the bailout of Fannie Mae and Freddie Mac.

With the exception of Puri et al. (2011), all other aforementioned papers still rely only on

aggregate data or bank balance sheet data, and therefore are still unable to fully control

individual borrower characteristics that may affect loan demand.

In this paper, by focusing on the effect of bank capital on banks’ allocation shares in

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syndicated loans, we also attempt to separate the effect of bank capital from demand side

factors. Our tests provide new insights because we study the relative movements conditional

on the total amount of credit. This is particular true for the within-loan estimations with

only the participant (non-lead) banks since these participant banks are unlikely to have

much influence on the determinations of the total loan size. So the fact that a participant

bank with more capital contributes more to a given loan provides a clean identification of

the impact of bank capital on lending.1 The positive and causal effect of bank capital on

syndicated loan shares found in our paper thus suggests that higher levels of bank capital

may contribute to more overall lending activities.

Our paper also contributes to the literature on loan syndicate structure. The existing

literature has focused on how asymmetric information between the lender and the borrower

or asymmetric information between lead banks and participant banks affects syndicate struc-

ture. Sufi (2007) finds that lead banks retain larger shares of syndicated loans and form more

concentrated syndicates when borrowers require more intense monitoring and due diligence.

Ivashina (2009) finds that asymmetric information between lead banks and participating

banks also leads to higher shares retained by lead banks. Gatev and Strahan (2009) examine

how bank liquidity affects commercial banks’ shares in syndicated loans. Ivashina and Scharf-

stein (2010) study how syndicate structure varies over the business cycle. To the best of our

knowledge, this paper is the first to study how supply side factors affect syndicate structure.

More importantly, studying the effect of bank capital on syndicate structure enables us to

better understand how bank capital levels affect real economic activities. As capital affects

the lead bank’s allocation share in syndicated lending, the lending share can in turn affect

the loan spreads that other participating banks may require (Ivashina (2009)). Therefore,

the effect of bank capital on syndicate structure establishes a link between bank capital and

the cost of external financing for the borrowers, which can then affect the borrowers’ real

activities.

1In this regard, our approach is similar to Kashyap et al. (1993), who use the relative movements betweenbank loans and commercial papers to identify the transmission of monetary policies through the lendingchannel.

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The rest of the paper is organized as follows. Section I describes the data. Section II

presents the baseline results on bank capital and lending. Section III presents results from

alternative identification strategies. Section IV presents the results on bank capital and

syndicate size. Section V concludes.

I. Data and Descriptive Statistics

A. Sample Construction and Key Dependent Variables

Our sample construction begins with a sample of 222,991 distinct loan facilities between

January 1996 and December 2012 from LPC Dealscan. We begin our sample in 1996 because

only since then do banks report their risk-based capital ratios in the Call Report. These

222,991 facilities belong to 155,345 distinct deal packages.2 We then use the DealScan-

Compustat link file provided by Chava and Roberts (2008, updated in August 2012) to

match the loan sample with borrowers’ financial statement information from Compustat. The

match produces a sample of 97,924 facilities (72,258 deal packages) for which the borrowers’

financial information can be found in Compustat.

The key dependent variable of our empirical analysis is a lender’s allocation of a loan,

which is referred to as Bank Share.3 The DealScan database reports lender identities and

their loan allocations at the facility level. Our unit of analysis is at both the facility and the

deal package levels. To calculate a bank’s allocation in a package, we first obtain the bank’s

allocations in all facilities within the package and then aggregate these allocations at the

package level using individual facility amounts and the total package amount. For example,

if a bank participates in both two facilities in a deal package and the two facility amounts

are 60% and 40% of the total package amount, and if the bank contributes 30 percent in the

first facility and 50 percent in the other facility, we calculate the bank’s share in the entire

deal package as: 60%× 30% + 40%× 50% = 38%.

2A deal package can contain multiple facilities and each facility within a package can be of the same ordifferent types of credit.

3or Lead Bank Share for lead banks

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We calculate bank shares for both lead banks and participant banks. The DealScan

database reports the roles of lenders in each facility. We follow Ivashina (2009) to identify

the lead bank(s) of a facility. If a lender is reported as the “administrative agent”, it will be

defined as the lead bank. If no lender is reported as the “administrative agent”, we define

lender(s) who act as the “agent”, “arranger”, “book-runner”, “lead arranger”, “lead bank”,

or “lead manager” as the lead bank(s). A lead bank of any facility in the package will be

regarded as the lead bank of the package. Among the 72,258 deal packages (97,924 facilities)

for which the borrowers’ financial information can be found on Compustat, we are able to

calculate bank shares at the package level for 31,113 deal packages (38,968 facilities) between

1996 and 2012.4

We link loans in DealScan to bank information in the Call Report. Because there is no

common identifier across the two databases, we use a text matching program to match bank

names reported in DealScan with bank legal names in the Call Report. Wherever possible, we

also use a bank’s geographical information (city and/or state) reported in both databases to

facilitate the matching. We then manually check all the automated matching results to ensure

matching accuracy. We also rely on information provided by the FDIC institution search

(http://www2.fdic.gov/idasp/main.asp) and/or by the National Information Center (NIC)

to identify DealScan lenders that are not matched by computer programs. For all lenders in

the DealScan universe, we are able to identify 1,269 unique U.S. financial institutions that

have Call Report information. Note that the 1,269 financial institutions include lead and

participating banks in the entire DealScan universe, not just our sample.

For the sample of 31,113 packages with valid lead bank share information, we then match

4The reduction of the sample size is mainly due to the fact that the lender allocation information isentirely missing for about 72% of all facilities in the original DealScan database (also see Ivashina (2009)).There are also cases in which shares of only some (but not all) lenders of a facility are reported. To ensureaccuracy, we exclude packages with facilities that have missing information on any lender shares. The onlyexception is when a package has only one lender and its allocation information is missing. In this case, we setthe lender share to be 100%. We also exclude packages with incorrect lender share information (e.g., thosepackages in which the sum of all lender shares are more than 101% based on DealScan’s original information- we choose 101% because some small rounding errors could lead to the summation of all lender shares toslightly exceed 100%). We are indeed able to calculate shares of all lenders for 34,231 packages between 1996and 2012. However, we cannot reliably identify any lead bank among all lenders based on Ivashina (2009)’slead bank identifying method for the other 3,118 (34,231-31,113) packages.

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them with borrower characteristics from Compustat and bank characteristics from the Call

Report. In particular, borrower characteristics are obtained from the Compustat Annual

database as of the fiscal year ending immediately prior to the deal activation date. Bank

characteristics are obtained from the Call Report as of the filing in the calendar quarter

immediately prior to the deal activation date. We only keep loan packages made by U.S.

commercial banks. We also exclude loan packages made to regulated and financial borrowing

firms (two digits SIC code equals to 49 or between 60 and 69) and those to non-U.S. firms.

These requirements reduce our sample to 13,183 packages.5 The requirement of simulta-

neously having key firm characteristics from the Compustat and key bank characteristics

variables from the Call Report further reduces the sample to 4,772 unique loan packages.

Finally, we only keep packages that contain credit lines, or term loans, or both for subsequent

analysis. This leads to a final sample of 4,356 unique deal packages, our main sample at the

package level. We focus on credit lines and terms loans because we are interested in how

bank capital affects credit supply to non-financial corporate borrowers and credit lines and

terms loans are the most popular types of bank financing obtained by non-financial firms

(see, e.g., Rauh and Sufi (2012) and Colla et al. (2013)). Our main results are robust if we

include other infrequent packages such as bridge loans, standby letters of credit, and leases.

Our final sample involves 2,435 unique borrowing firms and 235 unique lead banks.

Among the 4,356 packages, 4,297 packages have only one lead bank, 56 packages have two

lead banks, one package has three lead banks, and two packages have four lead banks. Our

baseline analysis focuses on lead banks since they originate and are in a management position

of a deal package. In this case, the unit of observation is a loan-lead bank pair. Therefore

our main sample at the package level has 4,420 individual observations (package-lead bank

pairs). The 4,356 packages correspond to 5,634 facilities which constitute a facility level

sample of 5,698 individual observations (facility-lead bank pairs).

We report the detailed decomposition of the 4,356 loan packages according to loan types

in Panel A of Table I. Packages that contain only credit lines and both credit lines and terms

5The large reduction in sample size in this step results primarily from the exclusion of loans to non-U.S.firms.

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loans take a significant portion (93.89%) of our whole sample. This observation is consistent

with existing literature suggesting that credit line is an instrumental component of corporate

external finance (e.g., Sufi (2009), Campello et al. (2011)). Panel B of Table I shows the top

ten lead banks with the largest number of deals led by them. Not surprisingly, giant banks

such as JP Morgran Chase, Bank of America, and Wells Fargo appear on this list. The top

ten most active banks together lead about 67.97% of all packages in our sample. Because of

multiple deals led by these banks, we are able to estimate a lead bank fixed effects model

in addition to the simple OLS model to account for any unobservable time invariant bank

characteristics.

B. Independent Variables

In the following subsections, we discuss explanatory variables used in our regressions.

The detailed variable definitions can be found in Appendix A1.

B.1. Bank Level Explanatory Variables

The key independent variable of interest is a bank’s capital ratio. We use three different

measures of bank capital. Our first and main measure, Total Capital Ratio, is defined as

total capital divided by bank total risk-weighted assets. Our second capital measure is Tier

1 Capital Ratio, which is defined as tier 1 capital divided by bank total risk weighted assets.

Compared with tier 2 capital, tier 1 capital measures the core capital that is not subject to

withdrawal by equity holders. Our third measure is Leverage Ratio, which is defined as tier

1 capital divided by bank total (un-weighted) assets. The Leverage Ratio is a non-risk-based

capital measure. We use Leverage Ratio to make sure that our results are not driven by a

bank’s incentive to strategically manage its risk-based assets.

We include a broad set of bank level control variables in our regressions. These control

variables are defined as follows.

Log (Bank Total Assets): defined as the natural logarithm of bank total assets ($thou-

sand). This is a measure of bank size. Log (Bank Total Assets) can have a mechanical effect

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on Lead Bank Share, as larger banks are able to lend more. Meanwhile, larger banks have

greater capacities to absorb capital shocks because they tend to be better diversified and/or

are more likely to have access to cheaper funds. We therefore expect Log (Bank Total Assets)

to have a positive effect on lender shares.

Bank Liquidity : defined as the sum of cash and available-for-sale securities divided by

bank total assets. Banks with more liquid assets are more likely to be able to fund loans

on the margin. As documented in Khwaja and Mian (2008), bank liquidity shortage can

translate into loan declines of the borrowing firm. We therefore expect Bank Liquidity to

have a positive effect on lender shares.

Bank ROA: defined as bank operating income divided by total assets. More profitable

banks are likely to have access to lower cost funds thus are at better positions to absorb

negative capital shocks. We therefore expect Bank ROA to have a positive effect on lender

shares.

Loan Charge-Offs : defined as the total charge-offs on loans and leases divided by bank

total assets. It measures a bank’s loss incurred on previous loans it made (Santos (2011)).

Murfin (2012) shows that banks write contracts with tighter covenants after suffering from

payment defaults to their own loan portfolios. He argues that recent defaults update the

lender’s perception of its own screening ability. In this context, large losses could also change

a bank’s perception of its own screening ability, which could then affect the bank’s decision

to participate in subsequent loans. Overall, we expect Loan Charge-Offs to have a negative

effect on lender shares.

Loan Loss Allowance: defined as the total allowance for loan and lease losses divided by

bank total assets. It reflects the lender’s view on the future performance of its loan portfolio

and its expectations of future market conditions. On one hand, Loan Loss Allowance reflects

the quality of loans made in the past. On the other hand, Loan Loss Allowance is also

correlated with future charge-offs. We thus expect Loan Loss Allowance to have a negative

effect on lender shares.

Risk-Weighted Assets : defined as total risk weighted assets divided by bank total assets.

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It measures the overall riskiness of a bank’s existing assets. In itself, higher risk should

cause the bank to lend less. However, risk-weighted assets are also the denominator of risk-

based capital ratios, and it remains to be tested whether risk-weighted assets should have

additional effects other than its effect on risk-based capital ratios.

In addition, we also include two measures of the bank’s liability structure.

Subordinated Debt : defined as total subordinated debt divided by bank total assets. On

one hand, subordinated debt may act as a substitute for bank equity capital and its existence

may indicate that the bank has access to public debt market (Santos (2011)). On the other

hand, the literature argues that investors of subordinate debt can monitor and discipline

bank to lower asset risk (Chen and Hasan (2011)).

Deposits : defined as total deposits divided by bank total assets. Given that deposits are

considered to be a stable and low-cost source of funding for banks, we expect it will have a

positive effect on lender shares.

Finally, we also include an indicator for bank holding companies, Bank Holding Company

(BHC) Dummy, which equals one if a bank is controlled by a bank holding company and zero

otherwise. Banks controlled by bank holding companies may get support from the holding

company or other institutions under the same holding company, and therefore may exhibit

different lending behavior (Ashcraft (2008)).

B.2. Borrower Characteristics

We also include a number of borrower characteristics as regressors. We use these borrower

characteristics to partially control for a borrower’s demand for credit, which is impossible

in most previous studies using only bank balance sheet information. These firm level con-

trols can also capture the asymmetric information effect on lead banks share. Asymmetric

information affects lead bank allocation share because higher lead bank allocation share can

mitigate the potential moral hazard and adverse selection problems associated with syndi-

cated lending (see e.g., Sufi (2007), Ivashina (2009)).

Log (Firm Total Assets): defined as the natural logarithm of a firm’s total assets ($mil-

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lion). Mechanically, a lead bank may not be able to contribute a large portion to a large

borrower’s loan. A larger firm also implies less information asymmetry so that the lead bank

may not have to hold a large fraction to keep “skin in the game”. So all else being equal, we

expect Log (Firm Total Assets) to have a negative effect on lead bank allocation.

Tobin’s Q : defined as the market value of assets divided by book value of assets. High

Q firms have more growth opportunities. Because growth options are difficult to value, it is

often argued that high growth firms have more asymmetric information (e.g., McLaughlin

et al. (1998) and Ongena and Smith (2001)).

Tangibility : defined as total property, plant, and equipment divided by total assets.

Tangible assets can serve as collateral, and therefore reduces the need of screening and

monitoring (e.g., Barth et al. (2001) and Leary and Roberts (2010)). We therefore expect

Tangibility to have a negative effect on Lead Bank Share. Asset tangibility can also be seen

as a proxy for a borrower’s debt capacity, and thus can be positively correlated with the

borrower’s demand for external credit.

R&D : defined as R&D expenses divided by total assets. Firms with higher R&D expenses

are more difficult to value (e.g., Aboody and Lev (2000) and Officer et al. (2009)). Therefore,

high R&D firms are likely to have greater information asymmetry.

Cash Flow Volatility : defined as the standard deviation of quarterly cash flows calculated

over the last three years. High cash flow volatility implies higher degree of information

asymmetry. Firms with more volatile cash flows are also considered to be riskier by creditors

(Sufi (2009)).

Leverage: defined as the total debt divided by book value of assets.

Profitability : defined as the operating income before depreciation divided by total assets.

Cash Holdings : defined as cash and marketable securities divided by total assets.

Rated Dummy : an indicator variable, which equals one if the firm has an S&P long-term

credit rating and zero otherwise.

To sum up, including firm-level variables allows us to explicitly control for the effect of

demand side factors, which is often not possible in studies relying only on bank balance sheet

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data.

C. Summary Statistics

We present the summary statistics for the key variables on loan, bank, and borrower

characteristics in Table II. Panel A of Table II reports the descriptive statistics of key loan

characteristics at both the package and the facility levels. The sample is the 4,356 deal

packages (5,634 facilities). Note that the number of observations is greater due to multiple

lead or participant banks per package/facility. On average, a lead bank contributes 60.7%

of the total package amount. And this number is 61.3% at the facility level. The median

lead bank contribution at the package level (63.42%) is less than that at the facility level

(70.1%). In our sample each package has 1.03 lead banks on average. The average number

of all lenders (both lead and participant banks) of a package is about 6.20 and this number

is very similar at the facility level (6.30). For participant banks, the average contribution is

8.8% (8.6%) per bank at the package (facility) level.

Packages in our sample have an average total amount of $411.22 million (in 2012 dollars).

This implies that the average lead bank contribution at the package level is about $250.84

million (411.22 × 0.61). The median lead bank contribution is $66.56 million (105.64 ×

0.63). Other non-pricing loan characteristics display normal patterns. Loan covenants are

structured at the package level. On average, each package in our sample contains two

financial covenants and 1.66 non-financial covenants.6 Performance pricing schedule and

loan collateral requirements are negotiated at the facility level. A little more than half

(53.6% and 54.9%) of facilities in our sample have a performance pricing schedule and are

secured. At the facility level, about 64.4% of the facilities are leveraged. A loan is leveraged

if it is documented by DealScan as a “Highly Leveraged”, “Leveraged”, or “Non-Investment

Grade” loan. 81.7% of the facilities are syndicated in our sample. Here we implicitly assume

that a lead bank can choose whether or not to structure a deal as a sole lender loan or a

syndicated loan. Our main results are not sensitive to the inclusion or exclusion of the sole

6See the definition of financial and non-financial covenants in Appendix A2.

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lender loans. A loan package will be regarded as leveraged (syndicated) if all facilities in the

package are leveraged (syndicated).

Panel B of Table II reports the descriptive statistics for bank characteristics. The key

variable of interest is the Total Capital Ratio, which is defined as total capital over risk-

weighted assets. It has a mean of 11.8% and a median of 11.2% for lead banks. The

mean and median of Total Capital Ratio, 12.3% and 11.3%, are slightly larger for non-lead

(participant) banks. For lead banks, the 25 percentile is 10.8% and the minimum value (not

shown) is 9.1%, which indicates that lead banks are far from hitting the minimum capital

requirements, which is 8%. Since our sample period includes the recent financial crisis, the

high capital ratios of these banks may be partially due to capital accumulation during this

abnormal period. It is also possible that banks with higher capital ratios choose to lend in

the syndicated loan market or the banks choose to lend when their capital ratios are high.

As alternative capital measures, the average Tier 1 Capital Ratio for lead banks is 8.8%.

The average Leverage Ratio is 6.9%. Leverage ratios are smaller than the tier 1 capital ratios

because the denominator is bank total assets without weighting by risk. The mean (median)

value of lead bank total assets in our sample is $400.83 ($217.19) billion. For participant

banks, the mean (median) value is $232.57 ($80.59) billion. Bank assets are much larger

for our sample than the average size of commercial banks in the whole Call Report universe

because larger banks are more active in the syndicated loan market.7

The summary statistics of borrower characteristics, which are reported in Panel C of

Table II, suggest that firms that borrow in the syndicated loan market are larger, more

profitable, and more likely to have S&P ratings than average Compustat firms. These firms

also have fewer growth opportunities.

7In our sample, our average loan size is over $400 million. A lead bank’s average contribution to a packageis about 0.06% of the bank’s total asset and about 0.08% of a bank’s total risk weighted assets.

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II. The Baseline Results: The Effect of Bank Capital

on Lead Bank Share

In this section, we present the baseline results using lead banks only. Specifically, we

regress the natural logarithms of Lead Bank Share (in percentages) on bank capital ratios

measured at the calendar quarter that ends right before a loan’s origination date along

with other control variables. We focus on lead banks in our cross-loan analysis because it

is the lead bank(s) that originates and arranges the loan. We include participant banks

when we examine and compare the impact of capital on loan contributions within a loan

package/facility. We estimate variations of the following model:

Log(LeadBankShare)ijkt = α + β1BankCapitaljt−1 + γ1Xit−1 + γ2Yjt−1 + γ3Zijkt

+Bank, Y ear, Industry, LoanPurpose, and/orState× Y earF ixedEffects+ ǫijkt (1)

where subscript i indexes the borrowing firm, subscript j indexes the lead bank, subscript k

indexes the loan package/facility, and subscript t indexes time. The key variable of interest

is Bank Capital, which is Total Capital Ratio in the baseline results. (We use Tier 1 Capital

Ratio and Leverage Ratio in the robustness tests.) If bank capital has a positive effect on

lending, we would expect its coefficient to be positive and statistically significant. X is a

vector of borrower characteristics, Y is a vector of lead bank characteristics other than Bank

Capital, and Z is a vector of loan characteristics.

We report the results at the package level in Table III. In all regressions in Table III, we

include loan origination year dummies to capture changes in the macroeconomic environment

of bank credit demand and supply. We also include industry dummies defined according to

the 2-digit SIC codes to control for industry specific effects on lead bank allocations. Package

purpose dummies are also included to account for the possibility that banks with higher

capital may prefer to involve in some loans with specific purposes. We cluster the standard

errors at the lead bank level to account for the correlation between multiple loans made by

15

the same lead bank.

In Column (1) of Table III under the OLS model, the estimated coefficient on Total

Capital Ratio is positive and statistically significant at the one percent level. This suggests

that, after controlling for other factors, a lead bank with more capital contributes more

to a loan package. Economically, one standard deviation increase of Total Capital Ratio

(0.025 or 2.5%, see Table II for the summary statistics) is associated with an increase of

the lead bank holding by 1.26 × 2.5%=3.15%. This is equivalent to a 3.2% increase in lead

bank share after adjusting for logarithm. This is equivalent to about 8% (3.2%/40%) of

the standard deviation of the Lead Bank Share per package in our sample. Or in dollar

value, one standard deviation increase in a lead bank’s Total Capital Ratio is associated

with an increase of $411.22 million × 3.2%=$13.16 million contribution to a package on

average. Considering that a bank typically makes many loans in a year, the overall effect

is economically significant if the lead bank’s average contribution to each loan increases by

about $13 million after one standard deviation increase of its Total Capital Ratio. In fact,

given the effect of capital on lead bank share found here, it is reasonable to expect that bank

capital can affect bank lending at the extensive margin, i.e., banks with lower capital levels

may choose not to lend at all or not to lend to some borrowers. Therefore, the implied total

effect of bank capital on lending may be even larger. In Column (2), we add the number

of financial and non-financial covenants as additional controls for loan characteristics. The

results are similar to those in Column (1).

Although our framework is able to mitigate the concern that bank capital may be corre-

lated with demand side factors, the OLS regressions do not address the potential problem

that bank capital is correlated with unobserved bank characteristics. As a first step to

address this problem, we include bank fixed effects in the regressions to control for the corre-

lation between bank capital and unobserved time invariant bank characteristics. The results

with bank fixed effects are presented in Columns (3) and (4) of Table III. In both columns,

the coefficient estimates on Total Capital Ratio remain positive and have the same levels of

statistical significance of one percent as their OLS counterparties. The magnitudes of the

16

effect of Total Capital Ratio in fact increase relative to the OLS estimates.

To further alleviate the concern that demand side factors may simultaneously drive bank

capital and the lead share, we include instead State × Year fixed effects. The State × Year

fixed effects can absorb any confounding time-variant state level economic conditions that

can simultaneously affect bank capital and loan demand and are otherwise unobservable. The

results are presented in Columns (5) and (6) of Table III. In both columns, the coefficient

estimates on Total Capital Ratio remain positive and statistically significant, which suggests

that the positive effects of bank capital on lead share are unlikely to be driven by local

economic conditions.

In general, most of our control variables in the regressions carry expected signs. The

coefficient estimates of Log (Bank Total Assets) are positive and statistically significant at

the one percent level in Columns (1), (2), (5), and (6), which is consistent with the intuition

that larger and more diversified banks are more capable of taking more risk. However, the

effect disappears with bank fixed effects in Columns (3) and (4), which is probably due to the

fact that bank size is highly autocorrelated. The coefficient of Bank Liquidity is also positive

and statistically significant in all models, which is consistent with the argument that banks

with more liquid assets are more likely to be able to fund loans on the margin. Bank ROA is

positive in all columns and statistically significant in Columns (1), (2), (5), and (6), which

is consistent with the notion that a more profitable bank has greater capacity to absorb

negative capital shocks. The coefficient on Loan Charge-Offs is negative in all columns,

which is consistent with the notion that greater losses resulted from previous lending is

likely to curb a bank’s incentive to lend in the future. The coefficients on Risk Weighted

Assets are positive and significant at the one percent level in all columns. On the borrower

side, the statistically significant negative coefficients on Rated Dummy show that a lead bank

contributes less to a loan made to a rated firm, which is consistent with Sufi (2007) that

a firms with credit rating has less information asymmetry so that the lead bank does not

have to retain a large fraction. The coefficient on Cash Holdings is positive and statistically

significant in all models, which is consistent with the notion that higher cash reserve could

17

exacerbate the free cash flow problem (Jensen (1986)) so that the lead bank holds more of

a loan to facilitate monitoring.

In Table IV, we show similar results at the facility level. Taken together, the baseline

results in Tables 3 and 4 show that a bank with greater capital level is more likely to

provide more credit. These results are less likely to be subject to the common criticism

in the literature on bank lending channel that the link between bank capital and lending

can be driven by demand side factors. First, our matched sample allows us to explicitly

control for borrower characteristics that may matter for loan demand, which is usually not

possible in studies based only on aggregate, regional, or bank balance sheet information (e.g.,

Bernanke et al. (1991), Berger and Udell (1994), and Peek and Rosengren (1997)). Second,

the documented effect of bank capital on lending in this paper is already conditioned on the

total amount of a package/facility, so the demand side factor should matter less for the lead

bank’s allocation in a loan. Lastly, one susceptible channel through which the demand side

factors can work is that low quality firms may choose to borrow from a low capital bank

since a low capital bank may lack the ability to effectively monitor the low quality firm. Such

possibility can therefore lead to a positive relationship between bank capital and lending.

However, the existing literature (e.g., Sufi (2007) and Ivashina (2009), among others) has

shown that lead banks tend to contribute more when the borrower is riskier and/or when

the asymmetric information problem is more acute. Thus, the selection issue is unlikely to

explain the positive relationship between bank capital and lead bank share found here.

Nevertheless, to further mitigate the concerns that the results are driven by the corre-

lation between local economic conditions, which can affect loan demand and bank capital

simultaneously, we conduct a falsification test with matched borrowers. For each loan in the

sample, whenever possible, we match it to a loan that is made to another borrower who is in

the same state, in the same 2-digit SIC industry in the same year and with the closest total

book asset value, but borrows from a different lender. We then regress lead bank share of

the matched loan on capital of the lead banks of the original loans. For each original loan

in the sample, because the matched borrower is in the same state as the true borrower, the

18

capital ratios of the true lead bank should still have a positive effect on lead bank share of

the matched borrower, if the baseline results were driven by the correlations between local

economic conditions and bank capital.

The falsification tests are presented in Table V. In this table, all bank characteristics

are for the sample lead bank in the original sample, but the lead bank share and borrower

characteristics are for the matched borrower. To save space, only coefficients and standard

errors on bank characteristics are reported. Columns (1) and (2) present the regression

results at the package level, and Columns (3) and (4) present the results at the facility

level. The results show that: (1) The coefficients on Total Capital Ratio are statistically and

economically insignificant; (2) None of the coefficients on bank characteristics are statistically

significant. The results suggest that our baseline results are unlikely to be driven by the

correlations between local economic conditions and bank capital.

III. Alternative Identification Strategies

To further establish causality, we use two alternative identification strategies in this

section. We first use a within-package/within-facility estimation to further difference out any

confounding loan and borrower characteristics that are otherwise unobservable. In the second

identification method, we exploit plausibly exogenous variations in bank capital generated by

the Troubled Asset Relief Program (TARP) and combine it with the within-package/within-

facility estimation to further establish the causality.

A. Within-Loan Estimations

Although we argue that the positive relationship between bank capital and lead bank

share is unlikely to be explained by the demand side factors, it is still possible that some

unobservable borrower characteristics, especially those related to asymmetric information

about borrower quality, may be correlated with lead bank capital. This could cause the

omitted variables problem. For example, it is possible that banks with higher levels of

19

capital are more willing to lend to firms with greater degree of information asymmetry, and

at the same time, lead banks take more allocations to mitigate the moral hazard problem

associated with monitoring and screening (Sufi (2007) and Ivashina (2009)). We address this

omitted variables problem by looking into the within package/facility allocation differences

between banks participating in the same loan package/facility. Empirically, we execute this

strategy by estimating the following model:

Log(BankShare)ijkt = αk + β1BankCapitaljt−1 + γ2Yjt−1 + ǫijkt, (2)

where subscript k indexes packages/facilities, and αk is the package/facility fixed effect. Y

is a vector of bank characteristics. Note that once package/facility fixed effects are included,

both borrower characteristics and loan characteristics drop out. By including package or

facility fixed effects in the model specification, we thus remove any effect due to confounding

borrower characteristics that are otherwise unobservable. The within-loan estimation is also

free from any confounding effects caused by endogenously determined loan characteristics.8

Therefore, any remaining differences in the relative loan shares between different lenders

within a package/facility are unlikely to be driven by firm-level or loan-level factors. Rather,

this difference is likely to be a function of bank-level factors.

At the package level, we include 2,044 syndicated packages that have more than one

lender per package between 1996 and 2012 because we require within package differences

between multiple banks.9 For the facility level analysis, we use 2,606 syndicated facilities

with more than one lender per facility.

We present the within-loan estimation results at the package level in Panel A of Table VI.

In Column (1), we first include all banks (both lead and non-lead), and estimate the model

with a lead bank dummy, which equals one if a bank is the lead bank in the package, and

8For papers that use a similar approach for other estimation purposes, see, e.g., Ivashina and Sun (2011)and Lim et al. (2014)

9According to DealScan, there are 3,516 syndicated packages (out of 4356) in our sample. But 1472of them have only one lender recorded by DealScan. So for this analysis, we keep the 2044 (3516-1472)syndicated packages with at least two lenders.

20

zero otherwise. In Column (2), we include only non-lead banks. In this sample, all non-lead

banks are homogenous in terms of their roles in a package so we can rule out any confounding

effect resulted from unobservable difference between lead and non-lead banks during the loan

arrangement process.10 The coefficients on Total Capital Ratio in both columns are positive

and statistically significant at the one percent level. This result suggests that, within the

same loan package, a bank with a higher capital level contributes more to the entire package.

Note that the coefficient on the Lead Bank Dummy in Column (1) is positive and statistically

significant, which suggests that lead banks tend to contribute more than participant banks.

As pointed out by Sufi (2007), a lead bank’s holding would signal its commitment to screening

and monitoring of the borrower. In Columns (3) and (4), we further add bank fixed effects

in the models. The coefficients on Total Capital Ratio are still positive but only marginally

significant. Since the magnitudes of the coefficient estimates do not drop, the decrease in the

significance levels is likely due to the decrease in statistical power. We re-estimate models

with the facility level data, and the results are presented in Panel B of Table VI. The

coefficients on Total Capital Ratio are all positive and statistically significant at least at the

five percent level. The results are consistent with those shown in Panel A of Table VI.

In summary, the evidence from the within-loan estimations in Table VI indicates that

the effect of bank capital on lending is unlikely to be driven by unobserved borrower char-

acteristics, or generally other demand side factors.

B. Using TARP as a Quasi-Natural Experiment

Although the baseline results and the within-loan estimation results can effectively sepa-

rate the effects of bank capital from confounding demand side factors, they do not address the

endogeneity concerns induced by other problems. In this subsection, we provide additional

evidence on the casual effect of bank capital on lending by exploiting plausibly exogenous

variations in bank capital levels generated by the Troubled Asset Relief Program (TARP).

10We have only 59 packages with multiple lead banks so it is not statistically reliable to make inferencefrom a similar test with lead banks only.

21

The TARP program was established in October 2008 in pursuant to the 2008 Emergency

Economic Stabilization Act. As the most important component of the TARP program, the

Capital Purchase Program (CPP) authorized the U.S. Treasury Department to invest up

to $250 billion in preferred equity of selected financial institutions to boost their capital

adequacy (Duchin and Sosyura (2014), Li (2013), and Berger and Roman (2013)). The

amount that each financial institution receives ranges from one percent to three perrcent

of its risk-weighted assets or $25 billion, whichever is smaller. The CPP program can be

viewed as an exogenous and significant shock to the recipients’ capital. All else being equal,

we expect a bank that receives more TARP money to increase lending compared with non-

TARP recipient banks. We estimate the impact of TARP money injection on bank share

in the within-loan setting. As stated before, the advantage of the within-loan estimation is

that it is free from the impacts of the confounding firm and loan characteristics. The model

specification is:

Log(BankShare)ijkt = αk + β1TARPRecipient + β2TARPAmount+ γ2Yjt−1 + ǫijkt (3)

In the model, TARPRecipient is a dummy variable that takes the value of one if the bank

is one of the CPP recipients and zero otherwise, regardless of the timing of its TARP funding.

This variable is used to capture the difference between TARP recipient and non-TARP

recipient. The variable TARPAmount is the amount of funding received under the CPP

divided by the bank’s total risk weighted assets (in decimals), which equals zero if the bank

is not a TARP recipient or if the loan activation date is before the TARP receiving date for

TARP recipients. Y is a vector of other bank characteristics and αk denotes package/facility

fixed effects. We expect the coefficient on TARP Amount to be positive if more capital

injection leads to more lending. Note that TARP Amount can be viewed as an interaction

between a AfterTARP dummy and TARPAmount, where the AfterTARP dummy equals

one if a bank has received TARP funding at the loan activation date and zero otherwise.

22

With the TARPRecipient and the AfterTARP dummies being included, the above model

specification is similar to a difference-in-difference (DID) framework, with the addition of

package/facility fixed effects. And because the equation is estimated with the within-loan

framework, time fixed effects are dropped.

We report the descriptive statistics for the TARP Recipient Dummy and TARP Amount

in Panel A of Table VII. The mean value for the TARP Recipient Dummy for the dif-

ferent samples used in the regressions in Panel B of Table VII ranges from 0.79 to 0.82.

The percentage of TARP recipients in our sample is high for a few reasons. First, the

TARPRecipientDummy is set to one for a bank that receives TARP funding, regardless of

its timing. Second, most banks in the syndicated loan market covered by Dealscan are rela-

tively large banks and many of them received TARP funding. Third, the observation in our

samples is a loan-bank pair, and large banks tend to make more loans. The unconditional

mean value for TARPAmount is 0.012 or 1.2%. Conditioning on receiving TARP funding,

the mean value of TARP Amount is about 1.5%. Note that both the unconditional and

the conditional medians for TARPAmount are zero since we set TARPAmount to zero for

non-TARP recipients and for TARP recipients before TARP funding.

The regression results are presented in Panel B of Table VII, with Columns (1) and (2)

for package level results and Columns (3) and (4) for facility level results. The package and

facility level samples are subsamples (between 2007 and 2012) of those used in Table VI.

We focus on the sample period between 2007 and 2012 as TARP was largely implemented

in 2008 and 2009. In Column (1), the coefficient estimates on TARP Amount is positive

and statistically significant at the five percent level. This is consistent with the notion that

capital injection has a significantly positive effect on a TARP recipient bank’s contribution

to a loan. The economic magnitude of this variable is also large. An injection of 2% of

capital to a bank would increase its share by 7.66% (exp(0.02 × 3.69) = 1.0766). In the

baseline results (see Table III), one standard deviation increase (or equivalently 2.5%) in

Total Capital Ratio would lead to a bank to increase its share by 3.2%. Column (2) of

Panel A presents the results with only non-lead banks. The coefficients on TARP Amount

23

remain positive and statistically significant at the five percent level. In Columns (3) and (4),

we re-examine the effects of TARP funding on lending at the facility level, and the results

remain qualitatively the same.11

Because the above model is estimated under the difference-in-difference framework, it is

important to check that the parallel trend assumption is satisfied, i.e., the results are not

driven by the differences of TARP banks versus non-TARP bank before treatment. To show

that, we conduct a placebo test similar to that in Berger and Roman (2013). In the placebo

test, we consider a “fictional” TARP program for the period 2001-2006, prior to the actual

effective TARP period. The fictional TARP program differs from the true TARP program

only on the years it occurred. Specifically, we assume that the fictional TARP program took

place six years earlier than the true TARP program (i.e. the hypothetical TARP program

took place in 2002 and 2003). If the results are driven by pre-existing trend differences

between TARP and non-TARP banks, we should observe similar effects of the fictional

TARP program on lender shares. We present the placebo test results in Panel C of Table

VII. It shows that none of the coefficients on TARP Amount is statistically significant, and

in fact the magnitudes are also much smaller compared with those in Panel B. The placebo

test results therefore suggest that the actual effect of TARP capital injection on lender shares

is not driven by pre-existing trend differences between TARP and non-TARP banks.

Overall, our estimation suggests that more capital injection through the TARP program

significantly increases a TARP recipient bank’s contribution in a loan. This result is con-

sistent with the findings in several recent studies. For example, Duchin and Sosyura (2014)

show that CPP-approved banks tend to increase the fraction of credit supplied in a loan after

CPP. But they find this increase only happens to riskier borrowers. Li (2013) shows that

TARP investments increased loan supply for banks with low Tier 1 capital ratios. Our result

is also consistent with Berger and Roman (2013), who find that TARP recipients received

11We use only TARP Amount to capture the changes in capital levels for a bank in the regressions reportedin Panel B of Table VII because it is the most significant change of capital levels for banks in our sample.But nevertheless, we also include Total Capital Ratio in the regressions in unreported analysis. The resultsremain virtually the same for the package level regressions. The point estimates for the coefficients on TARPAmount remain largely unchanged for the facility level regressions, although both coefficients in Models (3)and (4) of Panel B of Table VII become statistically insignificant.

24

competitive advantages and increased their market shares.

To sum up, the two alternative identification strategies (within-loan estimations and the

TARP experiment) presented in this section further confirm that the positive effect of bank

capital on lending is not driven by the endogeneity problem. These results enable a causal

interpretation of the positive impact of bank capital on lending.

IV. The Effect of Lead Bank Capital on Syndicate

Size

Another important aspect of a syndicated loan is the composition of the syndicate. Al-

though the composition of syndicate bears no direct relationship with credit provision, it has

implicit implication to the borrower in that the monitoring function of the syndicate may

be compromised if the syndicate involves too many lenders (Sufi (2007) and Gopalan et al.

(2011)). In our context, if a lead bank cannot contribute as much to the syndicate due to its

own capital constraints, the lead bank may have to include more participating banks. If too

many lenders cause coordination/free-rider problems in ex-post monitoring and/or contract

renegotiation issues (Bolton and Scharfstein (1996)), this would become another channel for

the lead bank’s capital constraints to affect credit provisions. We thus estimate the effect

of lead banks capital levels on the total number of lenders of a loan syndicate. The sample

used in this analysis is the main sample of 4,356 packages between 1996 and 2012. We only

report the results estimated from the package level sample as the facility level results are

qualitatively similar.

The regression results are reported in Table VIII. We present the results for the OLS

model, the bank-fixed effects model, and the Poisson model. The dependent variable in

the OLS and the bank-fixed effects models is the natural logarithm of the total number of

lenders (both lead and participant banks) in a loan package. The dependent variable in the

Poisson regression is the raw total number of all lenders. Note that we control for number of

loan covenants in Columns (3), (4), and (6). The coefficients on Total Capital Ratio are all

25

negative regardless of the model specifications. The statistical significance of the coefficients

is at the one percent level in all OLS models and either one percent or five percent level in the

bank-fixed effects models. In the Poisson regressions, once the numbers of different covenants

are controlled for, the coefficient on Total Capital Ratio becomes statistically significant at

the five percent level. Taken together, these results suggest that a lead bank with higher

capital level would form a syndicate with fewer lenders.

V. Robustness Tests

A. Alternative Measures of Bank Capital

We have shown that a lead bank’s contribution to a loan is positively related to its

Total Capital Ratio. Several other measures of bank capital adequacy are also often used

by practitioners and regulators. In this subsection, we show that our results are robust

with alternative capital adequacy measures including Tier 1 Capital Ratio, defined as tier 1

capital divided by risk weighted assets, and Leverage Ratio, defined as tier 1 capital divided

the total assets. Leverage Ratio, in particular, as argued by commentaries,12 can be an

effective backstop to the risk-based capital measures because it is less subjective to banks’

own discretion in manipulating their risk weighted assets. Beside these two measures, we also

construct an Average Total Capital Ratio, which is the average total capital ratios of the lead

bank over the four quarters prior to a loan. The Average Total Capital Ratio eliminates the

possible effect of short-term fluctuations in the capital ratios on lending. It instead captures

how a bank’s lending responds to its long-term capital ratio trend.

We report the regression results using these alternative bank capital measures in Table

IX. Again, we only report results for the package level sample as the facility level results

are qualitatively similar. We also suppress all other control variables to save space. For the

OLS model in Column (1), the coefficient on Tier 1 Capital Ratio is 1.25 and is statistically

significant at the one percent level, suggesting that higher Tier 1 Capital Ratio has a similar

12Banks Get a Break on Leverage-Ratio Rules, the Wall Street Journal, 01/12/2014.

26

positive effect on a lead bank’s contribution to a loan. The results remain robust after adding

bank fixed effects as shown in Column (2). Estimations using Leverage Ratio are reported in

Columns (3) and (4). Leverage Ratio also exhibits a significantly positive impact on the lead

bank share. Table IX also shows that the Average Total Capital Ratio has a significantly

positive effect on a lead bank’s loan allocation as well.

To summarize, both the regulatory capital ratios (Total Capital Ratio and Tier 1 Capital

Ratio) and the market-based capital ratio (Leverage Ratio), as well as the average capital

ratios over a year before the loan activation date, have a positive impact on a bank’s con-

tribution to a loan. The positive impact of bank capital on lending is not limited just to a

particular capital measure.

B. Additional Robustness Tests

We present additional tests to ensure the robustness of our results in Table X. For brevity,

we only report the bank-fixed effects estimations. The OLS results are similar.

In Column (1), we check if the documented positive effect of bank capital on lead bank

share is driven by packages with multiple lead banks. We exclude the 59 loan packages that

have multiple lead banks and re-estimate the main model using the 4,297 sole lead packages.

The coefficient of Total Capital Ratio is still positive and statistically significant at the 1%

level. The magnitude of Total Capital Ratio is virtually unchanged as compared to Column

(3) of Table III. In Column (2), we examine if the impact of capital on lead bank share

is more pronounced when the loan is made to a risky borrower. We re-estimate our main

model based on a sub-sample of leveraged packages. We find that the point estimate of the

coefficient on Total Capital Ratio indeed increases. In Column (3), we check if our previous

results are driven by sole lender packages.13 Recall that we initially include these sole lender

packages based on the implicit assumption that a lead bank can decide whether to structure

a loan package as a sole lender loan or a syndicated loan. As shown in Column (3), the

13Note that sole lender package is not equivalent to sole lead bank package. A sole lead bank package canstill have multiple participant banks.

27

coefficient on Total Capital Ratio remains positive and statistically significant at the one

percent level when estimated using the syndicated packages only (i.e., sole lender packages

are excluded).

While the estimations in Columns (1) through (3) are at the package level, the results

in Columns (4) and (5) are at the facility level. In Column (4), we examine the impact

of capital on lead bank share in short-term (less than a year) credit lines. The reason to

separate out short-term credit lines is that short-term credit lines have lower risk weights,

and therefore risk-based capital requirements are expected to have less impact on short-

term credit lines. Note that our estimation is based on the facility level data because a

loan type is only clearly identified at the facility. Two observations emerge. First, while

the coefficient of Total Capital Ratio is still positive, it is no longer statistically significant.

This is consistent with the fact that short-term credit lines have lower risk weights so that

participating in short-term credit lines has smaller effect on a bank’s risk-weighted capital.

Second, the coefficient on Bank Liquidity has a much larger positive point estimate and a

much stronger statistical significance than those in Column (3) of Table IV, which are also

at the facility level. This is consistent with the notion that short-term credit lines expose

banks to short-run liquidity risk.

We also examine the effect of Total Capital Ratio on lending using the sub-sample of

long-term credit lines and term loans in Column (5). The coefficient on Total Capital Ratio

is positive and statistically significant at the one percent level, which suggests that bank

capital has a greater effect on long-term credit lines and term loans than on short-term

credit lines.

Overall, the results in Table X suggest that, regardless of syndicate structure, borrower

risk, and loan types, a bank’s capital level always has a positive impact on lending.

28

VI. Conclusion

A thorough understanding of the relationships between bank capital and lending is im-

portant for bank regulations and for understanding credit supplies for corporations. This is

especially true during the transition period of implementing new bank capital regulations

due to Basel III and the Dodd-Frank Act. This paper provides interesting new evidence on

how a bank’s capital levels affect its lending behavior. More specifically, we use a matched

sample between syndicated loans from DealScan, the Call Report, and Compustat to identify

a causal effect of bank capital on lending. We find that, conditional on loan demand, high

levels of bank capital allow banks to contribute significantly more to syndicated loans that

they lead or participate in. The results are robust if we remove the effects of confounding

firm and loan factors with within-loan estimations. Exploiting exogenous variations in bank

capital generated by the TARP program, we further show that the effect of bank capital

on lending is unlikely to be driven by the omitted variables that could have a simultaneous

positive effect on both bank capital and lending. Thus, our paper contributes to a large

body of literature on the relationship between bank capital and credit supply. The results

presented here also add to the recent policy debate over whether stringent regulatory capital

requirements would jeopardize bank lending per se.

29

REFERENCES

Aboody, David, and Baruch Lev, 2000, Information asymmetry, R&D, and insider gains,

Journal of Finance 55, 2747–2766.

Ashcraft, Adam B., 2008, Are bank holding companies a source of strength to their banking

subsidiaries? Journal of Money, Credit and Banking 40, 273–294.

Barth, Mary E., Ron Kasznik, and Maureen F. McNichols, 2001, Analyst coverage and

intangible assets, Journal of Accounting Research 39, 1–34.

Berger, Allen N, and Raluca A Roman, 2013, Did tarp banks get competitive advantages?

Working paper, University of South Carolina.

Berger, Allen N., and Gregory F. Udell, 1994, Did risk-based capital allocate bank credit

and cause a “credit crunch” in the united states? Journal of Money, Credit and Banking

585–628.

Bernanke, Ben S., Cara S. Lown, and Benjamin M. Friedman, 1991, The credit crunch,

Brookings papers on economic activity 205–247.

Bolton, Patrick, and David S Scharfstein, 1996, Optimal debt structure and the number of

creditors, Journal of Political Economy 104, 1–25.

Brinkmann, Emile J., and Paul M. Horvitz, 1995, Risk-based capital standards and the

credit crunch, Journal of Money, Credit and Banking 27, 848–863.

Campello, Murillo, Erasmo Giambona, John R. Graham, and Campbell R. Harvey, 2011,

Liquidity management and corporate investment during a financial crisis, Review of Fi-

nancial Studies 24, 1944–1979.

Chen, Yehning, and Iftekhar Hasan, 2011, Subordinated debt, market discipline, and bank

risk, Journal of Money, Credit and Banking 43, 1043–1072.

30

Colla, Paolo, Filippo Ippolito, and Kai Li, 2013, Debt specialization, Journal of Finance 68,

2117–2141.

Diamond, Douglas W., and Raghuram G. Rajan, 2000, A theory of bank capital, Journal of

Finance 55, 2431–2465.

Duchin, Ran, and Denis Sosyura, 2014, Safer ratios, riskier portfolios: Banks’ esponse to

government aid, Journal of Financial Economics 113, 1–28.

Gatev, Evan, and Philip E. Strahan, 2009, Liquidity risk and syndicate structure, Journal

of Financial Economics 93, 490–504.

Gopalan, Radhakrishnan, Vikram Nanda, and Vijay Yerramilli, 2011, Does poor performance

damage the reputation of financial intermediaries? Evidence from the loan syndication

market Journal of Finance 66, 2083–2120.

Hancock, Diana, and James A Wilcox, 1993, Has there been a “capital crunch” in banking?

The effects on bank lending of real estate market conditions and bank capital shortfalls

Journal of Housing Economics 3, 31–50.

Ivashina, Victoria, 2009, Asymmetric information effects on loan spreads, Journal of Finan-

cial Economics 92, 300–319.

Ivashina, Victoria, and David Scharfstein, 2010, Loan syndication and credit cycles, Ameri-

can Economic Review 100, 57–61.

Ivashina, Victoria, and Zheng Sun, 2011, Institutional demand pressure and the cost of

corporate loans, Journal of Financial Economics 99, 500–522.

Jayaratne, Jith, and Donald P Morgan, 2000, Capital market frictions and deposit con-

straints at banks, Journal of Money, Credit and Banking 32, 74–92.

Jensen, Michael C., 1986, Agency costs of free cash flow, corporate finance, and takeovers,

American Economic Review 76, 323–329.

31

Kashyap, Anil K., Jeremy C. Stein, and David W. Wilcox, 1993, Monetary policy and

credit conditions: Evidence from the composition of external finance, American Economic

Review 83, 78–98.

Khwaja, Asim Ijaz, and Atif Mian, 2008, Tracing the impact of bank liquidity shocks: Evi-

dence from an emerging market, American Economic Review 1413–1442.

Kishan, Ruby P., and Timothy P. Opiela, 2000, Bank size, bank capital, and the bank

lending channel, Journal of Money, Credit and Banking 32, 121–141.

Leary, Mark T., and Michael R. Roberts, 2010, The pecking order, debt capacity, and

information asymmetry, Journal of Financial Economics 95, 332–355.

Li, Lei, 2013, Tarp funds distribution and bank loan supply, Journal of Banking & Finance

37, 4777–4792.

Lim, Jongha, Bernadette A. Minton, and Michael S. Weisbach, 2014, Syndicated loan spreads

and the composition of the syndicate, Journal of Financial Economics 111, 45–69.

McLaughlin, Robyn, Assem Safieddine, and Gopala K. Vasudevan, 1998, The information

content of corporate offerings of seasoned securities: An empirical analysis, Financial

Management 27, 31–45.

Murfin, Justin, 2012, The supply-side determinants of loan contract strictness, Journal of

Finance 67, 1565–1601.

Officer, Micah S., Annette B. Poulsen, and Mike Stegemoller, 2009, Target-firm information

asymmetry and acquirer returns, Review of Finance 13, 467–493.

Ongena, Steven, and David C Smith, 2001, The duration of bank relationships, Journal of

Financial Economics 61, 449–475.

Peek, Joe, and Eric S. Rosengren, 1997, The international transmission of financial shocks:

The case of Japan, American Economic Review 87, 495–505.

32

Peek, Joe, and Eric S. Rosengren, 2000, Collateral damage: Effects of the Japanese bank

crisis on real activity in the united states, American Economic Review 90, 30–45.

Puri, Manju, Jorg Rocholl, and Sascha Steffen, 2011, Global retail lending in the aftermath

of the US financial crisis: Distinguishing between supply and demand effects, Journal of

Financial Economics 100, 556–578.

Rauh, Joshua D, and Amir Sufi, 2012, Explaining corporate capital structure: Product

markets, leases, and asset similarity, Review of Finance 16, 115–155.

Rice, Tara, and Jonathan Rose, 2012, When good investments go bad: The contraction in

community bank lending after the 2008 GSE takeover, Working paper, Board of Governors

of the Federal Reserve System.

Santos, Joao A., 2011, Bank corporate loan pricing following the subprime crisis, Review of

Financial Studies 24, 1916–1943.

Sufi, Amir, 2007, Information asymmetry and financing arrangements: Evidence from syn-

dicated loans, Journal of Finance 62, 629–668.

Sufi, Amir, 2009, Bank lines of credit in corporate finance: An empirical analysis, Review of

Financial Studies 22, 1057–1088.

Van den Heuvel, Skander J., 2002, Does bank capital matter for monetary transmission?

Economic Policy Review 8, 259–265.

33

Table I

Sample Composition

This table reports the compositions of the sample of 4,356 packages between 1996 and 2012.Panel A reports the sample compositions by package category. Short-Term Credit Linedenotes facilities with the type of either ”364-Day Facility” or “Revolver/Line < 1 Yr.”.Long-Term Credit Line denotes facilities with the type of “Revolver/Line ≥ 1 Yr.”. CreditLine (CL) Only Package denotes packages with only credit lines. Term A Loan denotesfacilities with the type of either “Term Loan” or“Term Loan A”. Term B Loan denotesfacilities with the type of “Term Loan B”. Term Loan Only Package denotes packages withonly term loans. Credit Line and Term Loan Package denotes packages with both credit linesand term loans. Panel B reports the top ten lead banks in our sample that are ranked by thetotal number of deal packages each bank has led from 1996 to 2012. The total deal amountis the sum of entire package amounts led by these banks. All dollar values are adjusted usingCPI to the December 2012 value.

Panel A: Loan Categories

Total Leveraged Percent Syndicated Percent

Whole Sample 4,356 2,715 62.30% 3,516 80.70%

Credit Line (CL) Only Package 3,460 1,980 57.20% 2,864 82.80%Short-Term Credit Line Only 772 356 46.10% 626 81.10%Long-Term Credit Line Only 2,501 1,585 63.40% 2,055 82.25Short and Long-Term Credit Lines 187 39 20.90% 183 97.90%

Term Loan Only Package 266 201 75.60% 174 65.40%Term A Loan Only 240 175 72.90% 152 63.30%Term B Loan Only 19 19 100% 19 100%Term A and Term B Loan 7 7 100% 3 42.90%

Credit Line and Term Loan Package 630 534 84.80% 478 75.90%Term Loan and Short-Term CL 66 60 90.90% 37 56.10%Term Loan and Long-Term CL 555 468 84.30% 432 77.80%Term Loan and Both CL 9 6 66.70% 9 100%

Panel B: Top Ten Lenders

No. of % of Total AmountLender Name Deals No. of Deals ($million)

JP Morgan Chase Bank 864 19.83% 728,530Bank of America 787 18.07% 277,362Citi Bank 304 6.98% 391,801Wells Fargo Bank 240 5.51% 28,441Wachovia Bank 207 4.75% 58,693Silicon Valley Bank 173 3.97% 2,445PNC BANK 160 3.67% 27,606LaSalle Bank 97 2.23% 6,988Union Bank 65 1.49% 9,698Comerica Bank 64 1.47% 6,843Total 2,961 67.97% 1,538,407

34

Table II

Summary Statistics

This table reports summary statistics of the sample of 4,356 packages and the corresponding5,634 facilities between 1996 and 2012. Note that the number of observations (N) is greaterdue to multiple lead or participant banks per package/facility. Panel A reports loan char-acteristics at both the package and the facility levels. Panel B reports bank characteristics(both lead and participant banks) at the package level. Panel C reports borrower charac-teristics at the package level. Bank Share for a lead and a participant bank is the dollaramount of a loan contributed by the bank over the total loan amount for a loan package ora facility (in decimals). Total Capital Ratio for a bank is defined as total capital over risk-weighted assets, Tier 1 Capital Ratio is defined as tier 1 capital over risk-weighted assets,and Leverage Ratio is defined as tier 1 capital over total assets. See detailed informationfor the definitions of other variables in Appendix A1. Note that Log(X) denotes the naturallogarithm of variable X. All dollar values are adjusted using CPI to the December 2012 value.

Panel A: Loan Characteristics

Variables N Mean Median Std. P25 P75

Package Level

Lead Bank Share/Package 4,420 0.607 0.634 0.400 0.175 1.000Log (Lead Bank Share*100) 4,420 3.742 4.150 0.977 2.862 4.605Participant Bank Share/Package 8,580 0.088 0.068 0.075 0.040 0.111Log (Participant Bank Share*100) 8,580 1.862 1.916 0.833 1.386 2.407No. of Lead Banks/Package 4,420 1.032 1.000 0.208 1.000 1.000No. of All Lenders/Package 4,420 6.201 2.000 8.036 1.000 9.000Package Amount ($million) 4,420 411.215 105.641 1062.688 24.309 366.026Log(Package Amount $million) 4,420 4.543 4.660 1.843 3.191 5.903No. of Financial Covenants 4,420 2.016 2.000 1.466 1.000 3.000No. of Non-Financial Covenants 4,420 1.659 1.000 2.143 0.000 2.000Leveraged Package Dummy 4,420 0.621 1.000 0.485 0.000 1.000Syndicated Package Dummy 4,420 0.813 1.000 0.388 1.000 1.000

Facility Level

Lead Bank Share/Facility 5,698 0.613 0.701 0.401 0.171 1.000Log (Lead Bank Share*100) 5,698 3.748 4.250 0.987 2.837 4.605Participant Bank Share/Facility 11,156 0.086 0.066 0.075 0.037 0.106Log (Participant Bank Share*100) 11,156 1.825 1.897 0.848 1.328 2.367No. of Lead Banks/Facility 5,698 1.025 1.000 0.179 1.000 1.000No. of All Lenders/Facility 5,698 6.307 2.000 8.320 1.000 9.000Facility Amount ($million) 5,698 334.558 73.166 906.455 15.953 303.857Log(Facility Amount $million) 5,698 4.219 4.293 1.965 2.770 5.717Secured Facility Dummy 5,698 0.549 1.000 0.498 0.000 1.000Performance Pricing Dummy 5,698 0.536 1.000 0.499 0.000 1.000Leveraged Facility Dummy 5,698 0.644 1.000 0.479 0.000 1.000Syndicated Facility Dummy 5,698 0.817 1.000 0.387 1.000 1.000

35

Panel B: Bank Characteristics

Variables N Mean Median Std. P25 P75

Lead Bank

Total Capital Ratio 4,420 0.118 0.112 0.025 0.108 0.120Tier 1 Capital Ratio 4,420 0.088 0.082 0.027 0.077 0.090Leverage Ratio 4,420 0.069 0.064 0.022 0.059 0.072Bank Total Assets ($billion) 4,420 400.825 217.187 456.517 43.505 690.878Log (Bank Total Assets $thousand) 4,420 18.708 19.196 1.933 17.588 20.354Bank Liquidity 4,420 0.209 0.194 0.088 0.147 0.252Bank ROA 4,420 0.006 0.006 0.005 0.003 0.009Loan Charge-Offs 4,420 0.003 0.002 0.004 0.001 0.003Loan Loss Allowance 4,420 0.010 0.009 0.009 0.007 0.012Risk Weighted Assets 4,420 0.792 0.777 0.126 0.713 0.852Subordinated Debt 4,420 0.017 0.017 0.010 0.012 0.021Deposits 4,420 0.644 0.643 0.131 0.567 0.719BHC Dummy 4,420 0.996 1.000 0.060 1.000 1.000

Participant Bank

Total Capital Ratio 8,580 0.123 0.113 0.088 0.108 0.121Bank Total Assets ($billion) 8,580 232.566 80.585 352.241 38.559 250.515Log (Bank Total Assets $thousand) 8,580 18.242 18.204 1.579 17.466 19.339Bank Liquidity 8,580 0.225 0.202 0.116 0.150 0.269Bank ROA 8,580 0.007 0.006 0.005 0.003 0.010Loan Charge-Offs 8,580 0.002 0.001 0.004 0.000 0.003Loan Loss Allowance 8,580 0.009 0.008 0.011 0.006 0.011Risk Weighted Assets 8,580 0.842 0.820 0.186 0.736 0.918Subordinated Debt 8,580 0.020 0.018 0.016 0.011 0.025Deposits 8,580 0.651 0.662 0.129 0.591 0.730BHC Dummy 8,580 0.971 1.000 0.167 1.000 1.000

Panel C: Borrower Characteristics

Variables N Mean Median Std. P25 P75

Log (Firm Total Assets $million) 4,420 6.266 6.135 2.048 4.763 7.650Tobin’s Q 4,420 1.581 1.177 1.652 0.830 1.811Profitability 4,420 0.115 0.131 0.156 0.077 0.182Tangibility 4,420 0.304 0.241 0.230 0.121 0.438Cash Holdings 4,420 0.105 0.048 0.137 0.015 0.142Leverage 4,420 0.249 0.225 0.208 0.096 0.354Rated Dummy 4,420 0.341 0.000 0.474 0.000 1.000R&D 4,420 0.046 0.000 0.139 0.000 0.027Cash Flow Volatility 4,420 0.230 0.042 0.087 0.020 0.107

36

Table III

Package Level Baseline Results

This table reports the package level baseline results for the effect of bank capital on leadbank share using OLS, bank fixed effects, and state-year fixed effects models. The sampleconsists of 4,356 deal packages (4,420 package-lead bank observations due to multiple leadbanks for some packages) between 1996 and 2012 for which lead bank share information isavailable. The dependent variable is the natural logarithm of lead bank share in percentages.The key independent variable in all regressions is the Total Capital Ratio, which is defined asthe ratio of a bank’s total capital over its risk-weighted assets. The OLS and the bank fixedeffects regressions also control for year, industry (2-digit SIC code), and package purpose fixedeffects. The state-year fixed effects regressions also control for industry (2-digit SIC code) andpackage purpose fixed effects. Note that for the state-year fixed effects model, each uniquestate-year combination has its own fixed effect, so 50 states over 17 years would have 850 fixedeffects instead of 67 fixed effects. The t-statistics in the parentheses below the coefficientestimates are calculated using robust standard errors corrected for heteroskedasticity andclustered at the lead bank level. ***, **, and * denote statistical significance at the 1%, 5%,and 10% levels, respectively, in a two-tailed test.

37

OLS Bank FE State×Year FE

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

Total Capital Ratio 1.26*** 1.38** 1.71*** 1.68** 1.56*** 1.58**(2.86) (2.56) (2.79) (2.44) (2.87) (2.41)

Log(Bank Total Assets) 0.05*** 0.05*** -0.00 0.00 0.05*** 0.05***(4.09) (4.00) (-0.01) (0.03) (3.71) (3.46)

Bank Liquidity 0.58** 0.56** 0.66* 0.53* 0.59*** 0.57***(2.46) (2.36) (1.92) (1.67) (2.74) (2.65)

Bank ROA 6.26*** 6.42*** 1.53 2.05 6.13*** 6.29***(2.97) (2.80) (0.70) (1.00) (3.13) (3.03)

Loan Charge-Offs -7.98* -8.01* -5.28 -5.50 -7.10* -7.27(-1.83) (-1.76) (-0.93) (-0.94) (-1.78) (-1.53)

Loan Loss Allowance 1.23 0.79 -3.22 -2.45 0.15 -0.09(0.53) (0.37) (-1.46) (-1.07) (0.06) (-0.04)

Risk Weighted Assets 0.66*** 0.71*** 0.81*** 0.75*** 0.66*** 0.71***(4.36) (4.33) (3.81) (3.94) (4.15) (4.17)

Subordinated Debt -0.37 -0.63 0.52 0.05 0.25 -0.02(-0.18) (-0.32) (0.28) (0.03) (0.13) (-0.01)

Deposits 0.04 -0.01 -0.20 -0.13 -0.01 -0.07(0.21) (-0.07) (-1.03) (-0.68) (-0.04) (-0.47)

BHC Dummy -0.17 -0.08 -0.17 -0.09(-1.03) (-0.55) (-0.90) (-0.55)

Log (Firm Total Assets) 0.00 -0.03* 0.01 -0.03 0.01 -0.03*(0.24) (-1.84) (0.76) (-1.52) (0.39) (-1.86)

Tobin’s Q -0.01 -0.01 -0.01 -0.01* -0.01 -0.01(-1.31) (-1.25) (-1.36) (-1.74) (-1.10) (-1.31)

Profitability 0.08 0.10 0.01 0.02 0.11 0.13*(1.16) (1.47) (0.15) (0.29) (1.55) (1.80)

Tangibility -0.01 -0.03 -0.00 -0.03 0.01 -0.01(-0.09) (-0.38) (-0.04) (-0.43) (0.11) (-0.13)

Cash Holdings 0.29*** 0.28*** 0.27*** 0.25*** 0.27*** 0.26***(4.87) (5.08) (3.88) (4.18) (3.97) (4.25)

Leverage 0.13*** 0.16*** 0.13*** 0.16*** 0.11*** 0.16***(3.30) (3.53) (3.02) (3.07) (3.02) (3.74)

Rated Dummy -0.21*** -0.25*** -0.19*** -0.22*** -0.22*** -0.26***(-5.03) (-5.98) (-4.40) (-5.09) (-5.32) (-6.12)

R&D -0.04 -0.07 -0.07 -0.10 -0.04 -0.07(-0.57) (-0.93) (-0.99) (-1.25) (-0.52) (-0.86)

Cash Flow Volatility 0.01 0.00 0.02 0.01 0.01 -0.01(1.11) (0.10) (1.48) (0.75) (0.45) (-0.45)

Log (Package Amount) -0.38*** -0.34*** -0.38*** -0.34*** -0.38*** -0.33***(-21.27) (-24.67) (-19.88) (-22.99) (-18.02) (-20.11)

No. of Fin. Covenants -0.06** -0.07*** -0.06**(-2.21) (-2.71) (-2.51)

No. of Non-Fin. Covenants -0.04*** -0.04*** -0.05***(-5.52) (-4.78) (-5.78)

Constant 3.59*** 3.75*** 4.78*** 5.07*** 3.31*** 3.47***(8.28) (9.35) (5.11) (5.62) (8.60) (9.24)

Observations 4,420 4,420 4,420 4,420 4,420 4,420Adj. R-squared 0.62 0.64 0.63 0.65 0.63 0.65

38

Table IV

Facility Level Baseline Results

This table reports the facility level baseline result for the effect of bank capital on lead bankshare using OLS, bank fixed effects, and state-year fixed effects models. The sample consistsof 5,634 facilities (5,698 facility-lead bank observations due to multiple lead banks for somefacilities) between 1996 and 2012 for which lead bank share information is available. Thedependent variable is the natural logarithm of lead bank share in percentages in a facility.The key independent variable in all regressions is the Total Capital Ratio, which is defined asthe ratio of a bank’s total capital over its risk-weighted assets. The OLS and the bank fixedeffects regressions also control for year, industry (2-digit SIC code), and facility purpose fixedeffects. The state-year fixed effects regressions also control for industry (2-digit SIC code)and facility purpose fixed effects. The t-statistics in the parentheses below the coefficientestimates are calculated using robust standard errors corrected for heteroskedasticity andclustering at the lead bank level. ***, **, and * denote statistical significance at the 1%,5%, and 10% levels, respectively, in a two-tailed test.

39

OLS Bank FE State×Year FE

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

Total Capital Ratio 1.23** 1.15** 1.86** 1.72** 1.68*** 1.49***(2.36) (2.08) (2.36) (2.32) (3.08) (2.72)

Log(Bank Total Assets) 0.04*** 0.05*** -0.02 0.01 0.04*** 0.05***(3.34) (3.44) (-0.56) (0.26) (3.28) (3.24)

Bank Liquidity 0.40* 0.45** 0.48 0.34 0.40** 0.46**(1.81) (2.26) (1.63) (1.38) (2.01) (2.46)

Bank ROA 6.93*** 6.08*** 3.39 2.87 6.13** 5.36**(2.80) (2.64) (1.59) (1.24) (2.59) (2.40)

Loan Charge-Offs -5.81* -2.70 -4.08 -1.90 -5.66* -2.78(-1.74) (-0.83) (-0.84) (-0.40) (-1.84) (-0.85)

Loan Loss Allowance 0.57 -0.13 -4.27* -4.91** -0.67 -1.18(0.26) (-0.07) (-1.95) (-2.26) (-0.37) (-0.73)

Risk Weighted Assets 0.58*** 0.67*** 0.70*** 0.62*** 0.58*** 0.68***(3.64) (4.03) (2.86) (2.63) (3.55) (3.88)

Subordinated Debt -0.28 -0.45 0.01 0.64 0.29 -0.06(-0.15) (-0.24) (0.00) (0.35) (0.16) (-0.03)

Deposits 0.13 0.08 -0.24 -0.10 0.14 0.09(0.75) (0.56) (-1.62) (-0.71) (0.94) (0.75)

BHC Dummy -0.34* -0.20 -0.37** -0.21(-1.90) (-1.44) (-2.17) (-1.49)

Log (Firm Total Assets) -0.06*** -0.08*** -0.06*** -0.08*** -0.06*** -0.08***(-4.24) (-5.55) (-4.37) (-5.41) (-3.14) (-4.55)

Tobin’s Q -0.01* -0.01** -0.01** -0.01*** -0.01 -0.01*(-1.90) (-1.98) (-2.28) (-2.63) (-1.60) (-1.87)

Profitability -0.00 0.11* -0.10 0.02 0.02 0.11(-0.04) (1.69) (-1.04) (0.32) (0.19) (1.46)

Tangibility 0.06 0.06 0.06 0.06 0.07 0.06(0.67) (0.62) (0.62) (0.57) (0.79) (0.68)

Cash Holdings 0.42*** 0.25*** 0.42*** 0.26*** 0.34*** 0.18*(4.36) (2.69) (4.13) (2.71) (4.03) (1.95)

Leverage 0.07 0.03 0.08* 0.03 0.02 -0.01(1.52) (0.79) (1.74) (0.88) (0.65) (-0.18)

Rated Dummy -0.20*** -0.21*** -0.19*** -0.19*** -0.21*** -0.21***(-5.19) (-4.89) (-4.98) (-4.64) (-5.78) (-4.66)

R&D 0.07 -0.00 0.04 -0.01 0.05 -0.01(0.78) (-0.03) (0.34) (-0.13) (0.65) (-0.09)

Cash Flow Volatility 0.00 0.01 0.01 0.02 -0.01 -0.00(0.20) (0.66) (0.89) (1.55) (-0.49) (-0.13)

Log (Facility Amount) -0.28*** -0.21*** -0.28*** -0.21*** -0.27*** -0.21***(-14.76) (-13.37) (-13.44) (-12.12) (-12.91) (-11.72)

Secured Facility Dummy -0.01 -0.01 -0.02(-0.39) (-0.43) (-0.60)

Performance Pricing Dummy -0.46*** -0.47*** -0.45***(-7.28) (-7.10) (-7.46)

Constant 3.80*** 3.73*** 4.95*** 4.54*** 3.13*** 3.23***(7.12) (8.12) (5.54) (5.00) (7.52) (8.78)

Observations 5,698 5,698 5,698 5,698 5,698 5,698Adj. R-squared 0.60 0.64 0.61 0.65 0.62 0.66

40

Table V

Falsification Tests with Matched Borrowers

This table reports the falsification tests with matched loans/borrowers. The matched loansample consists of 1,726 packages and 2,281 facilities between 1996 and 2012 for which leadbank share information is available. The dependent variable is the natural logarithm of thelead bank share in percentages in a matched package/facility, while the key independentvariable in all regressions is the Total Capital Ratio, which is defined as the ratio of a bank’stotal capital over its risk-weighted assets, of the lead bank in the original loan package/facilitythat is used to identify the matched loan package/facility whose lead bank share is used asthe dependent variable. The OLS and the bank fixed effects regressions also control for year,industry (2-digit SIC code), and package/facility purpose fixed effects. The t-statistics inthe parentheses below the coefficient estimates are calculated using robust standard errorscorrected for heteroskedasticity and clustering at the lead bank level. ***, **, and * denotestatistical significance at the 1%, 5%, and 10% levels, respectively, in a two-tailed test.

Package Level Facility Level

OLS Bank FE OLS Bank FEVARIABLES (1) (2) (3) (4)

Total Capital Ratio 0.27 1.23 0.21 0.98(0.37) (0.51) (0.36) (0.53)

Log(Bank Total Assets) 0.00 0.03 0.00 0.02(0.20) (0.97) (0.02) (0.59)

Bank Liquidity 0.11 0.23 -0.02 0.24(0.57) (0.70) (-0.12) (0.60)

Bank ROA 2.77 2.74 0.65 -1.57(0.74) (0.59) (0.17) (-0.33)

Loan Charge-Offs 6.18 -0.39 3.91 2.53(1.61) (-0.08) (0.92) (0.47)

Loan Loss Allowance -1.47 -6.27 -1.10 -5.09(-0.87) (-1.20) (-0.78) (-1.28)

Risk Weighted Assets -0.19 -0.49 -0.21 -0.38(-1.26) (-1.19) (-1.43) (-1.01)

Subordinated Debt 1.38 2.29 1.48 1.44(0.98) (0.84) (0.90) (0.50)

Deposits -0.00 -0.01 0.09 -0.08(-0.04) (-0.02) (0.79) (-0.29)

BHC Dummy -0.78*** -0.79***(-4.10) (-4.28)

Observations 1,726 1,726 2,281 2,281Adj. R-squared 0.53 0.53 0.47 0.48

41

Table VI

Within-Loan Estimations

We include a package dummy for all the regressions in Panel A to difference out the impactsof loan- and borrower-related factors, which we call the within-package estimations. PanelB reports the within-facility estimations using facility dummies. The dependent variablein all regressions is the natural logarithm of the bank share (in percentages) of a lead ora participant bank at the package (Panel A) or the facility (Panel B) level, and the keyindependent variable is the Total Capital Ratio, which is defined as the ratio of a lead orparticipant bank’s total capital over its risk-weighted assets. The sample used in models (1)and (3) of Panel A consists of 2,044 syndicated packages between 1996 and 2012 for whichthere exist at least two lenders and package-level share information of all lenders is available.The sample used in models (2) and (4) of Panel A consists of 1,668 syndicated packagesbetween 1996 and 2012 for which there exist at least two non-lead lenders and package-levelshare information of all non-lead lenders is available. Note that lead banks are excluded inmodels (2) and (4) of Panel A. Similarly, the sample used in models (1) and (3) of PanelB consists of 2,606 syndicated facilities between 1996 and 2012 with at least two lenders,and the sample used in models (2) and (4) of Panel B consists of 2,124 syndicated facilitiesbetween 1996 and 2012 with at least two non-lead lenders (lead banks are excluded). Thet-statistics in the parentheses below the coefficient estimates are calculated using robuststandard errors corrected for heteroskedasticity and clustering at the package/facility level.***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively, ina two-tailed test.

42

Panel A: Package Level

Package andPackage FE Bank FE

VARIABLES (1) (2) (3) (4)

Total Capital Ratio 0.51*** 0.53*** 0.55 0.63*(3.86) (3.31) (1.63) (1.72)

Log(Bank Total Assets) 0.16*** 0.18*** 0.10*** 0.12***(23.60) (23.57) (5.77) (6.33)

Bank Liquidity -0.09 -0.11 -0.13 -0.15(-1.34) (-1.46) (-1.28) (-1.34)

Bank ROA -4.94*** -5.33*** 2.23 3.41(-2.70) (-2.60) (1.16) (1.55)

Loan Charge-Offs 4.63** 3.72 5.61** 5.77**(2.23) (1.61) (2.42) (2.25)

Loan Loss Allowance 1.52* 1.57* 0.02 0.43(1.92) (1.76) (0.02) (0.37)

Risk Weighted Assets 0.33*** 0.29*** 0.15 0.18*(5.87) (4.60) (1.52) (1.67)

Subordinated Debt -1.63*** -1.72*** -1.64** -1.38(-3.13) (-2.99) (-1.99) (-1.54)

Deposits -0.17*** -0.32*** 0.04 0.04(-3.53) (-5.29) (0.36) (0.34)

BHC Dummy -0.31*** -0.37*** 0.06 0.11(-6.34) (-7.27) (0.36) (0.60)

Lead Bank Dummy 0.45*** 0.41***(39.21) (34.77)

Constant -0.84*** -1.11*** -0.14 -0.67(-5.22) (-5.94) (-0.35) (-1.56)

Observations 10,655 8,192 10,655 8,192Adj. R-squared 0.76 0.68 0.79 0.72

43

Panel B: Facility Level

Package andPackage FE Bank FE

VARIABLES (1) (2) (3) (4)

Total Capital Ratio 0.59*** 0.58*** 0.80** 0.69**(3.92) (3.59) (2.54) (2.14)

Log(Bank Total Assets) 0.17*** 0.18*** 0.10*** 0.12***(22.94) (23.38) (5.86) (6.59)

Bank Liquidity -0.10 -0.11 -0.20* -0.23**(-1.53) (-1.46) (-1.96) (-2.09)

Bank ROA -5.63*** -5.96*** 2.00 3.31(-3.39) (-3.24) (1.09) (1.63)

Loan Charge-Offs 4.94** 3.38 4.98** 4.82**(2.38) (1.47) (2.20) (1.98)

Loan Loss Allowance 1.40* 1.62* -0.82 0.15(1.82) (1.95) (-0.76) (0.13)

Risk Weighted Assets 0.34*** 0.30*** 0.20** 0.18*(5.79) (4.57) (2.09) (1.77)

Subordinated Debt -1.75*** -1.86*** -2.24*** -1.79**(-3.22) (-3.14) (-2.70) (-2.04)

Deposits -0.21*** -0.36*** -0.03 -0.04(-3.77) (-5.49) (-0.28) (-0.33)

BHC Dummy -0.29*** -0.37*** 0.12 0.13(-5.69) (-6.81) (0.78) (0.77)

Lead Bank Dummy 0.45*** 0.41***(37.10) (33.02)

Constant -0.99*** -1.24*** -0.28 -0.74*(-5.87) (-6.56) (-0.76) (-1.83)

Observations 13,806 10,670 13,806 10,670Adj. R-squared 0.76 0.69 0.80 0.74

44

Table VII

Using TARP as a Quasi-Natural Experiment

This table reports the within-loan estimations over the effect of TARP on lender share. PanelA reports summary statistics of the TARP-related variables. Panel B reports the actual testand Panel C reports the placebo test. The sample used in model (1) of Panel B consists of asubsample of syndicated packages between 2007 and 2012 for which there exist at least twolenders and package-level share information of all lenders is available. The sample used inmodel (2) of Panel B consists of a subsample of syndicated packages between 2007 and 2012for which there exist at least two non- lead lenders and package-level share information of allnon-lead lenders is available. Similarly, the sample used in model (3) of Panel B consists of asubsample of syndicated facilities between 2007 and 2012 with at least two lenders, and thesample used in model (4) of Panel B consists of a subsample of syndicated facilities between2007 and 2012 with at least two non-lead lenders. The lead banks are excluded in models (2)and (4). Samples used in Panel C follow the same selection standards as of those in PanelB except for the sample period being between 2001 and 2006. The dependent variable in allregressions is the natural logarithm of a lender’s share in percentages in a package or facility.TARP Recipient Dummy is equal to one if a bank is a TARP receiver (regardless of timing)and zero otherwise. TARP Amount is the amount of TARP capital received by a bank overits total risk-weighted assets (in decimals). Note that TARP Amount is zero for both anon-recipient bank and for a recipient bank before it received TARP capital injections. Fora bank in the placebo tests in Panel C, the values for TARP Recipient Dummy and TARPAmount are the same as those used in the regressions in Panel B for the same bank, exceptthat the timing of the TARP capital injections (and hence the value for TARP Amountfor a particular period for the bank) is moved five years earlier than actual (from 2007 or2008 to 2002 or 2003). The t-statistics in the parentheses below the coefficient estimates arecalculated using robust standard errors corrected for heteroskedasticity and clustering at thelead bank level. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels,respectively, in a two-tailed test.

Panel A: Summary Statistics for TARP-related Variables

Sample/ModelVariable as in Panel B N Mean Median Std.

TARP Recipient Dummy (1) 1569 0.823 1 0.381(2) 1233 0.793 1 0.405(3) 2015 0.821 1 0.384(4) 1588 0.792 1 0.406

TARP Amount (1) 1569 0.012 0 0.019(2) 1233 0.012 0 0.02(3) 2015 0.012 0 0.019(4) 1588 0.012 0 0.02

TARP Amount (TARP Recipient Dummy=1) (1) 1291 0.015 0 0.02(2) 978 0.015 0 0.021(3) 1654 0.015 0 0.02(4) 1258 0.016 0 0.021

45

Panel B: TARP

Package Level Facility Level

VARIABLES (1) (2) (3) (4)

TARP Recipient Dummy -0.23*** -0.22*** -0.24*** -0.23***(-4.50) (-4.04) (-5.61) (-5.11)

TARP Amount 3.69** 3.60** 2.29* 2.17(2.51) (2.12) (1.75) (1.48)

Log(Bank Total Assets) 0.19*** 0.19*** 0.19*** 0.21***(12.03) (12.17) (14.23) (14.71)

Bank Liquidity -0.19 -0.21 -0.12 -0.12(-1.23) (-1.30) (-0.89) (-0.84)

Bank ROA -0.57 -1.43 0.54 -0.33(-0.22) (-0.48) (0.26) (-0.15)

Loan Charge-Offs 8.18* 8.19 9.36** 8.86*(1.73) (1.57) (2.16) (1.85)

Loan Loss Allowance -10.46*** -11.45*** -12.08*** -13.41***(-3.03) (-2.86) (-3.71) (-3.53)

Risk Weighted Assets 0.80*** 0.81*** 0.83*** 0.85***(6.51) (6.29) (7.68) (7.51)

Subordinated Debt -8.57*** -9.74*** -8.20*** -9.49***(-4.63) (-4.90) (-5.25) (-5.63)

Deposits -0.44*** -0.60*** -0.48*** -0.65***(-3.28) (-4.19) (-4.17) (-5.18)

BHC Dummy -0.17 -0.18 -0.10 -0.14(-1.41) (-1.50) (-1.02) (-1.28)

Lead Bank Dummy 0.41*** 0.42***(15.86) (17.74)

Constant -1.38*** -1.49*** -1.65*** -1.82***(-3.88) (-4.13) (-5.21) (-5.64)

Observations 1,569 1,233 2,015 1,588Adj. R-squared 0.71 0.63 0.73 0.63

46

Panel C: Placebo Test

Package Level Facility Level

VARIABLES (1) (2) (3) (4)

TARP Recipient Dummy -0.19*** -0.17*** -0.17*** -0.16***(-6.20) (-4.97) (-6.17) (-4.98)

TARP Amount 0.28 0.13 0.26 0.08(1.25) (0.55) (1.26) (0.38)

Log(Bank Total Assets) 0.21*** 0.23*** 0.22*** 0.24***(16.29) (16.14) (18.23) (18.21)

Bank Liquidity 0.18 0.17 0.10 0.12(1.45) (1.24) (0.92) (0.99)

Bank ROA -0.98 -0.51 -2.89 -2.12(-0.19) (-0.09) (-0.62) (-0.39)

Loan Charge-Offs 4.48 10.88* 11.82*** 17.75***(0.90) (1.95) (2.60) (3.45)

Loan Loss Allowance 13.44*** 7.21 9.03** 3.25(3.31) (1.55) (2.40) (0.73)

Risk Weighted Assets 0.19* 0.18 0.20** 0.20**(1.87) (1.65) (2.16) (2.00)

Subordinated Debt 1.95*** 1.99*** 2.01*** 2.05***(2.86) (2.62) (3.22) (2.96)

Deposits -0.18* -0.30** -0.13 -0.23**(-1.69) (-2.37) (-1.31) (-2.03)

BHC Dummy -0.34*** -0.43*** -0.35*** -0.45***(-3.65) (-4.39) (-4.10) (-4.99)

Lead Bank Dummy 0.43*** 0.44***(23.98) (26.60)

Constant -1.80*** -2.12*** -1.88*** -2.23***(-5.34) (-5.53) (-6.23) (-6.49)

Observations 4,523 3,455 5,740 4,407Adj. R-squared 0.74 0.66 0.74 0.66

47

Table VIII

Bank Capital and Number of Lenders

This table reports the results for the effect of bank capital on number of lenders per packageusing OLS, bank fixed effects, and Poisson models. The sample consists of 4,356 deal pack-ages between 1996 and 2012 with lead bank share information. The dependent variable inthe OLS and the bank fixed effects models is the natural logarithm of total number of lendersin a package. The dependent variable in the Poisson models is the total number of lendersin a package. The key independent variable is the Total Capital Ratio, which is a bank’stotal capital over its risk-weighted assets. All regressions control for year, industry (2-digitSIC code), and package purpose fixed effects. The t-statistics in the parentheses below thecoefficient estimates are calculated using robust standard errors corrected for heteroskedas-ticity and clustering at the lead bank level. ***, **, and * denote statistical significance atthe 1%, 5%, and 10% levels, respectively, in a two-tailed test.

48

OLS Bank FE OLS Bank FE Poisson PoissonVARIABLES (1) (2) (3) (4) (5) (6)

Total Capital Ratio -1.91*** -2.32*** -2.07*** -2.28** -0.62 -0.95**(-3.59) (-3.05) (-3.09) (-2.58) (-1.51) (-2.12)

textitLog(Bank Total Assets) -0.07*** 0.01 -0.07*** 0.01 -0.03 -0.02(-4.77) (0.27) (-4.65) (0.25) (-1.48) (-0.92)

Bank Liquidity -0.63** -0.79* -0.61** -0.62 -0.40* -0.32(-2.16) (-1.77) (-2.07) (-1.50) (-1.70) (-1.51)

Bank ROA -6.85*** -1.42 -7.07*** -2.10 -5.01 -5.46*(-2.80) (-0.48) (-2.82) (-0.77) (-1.31) (-1.72)

Loan Charge-Offs 11.46** 8.21 11.47** 8.41 2.54 1.66(2.32) (1.31) (2.25) (1.29) (0.75) (0.49)

Loan Loss Allowance -1.62 4.50* -1.04 3.49 -3.77 -2.23(-0.57) (1.68) (-0.41) (1.23) (-1.30) (-0.99)

Risk Weighted Assets -0.61*** -0.91*** -0.68*** -0.83*** -0.08 -0.18(-3.54) (-3.38) (-3.71) (-3.52) (-0.40) (-0.87)

Subordinated Debt -1.15 -2.09 -0.81 -1.52 -3.45* -2.66(-0.52) (-0.89) (-0.38) (-0.63) (-1.67) (-1.24)

Deposits 0.02 0.16 0.09 0.08 -0.12 -0.06(0.11) (0.70) (0.46) (0.32) (-0.63) (-0.33)

BHC Dummy 0.22 0.10 0.73** 0.50*(0.98) (0.51) (2.38) (1.80)

Log (Firm Total Assets) -0.00 -0.01 0.04* 0.04* -0.02 0.05(-0.24) (-0.57) (1.95) (1.68) (-0.76) (1.61)

Tobin’s Q 0.01 0.01* 0.01 0.01** -0.02 -0.00(1.49) (1.70) (1.36) (2.06) (-1.47) (-0.36)

Profitability -0.12 -0.06 -0.14* -0.07 0.24 0.25(-1.61) (-0.59) (-1.91) (-0.71) (1.18) (1.13)

Tangibility 0.02 0.01 0.05 0.05 -0.08 -0.05(0.24) (0.07) (0.55) (0.47) (-0.77) (-0.51)

Cash Holdings -0.36*** -0.33*** -0.34*** -0.31*** -0.41*** -0.41***(-4.85) (-3.84) (-4.88) (-3.96) (-3.88) (-3.49)

Leverage -0.14*** -0.14*** -0.18*** -0.18*** 0.06 -0.02(-2.93) (-2.63) (-3.58) (-3.01) (0.78) (-0.26)

Rated Dummy 0.22*** 0.20*** 0.27*** 0.24*** 0.12*** 0.15***(4.73) (4.08) (5.84) (4.88) (3.97) (4.71)

R&D -0.02 0.01 0.02 0.05 -0.00 0.09(-0.22) (0.07) (0.23) (0.51) (-0.03) (0.79)

Cash Flow Volatility -0.01 -0.02 0.00 -0.00 -0.02 -0.00(-0.77) (-1.03) (0.22) (-0.26) (-1.10) (-0.02)

Log (Deal Amount) 0.49*** 0.50*** 0.44*** 0.45*** 0.57*** 0.52***(20.99) (19.86) (24.39) (23.07) (12.67) (11.51)

No. of Fin. Covenants 0.07** 0.09*** 0.09***(2.51) (2.98) (4.74)

No. of Non-Fin. Covenants 0.06*** 0.06*** 0.06***(5.59) (4.76) (6.82)

Constant 1.04* -0.60 0.84 -0.98 -0.50 -1.02**(1.81) (-0.52) (1.57) (-0.86) (-0.87) (-2.01)

Observations 4,420 4,420 4,420 4,420 4,420 4,420Adj. R-squared 0.65 0.66 0.67 0.68 0.55 0.57

49

Table IX

Alternative Measures of Bank Capital

This table reports the results for the effect of bank capital on lead bank share using bothOLS and bank fixed effects models with alternative measures of bank capital. The sampleconsists of 4,356 deal packages (4,420 package-lead bank observations due to multiple leadbanks for some packages) between 1996 and 2012 with lead bank share information. Thedependent variable is the natural logarithm of lead bank share in percentages in a package.The alternative measures of bank capital are the Tier1 Capital Ratio, the Leverage Ratio,and the Avg. Total Capital Ratio. Tier1 Capital Ratio is defined as the ratio of a bank’stier 1 capital over its risk-weighted assets, and Leverage Ratio is defined as the ratio of abank’s tier 1 capital over its total assets. Avg. Total Capital Ratio is defined as the averageof the Total Capital Ratio over the four quarters before a package origination date. Allregressions control for year, industry (2-digit SIC code), and package purpose fixed effects.The t-statistics in the parentheses below the coefficient estimates are calculated using robuststandard errors corrected for heteroskedasticity and clustering at the lead bank level. ***,**, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively, in atwo-tailed test.

Tier 1 Capital Leverage Ratio Avg. Total Capital Ratio

OLS Bank FE OLS Bank FE OLS Bank FEVARIABLES (1) (2) (3) (4) (5) (6)

Tier 1 Capital Ratio 1.25*** 1.58**(2.72) (2.46)

Leverage Ratio 1.84*** 2.22**(2.84) (2.09)

Avg. Total Capital Ratio 1.33** 1.63*(2.47) (1.70)

Observations 4,420 4,420 4,420 4,420 3,858 3,858Adj. R-squared 0.62 0.63 0.62 0.63 0.65 0.66

50

Table X

Additional Robustness Tests

This table reports several additional robustness tests. The dependent variable in all regres-sions is the natural logarithm of lead bank share in percentages in a package or facility.The samples in models (1) to (3) are subsamples of the 4,356 deal packages between 1996and 2012 with lead bank share information, while the samples in model (4) and (5) are thesubsamples of the 5,634 facilities between 1996 and 2012 with lead bank share information.The dependent variable in all regressions is the natural logarithm of lead bank share in per-centages in a facility. The key independent variable in all regressions is the Total CapitalRatio, which is defined as the ratio of a bank’s total capital over its risk-weighted assets. Forbrevity, only bank fixed effects models are reported. We include the same control variables asthose in Tables III (for package level regressions) and IV (for facility level regressions), andall regressions also control for year, industry (2-digit SIC code), and package/facility purposefixed effects. The coefficients for the control variables are not reported. The t-statistics inthe parentheses below the coefficient estimates are calculated using robust standard errorscorrected for heteroskedasticity and clustering at the lead bank level. ***, **, and * denotestatistical significance at the 1%, 5%, and 10% levels, respectively, in a two-tailed test.

51

Term Loan andSole Lead

Package

Leveraged

Package

Syndicated

Package

Short-Term

Credit Lines

Long-Term

Credit LinesVARIABLES (1) (2) (3) (4) (5)

Total Capital Ratio 1.70*** 2.40*** 2.04*** 1.31 1.88***(2.72) (4.11) (3.25) (0.69) (2.90)

Log(Bank Total Assets) 0.00 0.04 0.01 -0.04 -0.00(0.00) (0.86) (0.22) (-0.55) (-0.08)

Bank Liquidity 0.67* 0.49 0.74** 1.30** -0.01(1.88) (1.35) (2.38) (2.37) (-0.03)

Bank ROA 0.84 1.41 1.53 -0.68 5.20**(0.39) (0.58) (0.69) (-0.10) (2.38)

Loan Charge-Offs -4.01 -4.87 -5.78 7.00 -6.51(-0.70) (-1.02) (-0.81) (0.61) (-1.08)

Loan Loss Allowance -3.70 -4.51*** -4.99* -20.06** -4.06*(-1.59) (-2.89) (-1.97) (-2.25) (-1.95)

Risk Weighted Assets 0.80*** 0.63*** 0.72*** 1.29*** 0.42*(3.74) (2.62) (2.77) (2.97) (1.89)

Subordinated Debt 0.55 -2.82 -0.47 7.21 -2.07(0.28) (-1.53) (-0.24) (1.18) (-1.42)

Deposits -0.19 -0.17 -0.24 -0.72* -0.07(-0.87) (-0.66) (-1.45) (-1.78) (-0.63)

Log (Firm Total Assets) 0.01 0.00 0.03 -0.04* -0.07***(0.72) (0.00) (1.58) (-1.76) (-3.70)

Tobin’s Q -0.01* -0.01 -0.00 0.00 -0.02**(-1.71) (-1.04) (-0.50) (0.04) (-2.09)

Profitability 0.00 0.09 -0.05 -0.10 -0.11(0.03) (1.19) (-0.35) (-0.45) (-1.31)

Tangibility -0.00 -0.08 -0.02 0.16 0.02(-0.03) (-0.94) (-0.23) (0.72) (0.20)

Cash Holdings 0.28*** 0.19** 0.35*** 0.15 0.48***(3.66) (2.29) (4.59) (1.31) (3.94)

Leverage 0.14*** 0.10** 0.14** 0.00 0.09*(3.43) (2.39) (2.58) (0.01) (1.70)

Rated Dummy -0.20*** -0.25*** -0.15*** -0.27*** -0.18***(-4.19) (-5.97) (-4.08) (-4.66) (-4.21)

R&D -0.07 -0.04 -0.10 0.22 0.07(-0.98) (-0.35) (-1.12) (0.71) (0.64)

Cash Flow Volatility 0.02 0.03* 0.01 -0.01 0.01(1.48) (1.84) (0.48) (-0.30) (0.65)

Log (Package Amount) -0.38*** -0.35*** -0.45***(-18.99) (-15.11) (-23.86)

Log (Facility Amount) -0.25*** -0.27***(-8.81) (-13.26)

Constant 4.77*** 3.75*** 5.12*** 4.95*** 4.75***(5.12) (3.97) (6.24) (3.36) (5.38)

Observations 4,297 2,740 3,576 1,085 4,613Adj. R-squared 0.62 0.56 0.60 0.63 0.63

52

Appendix A1: Variable Definitions

This appendix contains the detailed definitions of our regression variables. Note that forsome of the variables in this appendix, their natural logarithms are used in the regressions.

Variable Name Detailed Definition

Bank Share/Lead BankShare

A lenders share of the dollar amount of the loanpackage/facility in decimals. This variable is referred to asLead Bank Share if the lender is the lead bank of the loanpackage/facility. We set Lead Bank Share to 1 if No. of AllLenders equals one.

No. of Lead BanksThe total number of lead banks in a loan package/facilitysyndicate.

No. of All LendersThe total number of all banks (both lead and non-lead) in aloan package/facility syndicate.

Package Amount ($millions)The amount of a loan package committed by the packageslender pool, in millions of dollars of the December 2012purchasing power.

Facility Amount ($millions)The amount of a loan facility committed by the facilityslender pool, in millions of dollars of the December 2012purchasing power.

No. of Financial Covenants

The total number of covenants based on financial ratios (seeAppendix A2) at the package level. We first create a dummyvariable that equals one if a financial ratio covenant exists,and equals zero otherwise. Note that to avoid losing toomany observations, we set the dummy variable to zero ifthere is no covenant based on a financial ratio or informationabout it is missing. We then add up the dummy variables toobtain the total number of financial covenants.

No. of Non-FinancialCovenants

The total number of non-financial covenants (see AppendixA2). This variable is constructed in the same way as No. ofFin. Covenants based on non-financial ratio covenants.

Secured Facility DummyA dummy variable that equals one if a loan facility issecured, and equals zero otherwise.

Performance PricingDummy

A dummy variable that equals one if there is a griddisplaying different pricing levels based, and equals zerootherwise.

Leveraged Package/FacilityDummy

A dummy variable that equals one if the market segment ofa loan facility belongs to Highly Leveraged, or Leveraged, orNon-Investment Grade. A package is leveraged if all facilitieswithin the package are leveraged.

Syndicated Package/FacilityDummy

A dummy variable that equals one if the distributionmethod of a loan facility is syndication. A package issyndicated if all facilities within the package are syndicated.

53

Continued

Variable Name Detailed Definition

Facility Purpose Dummies

At the facility level. It includes (1) Acquisition Dummy, (2)General Corporate Purpose Dummy, (3) LBO Dummy, (4)Recapitalization Dummy, and (5) Miscellaneous Dummy.The omitted group includes Other purposes. The groupdefinition follows Drucker and Puri (2009).

Package Purpose Dummies

At the package level. It includes (1) Acquisition Dummy, (2)General Corporate Purpose Dummy, (3) LBO Dummy, (4)Recapitalization Dummy, and (5) Miscellaneous Dummy.The omitted group includes Other purposes. The groupdefinition follows Drucker and Puri (2009).

Total Capital RatioTotal Capital/Risk-Weighted Assets: (RCFD8274 +RCFD8275) / RCFDA223

Tier 1 Capital RatioTier 1 Capital/Risk-Weighted Assets:RCFD8274/RCFDA223

Leverage Ratio Tier 1 Capital/Bank Total Assets: RCFD8274/RCFD2170

Bank Total Assets($billions)

Bank Total Assets in billions of dollars of the December2012 purchasing power: RCFD2170 (after adjusted forCPI)/1,000,000

Bank Liquidity(Cash+Available-for-sale Securities)/ Bank Total Assets:(RCFD0010+RCFD1773)/RCFD2170

Bank ROA Net Income/Bank Total Assets: RIAD4340/RCFD2170

Loan Charge-OffsTotal Loan Charge-Offs/Bank Total Assets:RIAD4635/RCFD2170

Loan Loss AllowanceLoan Loss Allowance/Bank Total Assets:RCFD3123/RCFD2170

Risk-Weighted AssetsRisk-Weighted Assets/Bank Total Assets:RCFDA223/RCFD2170

Subordinated DebtSubordinated Debt/Bank Total Assets:RCFD3200/RCFD2170

Deposits Total Deposits/Bank Total Assets: RCFD2200/RCFD2170

BHC DummyBank Holding Company Dummy. A dummy variable thatequals one if the bank is held by a bank holding company,and equals zero otherwise.

TARP Recipient DummyA dummy variable that equals one if a bank is a TARPfunding recipient, and equals zero otherwise.

TARP Amount

TARP Amount/Risk-Weigthed Assets (RCFDA223). Thisvariable equals to zero for non-TARP recipients during alltimes and zero for TARP recipients before their TARPfunding infusions.

54

Continued

Variable Name Detailed Definition

Firm Total Assets($millions)

Book Value of Total Assets in millions of dollars of theDecember 2012 purchasing power: AT

Tobins Q

Market Value of Total Assets/Book Value of Total Assets,where Market Value of TotalAssets=PRCC F*CSHO+(DLC+DLTT)+PSTKL-TXDITC. TXDITC is set to zero ifmissing.

ProfitabilityOperating Income Before Depreciation/Book Value of TotalAssets: OIBDP/AT

TangibilityTotal Property, Plant, and Equipment/Book Value of TotalAssets: PPENT/AT

Cash HoldingsCash and Short-Term Investments/Book Value of TotalAssets: CHE/AT

Leverage(Total Debt in Current Liabilities+Total Long TermDebt)/Book Value of Total Assets: (DLC+DLTT)/AT

Rated DummyA dummy variable that equals one if a borrower has an S&Plong term credit rating in the fiscal year before the loanactivation date.

R&DResearch and Development Expense/Book Value of TotalAssets: XRD/AT. XRD is set to zero if missing.

Cash Flow Volatility

Standard deviation of previous 12 quarterly Cash Flows,where Cash Flow=(IBQ+DPQ)/SALEQ. DPQ is set to zeroif missing. We only keep the computed cash flow volatility ifCash Flows are non-missing for at least 8 quarters out of theprevious 12 quarters.

55

Appendix A2: Classification of Loan Covenants

Loan Covenant

CategoryCovenants

Financial Covenants

Max. Capex, Max. Debt to EBITDA, Max. Debt to Equity, Max.Debt to Tangible Net Worth, Max. Leverage Ratio, Max. Loan toValue, Max. Long-Term Investment to Net Worth, Max. NetDebt to Assets, Max. Senior Debt to EBITDA, Max. SeniorLeverage, Max. Total Debt (including Contingent Liabilities) toTangible Net Worth, Min. Cash Interest Coverage, Min. CurrentRatio, Min. Debt Service Coverage, Min. EBITDA, Min. Equityto Asset Ratio, Min. Fixed Charge Coverage, Min. InterestCoverage, Min. Net Worth to Total Asset, Min. Quick Ratio,Other Ratio, Net Worth, and Tangible Net Worth.

Non-FinancialCovenants

Insurance Proceeds Sweep, Dividend Restriction, Equity IssuanceSweep, Debt Issuance Sweep, Asset Sales Sweep, Excess CashFlow Sweep, Percentage of Net Income, and Percentage of ExcessCash Flow.

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