Bank Capital and Monitoring: Evidence from Loan Quality

Gauri Bhat

Cox School of Business Southern Methodist University

gbhat@smu.edu (214) 768-2964

Hemang Desai

Cox School of Business Southern Methodist University

hdesai@cox.smu.edu (214) 768-3185

April 1, 2016 Early Draft

Please do not quote or cite Comments Welcomed

We thank Anjan Thakor and seminar participants at Southern Methodist University and the University of Texas Arlington for comments.

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Abstract

There are two competing theories on how bank equity capital affects banks’ monitoring incentives. One perspective is that higher bank capital shields managers from market discipline that high leverage imposes and may result in underinvestment in monitoring efforts (Diamond and Rajan, 2001). This view suggests that higher bank capital may adversely affect bank value. An alternate perspective is that higher bank capital increases survival probability of banks and this in turn increases the odds that a manager will be able to realize the benefits of prior investment in monitoring effort suggesting a positive association between bank capital and monitoring incentives and therefore a positive association between capital and bank value (Mehran and Thakor, 2011). In this paper, we empirically test the association between bank capital and loan quality, an outcome of bank’s monitoring effort, for U.S. bank holding companies over the period 1994-2015. We document a positive and significant association between the bank capital and the quality of its loan portfolio, controlling for other determinants of loan quality. This association is statistically stronger for smaller banks relative to larger banks, and obtains for public as well private banks. This association is not affected by the financial crisis period and is not sensitive to banks’ participation in originate to distribute loan market. Importantly, using two salary based ex-ante measures of bank’s monitoring effort that capture labor input into monitoring, we show that bank capital is positively related to our ex-ante proxies of monitoring efforts, which in turn are positively related to loan quality. Our evidence is consistent with prediction in Mehran and Thakor that bank capital improves monitoring incentives which in turn increases the value of its loan portfolio.

JEL classification: G21, G32, M41

Key words: Bank capital, bank monitoring, loan quality.

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1 Introduction

A unique aspect of banks’ capital structure is its fragility – i.e., that banks use little equity

capital and are primarily financed by deposits. There are two opposing theoretical views on bank

capital structure and how bank capital affects bank’s monitoring incentives and hence the value of

its loan portfolio.1 One view is that the market discipline imposed by the fragile capital structure

(high leverage) encourages the bank to commit to monitoring its borrowers (Diamond and Rajan,

2001). Higher bank capital impedes this market discipline and adversely affects the bank’s

monitoring incentives. The opposing view is that bank capital strengthens bank’s incentives to

monitor its borrowers as bank failure is more costly for shareholders of well capitalized banks

(Holmstrom and Tirole 1997 and Mehran and Thakor 2011). Given the theoretical disagreement

on how bank capital affects bank’s monitoring incentives and hence bank value, empirical

evidence identifying costs and benefits of bank capital becomes particularly valuable. Our

objective in this paper is to contribute to this debate by providing an empirical test of the

association between bank capital and banks’ monitoring effort. Monitoring effort is unobservable

but a bank that monitors its borrower more, should have higher loan quality. Thus, we examine an

association between bank capital and loan quality, which is an outcome of banks’ monitoring

effort. In addition, we also examine the association between bank capital and salary based ex-ante

measures of monitoring effort that capture labor input into monitoring.

The amount of bank capital not only affects a bank’s survival when faced with an economic

shock but also its risk management incentives. Diamond and Rajan (2001) argue that the value of

                                                            1 We use the term ‘bank capital’ to refer to the book value of equity on the bank’s balance sheet. The regulatory view of capital is similar but broader and includes other sources of financing such as preferred stock. We conduct robustness tests using the alternate regulatory definition of bank capital in Section 5. 

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the bank’s loan portfolio will be affected by bank manager’s investments in loan collection efforts.

The bank managers may threaten to withhold their collection effort until given a higher share in

the surplus. But if the bank has a fragile capital structure, the deposit holders may threaten to

prematurely withdraw their funds and thus, the fragile capital structure resolves the hold-up

problem. In their model, higher capital shields the bank manager from market discipline (a run by

the depositors) thus, allowing the manager to underinvest in monitoring and hence, higher capital

adversely affects the value of bank’s loan portfolio. An alternate perspective is that higher capital

strengthens a bank’s monitoring incentives and enhances its survival probability. Specifically,

Mehran and Thakor (2011) argue that since higher capital increases the survival probability of the

bank, it increases the marginal benefit to monitoring as greater survival probability increases the

likelihood that the bank will be able to realize the gains from its prior investment in monitoring.

This increases the value of bank’s relationship loans, which in turn further strengthens banks’

incentives to monitor. Thus, Mehran and Thakor (2011) predict a positive association between

bank capital and value of the loan portfolio. Our study contributes to this debate by empirically

testing the association between bank capital and monitoring effort.

We conduct three tests to test the association between bank capital and monitoring effort:

(1) we examine the association between bank capital and three measures of loan quality (outcomes

of banks’ monitoring efforts). (2) We investigate whether the bank capital is associated with two

salary expense based (labor input) proxies of monitoring effort. (3) We correlate our ex-ante

measures of monitoring effort to loan quality, ex-post measure of monitoring effort.

We use three measures of default risks inherent in the loan portfolio of the bank, namely,

non-performing loans (NPL), loan loss provisions (LLP), and net loan charge-offs (NCO) to

capture loan quality. Non-performing loans involve relatively mechanical classification of loans

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as non-performing when payment on the loan is overdue. Typically, loans generally become non-

performing at 90 days past due or if loan is a nonaccrual loan. A manager has little control over

classification of a loan as non-performing. Thus, our first measure of loan quality, non-performing

loans, is considered a relatively non-discretionary indicator of loan quality. If losses on loans

appear probable and estimable, the bank managers are expected to record a provision by reducing

current income and increasing the allowance on loans, but they are required to use considerable

judgement in doing so. While provision is highly discretionary for heterogeneous loans for which

loss accruals are estimated at the individual loan level primarily based on the judgment of loan

officers under FAS 5 or FAS 114 relative to homogenous loans for which loss accruals are

estimated at the pool level based on historical loss statistics under FAS 5, it is still within the bank

manager’s discretion to apply the expected loss rates even to the homogenous loan pools. Thus,

our second measure of the loan quality, loan loss provisions, reflects the bank manager’s private

information regarding default risks inherent in the loan portfolio and is discretionary in nature.

Bank managers charge off loans when they are deemed uncollectible. While the bank managers

exercise some degree of discretion over the timing and the magnitude of the charge-offs, especially

for commercial loans, charge-offs are often driven by exogenous factors and also, some loans such

as consumer are automatically charged off when they become delinquent for a certain number of

days.2 Thus, our third measure of loan quality, charge-offs, is relatively non-discretionary measure.

In our main empirical tests, we examine the cross-sectional association between bank capital and

                                                            2 Closed-end consumer loans must be charged-off no later than 120 days past due, whereas open-end consumer loans and residential mortgages must be charged-off no later than 180 days past due as per the Federal Financial Institutions Examination Council, Uniform Retail Credit Classification and Account Measurement Policy, June 12, 2000.

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each of these three measures of loan quality – non-performing loans, loan loss provisions and

charge-offs.

We find a significant positive association between bank capital in quarter t-1 and each of

the three measures of loan quality in quarter t. This association is observed over our entire sample

period of 1994Q2-2015Q1 and in the crisis and non-crisis periods. The relation is observed for

both the public and the private banks. The economic magnitude of the relation is strong as well.

Our main tests reveal that one standard deviation increase in bank capital is associated with a

decrease of 27% in average NPL, 18% in average LLP and 28% in average NCO, respectively.

Our analysis controls for other determinants of loan quality such as loan growth, size, loan

portfolio composition, liquidity, maturity gap, cost inefficiency, ratio of fee income to total

income, change in GDP rate and earnings before provision. We include bank fixed effects and

quarter dummies to account for average differences in loan quality across banks and average

differences in loan quality across quarters, respectively, that are not captured by the other

exogenous variables, and to reduce correlation across error terms. To control for heteroscedasticity

and possible correlation among observations of the same bank in different years, we report robust

standard errors, clustered by bank.

We also find that the positive relation between bank capital and loan quality obtains for

both the public and the private banks. It is plausible that the effect of higher capital in strengthening

monitoring incentives differs across bank size. The role of close monitoring may be more

important to smaller banks that typically extend loans to small businesses.3 We find that the

                                                            3 Koch et al (2016) show that in the years preceding the financial crisis, the capital of the largest banks converged to the minimum allowed by law, while the smaller banks increased their capital in the period preceding the financial crisis. They attribute this behavior to the moral hazard at the largest banks resulting from too big to fail precedent established in the preceding years (Franklin in 1974, Continental Illinoi, 1984 etc.). We examine the association between equity and loan quality for the largest 20 banks each quarter and find it to be insignificant.  

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association between capital and loan quality is statistically stronger for smaller banks relative to

medium and larger banks. In fact, for larger banks, the association between capital and loan quality

is not consistently significant across various loan quality measures. The positive relation between

bank capital and loan quality obtains during the financial crisis (2007Q2-2008Q4) and the non-

crisis periods, and for banks that participate in the Originate-to-distribute market for loans and the

banks that do not. Results are robust to including additional control for loan performance (interest

on loans), and exclusion of banks with assets above 10 billion.

While, loan quality is a result of, and hence ex-post outcome of investment in monitoring

effort, theory predicts an association between bank capital and monitoring incentives. Thus, we

also examine whether bank capital is associated with ex-ante investment in monitoring effort.

Specifically, we use two ex-ante proxies for bank’s monitoring effort. These proxies are based on

the ratio of salary expense to total non-interest expense. The intuition being that ratio of the salary

expenses to total non-interest expense that captures the quality and quantity of labor input into

monitoring effort. The first proxy is the ratio salary expense to total non-interest expense adjusted

for size and portfolio composition. The second proxy is a comprehensive measure of monitoring

effort based on Coleman, Esho and Sharpe (2006). Coleman et al. estimate a fixed effects

regression with salary expense to total non-interest expense as the dependent variable and various

factors that are intended to capture the impact of non-monitoring activities of the bank on the salary

expense such as portfolio composition, fee income, transaction deposits, size, performance and

number of employees (details in section 3.2 and 5). The fixed effect regression coefficient is the

proxy for monitoring effort. Their out of sample tests show that the monitoring effort proxy is

reliably associated with loan quality indicators in the future. We compute our second proxy using

rolling regressions for 4 quarters at a time, t-4 to t-1. The fixed effects obtained from these

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regressions are the monitoring effort proxy for the bank at the beginning of quarter t. If bank capital

strengthens monitoring incentives then we should expect to see a positive association between

bank capital and our proxies for monitoring effort. Consistent with predictions in Holmstrom and

Tirole and Mehran and Thakor, we find that bank capital is positively associated with both of our

ex-ante proxies of monitoring incentives. Again, this association is stronger for small banks

relative to medium banks, and not significant for large banks. We correlate both our ex-ante

proxies of monitoring effort with the three ex-post measures, loan quality and find positive and

significant association.

Additionally, we identify a situation where banks’ monitoring efforts are expected to be

higher and test if in such an instance the relation between capital and loan quality is stronger. Such

a scenario is the relation between relationship loans and core deposits. Song and Thakor (2007)

argue that banks finance informationally opaque relationship loans with core deposits. The

rationale being that informationally opaque loans are likely to generate the most disagreement

between banks and depositors about their value and hence depositors are most likely to run if they

perceive that the value of the loan portfolio has declined. However, core deposits are sluggish as

banks also provide transaction and advisory services to these depositors. Thus, banks find it

optimal to finance relationship loans with core deposits. Based on this insight, we use banks’

funding mix as a proxy for the extent of banks’ relationship lending, Since relationship loans

require greater monitoring effort, the relation between bank capital and loan quality (outcome of

monitoring) should be stronger for banks with greater core deposits (proxy for relationship

lending). We divide our sample into two groups – those with above median ratio of core deposits

to total assets and those with below. Consistent with our conjecture, we find that the association

between bank capital and monitoring, and bank capital and loan quality is significantly stronger

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for banks with greater core deposits. Furthermore, we find this association is stronger for small

banks relative to large banks as one would expect given that relationship lending is dominant in

small banks.

While the theory suggests a causal relationship from bank capital to loan quality, an

outcome of the monitoring effort, in practice both may be jointly determined. We employ several

econometric techniques and additional tests to mitigate this endogeneity concerns. First, our bank

capital measure is lagged which partially mitigates the endogeneity concern as lagged capital and

current loan quality measures are less likely to be jointly determined. We have also replicated the

analysis by extending the lag to three periods and our results are robust. Second, we have panel

data and use a fixed effect estimation model to control for time invariant omitted variables. Third,

while we control for profitability, portfolio composition and risk in our regressions, we understand

that these controls may not be sufficient. Therefore, we conduct additional tests to control for loan

performance by including interest yield on loans as a control variable, and risk by conditioning our

analysis on participation in the OTD market.

Our final test to address endogeneity involves examining the association between change

in capital and change in loan quality. This research design allows the firm to act as its own control

and thus minimizing the likelihood that the relationship is due to an omitted variable. Over our

sample period, 443 banks (1944 bank-quarters) made secondary equity offerings. We examine

whether an increase in capital is associated with increase in monitoring effort and loan quality

(outcome). Using a changes specification, we first regress change in in our two monitoring proxies

on a dummy variable that takes the value of 1 for banks that issued equity capital. The results show

that banks that raise equity show a significant increase in monitoring effort after one year.

Assuming that increased monitoring efforts will result in improved loan quality with a lag, we find

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that banks that increase capital show improvement in NPL after 2 years and in each of three

measures after three years controlling for changes in control variables. This result shows that an

increase in capital is followed by an increased investment in monitoring effort as well as improved

loan quality.

Our study advances the literature forward along various dimensions. Though prior studies

have documented benefits of higher capital (for example, Calomiris and Mason, 2003, and Berger

and Bouwman, 2013), the empirical evidence on the positive impact of bank capital on monitoring

incentives is limited. For example, using a sample of 244 bank mergers from 1989 to 2007 that

were accounted for using the Purchase Method, Mehran and Thakor (2011) show that the purchase

price received by the target bank and the goodwill recognized in the acquisition are positively

related to the target bank’s equity capital suggesting that bank capital is positively related to bank

value in the cross-section.4 Our study complements Mehran and Thakor by showing the impact of

capital on loan quality using a large and comprehensive sample of banks. Furthermore, we

arguably provide more direct test of the prediction in Mehran and Thakor that bank capital is

positively associated with monitoring incentives as we show evidence on the mechanism by which

capital affects loan quality - through its effect on monitoring effort. In a related study,

Purnanandam (2011) finds that banks with greater participation in the originate-to-distribute model

originated loans of poor quality and that this effect was stronger for banks with lower capital

suggesting that lower capital reduces pre-lending screening incentives. However, this analysis is

conducted over a short period of seven quarters from 2006Q3 to 2008Q1 and the analysis is

                                                            4 Prior to 2001, there were two acceptable methods to account for mergers, the Purchase and the Pooling of Interests Method. The Purchase Method required the acquirer to revalue the Target’s assets and liabilities to their fair market values but Pooling Method permitted the acquirer to record the target’s assets and liabilities at their book values. Prior to the prohibition on the use of Pooling of Interests Method by the FASB in 2001, an overwhelming majority of bank mergers were accounted for using the Pooling method.

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restricted to mortgage loans. In contrast, our study spans a much larger period (1994 to 2015) and

examines total loans as well each major category of loans.

Finally, the issue of costs and benefits of bank capital has become particularly controversial

with some calling for drastic increase in equity capital for banks (Admati et al, 2010). Our study

contributes to this debate by identifying an important benefit of bank capital – its impact on

monitoring incentives and its positive association with loan quality (value). We recognize that our

evidence shows a cross-sectional association between bank capital and loan quality and hence our

evidence does not suggest or imply that banks should hold more capital. At a minimum, our

empirical evidence identifies an important benefit of capital – stronger monitoring incentives.

2 Background and Motivation

A commonly held view is that bank capital dissipates value (Mishkin, 2000). There are

multiple mechanisms by which bank capital may adversely affect value. One set of argument holds

that leverage disciplines the bank manager while higher capital shields a bank manager from

market discipline, thereby allowing the manager to shirk or underinvest in monitoring efforts.

While the disciplinary role of debt is well established in corporate finance theories, the disciplinary

role of bank debt is stronger because of the nature of bank deposits, which is the primary source

of bank debt. Demand deposits have a sequential service constraint feature where payments are

made to depositors on a first come first served basis till the funds are exhausted. This is a crucial

disciplinary mechanism that not only disciplines the bank but also enhances liquidity creation

(Calomiris and Kahn, 1991). The general argument proceeds along the following lines. Banks

make loans that are long-term and illiquid but are financed primarily with liquid demand deposits.

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Such a transformation of illiquid loans into liquid deposits creates liquidity on both sides of the

balance sheet. Such liquidity creation is the primary function of the banks (Diamond and Dybvig,

1983) and a fragile capital structure (financed primarily by deposits) is necessary for the bank to

perform this function. A fragile capital structure allows the depositors to delegate monitoring to

the banker who specializes in monitoring of borrowers (Diamond, 1984). However, once the funds

from depositors are raised, the banker may shirk or abscond. The depositors can guard against such

opportunistic behavior by becoming informed. Higher bank leverage increases the risk exposure

of depositors, giving them greater incentives to become informed. If they acquire adverse

information, they can respond by withdrawing their funds. Their action is likely to be emulated by

other depositors precipitating a run on the bank. Thus, the threat of a bank run then keeps the bank

manager diligent and prevents shirking and excessive risk taking (Calomiris and Kahn, 1991). In

contrast to demand deposits, claims such as equity lack this sequential servicing constraint and

hence cannot provide the same discipline as deposits.

Diamond and Rajan (2000 and 2001) model fragility (leverage) as helping solve the

contracting problem between the relationship lender (bank) and the depositors. They argue that the

fragility of bank capital structure helps alleviate the hold-up problem where the relationship lender

(the banker) may refuse to collect payments from the borrower on behalf of the lenders (depositors)

unless the depositors agree to give the banker a higher share of the surplus. However, if the bank

is primarily financed with deposits, then in response to the threat of hold-up by the banker, the

depositors can run, which would then drive the banker’s rent down to zero. Equity holders cannot

provide such a threat. Thus, it is the fragility of the bank’s capital structure that disciplines the

banker and a credible threat of a bank run that allows the bank to raise financing via deposits which

in turn allows it to fund illiquid loans and thereby creating liquidity. Of course, the banks does

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need to finance itself with some capital as it provides a buffer against economic shocks. Overall,

these studies identify negative effects of bank capital and argue that lower level of bank capital

(high leverage) should be associated with stronger monitoring incentives, more lending and greater

liquidity creation.

An alternate perspective is that higher level of bank capital has beneficial effects. There

are three broad arguments that highlight benefits of higher level of bank capital. First, very low

levels of equity can induce moral hazard by encouraging the manager to engage in excessive risk

taking or asset substitution (Jensen and Meckling, 1976). Higher of level of bank capital can

mitigate this moral hazard problem by reducing risk taking incentives (Furlong and Keeley, 1989).

Second, higher level of capital increases banks’ risk absorbing capacity and this in turn increases

the banks’ risk bearing capacity (Bhattacharya and Thakor, 1993). A third argument is that higher

level of bank capital strengthens the banks’ monitoring incentives because a bank failure is more

costly for shareholders of well capitalized banks (Holmstrom and Tirole, 1997 and Mehran and

Thakor, 2011). The key insight in Mehran and Thakor, which is a dynamic variant of the

Holmstrom and Tirole model is that higher level of capital increases the survival probability of the

bank. Higher survival probability increases the value of the relationship loans, which in turn

improves the banks’ monitoring incentives ex-ante, resulting in a positive association between

bank capital and monitoring incentives. Overall, the above mentioned papers provide an alternate

perspective that higher bank capital (lower leverage) has beneficial effects of improved monitoring

incentives. Thus, these papers predict a positive association between bank capital and value via

improved monitoring incentives.

The above discussion highlights two opposite theoretical predictions regarding the effect

of bank capital (leverage) on bank’s monitoring incentives. Given the theoretical disagreement,

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empirical evidence on the issue becomes particularly valuable. While bank capital is observable,

bank’s monitoring efforts and its ability are largely unobservable to outsiders. If banks are

heterogeneous rather than homogeneous in their loan monitoring incentives, then a bank’s superior

loan monitoring ability should be reflected in the quality of its loan portfolio. Banks that have

monitored their borrowers will have fewer loan defaults. Thus, if higher capital improves banks’

monitoring incentives ex-ante then its effect should be reflected in the ex-post measure of default

risk of its loan portfolio. In other words, we should observe a positive association between bank

capital in time period t-1 and various indicators of loan quality in time period t, a proposition we

test empirically in this paper.

3 Data and variable measurement

Our sample includes commercial bank holding companies in the United States that are in

business during the 1994 to 2015 period. We use the quarterly call report data (Y9C) from the

Federal Reserve Bank of Chicago website from 1994Q1 to 2015Q1. All domestic Federal Deposit

Insurance Corporation (FDIC)-insured commercial bank holding are required to file call reports

with the regulators on a quarterly basis. These reports contain detailed information on the bank’s

income statement, balance-sheet items, and off-balance-sheet activities. The items required to be

filed in this report change over time to reflect the changing nature of banking business. To ensure

that we maintain consistency in the measurement of our variables over time, we check the Y9C

report format available on the Federal Reserve Bank’s website for each quarter. We exclude banks

for the following criteria (1) total assets below 500 million in current or lagged year; (2) zero or

negative equity capital in the current or lagged year; (3) missing data to compute variables in our

tests. Panel A of Table 1 shows that our final sample includes 55,492 bank quarters with 2,006

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unique banks. We use the CRSP-FRB link file from the Federal Reserve Bank of New York’s

website to identify the public banks in our sample. We have 501 unique public (private) banks

with 21,174 (34,318) bank quarter observations.

3.1 Variable definitions

Our main independent variable is bank equity capital deflated by lagged total assets (CAP).

Out dependent variable, loan quality is measured in three different ways: (1) non-performing loan

(NPL); (2) provision for loan losses (LLP); and (3) net charge-offs on loans (NCO). All three are

deflated by lagged total assets. NPLs include all loans on which interest or principal payment is

due for more than 90 days, and are a required disclosure in the Y9C. NPL typically are based on

mechanical classifications and generally bank managers have very little discretion over the level

of NPLs. Loan loss provision reflects the expense accrued on the income statement to reflect an

increase in the future expected loan losses. Provisions are subject to accounting rules and subject

to the discretion of the bank’s management. Charge-offs reflect write-off of loans net of the

recoveries during the current period. While most charge-offs are triggered by exogenous factors,

with timing and magnitude dictated by policies (such as consumer loan charge-offs), there are

others such as commercial loans charge-offs over which the bank management may have higher

discretion on the magnitude and timing.

We control for a host of bank characteristics that can potentially affect the quality of loans.

We control for the growth in loans measured as change in loan over prior quarter deflated by total

assets to capture effects related to loan seasoning (LG). We include log of total assets to control

for the bank’s size (SIZE). We include the ratio of real estate loans to total assets (RE), ratio of

commercial and industrial loans to total assets (COMM) and ratio of consumer loans to total assets

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(CONS) to control for the broad business mix of the bank and credit risk. We control for the

liquidity risk by including the ratio of liquid assets to total assets (LIQUID).We include a measure

of the 12-month maturity gap to control for the interest rate risk faced by the banks (ABSGAP).

To account for cost inefficiency, we include the cost to income ratio measured as non-interest

expense to income before extraordinary items (EFF). We control for the ratio of the fee income to

the total income (FEE) as banks that derive a higher proportion of their income from fee-generating

advisory services would be expected to incur fewer credit-related losses. We include GDP growth

rate to capture macroeconomic conditions that affect loan quality (DELTAGDP). We include

earnings before provision deflated by lagged total assets (EBLLP) to control to capture banks’

management of their reported profitability. See Appendix A for details on data items.

3.2 Construction of proxy for Monitoring Effort

We use two proxies for monitoring effort based on salary expense. Salary expense

measures the labor input into the monitoring process. Although banks may have information

technology based automated processes to monitor loans, loan monitoring still requires significant

labor-intensive gathering of information and evaluation. Thus, monitoring effort is directly related

to the quantity and quality of bank staff, and hence monitoring-related salary expenses. We use

the median adjusted ratio of salary expense to total non-interest expense (SALEXP) in our

computation of our first proxy (ME1) of monitoring effort. One would expect the salary expense

ratio to be driven by size of the bank and its portfolio composition. For example, due to

heterogeneity in commercial loans, they require greater investment in monitoring. Thus, we divide

banks into small, medium and banks based on total assets and then we further divide each group

into sub-groups based on proportion of commercial loans to total assets (COMM). We form these

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9 sub-groups for each quarter. For each bank quarter, we compute the ME1 as the difference

between the SALEXP and the median for that sub group of banks.

Several other non-monitoring factors may also affect the salary expense ratio in addition

to bank size and portfolio composition. Thus, we employ the technique used in Coleman et al.

2006 to compute our second measure of monitoring effort (ME2) filtering out the effects of non-

monitoring factors that may affect this ratio. The proxy for monitoring effort in quarter t is based

on the estimation of a fixed effects regression, with SALEXP as dependent variable and the non-

monitoring factors as independent variables for the quarters t-4 to t-1. The fixed effect regression

coefficients are individual bank specific constants which become our proxy for Monitoring Effort

at the beginning of quarter t. To control for the effect of asset composition on the salary expense

ratio we include portfolio composition (RE, COMM and CONS). Fee-generating activities are

typically labor-intensive and require highly skilled staff. Therefore, we include fee to interest

income ratio (FEE). Salary expense ratio maybe affected by the profit efficiency and liability

composition. Hence, we control for return on assets (ROA) and the ratio of transitory deposits to

lagged total assets (TRANDEP), respectively. To control for scale effects and the network of the

bank, we include log of total assets (SIZE) and log of number of employees (LOGEMP).

3.3 Descriptive Statistics

Table 1 Panel B provides the descriptive statistics of the key variables used in the study for

all the banks and public and private banks separately. We winsorize data at 1% from both tails to

minimize the effects of outliers. The average bank in our sample has an asset base of $16.489

billion (median $1.123 billion) and the mean (median) equity capital to total assets ratio is 9.2%

(8.8%). We provide the distribution of other key variables in the panel. These numbers are in line

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with prior studies involving large bank samples. The statistics indicate that the public banks in our

sample are significantly larger and are statistically different (unreported in the table) on almost all

characteristics, consistent with the notion that differences in control structure and access to capital

markets cause public and private banks to be different. We classify banks by size into three

categories – small, medium and large. Small banks are banks with assets less than or equal to 1

billion. Medium banks are banks with assets more than 1 billion but less than or equal to 3 billion

and large banks are banks with assets above 3 billion. The descriptive statistics are provided

separately for based on size in Panel C of the Table 1.

4 Loan Quality and Equity Capital

Our main analysis is presented in the first three columns of Panel A of Table 2. Since lower

values of loan quality measures indicate higher loan quality, we have multiplied our loan quality

measures (dependent variables) LQ1 (NPL), LQ2 (LLP) and LQ3 (NCO) by -1 to facilitate the

interpretation of the coefficient. The first three columns of the Panel present the results for all the

banks in our sample. Our main variable of interest CAP is positively and significantly (at the 1%

level) associated with each of the three measure of loan quality. In terms of economic significance,

one standard deviation change in CAP (0.030) is associated with a decrease of 0.003 (0.030*0.103)

in NPL, a decrease of 0.0002 (0.030*0.007) in LLP and a decrease of 0.0003 (0.030*0.009) in

NCO, respectively Looking at the average values of these measures, it translates to a decrease of

27% in NPL, 18% in LLP and 28% in NCO, respectively. The coefficient on LG is positive and

significant (at the 1% level) in all three regression specifications consistent with the idea that

quality issues become more apparent as the loans season. The coefficient on SIZE is negative and

significant (at 1% level) for all three measure of loan quality consistent with larger banks having

lower loan quality. The coefficients on loan composition measures are negative and significant

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consistent with the idea that loan composition captures credit risk.5 LIQUID is positively and

significantly association with the LQ1 and LQ2 (the LLP based measure) while ABSGAP is

significantly associated with all three measures of loan quality. The coefficient on EFF is positive

and significant across all three regressions suggesting that banks that have higher cost in proportion

to income have higher loan quality. The coefficient on FEE is positive and significant across all

three measures consistent with the expectation that banks that derive a higher proportion of their

income from fee-generating activities would be expected to incur fewer credit-related losses. The

coefficient on DELTAGDP is positive and significant consistent with loan quality increasing

during economically more favorable times. The coefficient on EBLLP is positive and significant

only in specification where loan quality is measured as negative NCOs.

All regressions include quarter dummies to control for average differences in loan quality

across quarters that are not captured by other exogenous variables. We include bank fixed effects

to account for average differences over time across banks that are not captured by the other

exogenous variables. All regressions are estimated with robust standard errors, clustered by bank,

to control for heteroskedasticity, as well as possible correlation among observations of the same

bank in different years.

4.1 Public vs. Private

Next, we explore whether the association between bank capital and loan quality is different

for public vs. private banks. Public banks differ from private banks in terms of control structure

and ownership. Given the diffused ownership structure of public banks, there is greater potential

for agency conflicts in public banks. Private banks are more likely to be closely held among smaller

                                                            5 The omitted category of loans is other loans that includes loans to depository and non-depository financial institutions, loans to government etc. which are relatively less risky than commercial and agricultural loans, real estate and consumer loans.

19   

numbers of shareholders, with owner-managers more likely to be majority equity stakeholders.

Thus, with concentrated ownership, private banks likely have lower agency costs as principals can

more easily monitor the actions of the managers and hence private bank managers will be less

likely to shirk monitoring effort, amongst other things. In other words, we would expect bank

capital to provide stronger monitoring incentives for public banks than private banks.

We now estimate the effect of bank capital on loan quality for public and private banks.

The results for private (public) banks are presented in columns 4-6 (7-9) of Panel A of Table 2.

The results show bank capital is positively and significantly associated with all three measures of

loan quality for public as well as private banks. While the association between bank capital and

LQ1 and LQ2 measure is not statistically different for public vs. private banks, the association

between bank capital and LQ2, the LLP based measure of loan quality is statistically more

pronounced for public banks than for private banks at 10% level of significance.

Overall, the results suggest that bank’s equity capital is positively and significantly

associated with each of the three measures of loan quality. Thus, higher the book value of equity,

lower the NPL, LLP and NCO for a bank.

4.2 Size and the association between bank capital and loan quality

Next we investigate whether the positive association between bank capital and loan quality

is different for large and small banks. While the theories that posit a relation between bank capital

and monitoring apply to all banks, we expect monitoring to be more critical to smaller banks than

for larger banks. Small banks deal with more entrepreneurial type businesses where monitoring is

more important. They have the ability to closely monitor the borrowers and their organization

structures enable them to effectively use their informational advantage that arises from the long

20   

history of lending and access to confidential information due to geographical proximity (Nakamura

1994). For example, bank uses borrower specific skills while monitoring as referred to in the

Diamond and Rajan (2000, 2001), which is more in line with the relationship lending of smaller

banks. Thus, we would expect the positive association between bank capital and loan quality to

be stronger for smaller banks relative to larger banks. However, larger bank can presumably

monitor more effectively because they have more specialized staff and/or better monitoring

technology which predicts that the association would be stronger for larger banks relative to

smaller banks. In line with the analysis in Berger and Bouwman (2009), we divided our banks as

small, medium and large with total assets less than or equal to 1 billion, between 1 billion and 3

billion and above 3 billion, respectively. Results are presented in Panel B of Table 2. The first

(next) [last] three columns present the results of the main model for the small (medium) [large]

banks. The coefficient on CAP is positive and significant at 1% for all three measures of loan

quality for small and medium size banks. The coefficient on CAP is positive and significant at

10% for LQ1 and at 5% for LQ3 and insignificant for LQ2 for large banks. The association

between CAP and LQ is statistically stronger for all three measures of LQ for small banks relative

to large banks and for LQ1 measure for medium banks relative to large banks, consistent with our

expectations. Overall, bank equity capital is positively associated with loan quality and this

association is most pronounced for small banks.

5 Bank Capital and Ex-ante Measures of Monitoring Effort

5.1 Estimation of monitoring effort

Next we turn to analysis to establish the mechanism through which bank capital affects

loan quality. We use two labor input based proxies to capture banks’ monitoring effort. Our first

21   

proxy for monitoring effort is the median adjusted ratio of salary expense to total non-interest

expense (SALEXP). Since salary expense is likely to be affected by bank size and portfolio

composition (for example, commercial loans require greater monitoring effort), we use an adjusted

measure of salary expense (ME1). Specifically, we divide our sample banks into size terciles

(small, medium and large). Then, each size tercile is further divided into terciles based on the ratio

of commercial loans to total assets for each quarter. We compute ME1 as the difference between

the SALEXP and the median for a group of banks that fall in the same tercile for size and portfolio

composition for each quarter. Our empirical results (unreported) suggest that for each size tercile,

SALEXP is increasing in the proportion of commercial loans consistent with heterogeneous

commercial loans requiring greater monitoring effort. The statistics also suggest that SALEXP is

lower for larger banks.

To estimate our second measure ME2, we regress SALEXP on control variables to filter

out the effect of non-monitoring variables on the salary expense ratio. This measured in motivated

by analysis on Coleman et al (2006). The fixed effects regression estimates for the entire time

period are reported in Table 3. Please note that the reported regressions are for information purpose

only as the actual estimation was done on a quarterly basis for each bank involving fixed effects

regression over prior four quarters (quarters t-4 to t-1) to obtain 1,973 bank specific coefficients

across 53,039 bank quarters. These bank specific coefficients, which form our proxy for the

monitoring effort (ME2) at the beginning of quarter t, represent the component of the salary

expense ratio (SALEXP) that is not explained by the impact of non-monitoring factors on salary

expense ratio.

The table includes the regression estimates for all, small, medium and large banks in

columns 1, 2, 3 and 4, respectively. The signs of the coefficients are generally consistent with the

22   

analysis in Coleman et al. Consumer loans are typically homogenous in nature and are monitored

as pools. It is more likely that banks use automated models to monitor consumer loans than unique

large sized heterogeneous commercial loans. Consistent with our expectation, commercial loans

are associated with a significantly higher salary expense while consumer loans have a negative

association with the salary expense in the first column that reports the results for all banks. The

negative coefficient of the FEE suggests that banks with a greater proportion of fee income have

a lower ratio of salary to non-interest expense. The coefficient on ROA is positive and significant

suggesting more profitable banks have higher salary expense.

5.2 Bank Capital and Monitoring Effort

While our results show a positive association between bank capital and loan quality, an

outcome of the monitoring effort, theory predicts the link between bank capital and monitoring

incentives (Mehran and Thakor 2011). Hence, we conduct tests to examine the association between

bank capital and our labor input based proxies of monitoring effort.

The results of the fixed effects regression of ME1 and ME2 on bank capital for all banks

are presented in the first two columns of Table 4. We include additional columns 4-8 to present

the results for small, medium and large banks. We do not include any additional controls in this

regression as our ME1 measure is adjusted for size and portfolio composition, and our ME2

measure is computed after controlling for non-monitoring factors that may affect our proxy. In

addition, our fixed effects estimation model mitigates the concern for any time invariant omitted

variables. The coefficient on CAP is positive and significant at the 1% level for ME1 and at 5%

level for ME2 suggesting that bank capital is positively associated with monitoring efforts for all

the banks in our sample. When we divide the sample by size, we find that the association is

23   

strongest for small banks relative to medium banks and large banks. In fact, it is insignificant for

large banks consistent with the idea that the role of monitoring discussed in the theoretical models

is more relevant to small banks rather than large banks.

To validate our ex-ante proxies of monitoring effort, we correlate them to the ex-post

proxies of monitoring effort, loan quality. We include all control variables that affect loan quality

included in our main regressions in table 2. Results are presented in Table 5. The association is

positive and significant at 1% level for both the measure ME1 and ME2 for all three measures of

loan quality LQ1, LQ2 and LQ3.

Overall, the results in table 4 and table 5 indicate that monitoring effort is the mechanism

through which bank capital is positively associated with loan quality. These results provide

empirical support the predictions of the Mehran and Thakor (2011) model. A key to their result is

that bank monitoring and capital are positively related in the cross-section. Banks with higher

capital monitor more as higher monitoring increases the survival probability of the bank and

enhances the value of the relationship loan portfolio. Our results validate the mechanism identified

in their model that the positive association between bank capital and loan quality is due to a

positive association between bank capital and monitoring effort.

6 Additional Analysis and robustness tests

6.1 Relationship Lending

In additional tests, we attempt to explore circumstances under which the monitoring effort

would be important and examine whether the association between bank capital and loan quality

(outcome of monitoring effort) is stronger in such settings. One such setting is relationship lending.

Since relationship loans require greater monitoring effort, the relation between bank capital and

24   

monitoring effort should be stronger for banks that engage more in relationship lending. Our

empirical tests show that the association between bank capital and monitoring effort for both the

input based proxies and the outcome proxies is stronger for smaller banks relative to larger banks.

Smaller banks are known to engage in higher level of relationship lending than larger banks

(Berger et al. 2005). Hence, we test the association between bank capital and monitoring

conditioned on relationship lending. Song and Thakor (2007) show that banks find it optimal to

finance relationship loans with core deposits. Berlin and Mester (1999) provide empirical evidence

that suggests that banks’ access to core deposits is the foundation of relationship lending. Based

on this insight, we use ratio of core deposits as a proxy for the extent of banks’ relationship lending.

We interact CAP with CORED, a dummy variable that takes the value 1 if the bank has

ratio of core deposits to total assets above median for the quarter; 0 otherwise. Results for the

association between bank capital and monitoring effort and bank capital and loan quality are

presented in Panel A and Panel B of Table 6, respectively. We find that the positive association

between bank capital and monitoring effort obtains only for the sample banks with higher core

deposits as evidenced by the positive and significant coefficient on the interaction between CAP

and CORE for both the measures ME1 (1% level) and ME2 (10% level) but insignificant

coefficient on the main variable CAP in the first two columns. Partitioning on size, we find that

this relation obtains most consistently for small banks, where one would expect higher level of

relationship lending for both the measures. Within small banks, it is incrementally positive for

small banks with higher core deposits. The positive association between bank capital and

monitoring effort is incrementally positive and significant for medium banks and large banks for

ME1 but not ME2.

25   

Empirical evidence presented in Panel B of Table 6 confirms the above result using the

outcome measures of loan quality. The association between bank capital and LQ1 and LQ3 is

stronger for banks that engage in relationship lending. This is especially true for small banks where

one would expect the association to be stronger. We do not find consistent results for medium and

large banks. Overall, the results in Panel A and B of Table 6 confirm our conjecture that the

positive association between bank capital and monitoring effort is stronger for banks that engage

in relationship lending, where the role of monitoring is important.

6.2 Banks that issue new capital

Next we examine the association between change in capital and change in monitoring effort

and loan quality (outcome). The changes model allows the firm to act as its own control and

minimizes the concerns relating to omitted variable. Over our sample period, 443 banks (1944

bank-quarters) made secondary equity offerings. We first regress change in in our two monitoring

proxies on a dummy variable, NEWCAP, that takes the value of 1 for banks that issued equity

capital; 0 otherwise. While we do not expect sudden and immediate improvement in monitoring

effort and loan quality within one quarter, we do not have any theory or prior empirical evidence

to guide over what time period we should expect the improvement, if any. Hence, we compute the

change over a period of 4 quarters, 8 quarters and 12 quarters. Results relating to monitoring effort

are presented in Panel C and results relating to loan quality are presented in Panel D of Table 6.

The results in Panel C show that banks that raise capital show a significant increase in monitoring

effort after one year for both the measures ME1 and ME2. Assuming that increased monitoring

efforts will result in improved loan quality with a lag, we find that banks that increase bank capital

show improvement in NPL after 2 years and in each of three measures after three years controlling

for concurrent changes in control variables. Overall, these results suggest that an increase in

26   

capital is followed by an increased investment in monitoring effort as well as improved loan

quality.

6.3 Robustness tests

We conduct additional robustness tests to check the sensitivity of our results to financial

crisis, bank participation in the OTD market and loan performance.

Financial Crisis

We repeat the analysis in our main tests for all the banks in our sample to explore the effect

of the financial crisis. The financial crisis of 2007-2008 saw unprecedented losses in the banking

industry. We interact our main variable of interest CAP with CRISIS, a dummy variable equal to

1 if the bank quarter observation falls during the financial crisis period, and zero otherwise. We

define financial crisis period as the quarters spanning 2007Q2 to 2008Q4. Results (unreported)

show that the coefficient on CAP is positive and significant across all three LQ measures.

However, the coefficient on the interaction between CAP*CRISIS is not significant suggesting

that the positive association between bank capital and loan quality is more general and is not

sensitive to the crisis period.

OTD market

Purnanandam (2011) shows that banks with greater involvement in the OTD market during the

pre-crisis period did not expend enough resources in screening their borrowers and originated

excessively poor-quality mortgages, and therefore had higher NPLs and NCOs on these mortgages.

Furthermore, this effect is stronger for capital-constrained banks. Therefore, we repeat our analysis

to explore whether the association between bank capital and loan quality is different for banks that

participate in the OTD market vs. banks that do not. The OTD data is available from 2006Q2. As

27   

a result, the sample size is reduced to 26,563 in these tests. We interact CAP with OTD, a dummy

variable that takes the value 1 if that bank participates in the OTD market; 0 otherwise. The

(unreported) results show that while the coefficient on CAP is positive and significant across all

three measures of loan quality, we do not find evidence that this association between bank capital

and loan quality is different for banks that participate in the OTD market vs. banks that do not as

the coefficient on the interaction term is not significant. This result differs from Purnanandam

(2011) in that he finds that participation in the OTD adversely affected screening and monitoring

of mortgage loans. However, there are some important differences between our analysis and

Purnanandam (2011). First, Purnanandam (2011)’s focus is on whether securitization of mortgage

loans weakened screening incentives and hence he measures NLP and NCO for mortgage loans.

Second, his analysis is conducted over a shorter time period of seven quarters around the financial

crisis. However, with the benefit of time, we are able to examine a longer time period (from

2006Q2 to 2015Q1 for this test) and since we are interested in the broader question of the impact

of bank capital on loan quality, we use portfolio level loan quality measure instead of quality of

mortgage loans.

Loan Performance

It is possible that the positive association between bank capital and loan quality may be

obtained due to underlying risk preferences of the bank management. Bank managers may choose

high leverage and at the same time engage in risky loans. Similarly, bank managers may be risk

averse and may choose low leverage and engage in loans that are less risky. While our control

variables that capture risk and our fixed effects estimation (to control for time invariant omitted

variables) should address this concern, we run additional tests to control for loan pricing using

interest income on loans. Our results are robust to the inclusion of this additional control.

28   

Regulatory capital

Our analysis has used book value of equity as the proxy for bank capital. However, the

regulators view bank capital as the amount of equity the bank chooses to finance itself with but in

broader terms. Regulatory capital includes other sources of financing such as preferred stock and

adjustments. We have replicated our analysis using Tier One Capital ratio of the bank and find that

all of the results are robust to the use of Tier 1 Capital ratio instead of equity capital.

7 Conclusions

There is theoretical disagreement on whether higher capital incentivizes the bank to

monitor its borrowers or impedes monitoring by shielding the bank from market discipline

imposed by high leverage. One view posits that higher bank capital dissipates value as higher

capital shields manager from market discipline by lessening the discipline imposed by deposit

financing as equity capital lacks the sequential service feature of deposits (Calomiris and Kahn,

1991). Thus, higher capital may result in underinvestment in monitoring (Diamond and Rajan,

2001). An alternate perspective suggests that bank capital strengthens a banks’ monitoring

incentives as shareholders of well capitalized banks have more to lose from bank failure (Holstrom

and Tirole, 1997). In particular, higher capital improves survival probability of the bank and

thereby increases the odds that the manager will be able to harvest the gains from prior investment

in monitoring, which in turn further strengthens monitoring incentives (Mehran and Thakor, 2011).

Given the theoretical disagreement, empirical evidence on this issue is particularly important.

Moreover, the issue of costs and benefit of bank capital has become particularly controversial in

recent years with some advocating much higher capital requirements (Admati et al, 2010) while

others suggesting that higher capital imposes a variety of costs.

29   

We empirically examine the association between bank capital and loan quality (an outcome

of monitoring effort) as well as between capital and two proxies for labor input into monitoring

effort. We find that bank capital is positively associated with three measures of loan quality- non-

performing loans, loan loss provisions and net charge-offs, all outcomes of the monitoring process.

This association obtains in the financial crisis period as well as the non-crisis period, whether the

bank participates in the OTD market or not, and whether it is publicly listed or private.

Furthermore, we provide evidence on the mechanism by which bank capital affects loan quality.

Using two salary expense based ex-ante measures of monitoring effort that capture the labor input

into the monitoring process, we document that monitoring effort is the mechanism through which

the bank capital affects loan quality. Overall, our evidence is consistent with predictions in Mehran

and Thakor (2011) that bank capital improves monitoring incentives and hence is positively related

to value in the cross-section. Additional tests show that the association between capital and loan

quality is stronger for smaller banks and banks with higher proportion of relationship loans. We

also find that there is increase in monitoring effort and loan quality for banks that issue capital

during our sample period.

We recognize that our evidence shows a cross-sectional association between bank capital

and loan quality and hence are careful not to draw any inference regarding optimal capital structure

of banks. However, our evidence is important as it provides large sample evidence on an important

benefit of bank capital – its favorable impact of monitoring incentives and loan quality, which is

an important component of bank value.

30  

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Appendix A Definitions of Variables and Other Acronyms VARIABLE DESCRIPTION SOURCE ABSGAP Absolute value of one-year maturity gap deflated by total assets Y9C - bhck3197,bhck3296, bhck3298, bhck3409, bhck2170 COMM Agricultural and commercial loans deflated by total assets Y9C - bhck1763, bhck1764, bhck1590, bhck2170 CONS Consumer loans deflated by total assets Y9C - bhck2008, bhck2011, bhckb538, bhckb539, bhck2170 CORE Deposits less than $100,000 deflated by total assets. Y9C - bhcb6648, bhod6648, bhck2170 CORED Dummy variable takes the value 1 if core deposits are above the quarter

median; 0 otherwise.

DELTAGDP GDP growth rate St Louis Federal Reserve website https://research.stlouisfed.org/fred2/ EBLLP Earnings before provision deflated by total assets Y9C - bhck4300, bhck4230, bhck2170 EFF Ratio of non-Interest Expense to income before extraordinary items Y9C - bhck4093, bhck4300, bhck2170 CAP Equity Capital deflated by total assets Y9C - bhck3210, bhck2170 FEE Ratio of fee income to total income (interest income and non-interest

income) Y9C - bhck4079, bhck4107, bhck4079

LARGE Banks with total assets more than $3 billion LG Change in loans deflated by total assets Y9C - bhck2122, bhck2170 LIQUID Bank’s liquid assets deflated by total assets Y9C - bhck0081, bhck0395, bhck0397, bhck1773, bhck1350, bhck0276,bhck0277,

bhdmB987, bhdmB989, bhck2170 LLP Provision deflated by total assets Y9C - bhck4230, bhck2170 LOGEMP Log of total number of employees Y9C - bhck4150 LQ1 NPL multiplied by -1 LQ2 LLP multiplied by -1 LQ3 NCO multiplied by -1 ME1 Adjusted monitoring effort computed as the difference between SALEXP

and MEDIAN SALEXP for the banks that belong to the same tercile based on COMM for small, medium and large banks each quarter

ME2 Adjusted monitoring effect computed as per Coleman, Esho & Sharpe (2006)

MEDIUM Banks with total assets more than $1 billion and less than or equal to $3 billion

NCO Net charge-offs deflated by total assets Y9C - bhck4635, bhck4605, bhck2170 NEWCAP Dummy variable equal to 1 if bank issued equity capital in that quarter Y9C - bhck3577 NPL Non-Performing Loans deflated by Total Assets Y9C - bhck5525,bhck5526, bhck2170 PUBLIC Dummy variable=1 if bank is a public bank; 0 otherwise CRSP-FRB link file from

https://www.newyorkfed.org/research/banking_research/datasets.html RE Real estate loans deflated by total assets Y9C - bhck1410, bhck2170 ROA Net Income deflated by total assets Y9C - bhck4300, bhck2170 SALEXP Ratio of salary expense to total non-interest expense Y9C - bhck4135,bhck4093 SIZE Log of total assets Y9C - bhck2170 SMALL Banks with total assets less than or equal to $1 billion TA Total assets Y9C - bhck2170 TRANDEP Transaction deposits deflated by total assets bhcb2210, bhcb3187, bhcb2389, bhod3189 , bhod3187, bhod2389, bhdm6631,

bhdm6636, bhfn6631, bhfn6636, bhck2170

34  

Table 1 Sample Selection and Descriptive Statistics

Panel A: Sample selection

ALL PUBLIC PRIVATE

#BANK-

QUARTERS #BANKS

#BANK- QUARTERS

#BANKS #BANK-

QUARTERS #BANKS

Y9C filings with non-missing total assets and net income between 1994Q1-2015Q1 123,305 3,887 30,777 584 92,528 3,303 After deleting banks with total assets < 500 million 62,906 2,119 23,565 504 39,341 1,615 After deleting banks with missing data (and using 1994Q1 for lag) 55,681 2,006 21,225 503 34,456 1,503 Final sample after deleting banks with negative equity capital 55,492 2,006 21,174 503 34,318 1,503

Panel B: Summary statistics

ALL PUBLIC PRIVATE VARIABLES N MEAN MEDIAN STDDEV N MEAN MEDIAN STDDEV N MEAN MEDIAN STDDEV NPLt 55,492 0.011 0.006 0.015 21,174 0.010 0.005 0.014 34,318 0.012 0.007 0.016

LLPt 55,492 0.001 0.001 0.002 21,174 0.001 0.001 0.002 34,318 0.001 0.000 0.002

NCOt 55,492 0.001 0.000 0.002 21,174 0.001 0.000 0.002 34,318 0.001 0.000 0.002

CAPt-1 55,492 0.092 0.088 0.030 21,174 0.091 0.088 0.024 34,318 0.092 0.087 0.032

SALEXPt-1 55,492 0.524 0.534 0.082 21,174 0.519 0.528 0.076 34,318 0.526 0.539 0.086

ME2t-1 55,492 (0.008) - 0.075 21,174 (0.008) - 0.071 34,318 (0.008) - 0.078

LGt-1 55,492 (0.000) 0.001 0.023 21,174 0.000 0.001 0.021 34,318 (0.000) 0.001 0.024

TAt-1(inbillions) 55,492 16.489 1.123 112.445 21,174 29.095 2.022 167.438 34,318 8.712 0.917 54.675

REt-1 55,492 0.460 0.470 0.161 21,174 0.454 0.460 0.153 34,318 0.464 0.476 0.166

COMMt-1 55,492 0.119 0.105 0.078 21,174 0.120 0.106 0.074 34,318 0.118 0.104 0.080

CONSt-1 55,492 0.046 0.022 0.063 21,174 0.053 0.032 0.060 34,318 0.042 0.016 0.064

LIQUIDt-1 55,492 0.242 0.226 0.115 21,174 0.235 0.222 0.103 34,318 0.247 0.230 0.121

ABSGAPt-1 55,492 0.166 0.136 0.131 21,174 0.160 0.133 0.125 34,318 0.170 0.139 0.134

EFFt-1 55,492 3.712 2.821 11.639 21,174 3.571 2.738 9.229 34,318 3.799 2.892 12.903

FEEt-1 55,492 0.178 0.153 0.130 21,174 0.185 0.160 0.119 34,318 0.174 0.148 0.136

DELTAGDPt 55,492 4.145 4.700 2.918 21,174 4.374 4.700 2.904 34,318 4.004 4.600 2.918

EBLLPt 55,492 0.003 0.003 0.002 21,174 0.003 0.003 0.002 34,318 0.003 0.003 0.003

TRANDEPt-1 55,492 0.580 0.579 0.155 21,174 0.585 0.580 0.151 34,318 0.576 0.578 0.158

ROAt-1 55,492 0.002 0.002 0.003 21,174 0.002 0.003 0.003 34,318 0.002 0.002 0.003

LOGEMPt-1 55,492 6.215 5.778 1.372 21,174 6.755 6.315 1.486 34,318 5.882 5.557 1.180

NEWCAPt-1 55,492 0.035 - 0.183 21,174 0.045 - 0.208 34,318 0.028 - 0.165

COREt-1 55,492 0.187 0.182 0.099 21,174 0.181 0.178 0.097 34,318 0.190 0.185 0.101

35  

Panel C: Summary statistics small, medium and large banks

SMALL MEDIUM LARGE VARIABLES N MEAN MEDIAN STDDEV N MEAN MEDIAN STDDEV N MEAN MEDIAN STDDEV NPLt 24,650 0.012 0.006 0.016 17,394 0.012 0.006 0.016 13,448 0.010 0.006 0.014

LLPt 24,650 0.001 0.000 0.002 17,394 0.001 0.001 0.002 13,448 0.001 0.001 0.002

NCOt 24,650 0.001 0.000 0.002 17,394 0.001 0.000 0.002 13,448 0.001 0.001 0.002

CAPt-1 24,650 0.090 0.087 0.028 17,394 0.091 0.087 0.030 13,448 0.094 0.089 0.031

SALEXPt-1 24,650 0.538 0.544 0.073 17,394 0.525 0.536 0.082 13,448 0.496 0.513 0.092

ME2t-1 24,650 (0.005) - 0.068 17,394 (0.009) - 0.075 13,448 (0.013) - 0.087

LGt-1 24,650 0.000 0.001 0.024 17,394 (0.000) 0.001 0.023 13,448 0.000 0.001 0.022

TAt-1(inbillions) 24,650 0.701 0.675 0.138 17,394 1.651 1.497 0.545 13,448 64.621 9.595 221.626

REt-1 24,650 0.498 0.503 0.143 17,394 0.476 0.486 0.153 13,448 0.370 0.376 0.169

COMMt-1 24,650 0.114 0.098 0.075 17,394 0.117 0.100 0.078 13,448 0.130 0.121 0.081

CONSt-1 24,650 0.037 0.018 0.053 17,394 0.041 0.017 0.058 13,448 0.069 0.047 0.078

LIQUIDt-1 24,650 0.244 0.230 0.114 17,394 0.244 0.229 0.114 13,448 0.238 0.216 0.116

ABSGAPt-1 24,650 0.151 0.120 0.124 17,394 0.157 0.127 0.127 13,448 0.207 0.189 0.139

EFFt-1 24,650 3.954 2.947 12.536 17,394 3.668 2.807 11.893 13,448 3.328 2.678 9.368

FEEt-1 24,650 0.152 0.134 0.109 17,394 0.167 0.148 0.120 13,448 0.240 0.206 0.155

DELTAGDPt 24,650 4.045 4.600 2.956 17,394 4.068 4.600 2.937 13,448 4.428 4.800 2.805

EBLLPt 24,650 0.003 0.003 0.002 17,394 0.003 0.003 0.003 13,448 0.004 0.003 0.003

TRANDEPt-1 24,650 0.561 0.555 0.145 17,394 0.585 0.582 0.152 13,448 0.606 0.618 0.173

ROAt-1 24,650 0.002 0.002 0.003 17,394 0.002 0.002 0.003 13,448 0.002 0.003 0.003

LOGEMPt-1 24,650 5.298 5.298 0.429 17,394 6.019 6.009 0.545 13,448 8.151 7.876 1.309

NEWCAPt-1 24,650 0.024 - 0.154 17,394 0.036 - 0.186 13,448 0.052 - 0.222

COREt-1 24,650 0.211 0.207 0.096 17,394 0.188 0.183 0.096 13,448 0.141 0.131 0.094

Panel A of this table reports the sample selection process. Panel B of this table reports the mean, median and standard deviation for the primary variables used in this study for all banks, public banks and private banks. Panel C reports the descriptive statistics for the small, medium and large banks. Variables are defined in Appendix A.

36  

Table 2 Capital and Loan Quality

PANEL A: Association between Capital and Loan Quality

ALL PUBLIC PRIVATE VARIABLES LQ1t LQ2t LQ3t LQ1t LQ2t LQ3t LQ1t LQ2t LQ3t (1) (2) (3) (4) (5) (6) (7) (9) (9) CAPt-1 0.103*** 0.007*** 0.009*** 0.107*** 0.009*** 0.009*** 0.109*** 0.006*** 0.009*** (8.592) (5.166) (6.895) (5.593) (4.326) (4.793) (7.309) (3.331) (5.387) LGt-1 0.017*** 0.002*** 0.002*** 0.014*** 0.002** 0.002*** 0.019*** 0.002*** 0.002*** (6.711) (3.498) (5.221) (3.112) (2.276) (2.771) (6.138) (2.661) (4.444) SIZEt-1 -0.000 -0.001*** -0.000*** -0.002* -0.000*** -0.000*** 0.001 -0.001*** -0.000* (-0.711) (-7.333) (-3.519) (-1.777) (-4.972) (-2.640) (1.346) (-5.290) (-1.950) REt-1 -0.007* -0.001*** -0.001** -0.014** -0.002*** -0.001** -0.002 -0.001* -0.001 (-1.722) (-3.641) (-2.508) (-2.119) (-3.189) (-2.300) (-0.415) (-1.880) (-1.361) COMMt-1 -0.000 -0.002*** -0.001* -0.006 -0.002*** -0.001* 0.003 -0.002** -0.001 (-0.043) (-3.258) (-1.865) (-0.650) (-2.982) (-1.675) (0.360) (-2.142) (-1.279) CONSt-1 -0.012** -0.004*** -0.004*** 0.009 -0.002* -0.002** -0.027*** -0.005*** -0.006*** (-2.071) (-4.990) (-5.806) (1.108) (-1.752) (-2.485) (-3.884) (-4.916) (-5.473) LIQUIDt-1 0.006* 0.001*** 0.000 0.008 0.001*** 0.001** 0.004 0.001** 0.000 (1.720) (3.039) (1.195) (1.485) (2.989) (2.343) (1.024) (2.021) (0.059) ABSGAPt-1 0.003* 0.000** 0.000** -0.000 0.000 0.000 0.005** 0.000* 0.000** (1.764) (2.288) (2.292) (-0.051) (1.146) (0.952) (2.492) (1.905) (2.218) EFFt-1 0.000*** 0.000*** 0.000*** 0.000 0.000*** 0.000*** 0.000*** 0.000** 0.000** (2.996) (3.004) (2.941) (1.568) (2.979) (3.223) (2.662) (2.036) (2.001) FEEt-1 0.007** 0.001*** 0.001*** 0.008** 0.002*** 0.002*** 0.006 0.001** 0.001** (2.498) (3.427) (3.386) (2.455) (3.007) (3.052) (1.616) (2.233) (2.198) DELTAGDPt-1 0.001*** 0.000*** 0.000*** 0.000** 0.000*** 0.000*** 0.001*** 0.000*** 0.000*** (4.710) (5.237) (5.339) (2.357) (5.245) (4.914) (4.312) (2.734) (3.263) EBLLPt-1 0.005 0.044*** 0.036 0.049*** -0.018 0.039** (0.318) (3.452) (1.472) (2.612) (-0.850) (2.268) QTRDUMMIES YES YES YES YES YES YES YES YES YES BANKFE YES YES YES YES YES YES YES YES YES N 55,492 55,492 55,492 21,174 21,174 21,174 34,318 34,318 34,318 ADJ.R-SQ. 0.364 0.234 0.225 0.457 0.300 0.290 0.316 0.205 0.195

37  

PANEL B: Size and Association between Capital and Loan Quality SMALL MEDIUM LARGE VARIABLES LQ1t LQ2 t LQ3 t LQ1t LQ2 t LQ3 t LQ1t LQ2 t LQ3 t (1) (2) (3) (4) (5) (6) (7) (8) (9) CAPt-1 0.171*** 0.009*** 0.014*** 0.132*** 0.007*** 0.011*** 0.039* 0.003 0.005** (7.859) (4.565) (6.212) (6.508) (2.884) (4.654) (1.751) (1.271) (2.030) LG t-1 0.015*** 0.001* 0.002*** 0.015*** 0.001 0.002** 0.012* 0.002* 0.002*** (4.261) (1.755) (3.113) (3.447) (1.099) (2.247) (1.847) (1.815) (2.590) SIZE t-1 0.006*** -0.001*** -0.000 -0.003 -0.001*** -0.001*** 0.001 -0.000* 0.000 (3.266) (-3.872) (-0.201) (-1.372) (-5.641) (-3.095) (0.916) (-1.781) (0.388) RE t-1 -0.004 -0.002* -0.001* -0.002 -0.002*** -0.001** -0.021** -0.001** -0.001 (-0.509) (-1.714) (-1.808) (-0.297) (-3.107) (-2.125) (-2.312) (-1.997) (-1.330) COMM t-1 -0.004 -0.005*** -0.003** 0.013 -0.002* -0.001 -0.010 -0.002 -0.001 (-0.394) (-3.348) (-2.288) (1.130) (-1.843) (-1.122) (-1.067) (-1.513) (-1.181) CONS t-1 -0.016* -0.003** -0.002** -0.014 -0.002** -0.003*** -0.002 -0.005*** -0.005*** (-1.844) (-2.280) (-2.527) (-1.326) (-2.485) (-3.824) (-0.249) (-3.097) (-3.750) LIQUID t-1 0.002 0.001 -0.000 0.010* 0.001* 0.000 0.004 0.001** 0.001 (0.292) (1.289) (-0.199) (1.672) (1.721) (0.798) (0.528) (2.363) (1.218) ABSGAP t-1 0.003 0.000** 0.000** 0.005** 0.001* 0.001** 0.001 0.000 -0.000 (1.239) (1.996) (2.199) (2.181) (1.685) (2.206) (0.176) (0.281) (-0.102) EFF t-1 0.000 0.000 0.000 0.000** 0.000* 0.000* 0.000 0.000*** 0.000*** (1.270) (0.705) (0.538) (2.308) (1.911) (1.956) (1.384) (3.774) (3.737) FEE t-1 0.012*** 0.001* 0.001** -0.000 0.001* 0.001** 0.008** 0.001** 0.002*** (3.951) (1.886) (2.082) (-0.077) (1.958) (2.061) (2.005) (2.326) (2.613) DELTAGDP t-1 0.001*** 0.000 0.000*** 0.001** -0.000 0.000 0.001*** 0.000*** 0.000*** (5.148) (1.589) (3.206) (2.023) (-0.078) (1.231) (3.507) (6.617) (6.200) EBLLP t-1 -0.034 0.031 0.028 0.067*** 0.014 0.014 (-1.289) (1.513) (1.060) (3.081) (0.449) (0.654) QTR DUMMIES YES YES YES YES YES YES YES YES YES BANK FE YES YES YES YES YES YES YES YES YES N 24,650 24,650 24,650 17,394 17,394 17,394 13,448 13,448 13,448 ADJ. R-SQ. 0.345 0.185 0.173 0.353 0.230 0.227 0.426 0.322 0.307

Notes: Panel A of this table reports the results of the fixed effects regression of loan quality measures on bank capital and other control variables for all the banks in the sample period 1994Q2-2015Q1 in columns 1-3, for public banks in columns 4-6 and private banks in columns 7-9. Panel B of this table reports the results of the fixed effects regression for small, medium and large banks in columns 1-3, columns 4-6 and columns 7-9, respectively. Variables are defined in Appendix A. T-statistics are in parenthesis. All standard errors are robust, clustered by bank. Levels of significance are denoted as follows: * if p<0.10; ** if p<0.05; *** if p<0.01.

38  

Table 3 Estimation of Monitoring Effort based on Coleman et al. 2006

ALL SMALL MEDIUM LARGE VARIABLES SALEXPt-1 SALEXPt-1 SALEXPt-1 SALEXPt-1 (1) (2) (3) (4) REt-1 0.028* 0.042** 0.045* -0.013 (1.879) (2.570) (1.950) (-0.471) COMMt-1 0.077*** 0.030 0.089* 0.072 (3.035) (1.019) (1.904) (1.625) CONSt-1 -0.151*** -0.075** -0.152*** -0.199*** (-4.679) (-2.158) (-3.349) (-3.304) FEEt-1 -0.103*** -0.092*** -0.073*** -0.143*** (-7.076) (-5.112) (-4.052) (-5.105) TRANDEPt-1 0.014 0.008 0.030 0.020 (0.969) (0.549) (1.284) (0.672) SIZEt-1 -0.032*** -0.002 -0.011 -0.042*** (-3.760) (-0.203) (-0.870) (-4.092) ROAt-1 8.428*** 7.074*** 8.657*** 9.374*** (35.198) (20.527) (24.409) (17.074) LOGEMP 0.032*** 0.042*** 0.015 0.046*** (3.044) (5.277) (0.923) (3.735) QTR DUMMIES YES YES YES YES BANK FE YES YES YES YES N 55,492 24,650 17,394 13,448 ADJ. R-SQ. 2,006 1,413 910 538

This table reports the results of the fixed effects regression of the salary expense ratio (SALEXP) on the non-monitoring factors for all, small, medium and large banks for the entire time period. These regressions for the pooled sample are given for the sake of brevity. We estimate monitoring effort for the quarter using rolling regressions. To estimate monitoring effort for the quarter by bank, we regress the SALEXP (salary expense ratio) on a set of control variables for the previous four quarters (t-1 to t-4). The regression produces bank specific constants which serve as our proxy for the ME2 by that bank at the beginning of quarter t. We run the regressions separately for small, medium and large banks. Variables are defined in Appendix A. T-statistics are in parenthesis. All standard errors are robust, clustered by bank. Levels of significance are denoted as follows: * if p<0.10; ** if p<0.05; *** if p<0.01.

39  

Table 4

Capital and Ex-ante Measures of Monitoring Effort

ALL SMALL MEDIUM LARGE

VARIABLES ME1t-1 ME2t-1 ME1t-1 ME2t-1 ME1t-1 ME2t-1 ME1t-1 ME2t-1

(1) (2) (3) (4) (5) (6) (7) (8)

CAPt-1 0.233*** 0.102** 0.513*** 0.316*** 0.386*** 0.127* -0.057 -0.057

(4.052) (2.057) (7.302) (4.692) (4.369) (1.887) (-0.562) (-0.523)

QTR DUMMIES YES YES YES YES YES YES YES YES

BANK FE YES YES YES YES YES YES YES YES

N 55,492 24,650 17,394 13,448 53,039 24,388 17,100 11,551

R-SQ. 0.013 0.003 0.031 0.229 0.032 0.126 0.0316 0.006

This table reports the results of the fixed effects regression of ex-ante measures of monitoring effort (ME1 and ME2) on bank capital. Variables are defined in Appendix A. T-statistics are in parenthesis. All standard errors are robust, clustered by bank. Levels of significance are denoted as follows: * if p<0.10; ** if p<0.05; *** if p<0.01.

40  

Table 5

Validation of Ex-ante measure of Monitoring Effort - Association between Loan Quality and Monitoring Effort

ME=ME1 ME=ME2

VARIABLES LQ1t LQ2 t LQ3 t LQ1t LQ2 t LQ3 t

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

ME t-1 0.044*** 0.004*** 0.004*** 0.013*** 0.001*** 0.001*** (15.958) (12.154) (14.157) (7.297) (3.679) (5.321)

CONTROL VARIABLES YES YES YES YES YES YES QTR DUMMIES YES YES YES YES YES YES BANK FE YES YES YES YES YES YES

N 55,492 55,492 55,492 53,039 53,039 53,039

ADJ. R-SQ. 0.384 0.241 0.234 0.353 0.233 0.221

This table reports results for the regressing of loan quality on ex-ante measures of monitoring effort computed in Table 4, controlling for other determinants of loan quality. . Variables are defined in Appendix A. T-statistics are in parenthesis. All standard errors are robust, clustered by bank. Levels of significance are denoted as follows: * if p<0.10; ** if p<0.05; *** if p<0.01.

41  

TABLE 6

Panel A: Monitoring Effort and Relationship Lending ALL SMALL MEDIUM LARGE

VARIABLES ME1t-1 ME2t-1 ME1t-1 ME2t-1 ME1t-1 ME2t-1 ME1t-1 ME2t-1

(1) (2) (3) (4) (5) (6) (7) (8)

CAPt-1 0.071 0.054 0.348*** 0.200*** 0.192* 0.065 -0.118 -0.017

(1.018) (0.868) (4.307) (2.676) (1.833) (0.717) (-1.018) (-0.136)

CAP*CORED t-1 0.366*** 0.097* 0.284*** 0.199*** 0.407*** 0.141 0.252** -0.162

(5.493) (1.811) (3.851) (2.941) (3.457) (1.259) (1.974) (-0.908)

CORED t-1 -0.043*** -0.015** -0.033*** -0.023*** -0.048*** -0.014 -0.031** 0.002

(-6.867) (-2.474) (-4.747) (-3.506) (-4.539) (-1.359) (-2.493) (0.085)

QTR DUMMIES YES YES YES YES YES YES YES YES

BANK DUMMIES YES YES YES YES YES YES YES YES

N 55,492 53,039 24,650 24,388 17,394 17,100 13,448 11,551 R-SQ. 0.013 0.032 0.003 0.126 0.031 0.0316 0.229 0.006

Panel B: Loan Quality, Capital and Relationship Lending ALL SMALL MEDIUM LARGE VARIABLES LQ1t LQ2t LQ3t LQ1t LQ2t LQ3t LQ1t LQ2t LQ3t LQ1t LQ2t LQ3t (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) CAPt-1 0.074*** 0.006*** 0.007*** 0.144*** 0.008*** 0.011*** 0.105*** 0.009*** 0.011*** 0.022 0.003 0.004** (6.479) (4.700) (5.596) (6.916) (3.480) (4.446) (5.443) (3.070) (4.269) (1.145) (1.396) (1.978) CAP*COREDt-1 0.061*** 0.000 0.003** 0.043** 0.001 0.005** 0.045 -0.005* -0.002 0.082** 0.002 0.005 (3.823) (0.199) (2.018) (2.173) (0.481) (2.302) (1.376) (-1.707) (-0.554) (2.112) (0.568) (1.300) COREDt-1 -0.010*** -0.000*** -0.001*** -0.007*** -0.000 -0.001*** -0.008*** 0.000 -0.000 -0.014*** -0.001** -0.001*** (-6.374) (-3.029) (-4.546) (-3.328) (-1.539) (-3.101) (-2.826) (0.057) (-0.873) (-3.303) (-2.462) (-2.943) CONTROLS YES YES YES YES YES YES YES YES YES YES YES YES QTRDUMMIES YES YES YES YES YES YES YES YES YES YES YES YES BANKDUMMIES YES YES YES YES YES YES YES YES YES YES YES YES N 55,492 55,492 55,492 24,650 24,650 24,650 17,394 17,394 17,394 13,448 13,448 13,448 DJ.R-SQ. 0.378 0.238 0.230 0.350 0.186 0.175 0.364 0.233 0.231 0.454 0.332 0.320

42  

Panel C: Changes in Monitoring Effort for banks that issued Capital

Δ IS MEASURED OVER 4 quarters 8 quarters 12 quarters VARIABLES ΔME1t-1 ΔME2t-1 ΔME1t-1 ΔME2t-1 ΔME1t-1 ΔME2t-1 (1) (2) (3) (4) (5) (6) NEWCAPt-1 0.009*** 0.009*** 0.010*** 0.004 0.011*** ‐0.002 (3.691) (3.095) (3.253) (0.882) (3.188) (‐0.564) QTR DUMMIES YES YES YES YES YES YES BANK FE YES YES YES YES YES YES N 47,037 44,362 40,006 37,462 33,985 32,006 R-SQ. 0.005 0.002 0.006 0.003 0.005 0.003

Panel D: Changes in Loan Quality Measures for banks that issued Capital

Δ IS MEASURED OVER 4 quarters 8 quarters 12 quarters VARIABLES ΔLQ1t ΔLQ2t ΔLQ3t ΔLQ1t ΔLQ2t ΔLQ3t ΔLQ1t ΔLQ2t ΔLQ3t (1) (2) (3) (4) (5) (6) (7) (8) (9) NEWCAPt-1 0.000 -0.001 -0.001 0.002** -0.000 -0.000 0.004*** 0.001** 0.001* (0.584) (-1.415) (-1.630) (2.043) (-0.004) (-0.272) (3.511) (2.138) (1.891) ΔCONTROLS YES YES YES YES YES YES YES YES YES QTR DUMMIES YES YES YES YES YES YES YES YES YES BANK FE YES YES YES YES YES YES YES YES YES N 47,037 47,037 47,037 40,006 40,006 40,006 33,985 33,985 33,985 ADJ. R-SQ. 0.180 0.341 0.346 0.284 0.352 0.358 0.341 0.360 0.364

Table 7 reports the results for the additional tests. Panel A reports the results of the regression of the ex-ante measures of monitoring effort (ME1 and ME2) on bank capital conditional on the level of CORE deposits in the bank. CORED is a dummy variable that takes the value 1 if the level of core deposits for the bank is above the quarter median; 0 otherwise. Panel B reports the results for the regression of loan quality on bank capital, controlling for other variables that affect loan quality. Panel C reports the results for the changes model for the regression of the ex-ante measures of monitoring effort (ME1 and ME2). The ME variable is differenced over 4 quarters, 8 quarters and 12 quarters, respectively. The variable NEWCAP is a dummy variable that takes value 1 if the bank issued new equity capital; 0 otherwise. Panel D reports the results for the changes model for the regression of loan quality on bank capital, controlling for other variables that affect loan quality. The three loan quality variables and all the control variables are differenced over 4 quarters, 8 quarters and 12 quarters, respectively.