Download Bank Capital and Monitoring: Evidence from Loan Quality Gauri

Bank Capital and Monitoring: Evidence from Loan Quality
Gauri Bhat
Cox School of Business
Southern Methodist University
[email protected]
(214) 768-2964
Hemang Desai
Cox School of Business
Southern Methodist University
[email protected]
(214) 768-3185
December 2016
JEL classification: G21, G32, M41
Key words: Bank capital, bank monitoring, loan quality.
We would like to thank Viral Acharya, Christa Bouwman, Charles Calomiris, Mike Davis, Amar
Gande, Doug Hanna, Urooj Khan, Christoffer Koch, Srini Krishnamurthy, Scott MacDonald,
Shiva Rajgopal, Anjan Thakor, Senyo Tse, Vijay Yerramilli and seminar participants at Columbia
University, Southern Methodist University, Texas A&M University, University of California at
Irvine, University of North Texas and the University of Texas Arlington for comments and
discussions.
Bank Capital and Monitoring: Evidence from Loan Quality
ABSTRACT
There are two competing theoretical perspectives on whether bank capital improves or adversely
affects banks’ monitoring incentives. We show that bank capital is positively associated with loan
quality, an outcome of bank’s monitoring effort, and two salary-based ex-ante measures of
monitoring effort that capture quality and quantity of labor input into monitoring. The evidence is
robust to a battery of additional tests including an instrumental variable approach. Overall, our
evidence is consistent with the prediction in Mehran and Thakor (2011) that bank capital
strengthens monitoring incentives which in turn increases loan quality and hence the value of its
loan portfolio.
1 A unique aspect of banks’ capital structure is its fragility – i.e., that banks are primarily
financed by debt that is in form of demand deposits and this fragility is further exacerbated by the
fact that banks are highly levered, i.e., they use little equity capital.1 Theory provides two
competing views on bank capital structure. One view suggests that higher equity capital insulates
bank managers from market discipline by deposit holders and can potentially exacerbate agency
problems at banks leading to lower investment in monitoring, reduced efficiency and lower
liquidity creation (Diamond and Rajan, 2000 and Kashyap, Rajan and Stein, 2008). An alternate
view is that higher capital strengthens banks’ monitoring incentives resulting in better risk
management and hence higher capital is positively associated with value of banks’ loan portfolio
(Holmstrom and Tirole 1997 and Mehran and Thakor 2011). Given the competing theoretical
predictions on the role of bank capital, 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.
While, monitoring effort is unobservable, a bank that monitors its borrowers more, should have
higher loan quality, ceteris paribus. Hence, 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 two salary based ex-ante measures of monitoring effort that
capture quality and quantity of labor input into monitoring. Thus, we provide evidence on the
mechanism by which bank capital is predicted to affect value – via its effect on monitoring
incentives.
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 adjustments relating to goodwill, intangible assets, accumulated
other comprehensive income amongst other things. We conduct robustness tests using Tier 1 regulatory capital in
Section 4.
2 The rationale for a fragile capital structure that is financed with demand deposits and little
equity capital has been articulated in several papers (Calomiris and Kahn, 1991, Diamond and
Rajan, 2000 and 2001, Kashyap, Rajan and Stein, 2008). A key argument in these papers is that
the sequential service constraint feature of demand deposit provides appropriate incentives and
discipline for bank managers and that higher equity capital may actually shield the bank manager
from the discipline imposed by depositors. For example, Calomiris and Kahn (1991) argue that
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. In contrast to demand deposits, claims such as equity lack this sequential
servicing constraint and hence cannot provide the same discipline as deposits. 2
Diamond and Rajan (2001) propose that a fragile capital structure helps address the holdup problem. In particular, they argue that the value of the bank’s loan portfolio will be affected by
bank manager’s investments in loan collection efforts and that bank managers may hold-up
depositors by threatening to withhold their collection effort until given a higher share in the
surplus. But if the bank is financed primarily by demand deposits, the deposit holders may threaten
to prematurely withdraw their funds which would eliminate the surplus for the bank manager.
Thus, a fragile capital structure resolves the hold-up problem and allows the bank to raise financing
via deposits which in turn allows it to fund illiquid loans and thereby create liquidity. In Diamond
2
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. 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 as once the funds from depositors are raised, the banker may shirk or abscond. The depositors
can guard against such opportunistic behavior by becoming informed.
3 and Rajan (2000), higher capital by providing a cushion, shields the bank manager from market
discipline (a run by the depositors) allowing the manager to underinvest in monitoring and hence,
higher capital adversely affects the value of bank’s loan portfolio. Overall, the above mentioned
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). Third, 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). In particular, Mehran and Thakor (2011) which
is a dynamic variant of Holmstrom and Tirole (1997), 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. Overall, the above
mentioned papers provide an alternate perspective that higher bank capital (lower leverage) has
4 beneficial effects on firm value and they also identify the channel or the mechanism by which bank
capital affects – improved monitoring incentives.
We contribute to this debate by providing an empirical test of the association between bank
capital and banks’ 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 ex-ante salary expense based proxies of monitoring effort (to
capture quality and quantity of labor input into monitoring effort) (3) We correlate our ex-ante
measures of monitoring effort to loan quality, which is an ex-post measure of monitoring effort.
We recognize that equity capital is a choice variable and hence we conduct a battery of tests to
address the concern that the relation between capital and loan quality is endogenously determined.
In addition, we conduct a variety of tests to further validate the association between capital and
monitoring such as examining whether the relation is robust to financial crisis and banks’ loan
portfolio choices.
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 (Wahlen 1994). When payments on loans are overdue, typically beyond 90
days, the loan is classified as NPL. A provision expense is charged to income statement and a
reserve is created against the NPL if the losses appear probable and estimable. When the losses
actually occur, the loss is charged-off against the reserve that was created. In our main empirical
tests, we examine the cross-sectional association between bank capital and each of these three
measures of loan quality – non-performing loans, loan loss provisions and charge-offs.
5 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 (we multiply each of the loan quality measures by 1 so that higher values of LQ can be interpreted as higher loan quality). This association is observed
over our entire sample period of 1994Q2-2015Q1 as well as during the financial crisis period
(2007Q2-2008Q4) and during the non-crisis period. The positive relation between bank capital
and loan quality obtains for public as well as for private banks and for banks that participate in the
originate-to-distribute market for loans as well as for the banks that do not. 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 23% in average NPL, 17% in average LLP
and 24% 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 examine whether the impact of capital on monitoring incentives differs by bank size as
there are large differences in size among the banks and prior work finds that large banks differ
from small banks in many respects such as liquidity creation (Berger and Bouwman 2009, Koch,
Richardson and Van Horn 2016).3 Furthermore, monitoring may be more important to smaller
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
6 banks as loans comprise a higher proportion of assets for small banks and also because small banks
are more likely to extend loans to small businesses where close monitoring may have a significant
role to play. Consistent with our conjecture, we find that the association between bank capital and
loan quality is statistically stronger for smaller banks relative to medium and larger banks. In fact,
for larger banks, the association between bank capital and loan quality is not consistently
significant across various loan quality measures. 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 an 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 this measure 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.’s measure of monitoring effort is the fixed effects regression coefficient in a
regression of salary expense to total non-interest expense on 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 1.2 and 3.1). Consistent with predictions in Holmstrom and Tirole (1997) and Mehran
and Thakor (2011), we find that bank capital is positively associated with both of our ex-ante
established in the preceding years (Franklin in 1974, Continental Illinoi in 1984 etc.). We examine the association
between equity and loan quality for the largest 20 banks each quarter and find it to be insignificant.
7 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.4
Additionally, we identify a situation where banks’ monitoring efforts are expected to be
higher, ex-ante and test if in such an instance the relation between bank capital and loan quality is
stronger. Song and Thakor (2007) argue that banks finance informationally opaque relationship
loans with core deposits as informationally opaque loans are likely to generate the most
disagreement between the banks and the depositors about their value and hence depositors are most
likely to precipitate a bank 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. Empirical evidence is consistent with this prediction that banks finance
relationship loans with core deposits (Berlin and Mestor 1999). Based on this insight, we use
banks’ funding mix as a proxy for the extent of banks’ relationship lending and expect the relation
between bank capital and loan quality (outcome of monitoring) to be stronger for banks with higher
proportion of core deposits (proxy for relationship lending). Consistent with our conjecture, we
find that the association between bank capital and monitoring, and bank capital and loan quality is
significantly stronger for banks with above median ratio core deposits to total assets. Furthermore,
4
A potential criticism against our main findings may be that the results merely capture the mechanical relationship
between the boom and bust periods in the economy. On one hand, banks build up capital, have few non-performing
loans, provisions and charge-offs, and pay higher salaries and bonuses during good times. On the other hand, bank
experience losses, have more non-performing loans, provisions and charge-offs, and cut back on salaries and
bonuses, during bad times. But for this mechanical relationship to obtain, bank capital, loan quality measures and
salary based measures have to be contemporaneous. We measure bank capital lagged by a quarter relative to the loan
quality measures, and our salary based measure is based on prior four quarters, thus precluding the association to be
mechanical. Furthermore, our results are robust to exclusion of the financial crisis quarters, and lagging bank capital
by four quarters.
8 we find this association is stronger for small banks relative to large banks as one would expect that
relationship lending to dominate at 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 concern. First, our bank
capital measure is lagged which partially mitigates the endogeneity concern as lagged bank capital
is likely to reflect earlier decisions and mitigates the concern that equity capital and current loan
quality measures are jointly determined. We have also replicated the analysis by extending the lag
to three quarters 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 by conditioning our analysis on
participation in the OTD market and the level of commercial and agricultural loan portfolio.
Fourth, we address the endogeneity concerns directly by using an instrumental variable
approach and obtain consistent results. Given that our main results show that the positive
association between bank capital and loan quality (and ex-ante salary based measure of monitoring
effort) is significant only for small and medium sized banks, we use the fraction of seniors in the
markets in which a bank operates as an instrument for bank capital (Berger and Bouwman, 2009,
2013). First stage results show the instrument is valid for both small and medium banks. Using
this instrument, we find results that are consistent with our main findings—that the effect of bank
capital on loan quality (and ex-ante salary based measure of monitoring effort) is significantly
positive for small and medium banks.
9 Finally, we examine the association between change in bank 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 the change in our two monitoring proxies on change in equity capital
(due to issuance of new capital) and change in control variables. 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 that banks that
increase capital show improvement in NPL after one year and significant improvement in NPL
after two years and in all three loan quality measures after three years after controlling for the
change 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.
Finally, we find that there are diminishing returns to capital with respect to its effect on
monitoring incentives. We find that the association between bank capital and monitoring is
significant for banks with below median equity capital to total assets ratio but is not significant for
banks with above median equity capital. Such non-linear relation mitigates the concern of reverse
causality where a positive relation between bank capital and loan quality could be due to more
profitable banks having higher equity capital due to higher retained earnings.
Our study advances the literature forward along several dimensions. Though prior studies
have documented benefits of higher capital (for example, Calomiris and Mason 2003, Calomiris
and Wilson, 2004) the empirical evidence on the positive impact of bank capital on monitoring
incentives is limited and is indirect. For example, using a sample of 244 bank mergers from 1989
10 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.5 Berger and Bouwman (2013) show that bank capital is
positively related to survival probability for small banks. For large banks the relation holds only
during the crisis period. Our study complements above studies by focusing on the channel through
which bank capital impacts value/survival probability using a large and comprehensive sample of
public and private bank holding companies. We provide a more direct test of the prediction in
Holmstrom and Tirole (1997) and Mehran and Thakor (2011) as we provide evidence on the
mechanism by which capital affects loan quality - through its effect on monitoring effort.
Purnanandam (2011) finds that banks with greater participation in the originate-to-distribute model
originated loans of poor quality and that this effect is 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
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, financial crisis of 2007-2009 highlighted the adverse consequences of banks’
fragile capital structure. This has prompted some to call for a dramatic increase in use of equity by
banks (Admati and Hellwig, 2013). However, others have cautioned against requiring banks to
hold more equity as it might insulate bank managers from market discipline and thereby exacerbate
5
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.
11 agency problems at the banks (Kashyap, Rajan and Stein, 2008). 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). Furthermore, our evidence that capital
strengthens monitoring incentives at the small banks but not at large banks seems to suggest that
the trade-off between benefits and costs of higher capital differs by size. This evidence
complements evidence in other work that suggests that bank performance, liquidity creation and
moral hazard induced by too big to fail guarantees differs by size (Berger and Bouwman 2009,
2013, Koch et al. 2016).
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. However, at a minimum, our empirical evidence identifies an important benefit of bank
capital – stronger monitoring incentives.
1. 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 companies 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.
12 We exclude banks for the following criteria (1) total assets below 500 million (or equivalent of
real dollars in 2006Q1) 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 56,858 bank quarters with 2,008 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 510 (1,498) unique public (private) banks with 21,888 (34,970) bank quarter
observations.
1.1 Variable definitions
Our main independent variable is bank equity capital deflated by lagged total assets (CAP).
Our 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 total assets. Typically, loans 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 as
it involves a relatively mechanical classification when payment on the loan is overdue (Liu and
Ryan 1995, Liu, Ryan, and Wahlen 1997, and Liu and Ryan 2006). Thus, our first measure of loan
quality, non-performing loans, is considered a relatively non-discretionary indicator of loan
quality. Our second measure of loan quality, loan loss provision is more discretionary. According
to FAS 5, if losses on loans appear probable and reasonably estimable, the bank managers are
expected to record a provision by reducing current income and increasing the allowance on loans.
Moreover, loan loss provision is more discretionary for heterogeneous loans for which loss
accruals are estimated at the individual loan level primarily based on the judgment of loan officers
(FAS 114) relative to homogenous loans for which loss accruals are estimated at the pool level
based on historical loss statistics under FAS 5. Thus, our second measure of the loan quality, loan
13 loss provisions, reflects the bank manager’s private information regarding default risks inherent in
the loan portfolio and is inherently discretionary in nature. Bank managers charge off loans when
they are deemed uncollectible. While bank managers exercise some discretion over the timing and
magnitude of the NCOs, especially for commercial loans, they are often driven by exogenous
factors (Bhat, Lee and Ryan 2014). Moreover, consumer loans are automatically charged off when
they become delinquent for a certain number of days.6 Thus, our third measure of loan quality,
charge-offs, is a relatively non-discretionary measure. In our main empirical tests, we examine the
cross-sectional association between bank capital and each of these three measures of loan quality
– NPL, LLP and NCO.
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 loans 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
(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
6
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.
14 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 capture banks’ propensity to
manage their reported profitability. See Appendix A for details on data items.
1.2 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, larger banks have
a different business model than smaller banks (larger banks have higher trading revenue and lower
proportion of loans to total assets relative to smaller banks) and may also benefit from economies
of scale. Also, due to heterogeneity in commercial loans, they require greater investment in
monitoring. Thus, we divide banks into small, medium and large 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 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.
15 2006 to compute our second measure of monitoring effort (ME2) filtering out the effects of nonmonitoring 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 nonmonitoring 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 profitability as well as 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).
1.3 Descriptive Statistics
Panel B of Table 1 provides the descriptive statistics for the key variables used in the study
for all the banks, and public and private banks separately. We winsorize the data at the 1% from
both tails to minimize the effect of outliers. The average bank in our sample has an asset base of
$16.10 billion (median $1.09 billion) and the mean (median) equity capital to total assets ratio is
9.1% (8.8%). We provide the distribution of other key variables in the panel. Unreported test
statistics indicate that the public banks in our sample are significantly larger and are statistically
different 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. In line with
the analysis in Berger and Bouwman (2009), we divide our sample into three groups, small,
medium and large with total assets more than or equal to $500 million but less than 1 billion, equal
16 to or more than 1 billion and less than 3 billion and equal to or more than 3 billion, respectively.
The size classifications are based on real 2006Q1 dollars using the implicit GDP price deflator.
The descriptive statistics are provided separately based on size in Panel C of the Table 1.
2. 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 measures of loan quality. In terms of economic
significance, one standard deviation change in CAP (0.029) is associated with a decrease of 0.004
(0.029*0.130) in NPL, a decrease of 0.0003 (0.029*0.009) in LLP and a decrease of 0.0003
(0.029*0.011) in NCO, respectively Looking at the average values of these measures, it translates
to a decrease of 23% in NPL, 17% in LLP and 24% 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 the 1% level) for LQ2 (the LLP based measure) and LQ3 (the chargeoff based measure) suggesting that larger banks have higher provisions and higher charge-offs.7
The coefficients on loan composition measures are negative and significant for LQ2 measure
7
Purnanandam (2011) also finds a positive and significant association between size and charge offs.
17 consistent with the idea that loan composition captures credit risk.8,9 LIQUID is positively and
significantly associated with LQ2 while ABSGAP is significantly associated with LQ2 and LQ3.
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 suggesting that banks that derive a higher
proportion of their income from fee-generating activities incur fewer credit-related losses. The
coefficient on DELTAGDP is positive and significant consistent with loan quality increasing
during economically favorable times. The coefficient on EBLLP is not significantly associated
with LLP suggesting that earnings management does not appear to be an important determinant of
the provisions.
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. The above results suggest that there is a strong positive association between
bank capital and loan quality.10
2.1 Public vs. Private
8
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.
9
We use bank’s risk-weighted assets and off-balance sheet activities divided by total assets as an alternative proxy
for credit risk. We find our results are robust to this alternative proxy. 10
While we use quarterly data in our analysis, we conduct additional analysis keeping only the fourth quarters to
ensure that the results are not driven by whether the loan quality measures are audited or unaudited. Results are
robust.
18 Next, we explore whether the association between bank capital and loan quality is different
for public vs. private banks to alleviate concerns whether our main results are driven by the nature
of the ownership of bank capital rather than the bank capital itself. Public banks differ from private
banks on a couple of important dimensions. First, private banks are more likely to be closely held
by smaller number of shareholders, with owner-managers and directors more likely to be majority
equity stakeholders (Brickley, Linck and Smith 2003, Nichols, Wahlen and Wieland 2009)
compared to the diffused ownership structure of public banks. Second, private banks are typically
smaller in size compared to the public banks and prior work finds that the effect of bank capital on
liquidity creation and survival probability differs across firm size (Berger and Bouwman, 2009,
2013). Moreover, even amongst the very large banks, the public banks seem to have the ‘too big
to fail’ guarantee, whereas the private large banks typically may not and this implicit guarantee
could potentially influence a banks’ monitoring incentives.
The results for private (public) banks are presented in columns 4-6 (7-9) of Panel A of
Table 2. The results show that bank capital is positively and significantly associated with all three
measures of loan quality for public as well as private banks. This suggests that the positive
association between capital and monitoring incentives is not affected by ownership structure of
banks. An alternative (unreported) fixed effects model including a dummy variable PUBLIC,
interaction of CAP and all control variables with PUBLIC, and quarter dummies shows that the
association between bank capital and each of the three loan quality measures is not statistically
different for public vs. private banks.
Overall, the results suggest that bank’s equity capital is positively and significantly
associated with each of the three measures of loan quality for both the public and the private banks.
2.2 Size and the association between bank capital and loan quality
19 Next we investigate whether the positive association between bank capital and loan quality
differs by firm size. While the theories that posit a relation between bank capital and monitoring
apply to all banks, we expect monitoring to be more critical for smaller banks than for larger banks.
Small banks deal with more entrepreneurial type businesses where monitoring is more important.
Smaller banks also have the ability to closely monitor their borrowers and their organization
structures enable them to effectively use their informational advantage that arises from the long
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. Moreover, smaller banks are less likely to be bailed out and hence higher capital is likely
more important in increasing their survival probability. Thus, we would expect capital to have a
stronger effect in strengthening monitoring incentives for smaller banks and hence the positive
association between bank capital and loan quality to be stronger for smaller banks relative to larger
banks.
We divide our sample into three groups, small, medium and large based on real 2006Q1
dollars. 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 the 1% level for all three measures of loan quality for small and medium
sized banks. The coefficient on CAP is positive and significant at the 10% level for LQ3 (but not
for LQ1 or LQ2) for large banks. The association between CAP and loan quality 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. Thus, we find that the impact of bank capital on loan
quality is stronger for smaller banks. Overall, the results suggest that while higher capital appears
20 to strengthen monitoring incentives at small banks, it does not appear to improve monitoring
incentives at larger banks.11,12 One plausible interpretation of this evidence could be that the tradeoffs of benefits and costs of higher capital differ by size. Also, the implicit “too big to fail”
guarantees enjoyed by the large banks may contribute to underinvestment in monitoring effort.
3. Bank Capital and Ex-ante Measures of Monitoring Effort
3.1 Estimation of monitoring effort
While our results show a positive association between bank capital and loan quality, an
outcome of the monitoring effort, theory predicts the association between bank capital and
monitoring incentives (Mehran and Thakor 2011). Thus, we now turn to identifying the mechanism
by which capital influences loan quality. It seems reasonable to argue that banks that have stronger
monitoring incentives would have greater investment in monitoring effort and hence should have
better loan quality, ceteris paribus. Thus, we conduct tests to examine the association between
bank capital and two proxies intended to capture quality and quantity of labor input into monitoring
effort. Our first proxy 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
11
The partitioning on size helps us address the concern whether the positive association between bank capital and
loan quality is driven by institutional ownership. Institutional investors typically tend to avoid investing in small
firms due to liquidity concerns. We find that the positive association between the bank capital and loan quality is
weakest in larger banks where one would expect the institutional investments would be higher.
12
Out results are also robust to excluding the top money center banks (large commercial banks in major city) and
banks above $10 billion in assets.
21 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 attempt to control for the effect of nonmonitoring factors on salary expense. Specifically, for each bank, we estimate a fixed effects
regression of SALEXP on control variables to filter out the effect of non-monitoring factors on
salary expense. The bank specific fixed effects coefficient is our proxy for monitoring effort for
each bank. This measure is motivated by analysis in 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 1,956 bank specific coefficients across 52,719 bank quarters. These bank specific
coefficients, which form our proxy for 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.
Table 3 includes 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 analysis in
Coleman et al. (2006). 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 larger
and heterogeneous commercial loans. Consistent with this conjecture, commercial loans are
associated with a significantly higher salary expense while consumer loans have a negative
22 association with the salary expense in column 1 of Table 3. The negative coefficient of the FEE
suggests that banks with a greater proportion of fee income have a lower ratio of salary to noninterest expense. The coefficient on ROA is positive and significant suggesting more profitable
banks have higher salary expense. The coefficient on LOGEMP is positive and significant
suggesting banks with more employees have higher salary expense.
3.2 Bank Capital and 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 3-8 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 salary expense.
In addition, fixed effects estimation model mitigates the concern of any time invariant omitted
variables. The results show that the coefficient on CAP is positive and significant at the 1% level
for ME1 and ME2 suggesting that on average, bank capital is positively associated with monitoring
effort for all the banks in our sample. When we divide the sample by size, we find that the
association is strongest for small banks relative to medium banks and large banks. In fact, it is
insignificant for large banks suggesting that the role of monitoring discussed in the theoretical
models is more relevant for small banks rather than large banks. Perhaps this could be due to
implicit too big to fail guarantees enjoyed by large banks, which may cause them to underinvest
in monitoring effort.
Given that we find that bank capital is related to loan quality and bank capital is related to
our proxy for monitoring effort, we should expect that our monitoring effort proxy would be related
to loan quality. Nonetheless, to validate our ex-ante proxies of monitoring effort, we correlate them
23 to the ex-post outcome 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 the 1% level for both the measures, ME1 and ME2,
and for all three measures of loan quality, LQ1, LQ2 and LQ3.
Overall, the results in Table 4 and Table 5 suggest that monitoring effort is the mechanism
through which bank capital is positively associated with loan quality. These results provide
empirical support for the prediction in Mehran and Thakor (2011) that bank capital and monitoring
are positively related in the cross-section. Banks with higher capital monitor more as higher
monitoring increases the survival probability of the bank, which in turn increases the likelihood
that banks will be able to harvest the prior period investment in monitoring. This positive feedback
mechanism results in higher monitoring, enhancing the value of the banks’ relationship loan
portfolio. Our results validate the mechanism suggested in their model that the positive association
between bank capital and loan quality/value is due to a positive association between bank capital
and monitoring effort.
4. Additional Analysis and Robustness Tests
An important empirical challenge for our paper is that bank capital is a choice variable and
hence the relation between capital and loan quality could be endogenous. While this concern is
partially mitigated due to the use of (i) lagged value of bank capital13, (ii) panel data and use a
fixed effect estimation model to control for time invariant omitted variables, (iii) control for
profitability, portfolio composition and risk in our regressions, we understand that these tests may
13
We have also replicated the analysis by extending the lag to four quarters and our results are robust.
24 not fully address the issue of identification. Therefore, we conduct a battery of additional tests as
well as several robustness tests to address endogeneity.
In these tests we attempt to further validate the association between bank equity capital,
monitoring incentives and loan quality by conducting a battery of additional tests as well as using
an instrumental variable approach to address endogeneity directly. The tests are discussed below.
4.1 Relationship Lending
To further validate the association between capital and monitoring incentives, we identify
a situation where role of monitoring is more important and test whether in such an instance the
relation between capital and loan quality is stronger. As discussed earlier, banks finance
informationally opaque relationship loans with core deposits, as informationally opaque loans are
likely to generate the most disagreement between the banks and the depositors about their value
and hence depositors are more likely to precipitate a bank run if they perceive that the value of the
loan portfolio has declined (Song and Thakor, 2007) However, core deposits are sluggish as banks
also provide transaction and advisory services to these depositors. Empirical evidence is consistent
with this prediction that banks finance relationship loans with core deposits (Berlin and Mestor
1999). Based on this insight, we use banks’ funding mix as a proxy for the extent of banks’
relationship lending and expect the relation between bank capital and loan quality (outcome of
monitoring) to be stronger for banks with higher proportion of core deposits (proxy for 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 that is above the median for the quarter; and 0 otherwise.
Results of the association between bank capital and loan quality and bank capital and monitoring
effort are presented in Table 6. We find that the positive association between bank capital and
25 monitoring effort is stronger for banks with higher core deposits as evidenced by the positive and
significant coefficient on the interaction between CAP and CORED for LQ1(at the 1% level), for
LQ3(at the 10% level) and for ME1(at the 1% level). Based on prior evidence that smaller banks
engage in higher level of relationship lending (Berger, Miller, Petersen and Rajan, 2005), we also
examine this association for small, medium and large banks separately. In unreported results, we
find that this relation obtains most consistently for small banks.14 Within small banks, the relation
between capital and loan quality and capital and monitoring effort is incrementally positive for
small banks with higher core deposits for LQ1 (at the 1% level), LQ3 (at the 10% level) and both
the measures of ex-ante monitoring effort, ME1(at the 1% level) and ME2 (at the 1% level). The
positive association between bank capital and loan quality is incrementally positive and significant
for banks with above median core deposits for medium banks and large banks but only for
nonperforming loans (LQ1) and ME1.
Overall, the results in Table 6 confirm our conjecture that the positive association between
bank capital and monitoring effort is stronger for banks that engage in more relationship lending,
where the role of monitoring is more important.
4.2 Instrumental Variable
To address the concern of endogeneity more directly, we use an instrumental variable
approach. We need an instrument that is correlated with the bank capital (valid instrument), but
that is not related directly to loan quality or the salary-based measure of monitoring effort
(exclusion restriction). Berger & Bouwman (2009, 2013) use SENIORS, the fraction of seniors
(people aged sixty-five and over) in the markets in which a bank operates as an instrument for
Descriptive statistics in Panel C of Table 1 show that the median level of CORE deposits in small banks is higher
(21%) than that in large banks (13%)
14
26 bank capital. The rationale is that seniors tend to hold larger equity portfolios than the average
family (Bucks, Kennickel and Moore, 2006) and since investors have a strong preference for
investing closer to home (Coval & Moskowitz 1999), Berger and Bouwman (2009, 2013) argue
that small and medium banks, operating in markets with more seniors have easier access to
equity financing and hence will operate with higher equity capital ratios. 15 Also, there is no
compelling argument or theory that predicts an association between the percentage of seniors in
the bank’s market and loan quality measures (monitoring effort measures) suggesting that the
exclusion restriction is also satisfied.
Thus, following Berger and Bouwman (2009, 2013), we use the county and MSA-level
population data in 2000 and 2010 Censuses to calculate the fraction of seniors and apply these
fractions to bank quarters in our sample from 1994Q2 to 2004Q4, and 2005Q1 to 2015Q1,
respectively. If a bank operates in multiple markets, we use its share of deposits in each market
as a percentage of its total deposits, as weights to calculate the bank’s deposit-weighted-average
fraction of seniors. We use the data from FDIC Summary of Deposits to compute the deposit
market share of the bank. Our instrument SENIORS varies considerably in the cross section, but
not so over time as we have data from just two US Censuses (2000 and 2010) weighted by the
annual deposit market share of the bank. Hence, while we include quarter dummies in our
analyses, we do not include bank fixed effects.
Panel A of Table 7 shows the results of our first stage regressions. We find that the
coefficient on lagged SENIORS is positive and statistically significant at the 1% level for small
SENIORS are unlikely to be an attractive instrument for large banks as they are not restricted geographically
when it comes to raising equity. Berger & Bouwman (2009, 2013) use state level income tax rates to instrument
bank capital for large banks. Our prior tests show that the association between bank capital and loan quality (Panel B
of Table 2) and bank capital and monitoring effort (Table 4) is not significant for large banks. Hence, we focus our
instrumental variable analyses on small and medium banks.
15
27 and medium banks, after controlling for all the variables from the second stage regression. The
Angrist-Pischke F-statistic for the first stage is significant at 1% the level for the small banks and
the medium banks, suggesting we do not have a weak instrument.
For the small banks, the Wu- Hausman F statistic for endogeneity is significant at the 1%
level for all the five regressions – the three regressions using the loan quality measures and the
two regressions using the ex-ante salary based monitoring effort measures as the dependent
variable suggesting that we can reject the null that bank capital is exogenous. Hence we perform
the instrumental variables regression for the small banks. Panel B of Table 7 contains the results
for the second stage instrumental variable regression of the three loan quality measures and the
two monitoring effort measures on the instrumented bank capital including control variables and
quarter dummies. The effect of bank capital on loan quality is positive and significant at the 1%
level in all five regressions.
We present the results for medium banks for completeness as the Wu- Hausman F statistic
is significant at the 1% level only for one of the three regressions using loan quality measures as
the dependent variable. The F statistic is significant at the 10% for the other two regressions using
loan quality measures and is insignificant for the two regressions with monitoring effort as the
dependent variable. An insignificant F statistic suggests that we cannot reject the null that bank
capital is exogenous. Panel C of Table 7 contains the results for the second stage instrumental
variable regression of the three loan quality measures and the two monitoring effort measures on
the instrumented bank capital including control variables and quarter dummies for medium banks.
The effect of bank capital on loan quality is positive and significant at the 1% level for all three
loan quality measures and at the 10% level for one of the two monitoring effort measures for the
medium banks. However, the insignificance on the two regressions using monitoring effort and
28 the low significance on the F statistic for two of the three regressions using loan quality measures
suggest that our original analyses exploring the relation between ex-ante monitoring effort and
bank capital is more appropriate for the medium banks.
Overall, we find that the results of our instrumental variable approach are consistent with
our main findings that bank equity capital is positively correlated with loan quality and monitoring
effort.
4.3. Banks that issue new capital
Next we examine the association between change in bank capital and change in monitoring
effort and loan quality. The changes model allows the firm to act as its own control and minimizes
the concerns relating to omitted variables. Over our sample period, 443 banks (1944 bank-quarters)
made secondary equity offerings. We regress change in our three loan proxies and two monitoring
proxies on NEWCAP, a variable that captures new equity issued in period t-1. While we do not
expect sudden and immediate improvement in monitoring effort and loan quality, we expect that
increased equity would bring about improvement, if any, initially in the monitoring effort,
followed by improvement in loan quality. We do not have any theory or prior empirical evidence
to guide us on the period over which we should expect the improvement. Hence, we compute the
change in monitoring effort and change in loan quality over a period of 4 quarters, 8 quarters and
12 quarters. Results relating to loan quality are presented in Panel A and results relating to
monitoring effort are presented in Panel B of Table 8. The coefficient on NEWCAP in Panel A is
positive and significant for LQ1 after 4 quarters (at the 10% level) and after 8 quarters (at the 1%
level). After 12 quarters, the coefficient on NEWCAP is significant at the 1% level for each of the
three measures of loan quality (LQ1, LQ2 and LQ3), after controlling for the concurrent changes
in control variables. The coefficient on NEWCAP in Panel B is positive and significant (at the 1%
29 level) after 4, 8 and 12 quarters for ME1 (but not ME2). These results suggest that banks that raise
new equity capital show an increase in monitoring effort after one year. The economic benefits of
the increase in monitoring efforts are first seen as a decrease in the level of NPL (increase in LQ1)
after one year, which eventually results in lower provisions (higher LQ2) and lower charge-offs
(lower LQ3) within three years.
Overall, these results suggest that an increase in capital is followed by an increased
investment in monitoring effort as well as improved loan quality. While it is difficult to draw
unambiguous causal conclusions, we view these results as complementing and strengthening our
main findings of the positive association between bank capital and loan quality; and bank capital
and monitoring effort.
4.4. Non-linearity in the relation between Equity and Monitoring Effort
The association between equity and monitoring is likely to be nonlinear as there may be
diminishing returns to equity capital with respect to monitoring. Thus, we explore whether the
positive association between bank capital and monitoring flattens out beyond a certain level of
capital. We allow for non-linearity in two alternate specifications. In the first specification, we
include a squared term of lagged equity in the main regression specification with loan quality
measures as the dependent variable (columns 1-3 in Panel A of Table 2) and the regressions with
monitoring effort as the dependent variable (columns 1-2 in Table 4). Results are reported in Panel
A of Table 9. We find that the coefficient on the squared lagged equity is negative and significant
at the 1% level for LQ1, LQ3 and ME1 and at the 5% level for ME2 suggesting that the association
between bank capital and loan quality and capital and ex-ante monitoring effort is non-linear. In
an alternate specification, we interact the lagged capital with a dummy variable that equals 1 if the
level of lagged capital is above median for the quarter; and 0 otherwise. We find that the
30 association between equity and loan quality and equity and monitoring effort is more pronounced
for the banks with lagged equity below the median compared to banks with lagged equity above
the median. Overall, these results suggest that there are diminishing returns to bank capital in terms
of monitoring effort. This non-linear relation between lagged bank capital and loan quality also
mitigates the concern of reverse causality where a positive relation between bank capital and loan
quality could be due to more profitable banks having higher equity capital due to higher retained
earnings.16
4.5 Risk as an alternative explanation
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. For example, bank managers
may choose high leverage and at the same time engage in risky loans. Alternatively, bank managers
may be risk averse and may choose low leverage and engage in loans that are less risky. While we
control for risk in our regressions, and our fixed effects estimation (to control for time invariant
omitted variables) should address this concern, we run additional tests conditioning on
participation in the OTD market, proportion of commercial and agricultural loans in the portfolio
and controlling for loan pricing.
4.5.1. OTD market
Purnanandam (2011) shows that banks with greater involvement in the OTD market during
the pre-crisis period originated excessively poor-quality mortgages, and therefore had higher NPLs
The reverse causality argument may still be valid and produce the non-linear relation between bank capital and
loan quality if the profitable banks pay out dividends at a higher rate relative to less profitable banks, thus, causing
the capital levels to be lower for the highly profitable banks and the non-linear relation between bank capital and
loan quality. Hence, we perform a robustness check, by replacing our equity variable with equity before dividend
payments. Results remain unaltered.
16
31 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 participated in the OTD market relative to the banks that did not.
The OTD data is only available from 2006Q2 onwards. As a result, the sample size is reduced to
25,752 bank quarter observations 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. Results reported in
Panel B of Table 9 show that the coefficient on CAP is positive and significant across all the three
measures of loan quality and the two ex-ante measures monitoring effort. With the exception of
the coefficient on CAP*OTD being negative and significant at 10% level in the regression with
dependent variable ME2, we do not find the coefficient on the interaction term to be significant in
any of the other regressions.17 These results suggest that the association between capital and loan
quality is not different for banks that participated in the OTD market vs. banks that did not. This
result differs from Purnanandam (2011) in that he finds that participation in the OTD market
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 only 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
17
We run the regressions with the dependent variable ME2 separately for the banks that participate in the OTD market
vs. banks that do not, allowing the coefficients on all the control variables and quarter dummies to differ and find that
the coefficient on CAP is positive and significant (at 1% level) for both the groups.
32 we are interested in the broader question of the impact of bank capital on loan quality, we use
portfolio level loan quality measures instead of quality of just mortgage loans.
4.5.2. High level of commercial and agricultural loans
Commercial and agricultural loans are heterogeneous and are considered riskier than
consumer loans. Thus, we use the proportion of commercial and agricultural loans as a proxy for
riskiness of the loan portfolio. We interact our main variable of interest CAP with
COMMDUMMY, a dummy variable equal to 1 if the bank has COMM (ratio of commercial and
agricultural loans to total assets) that is higher than the median for the quarter, and zero otherwise.
Results are reported in Panel C of Table 9. We find that the coefficient on CAP is positive and
significant across all the three LQ measures and both the ex-ante ME measures. However, the
coefficient on the interaction between CAP*COMMDUMMY is negative and significant at the
10% level only in the regression with LQ1 as the dependent variable suggesting that the association
between CAP and LQ1 is marginally weaker for banks with above median level of commercial
and agricultural loan portfolio than for banks with below median.18 Overall, the results suggest
that the positive association between bank capital and loan quality and capital and monitoring
incentives is more general and is not sensitive to the composition of the loan portfolio.
4.5.3 Loan Yield
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. While our control variables
capture risk and our fixed effects estimation (to control for time invariant omitted variables) should
We run the regressions with the dependent variable LQ1 separately for the banks with COMM portfolio above
median vs. below median, allowing the coefficients on all the control variables and quarter dummies to differ and
find that the coefficient on CAP is positive and significant (at 1% level) for both the groups. 18
33 address this concern, we run additional tests to control for loan pricing using interest income on
loans as an additional control for risk. Results reported in Panel D of Table 9 show that our main
results are robust to the inclusion of this additional control.
4.6 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 and we want to examine the sensitivity of our results to the financial crisis period. Thus,
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 (2007Q2 to 2008Q4) , and zero
otherwise. Results reported in Panel E of Table 9 show that the coefficient on CAP is positive and
significant (at the 1% level) 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. In
the regressions with the two ME measures, the coefficient on CAP is positive and significant, while
the coefficient on the interaction between CAP*CRISIS is negative and significant (at the 5%
level) suggesting that the positive association between bank capital and monitoring effort was
weaker during the crisis. We run the regressions with the dependent variable ME1 and ME2
separately for the crisis and non-crisis periods allowing the coefficients on all the control variables
and quarter dummies to differ and find that the coefficient on CAP is positive and significant (at
the 1% level) for both the crisis and the non-crisis period for each of the measures suggesting that
the positive association between capital and monitoring effort obtains during the crisis and the
non-crisis period.
34 4.7.Regulatory capital
Our analysis uses the 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 several adjustments to book value of equity relating to
goodwill, intangible assets, accumulated other comprehensive income amongst other things in the
numerator (tier one capital) and inclusion of off balance sheet items in the denominator (riskweighted total assets) . We have replicated our analysis using Tier One Capital ratio (REGCAP)
of the bank to see whether the association between bank capital and monitoring effort is sensitive
to this definition. Results are reported in Panel F of Table 9. We find that the coefficient on
REGCAP is positive and significant (at the 1% level) in all five regressions (three loan quality
regressions and two monitoring effort regressions) suggesting that results are robust to the use of
Tier 1 Capital ratio instead of equity capital.
5. Conclusions
Our study provides large sample empirical evidence on the association between bank
capital and monitoring effort. We use three loan quality based ex-post measures that are the
outcome of monitoring effort, as well as two salary based ex-ante measures that capture quality
and quantity of labor input into monitoring effort. Given the theoretical disagreement on whether
higher equity capital incentivizes the bank to monitor its borrowers or impedes monitoring by
shielding the bank from market discipline imposed by high leverage, 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. 2013, Admati and Hellwig 2013) while others suggesting that higher capital imposes
a variety of costs (Kashyap, Rajan and Stein 2008).
35 We find that bank capital is positively associated with each of the three measures of loan
quality- non-performing loans, loan loss provisions and net charge-offs, all outcomes of the
monitoring process. This association obtains, during the financial crisis period as well as during
the non-crisis period, whether the bank participates in the OTD market or not, and whether the
bank 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, we document that monitoring effort is the mechanism through which the bank capital affects
loan quality. Additional tests show that the association between bank capital and loan quality is
stronger for banks where monitoring is expected to play an important role, i.e., banks with higher
proportion of relationship loans.
We also find that there is an increase in monitoring effort and loan quality for banks that
issue capital during our sample period. We perform several additional robustness checks including
using an instrumental variable and find that our results are robust. Overall, our evidence is
consistent with predictions in Mehran and Thakor (2011) that bank capital improves monitoring
incentives and hence is positively related to loan value in the cross-section.
While we are careful not to draw any inference regarding optimal capital structure of banks,
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. Furthermore, our evidence that capital strengthens monitoring
incentives at small banks but not at large banks seems to suggest that the trade-off between benefits
and costs of higher capital differs by size, and that size should be an important consideration in
considering regulatory capital requirements.
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39 Appendix A Definitions of Variables and Other Acronyms
VARIABLE
DESCRIPTION
ABSGAP
Absolute value of one-year maturity gap deflated by total assets
CAP
Equity Capital deflated by total assets
COMM
Agricultural and commercial loans deflated by total assets
COMMDUMMYDummy variable takes the value 1 if bank has COMM above median for
the quarter; 0 otherwise
CONS
Consumer loans deflated by total assets
CORE
Deposits less than $100,000 deflated by total assets.
CORED
Dummy variable takes the value 1 if core deposits are above the quarter
median; 0 otherwise.
CRISIS
Dummy variable takes the value 1 if the bank quarter observation falls
during the financial crisis period (2007Q2 to 2008Q4); 0 otherwise
DELTAGDP
GDP growth rate
EBLLP
Earnings before provision deflated by total assets
EFF
Ratio of non-Interest Expense to income before extraordinary items
FEE
Ratio of fee income to total income (interest income and non-interest
income)
LARGE
Banks with total assets above or equal to $3 billion. The size
classifications are based on total assets expressed in real 2006Q1 dollars
using the implicit GDP price deflator.
LG
Change in loans deflated by total assets
LIQUID
Bank’s liquid assets deflated by total assets
SOURCE
Y9C - bhck3197,bhck3296, bhck3298, bhck3409, bhck2170
Y9C - bhck3210, bhck2170
Y9C - bhck1763, bhck1764, bhck1590, bhck2170
Y9C - bhck2008, bhck2011, bhckb538, bhckb539, bhck2170
Y9C - bhcb6648, bhod6648, bhck2170
St Louis Federal Reserve website https://research.stlouisfed.org/fred2/
Y9C - bhck4300, bhck4230, bhck2170
Y9C - bhck4093, bhck4300, bhck2170
Y9C - bhck4079, bhck4107, bhck4079
Y9C - bhck2122, bhck2170
Y9C - bhck0081, bhck0395, bhck0397, bhck1773, bhck1350, bhck0276,bhck0277,
bhdmB987, bhdmB989, bhck2170
Y9C - bhck4230, bhck2170
Y9C - bhck4435, bhck4436, bhckF821, bhck4059, bhck4010, bhck4393, bhck4503,
bhck4504
Y9C - bhck4150
LLP
LOANYIELD
Provision deflated by total assets
Interest income divided by loans
LOGEMP
LQ1
LQ2
LQ3
ME1
Log of total number of employees
NPL multiplied by -1
LLP multiplied by -1
NCO multiplied by -1
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
Adjusted monitoring effect computed as per Coleman, Esho and Sharpe
(2006)
Banks with total assets above or equal to $1 billion and less than $3
billion. The size classifications are based on total assets expressed in real
2006Q1 dollars using the implicit GDP price deflator.
Net charge-offs deflated by total assets
Y9C - bhck4635, bhck4605, bhck2170
Equity capital issued by the bank in that quarter
Y9C - bhck3577, bhck3579
Non-Performing Loans deflated by Total Assets
Y9C - bhck5525,bhck5526, bhck2170
ME2
MEDIUM
NCO
NEWCAP
NPL
40 OTD
PUBLIC
RE
REGCAP
ROA
SALEXP
SENIORS
SIZE
SMALL
TA
TRANDEP
Dummy variable takes the value 1 if bank participates in the OTD market; Y9C - bhckF066, bhckF067, bhckF068, bhckF069, bhckF070, bhckF071,
0 otherwise
bhckF072, bhckF073
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
Real estate loans deflated by total assets
Y9C - bhck1410, bhck2170
Tier one capital ratio
Y9C - bhck7206, bhck8274, bhcka223
Net Income deflated by total assets
Y9C - bhck4300, bhck2170
Ratio of salary expense to total non-interest expense
Y9C - bhck4135,bhck4093
The fraction of seniors in all markets in which a bank has deposits, using 2000 and 2010 Census of population from U.S. Census Bureau
FDIC Summary of Deposits
the proportion of deposits held by a bank in each market as weights.
Log of total assets
Y9C - bhck2170
Banks with total assets above or equal to $500 million and less than $1
billion. The size classifications are based on total assets expressed in real
2006Q1 dollars using the implicit GDP price deflator.
Total assets
Y9C - bhck2170
Transaction deposits deflated by total deposits
Y9C - bhcb2210, bhcb3187, bhcb2389, bhod3189 , bhod3187, bhod2389,
bhdm6631, bhdm6636, bhfn6631, bhfn6636
41 Table 1
Sample Selection and Descriptive Statistics
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. Bank variables are obtained from the Y9C call reports available from the Chicago Federal Reserve website. The banks are identified as
public banks based on the CRSP-FRB link file available from the New York Federal Reserve website. Panel C reports the descriptive statistics for the small (TA $500 million
>=TA< $1 billion), medium (TA $1 billion >=TA< $3 billion) and large banks (TA>=$3 billion). The banks are classified into the size categories based on real 2006Q1 dollars
using the implicit GDP price deflator. The Variables are defined in Appendix A.
Panel A: Sample selection
Y9C filings with non-missing total assets and net income between 1994Q1-2015Q1
After deleting banks with total assets less than 500 million (in 2006Q1) or equivalent
(adjusted per quarter based on GDP price deflator)
After deleting banks with missing data (and using 1994Q1 for lag)
Final sample after deleting banks with negative equity capital
ALL
#BANK#BANKS
QUARTERS
123,305
3,887
PUBLIC
#BANK#BANKS
QUARTERS
30,777
584
PRIVATE
#BANK#BANKS
QUARTERS
92,528
3,303
64,688
2,117
24,486
511
40,202
1,606
57,045
56,858
2,009
2,008
21,941
21,888
510
510
35,104
34,970
1,499
1,498
42 Panel B: Summary statistics
VARIABLES
NPLt
LLPt
NCOt
CAPt-1
SALEXPt-1
ME2t-1
LGt-1
TAt-1(in billions)
REt-1
COMMt-1
CONSt-1
LIQUIDt-1
ABSGAPt-1
EFFt-1
FEEt-1
DELTAGDPt
EBLLPt
TRANDEPt-1
ROAt-1
LOGEMPt-1
NEWCAPt-1
COREt-1
N
56,858
56,858
56,858
56,858
56,858
52,719
56,858
56,858
56,858
56,858
56,858
56,858
56,858
56,858
56,858
56,858
56,858
56,858
56,858
56,858
56,858
56,858
MEAN
0.016
0.002
0.001
0.091
0.524
0.000
0.000
16.103
0.457
0.120
0.047
0.242
0.167
3.688
0.176
4.224
0.003
0.578
0.002
6.188
0.381
0.190
ALL
MEDIAN
0.009
0.001
0.001
0.088
0.534
0.002
0.001
1.094
0.466
0.106
0.024
0.226
0.137
2.816
0.151
4.700
0.003
0.576
0.002
5.756
0.000
0.186
STDDEV
0.020
0.003
0.002
0.029
0.081
0.081
0.023
111.113
0.161
0.078
0.059
0.114
0.131
10.789
0.127
2.900
0.002
0.154
0.003
1.328
0.486
0.101
N
21,888
21,888
21,888
21,888
21,888
20,016
21,888
21,888
21,888
21,888
21,888
21,888
21,888
21,888
21,888
21,888
21,888
21,888
21,888
21,888
21,888
21,888
PUBLIC
MEAN
MEDIAN
0.015
0.008
0.002
0.001
0.001
0.001
0.091
0.088
0.520
0.528
-0.001
-0.003
0.000
0.001
28.161
1.902
0.454
0.459
0.120
0.106
0.054
0.032
0.235
0.221
0.160
0.133
3.557
2.745
0.183
0.158
4.419
4.800
0.003
0.003
0.584
0.578
0.002
0.003
6.691
6.271
0.537
1.000
0.183
0.180
STDDEV
0.019
0.003
0.002
0.024
0.075
0.083
0.021
164.763
0.152
0.074
0.058
0.103
0.125
8.466
0.117
2.884
0.002
0.149
0.003
1.441
0.499
0.098
N
34,970
34,970
34,970
34,970
34,970
32,703
34,970
34,970
34,970
34,970
34,970
34,970
34,970
34,970
34,970
34,970
34,970
34,970
34,970
34,970
34,970
34,970
PRIVATE
MEAN
MEDIAN
0.017
0.010
0.002
0.001
0.001
0.000
0.092
0.087
0.527
0.539
0.001
0.006
0.000
0.001
8.556
0.902
0.460
0.470
0.119
0.105
0.043
0.019
0.246
0.229
0.171
0.140
3.771
2.876
0.172
0.146
4.101
4.700
0.003
0.003
0.574
0.575
0.002
0.002
5.873
5.553
0.284
0.000
0.195
0.190
STDDEV
0.021
0.003
0.003
0.032
0.084
0.080
0.024
54.174
0.166
0.081
0.060
0.121
0.134
12.016
0.133
2.903
0.002
0.156
0.003
1.145
0.451
0.103
43 Panel C: Summary statistics small, medium and large banks
VARIABLES
NPLt
LLPt
NCOt
CAPt-1
SALEXPt-1
ME2t-1
LGt-1
TAt-1(in billions)
REt-1
COMMt-1
CONSt-1
LIQUIDt-1
ABSGAPt-1
EFFt-1
FEEt-1
DELTAGDPt
EBLLPt
TRANDEPt-1
ROAt-1
LOGEMPt-1
NEWCAPt-1
COREt-1
N
26,071
26,071
26,071
26,071
26,071
25,718
26,071
26,071
26,071
26,071
26,071
26,071
26,071
26,071
26,071
26,071
26,071
26,071
26,071
26,071
26,071
26,071
MEAN
0.016
0.001
0.001
0.090
0.538
0.001
0.000
0.692
0.495
0.115
0.039
0.243
0.152
3.936
0.152
4.109
0.003
0.562
0.002
5.294
0.350
0.214
SMALL
MEDIAN
0.009
0.001
0.000
0.087
0.544
0.003
0.001
0.664
0.500
0.099
0.020
0.230
0.123
2.947
0.133
4.600
0.003
0.554
0.002
5.303
0.000
0.211
STDDEV
0.020
0.003
0.002
0.028
0.072
0.066
0.024
0.161
0.144
0.076
0.051
0.113
0.124
11.624
0.108
2.938
0.002
0.145
0.003
0.413
0.477
0.097
N
17,078
17,078
17,078
17,078
17,078
15,381
17,078
17,078
17,078
17,078
17,078
17,078
17,078
17,078
17,078
17,078
17,078
17,078
17,078
17,078
17,078
17,078
MEDIUM
MEAN
MEDIAN
0.016
0.009
0.002
0.001
0.001
0.001
0.091
0.087
0.526
0.536
0.000
0.003
(0.000)
0.001
1.634
1.488
0.472
0.480
0.118
0.102
0.044
0.020
0.242
0.227
0.157
0.127
3.609
2.786
0.165
0.146
4.172
4.700
0.003
0.003
0.582
0.578
0.002
0.002
6.022
6.016
0.411
0.000
0.191
0.186
STDDEV
0.021
0.003
0.003
0.030
0.080
0.073
0.023
0.559
0.154
0.079
0.058
0.114
0.127
11.041
0.117
2.928
0.002
0.150
0.003
0.514
0.492
0.097
N
13,709
13,709
13,709
13,709
13,709
11,620
13,709
13,709
13,709
13,709
13,709
13,709
13,709
13,709
13,709
13,709
13,709
13,709
13,709
13,709
13,709
13,709
MEAN
0.016
0.002
0.002
0.094
0.496
(0.001)
0.000
63.436
0.368
0.130
0.068
0.239
0.207
3.317
0.236
4.507
0.004
0.604
0.002
8.095
0.404
0.144
LARGE
MEDIAN
0.010
0.001
0.001
0.089
0.512
0.001
0.001
9.343
0.375
0.121
0.050
0.217
0.189
2.672
0.204
4.800
0.003
0.614
0.003
7.849
0.000
0.134
STDDEV
0.019
0.003
0.002
0.030
0.090
0.115
0.022
219.670
0.165
0.081
0.070
0.117
0.140
8.586
0.151
2.771
0.002
0.169
0.003
1.215
0.491
0.096
44 Table 2
Capital and Loan Quality
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. Bank variables are obtained from the Y9C call reports available from the Chicago
Federal Reserve website. The banks are identified as public banks based on the CRSP-FRB link file available from the New York Federal Reserve website. The banks are
classified as small (TA $500 million >=TA< $1 billion), medium (TA $1 billion >=TA< $3 billion) and large banks (TA>=$3 billion) based on real 2006Q1 dollars using the
implicit GDP price deflator. 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.
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
LGt-1
SIZEt-1
REt-1
COMMt-1
CONSt-1
LIQUIDt-1
ABSGAPt-1
EFFt-1
FEEt-1
DELTAGDPt-1
0.130***
(8.219)
0.025***
(7.376)
-0.000
(-0.477)
0.004
(0.697)
0.010
(1.292)
-0.003
(-0.354)
0.002
(0.492)
0.003
(1.484)
0.000***
(2.988)
0.009***
(2.648)
0.001***
(4.791)
0.009***
(4.953)
0.002***
(4.410)
-0.001***
(-6.760)
-0.001*
(-1.824)
-0.002**
(-2.243)
-0.003***
(-4.022)
0.001**
(2.002)
0.001**
(2.326)
0.000***
(2.913)
0.001***
(3.720)
0.000***
(6.436)
0.005
(0.242)
0.011***
(6.713)
0.003***
(6.565)
-0.000***
(-2.861)
-0.000
(-0.408)
-0.000
(-0.589)
-0.005***
(-5.386)
0.000
(0.354)
0.000**
(2.124)
0.000***
(3.156)
0.001***
(3.217)
0.000***
(6.412)
0.058***
(3.821)
0.134***
(5.321)
0.018***
(3.147)
-0.002*
(-1.771)
-0.005
(-0.618)
0.004
(0.342)
0.022*
(1.862)
0.005
(0.677)
0.000
(0.024)
0.000
(1.568)
0.008*
(1.732)
0.001**
(2.205)
0.010***
(4.215)
0.002**
(2.410)
-0.000***
(-4.496)
-0.001
(-1.150)
-0.002*
(-1.714)
-0.000
(-0.166)
0.001**
(2.302)
0.000
(1.448)
0.000***
(2.926)
0.002***
(3.487)
0.000***
(5.853)
0.040
(1.385)
0.010***
(4.516)
0.003***
(3.434)
-0.000**
(-2.196)
-0.000
(-0.261)
-0.000
(-0.457)
-0.001
(-1.364)
0.001*
(1.811)
0.000
(0.857)
0.000***
(3.092)
0.002***
(3.305)
0.000***
(4.976)
0.060***
(2.722)
0.141***
(7.230)
0.030***
(7.094)
0.002*
(1.648)
0.009
(1.318)
0.014
(1.350)
-0.025**
(-2.356)
-0.000
(-0.044)
0.005**
(2.190)
0.000***
(2.641)
0.009**
(2.000)
0.001***
(4.683)
0.008***
(3.305)
0.003***
(3.688)
-0.001***
(-4.556)
-0.001
(-1.126)
-0.002
(-1.579)
-0.006***
(-4.748)
0.001
(1.070)
0.001*
(1.838)
0.000**
(1.975)
0.001**
(2.223)
0.000***
(3.771)
-0.023
(-0.875)
0.012***
(5.504)
0.003***
(5.705)
-0.000
(-1.273)
-0.000
(-0.242)
-0.000
(-0.391)
-0.007***
(-5.710)
-0.000
(-0.691)
0.001**
(2.170)
0.000**
(2.218)
0.001*
(1.864)
0.000***
(4.454)
0.053***
(2.605)
YES
YES
56,858
0.411
YES
YES
56,858
0.265
YES
YES
56,858
0.254
YES
YES
21,888
0.499
YES
YES
21,888
0.339
YES
YES
21,888
0.329
YES
YES
34,970
0.364
YES
YES
34,970
0.230
YES
YES
34,970
0.220
EBLLPt-1
QTRDUMMIES
BANKFE
N
ADJ.R-SQ.
45 VARIABLES
CAPt-1
LG t-1
SIZE t-1
RE t-1
COMM t-1
CONS t-1
LIQUID t-1
ABSGAP t-1
EFF t-1
FEE t-1
DELTAGDP t-1
LQ1t
(1)
N
ADJ. R-SQ.
Panel B: Size and Association between Capital and Loan Quality
MEDIUM
LQ3 t
LQ1t
LQ2 t
LQ3 t
(3)
(4)
(5)
(6)
LQ1t
(7)
LARGE
LQ2 t
(8)
LQ3 t
(9)
0.228***
(9.069)
0.023***
(5.025)
0.009***
(3.929)
0.004
(0.463)
0.006
(0.460)
-0.013
(-1.092)
-0.004
(-0.547)
0.004
(1.509)
0.000
(0.845)
0.015***
(3.632)
0.002***
(6.522)
0.013***
(5.041)
0.002***
(2.935)
-0.001***
(-3.236)
-0.002
(-1.475)
-0.004***
(-2.728)
-0.002*
(-1.690)
0.000
(0.446)
0.001**
(2.158)
0.000
(0.570)
0.001**
(2.539)
0.000**
(2.506)
-0.041
(-1.348)
0.018***
(6.674)
0.003***
(4.051)
0.000
(0.356)
-0.001
(-1.130)
-0.002
(-1.395)
-0.003**
(-2.221)
-0.001
(-0.859)
0.001**
(2.489)
0.000
(0.502)
0.001**
(2.460)
0.000***
(3.950)
0.041*
(1.659)
0.169***
(6.435)
0.027***
(4.399)
-0.002
(-0.805)
0.006
(0.684)
0.021
(1.464)
-0.016
(-1.104)
0.009
(1.217)
0.005
(1.623)
0.000**
(1.987)
0.002
(0.241)
0.001**
(2.128)
0.010***
(2.872)
0.001
(1.148)
-0.002***
(-5.706)
-0.002**
(-1.983)
-0.003**
(-2.019)
-0.003**
(-2.085)
0.000
(0.544)
0.001
(1.314)
0.000*
(1.707)
0.001**
(1.966)
0.000
(0.014)
0.030
(0.920)
0.012***
(4.402)
0.003***
(3.034)
-0.001***
(-3.167)
-0.001
(-0.814)
-0.002
(-1.302)
-0.005***
(-3.460)
0.000
(0.120)
0.001*
(1.843)
0.000**
(2.282)
0.001*
(1.942)
0.000
(1.506)
0.082***
(3.168)
0.039
(1.416)
0.015*
(1.782)
0.000
(0.061)
-0.007
(-0.628)
0.005
(0.426)
0.011
(0.865)
0.001
(0.137)
0.001
(0.230)
0.000**
(2.090)
0.011*
(1.934)
0.001**
(2.539)
0.004
(1.318)
0.003**
(2.494)
-0.000*
(-1.827)
-0.001
(-0.816)
-0.001
(-0.880)
-0.004**
(-2.407)
0.001**
(2.228)
0.000
(0.614)
0.000***
(3.741)
0.002***
(2.636)
0.000***
(7.495)
0.017
(0.466)
0.005*
(1.765)
0.003***
(3.324)
0.000
(0.479)
0.000
(0.129)
-0.000
(-0.303)
-0.005***
(-3.338)
0.001
(1.033)
0.000
(0.143)
0.000***
(3.561)
0.002***
(2.606)
0.000***
(6.520)
0.028
(1.096)
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
26,071
0.397
26,071
0.213
26,071
0.201
17,078
0.408
17,078
0.263
17,078
0.262
13,709
0.490
13,709
0.377
13,709
0.359
EBLLP t-1
QTR DUMMIES
BANK FE
SMALL
LQ2 t
(2)
46 Table 3
Estimation of Monitoring Effort based on Coleman et al. 2006
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. Bank variables are obtained from the Y9C call reports available from the Chicago Federal Reserve website. The banks are classified as small (TA $500 million >=TA< $1
billion), medium (TA $1 billion >=TA< $3 billion) and large banks (TA>=$3 billion) based on real 2006Q1 dollars using the implicit GDP price deflator. 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.
VARIABLES
REt-1
COMMt-1
CONSt-1
FEEt-1
TRANDEPt-1
SIZEt-1
ROAt-1
LOGEMP
QTR DUMMIES
BANK FE
N
ADJ. R-SQ.
ALL
SALEXPt-1
(1)
SMALL
SALEXPt-1
(2)
MEDIUM
SALEXPt-1
(3)
LARGE
SALEXPt-1
(4)
0.019
(1.341)
0.071***
(2.880)
-0.116***
(-3.814)
-0.099***
(-7.179)
0.018
(1.270)
-0.032***
(-3.803)
8.553***
(35.850)
0.031***
(3.061)
0.022
(1.441)
0.015
(0.507)
-0.038
(-1.073)
-0.092***
(-5.384)
0.017
(1.154)
-0.009
(-1.144)
7.460***
(21.358)
0.039***
(4.663)
0.046*
(1.850)
0.113**
(2.562)
-0.149***
(-3.070)
-0.069***
(-3.676)
0.025
(0.970)
-0.009
(-0.594)
8.725***
(22.937)
0.008
(0.418)
-0.015
(-0.587)
0.079*
(1.848)
-0.165***
(-2.756)
-0.136***
(-5.170)
0.016
(0.578)
-0.046***
(-4.108)
9.488***
(17.912)
0.051***
(3.864)
YES
YES
YES
YES
YES
YES
YES
YES
56,858
0.284
26,071
0.271
17,078
0.332
13,709
0.293
47 Table 4
Capital and Ex-ante Measures of Monitoring Effort
This table reports the results of the fixed effects regression of ex-ante measures of monitoring effort (ME1 and ME2) on bank capital. Bank variables are obtained from the Y9C
call reports available from the Chicago Federal Reserve website. The banks are classified as small (TA $500 million >=TA< $1 billion), medium (TA $1 billion >=TA< $3 billion)
and large banks (TA>=$3 billion) based on real 2006Q1 dollars using the implicit GDP price deflator. 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.
ALL
VARIABLES
CAPt-1
QTR DUMMIES
BANK FE
N
R-SQ.
SMALL
MEDIUM
LARGE
ME1t-1
(1)
ME2t-1
(2)
ME1t-1
(3)
ME2t-1
(4)
ME1t-1
(5)
ME2t-1
(6)
ME1t-1
(7)
ME2t-1
(8)
0.277***
(4.640)
0.154***
(3.147)
0.597***
(8.296)
0.345***
(5.342)
0.403***
(3.384)
0.167**
(2.018)
-0.030
(-0.295)
0.033
(0.319)
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
56,858
0.013
52,719
0.003
26,071
0.033
25,718
0.019
17,078
0.024
15,381
0.007
13,709
0.031
11,620
0.005
48 Table 5
Validation of Ex-ante measure of Monitoring Effort - Association between Loan Quality and Monitoring Effort
This table reports results for the regressing of loan quality on ex-ante measures of monitoring effort computed in Table 5, controlling for other determinants of loan quality. Bank
variables are obtained from the Y9C call reports available from the Chicago Federal Reserve website. 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.
VARIABLES
ME t-1
CONTROL VARIABLES
QTR DUMMIES
BANK FE
N
ADJ. R-SQ.
LQ1t
ME=ME1
LQ2 t
LQ1t
ME=ME2
LQ2 t
LQ3 t
(1)
(2)
(3)
LQ3 t
(4)
(5)
(6)
0.057***
(16.630)
0.005***
(12.714)
0.005***
(14.926)
0.020***
(7.792)
0.001***
(3.777)
0.002***
(6.320)
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
56,858
0.430
56,858
0.271
56,858
0.264
52,719
0.405
52,719
0.265
52,719
0.254
49 TABLE 6
Loan Quality, Capital and Relationship Lending
This table reports the results for the regression of loan quality (LQ1, LQ2 and LQ3) on bank capital, controlling for other variables that affect loan quality in columns 1-3 and the
results of the regression of the ex-ante measures of monitoring effort (ME1 and ME2) on bank capital in columns 4-5, 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. Bank variables are obtained from the Y9C call reports
available from the Chicago Federal Reserve website. 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.
VARIABLES
CAPt-1
CAP*COREDt-1
COREDt-1
CONTROLS
CONTROLS *COREDt-1
QTRDUMMIES
BANK FE
N
ADJ.R-SQ.
R-SQ.
LQ1t
(1)
LQ2t
(2)
LQ3t
(3)
ME1t-1
(4)
ME2t-1
(5)
0.082***
(5.262)
0.103***
(4.389)
0.014
(1.245)
0.008***
(4.595)
-0.002
(-0.684)
0.004***
(3.883)
0.008***
(5.145)
0.004*
(1.846)
0.003***
(2.866)
0.139**
(2.012)
0.326***
(4.785)
-0.040***
(-6.382)
0.115*
(1.957)
0.079
(1.124)
-0.013**
(-1.964)
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
NO
NO
YES
YES
NO
NO
YES
YES
56,858
0.430
56,858
0.272
56,858
0.262
56,858
52,719
0.020
0.003
50 TABLE 7
Capital and Loan Quality based on instrumental variable regressions
This table reports the results for the instrumental variable approach for small and medium banks. Bank capital is instrumented using SENIORS, the fraction of seniors calculated
using county- and MSA-level population data from the 2000 and 2010 Census weighted by the deposit market share of the bank at county and MSA level. Panel A includes the
first stage regression results and shows the coefficients on the instrument, SENIORS. Panel B shows the coefficient on instrumented bank capital in the regression of loan quality
(LQ1, LQ2 and LQ3) and monitoring effort (ME1 and ME2) on instrumented bank capital for small banks in columns 1-3 and in columns 4-5 for, respectively. Panel C shows the
coefficient on instrumented bank capital in the regression of loan quality (LQ1, LQ2 and LQ3) and monitoring effort (ME1 and ME2) on instrumented bank capital for medium
banks in columns 1-3 and in columns 4-5 for, respectively. In interest of parsimony, we show the coefficients on the instrumented bank capital only in Panel B and Panel C. Bank
variables are obtained from the Y9C call reports available from the Chicago Federal Reserve website. The banks are classified as small (TA $500 million >=TA< $1 billion) and
medium (TA $1 billion >=TA< $3 billion) based on real 2006Q1 dollars using the implicit GDP price deflator. 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.
Panel A: First stage regression results
VARIABLES
INSTRUMENT: SENIORS
SENIORS
SMALL
(4)
MEDIUM
(5)
0.006***
(6.41)
0.011***
(17.34)
YES
YES
YES
YES
24,240
14,963
41.10***
300.55***
CONTROLS
QTRDUMMIES
N
F- Statistic first stage
Panel B: Second stage regression for small banks
VARIABLES
CAPt-1(instrumented)
CONTROLS
QTRDUMMIES
N
Wu-Hausman F
LQ1t
(1)
LQ2t
(2)
LQ3t
(3)
ME1t-1
(4)
ME2t-1
(5)
0.577***
(3.35)
0.055***
(3.35)
0.046***
(3.17)
1.959***
(4.04)
1.208***
(2.79)
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
24,240
24,240
24,240
24,240
23,911
14.68***
11.94***
9.90***
18.73***
8.39***
51 VARIABLES
CAPt-1(instrumented)
Panel C: Second stage regression for medium banks
LQ2t
LQ3t
LQ1t
(1)
(2)
(3)
ME1t-1
(4)
ME2t-1
(5)
0.210***
(3.79)
0.019***
(3.26)
0.012**
(2.35)
0.275*
(1.74)
0.094
(0.60)
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
N
14,963
14,963
14,963
14,963
13,614
Wu-Hausman F
2.78*
8.37***
3.34*
0.39
0.42
CONTROLS
QTRDUMMIES
52 TABLE 8
Panel A of Table 8 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. The variable NEWCAP is a dummy variable that takes value
1 if the bank issued new equity capital; 0 otherwise. Panel B 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. Bank variables are obtained from the Y9C call reports available from the Chicago Federal
Reserve website. 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.
Panel A: Changes in Loan Quality Measures for banks that issued Capital
Δ IS MEASURED OVER
VARIABLES
NEWCAPt-1
ΔCONTROLS
QTR DUMMIES
BANK FE
N
ADJ. R-SQ.
Δ IS MEASURED OVER
VARIABLES
NEWCAPt-1
QTR DUMMIES
BANK FE
N
R-SQ.
ΔLQ1t
(1)
4 quarters
ΔLQ2t
(2)
ΔLQ3t
(3)
ΔLQ1t
(4)
8 quarters
ΔLQ2t
(5)
ΔLQ3t
(6)
ΔLQ1t
(7)
12 quarters
ΔLQ2t
(8)
ΔLQ3t
(9)
0.051*
(1.882)
-0.053*
(-1.958)
-0.054**
(-1.962)
0.134***
(3.149)
0.014
(0.504)
0.011
(0.378)
0.178***
(3.512)
0.055*
(1.909)
0.053*
(1.788)
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
48,108
0.206
48,108
0.394
48,108
0.398
41,094
0.320
41,094
0.405
41,094
0.411
35,159
0.382
35,159
0.415
35,159
0.419
ΔME1t-1
(1)
Panel B: Changes in Monitoring Effort for banks that issued Capital
4 quarters
8 quarters
ΔME2t-1
ΔME1t-1
ΔME2t-1
(2)
(3)
(4)
ΔME1t-1
(5)
12 quarters
ΔME2t-1
(6)
0.344***
(3.246)
-0.155
(-1.500)
0.375***
(2.940)
-0.163
(-1.216)
0.342***
(2.695)
-0.154
(-1.115)
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
48,108
0.006
43,444
0.001
41,094
0.006
36,559
0.002
35,159
0.006
31,591
0.003
53 TABLE 9
This table includes the results of the additional analyses and robustness tests discussed under Section 4.4 to 4.7. All the panels of this table reports the results of the fixed effects
regression of loan quality measures on bank capital and other control variables in columns 1-3 and the results of the fixed effects regression of ex-ante measures of monitoring
effort (ME1 and ME2) on bank capital in columns 4-5 for all the banks in the sample period 1994Q2-2015Q1. Each panel includes an additional variable(s) of interest for
robustness check. Panel A includes squared CAP to test for non-linearity. Panel B includes a dummy variable OTDt-1 equal to 1 if bank participates in the OTD market; 0
otherwise; and interactions with OTD t-1. Sample size is smaller as banks started reporting OTD activity in 2006Q2. Panel C includes a dummy variable COMMDUMMYt-1 equal
to 1 if bank has COMM (commercial portfolio) above median for the quarter; 0 otherwise; and interactions with COMMDUMMY t-1. Panel D includes LOANYIELD computed as
interest income on loans. Panel E includes a dummy variable CRISIS equal to 1 a dummy variable equal to 1 if the bank quarter observation falls during the financial crisis period,
and zero otherwise; and interactions with CRISIS. Panel F includes REGCAP (tier one regulatory capital ratio) instead of equity capital. Bank variables are obtained from the Y9C
call reports available from the Chicago Federal Reserve website. 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.
Panel A: Non-linear association between capital and monitoring effort, and capital and loan quality.
VARIABLES
CAPt-1
CAPt-1 *CAPt-1
CONTROLS
QTRDUMMIES
BANKFE
N
R-SQ.
ADJ.R-SQ.
LQ1t
(1)
LQ2t
(2)
LQ3t
(3)
ME1t-1
(4)
ME2t-1
(5)
0.437***
(7.645)
-1.411***
(-5.444)
0.016***
(2.700)
-0.034
(-1.238)
0.029***
(5.532)
-0.086***
(-3.626)
1.176***
(6.678)
-4.128***
(-5.267)
0.477***
(3.334)
-1.484**
(-2.539)
YES
YES
YES
YES
YES
YES
YES
YES
YES
NO
YES
YES
NO
YES
YES
56,858
56,858
56,858
56,858
0.022
52,719
0.003
0.421
0.265
0.256
54 Panel B: Association between capital and monitoring effort, and capital and loan quality for banks engaging in OTD market vs. banks not engaging in OTD market.
VARIABLES
CAPt-1
CAPt-1 *OTDt
OTDt
CONTROLS
CONTROLS* OTDt-1
QTRDUMMIES
BANKFE
N
R-SQ.
ADJ.R-SQ.
LQ1t
(1)
LQ2t
(2)
LQ3t
(3)
ME1t-1
(4)
ME2t-1
(5)
0.232***
(7.300)
-0.017
(-0.549)
0.029
(1.524)
0.010**
(2.503)
0.003
(0.664)
-0.000
(-0.008)
0.019***
(6.436)
0.002
(0.531)
0.003
(1.142)
0.688***
(7.924)
-0.061
(-0.777)
0.011
(1.450)
0.414***
(5.533)
-0.139*
(-1.932)
0.019***
(2.735)
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
NO
NO
YES
YES
NO
NO
YES
YES
25,752
25,752
25,752
25,752
0.040
25,752
0.008
0.392
0.253
0.238
Panel C: Association between capital and monitoring effort, and capital and loan quality for banks with commercial loan portfolio above median vs. below median
VARIABLES
CAPt-1
CAPt-1 *COMMDUMMYt-1
COMMDUMMYt-1
CONTROLS
CONTROLS*COMMDUMMYt-1
QTRDUMMIES
BANKFE
N
R-SQ.
ADJ.R-SQ.
LQ1t
(1)
LQ2t
(2)
LQ3t
(3)
ME1t-1
(4)
ME2t-1
(5)
0.149***
(7.349)
-0.045*
(-1.914)
0.012
(1.363)
0.009***
(4.307)
-0.001
(-0.520)
0.001
(1.227)
0.011***
(6.189)
-0.002
(-0.852)
0.001*
(1.750)
0.322***
(4.997)
-0.100
(-1.335)
0.011
(1.459)
0.133**
(2.368)
0.047
(0.764)
0.004
(0.583)
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
NO
NO
YES
YES
NO
NO
YES
YES
56,858
56,858
56,858
56,858
0.014
52,719
0.004
0.413
0.266
0.255
55 Panel D: Association between capital and monitoring effort, and capital and loan quality controlling for loan yields
VARIABLES
CAPt-1
LOANYIELDt-1
CONTROLS
QTRDUMMIES
BANKFE
N
R-SQ.
ADJ.R-SQ.
LQ1t
(1)
LQ2t
(2)
LQ3t
(3)
ME1t-1
(4)
ME2t-1
(5)
0.124***
(8.010)
0.692***
(4.811)
0.008***
(4.675)
0.060***
(4.095)
0.010***
(6.465)
0.061***
(3.913)
0.279***
(4.686)
-0.292
(-0.755)
0.163***
(3.298)
-1.394***
(-3.375)
YES
YES
YES
YES
YES
YES
YES
YES
YES
NO
YES
YES
NO
YES
YES
56,858
56,858
56,858
56,858
0.022
52,719
0.003
0.421
0.265
0.256
Panel E: Association between capital and monitoring effort, and capital and loan quality during the Crises vs. non-crisis periods
VARIABLES
CAPt-1
CAPt-1 *CRISISt
CRISISt
CONTROLS
QTRDUMMIES
BANKFE
N
R-SQ.
ADJ.R-SQ.
LQ1t
(1)
LQ2t
(2)
LQ3t
(3)
ME1t-1
(4)
ME2t-1
(5)
0.130***
(8.138)
-0.008
(-0.452)
0.001
(0.108)
0.009***
(5.377)
-0.003
(-1.234)
0.006***
(4.147)
0.011***
(6.851)
-0.002
(-0.802)
0.000
(0.110)
0.285***
(4.725)
-0.097**
(-2.249)
-0.004
(-0.616)
0.165***
(3.331)
-0.122**
(-2.288)
0.004
(0.566)
YES
YES
YES
YES
YES
YES
YES
YES
YES
NO
YES
YES
NO
YES
YES
56,858
56,858
56,858
56,858
0.013
52,719
0.003
0.412
0.267
0.255
56 Panel F: Association between regulatory capital and monitoring effort
VARIABLES
REGCAPt-1
CONTROLS
QTRDUMMIES
BANKFE
N
R-SQ.
ADJ.R-SQ.
LQ1t
(1)
LQ2t
(2)
LQ3t
(3)
ME1t-1
(4)
ME2t-1
(5)
0.054***
(5.382)
0.004***
(3.796)
0.005***
(4.941)
0.181***
(5.412)
0.080***
(2.667)
YES
YES
YES
YES
YES
YES
YES
YES
YES
NO
YES
YES
NO
YES
YES
51,279
51,279
51,279
51,279
0.014
48,854
0.003
0.409
0.267
0.256
57