Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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. 36 References Admati, Anat R., and Martin F. Hellwig. 2013. The Bankers’ New Clothes: What’s Wrong with Banking and What to Do about It, Princeton University Press. Admati, Anat R., Peter M. DeMarzo, Martin F. Hellwig, and Paul C. Pfleiderer. 2013. Fallacies, Irrelevant Facts, and Myths in the Discussion of Capital Regulation: Why Bank Equity is Not Socially Expensive. Available at SSRN: http://ssrn.com/abstract=2349739 or http://dx.doi.org/10.2139/ssrn.2349739 Berger, A. N., and C. H. S. Bouwman. 2013. How does capital affect bank performance during financial crises? Journal of Financial Economics, vol 109, pp 146–76 Berger, A. N., and C. H. S. Bouwman. 2009. Bank liquidity creation. Review of Financial Studies 22, 3779–3837 Berger, A.N, N.H. Miller, M.A. Petersen, R.G. Rajan, and J.C. Stein. 2005. Does function follow organizational form? Evidence from the lending practices of large and small banks. Journal of Financial Economics, Vol. 76, pp. 237–269. Berlin, M., and L. Mester. 1999. Deposits and Relationship Lending. Review of Financial Studies, 12 (Fall 1999), pp. 579-607 Bhat, G., J. Lee, and S. Ryan. 2014. Working Paper. Utilizing Loan Loss Indicators by Loan Type to Sharpen the Evaluation of the Information Content and Economic Consequences of Banks’ Loan Loss Reserving Bhattacharya, S., and A. V. Thakor. 1993. Contemporary Banking Theory. Journal of Financial Intermediation 3:2–50. Brickley, J., J. Linck, and C. Smith. 2003, Boundaries of the firm: Evidence from the banking industry. Journal of Financial Economics 70 (3): 351-383. Bucks, B. K., A. B. Kennickell, and K. B. Moore. 2006. Recent Changes in US Family Finances: Evidence from the 2001 and 2004 Survey of Consumer Finances. Federal Reserve Bulletin A1–A38. Calomiris, C. W., and C. M. Kahn. 1991. The Role of Demandable Debt in Structuring Optimal Banking Arrangements. American Economic Review 81:497–513. Calomiris, C., and J. Mason. 2003. Consequences of bank distress during great depression. American Economic Review 93: 937-947. Calomiris, C.W., Wilson, B., 2004. Bank capital and portfolio manage- ment: the 1930s ‘‘capital crunch’’ and the scramble to shed risk. Journal of Business 77, 421–455. 37 Coleman, A. D. F., N. Esho, and I. G. Sharpe. 2006. Does Bank Monitoring Influence Loan Contract Terms? Journal of Financial Services Research, Vol. 30, No. 2, pp. 177-198. doi:10.1007/s10693-006-0017-5 Coval, J.D., Moskowitz, T.J., 1999. Home bias at home: local equity preferences in domestic portfolios. Journal of Finance 54, 2045–2073. Diamond, D. W. 1984. Financial intermediation and delegated monitoring. Review of Economic Studies 51, 393-414. Diamond, D. W., and P. H. Dybvig. 1983. Bank Runs, Deposit Insurance, and Liquidity. Journal of Political Economy 91:401–19. Diamond, D. W., and R. G. Rajan. 2000. A Theory of Bank Capital. Journal of Finance 55:2431– 65. Diamond, D. W., and R. G. Rajan. 2001. Liquidity Risk, Liquidity Creation, and Financial Fragility: A Theory of Banking. Journal of Political Economy 109:287–327. Furlong, F., and M. Keeley. 1989. Capital regulation and bank risk taking: a note. Journal of Banking and Finance 13:883-891. Holmstrom, B., and J. Tirole. 1997. Financial intermediation, loanable funds, and the real sector, Quarterly Journal of Economics 112:663-691 Jensen, M., and W. Meckling. 1976. A theory of the firm: managerial behavior, agency costs and ownership structure. Journal of Financial Economics 3-4. Kashyap, A., R. Rajan, and J. Stein. 2008. Rethinking Capital Regulation. Federal Reserve Bank of Kansas City Symposium on Maintaining Stability in a Changing Financial System, pp. 431471, Federal Reserve Bank of Kansas City, Kansas City. Koch, C., G. Richardson, and P. Van Horn. 2016. Bank leverage and regulatory regimes: Evidence from Great Depression and Great Recession. Forthcoming, American Economic Review: Papers and Proceedings. Liu, C., and S. Ryan. 1995. The Effect of Bank Loan Portfolio Composition on the Market Reaction to and Anticipation of Loan Loss Provisions. Journal of Accounting Research 33 (1): 77–94. Liu, C., and S. Ryan. 2006. Income Smoothing over the Business Cycle: Changes in Banks’ Coordinated Management of Provisions for Loan Losses and Loan Charge-Offs form the Pre1990 Bust to the 1990s Boom. The Accounting Review 81 (2): 421-441. Liu, C., S. Ryan, and J. Wahlen. 1997. Differential Valuation Implications of Loan Loss Provisions across Banks and Fiscal Quarters. The Accounting Review 72 (1): 133-146. 38 Mehran, H., and A. Thakor. 2011. Bank capital and value in the cross-section. Review of Financial Studies 24:1019-1067. Mishkin, Frederic 2000. What Should Central Banks Do? Federal Reserve Bank of St. Louis Review, vol. 82 (November/December), pp. 1-14. Nakamura, N. 1994. Small Borrowers and the Survival of the Small Banks: Is Mouse Bank Mighty or Mickey? Business review (Federal Reserve Bank of Philadelphia) 02/1994 Nichols, D., J. Wahlen, and M. Wieland. 2009. Publicly-traded versus privately-held: implications for conditional conservatism in bank accounting. Review of Accounting Studies, 14, 88–122. Purnanandam, A. 2011. Originate-to-distribute Model and the Subprime Mortgage Crises. Review of Financial Studies 24: 1881-1915. Ramakrishnan, R., and A. Thakor. 1984. Information Reliability and a Theory of Financial Intermediation. Review of Economic Studies 51-3, 415–432. Song, F., and A. Thakor. 2007. Relationship banking, fragility and the asset-liability matching problem. Review of Financial Studies 20-6:2129-2177 Thakor, A. 2014. Bank Capital and Financial Stability: An Economic Tradeoff or a Faustian Bargain? Annual Review of Financial Economics Vol. 6, December 2014, pp. 185–223. Wahlen J. 1994. The Nature of Information in Commercial Bank Loan Loss Disclosures. The Accounting Review 69 (3): 455-478. 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