How Does Capital Affect Bank Performance

During Financial Crises?

Allen N. Berger †

University of South Carolina, Wharton Financial Institutions Center, and CentER – Tilburg University

Christa H.S. Bouwman ‡

Case Western Reserve University and Wharton Financial Institutions Center

March 2011

The recent financial crisis has raised important issues regarding bank capital. Various reform proposals involve requiring banks to hold more capital. But assessing these proposals requires an understanding of how capital affects bank performance. Existing theories produce conflicting predictions regarding the effect of capital on bank performance during normal times and have little to say about the effect during financial crises. This paper addresses these issues empirically by formulating and testing hypotheses regarding the effect of capital on three dimensions of bank performance – survival, market share, and profitability – during financial crises and normal times. We distinguish between two banking crises and three market crises that occurred in the U.S. over the past quarter century. We have two main results. First, capital helps banks of all sizes during banking crises. Higher capital helps these banks increase their probability of survival, market share, and profitability during such crises. Second, higher capital improves the performance of small banks in all three dimensions during market crises and normal times as well, but the effect on medium and large banks during these periods is less pronounced. Overall, our results suggest that capital is important for small banks at all times and is important for medium and large banks primarily during banking crises. † Contact details: Moore School of Business, University of South Carolina, 1705 College Street, Columbia, SC 29208. Tel: 803-576-8440. Fax: 803-777-6876. E-mail: aberger@moore.sc.edu. ‡ Contact details: Weatherhead School of Management, Case Western Reserve University, 10900 Euclid Avenue, 362 PBL, Cleveland, OH 44106. Tel.: 216-368-3688. Fax: 216-368-6249. E-mail: christa.bouwman@case.edu. Keywords: Financial Crises, Survival, Market Share, Profitability, Liquidity Creation, and Banking. JEL Classification: G01, G28, and G21. This is a significantly expanded version of the second part of an earlier paper, “Financial Crises and Bank Liquidity Creation.” A previous version of this paper was entitled: “Bank Capital, Survival and Performance Around Financial Crises.” The authors thank Nittai Bergman, Lamont Black, Rebel Cole, Bob DeYoung, Mark Flannery, Paolo Fulghieri, Steven Ongena, Bruno Parigi, Peter Ritchken, Katherine Samolyk, Asani Sarkar, James Thomson, Greg Udell, Todd Vermilyae, and participants at presentations at the American Finance Association Meeting, the Bank for International Settlements, the University of Venice, the European School of Management and Technology in Berlin, the CREI / JFI / CEPR Conference on Financial Crises at Pompeu Fabra, the Boston Federal Reserve, the Philadelphia Federal Reserve, the San Francisco Federal Reserve, the Cleveland Federal Reserve, the International Monetary Fund, the Summer Research Conference in Finance at the ISB in Hyderabad, the Unicredit Conference on Banking and Finance, the University of Kansas’ Southwind Finance Conference, Erasmus University, and Tilburg University for useful comments.

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1. Introduction

The recent financial crisis has raised fundamental issues about the role of bank equity capital. Various

proposals have been put forth which argue that banks should hold more capital (e.g., Kashyap, Rajan, and

Stein 2009, Hart and Zingales 2009, Acharya, Mehran, and Thakor 2010, Basel III (2010)).1

Theories predict that the effect of bank capital on any of these three dimensions of performance could

be positive or negative. As a prelude to a more extensive discussion of this in the next section, here we briefly

present the main arguments. Consider survival probability first. Holding fixed the bank’s asset and liability

portfolios, higher capital mechanically implies a higher survival probability (also suggested by monitoring-

based theories like Holmstrom and Tirole 1997), but theories on the disciplining role of demandable debt

suggest that a bank with more demandable debt and less capital may make better loans that are less likely to

default and cause bank failure (e.g., Calomiris and Kahn 1991). Next, consider the issue of market share.

While recent banking theories suggest a positive relationship between capital and market share (e.g., Allen,

Carletti, and Marquez forthcoming, Mehran and Thakor forthcoming), the literature on the interaction between

a nonfinancial firm’s leverage and its product-market dynamics argues that the relationship is negative (e.g.,

Brander and Lewis 1986). Finally, consider profitability. If capital improves monitoring incentives as in

Holmstrom and Tirole (1997), higher capital could enhance return on equity, whereas the literature on the

disciplining role of debt suggests the opposite (Calomiris and Kahn 1991).

An underlying

premise in all of these proposals is that there are externalities due to the safety net provided to banks and thus

social efficiency can be improved by requiring banks to operate with more capital, especially during financial

crises. Bankers, however, have typically argued that being forced to hold more capital would jeopardize their

performance, especially profitability, and the argument that higher capital need not be beneficial has found

some support in the academic literature as well (e.g., Calomiris and Kahn 1991). The issue of what effects

capital has on bank performance, and how these effects might differ between crises and normal times, thus

boils down to an empirical question, and one that we confront in this paper. In particular, the goal of this

paper is to empirically examine the effects of bank capital on three dimensions of bank performance –

probability of survival, market share, and profitability – during different types of financial crises and normal

times.

1 Hoshi and Kashyap (2010) draw lessons for the U.S. from Japan’s efforts to recapitalize its banking sector.

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This discussion suggests that although a substantial body of theoretical research is available, these

theories focus on different forces and hence produce conflicting implications, pointing strongly to the need for

empirical mediation. Understanding the effects of capital is interesting in its own right, but is particularly

compelling during times of stress, such as financial crises. While the papers discussed above focus on the role

capital plays during normal times, we examine both crises and normal times. We therefore need to extrapolate

these theoretical results to the crisis context in a manner which is still consistent with the intuition of the

theories, which we do in the next section. For each of the three performance dimensions, we take our cue from

the theories and formulate hypotheses that allow us to assess whether capital helps or hurts bank performance.

These hypotheses are then tested using data on virtually every U.S. bank from 1984:Q1 until 2009:Q4.

We test our survival hypotheses using logit regressions. We regress the log odds ratio of the

probability of survival on the bank’s pre-crisis capital ratio interacted with a banking crisis dummy, a market

crisis dummy, and a normal times dummy, plus a set of control variables. As discussed below, banking crises

are those that originated in the banking sector, and market crises are those that originated outside banking in

the financial markets. The interaction terms capture the effect of capital on survival during banking crises,

market crises, and normal times, respectively. Moreover, we examine small banks (gross total assets or GTA

up to $1 billion), medium banks (GTA exceeding $1 billion and up to $3 billion), and large banks (GTA

exceeding $3 billion) as three separate groups, since the effect of capital likely differs by bank size (e.g.,

Berger and Bouwman 2009).2

We test our market share hypotheses by defining market share in terms of the bank’s share of

aggregate bank liquidity creation. We prefer this measure over the bank’s market share of assets because

liquidity creation is a better representation of bank output than assets alone, since liquidity creation takes into

account all on- and off-balance sheet activities.

Our results support the hypothesis that capital enhances the survival probability

for banks of all sizes during banking crises and, in the case of small banks, also during market crises and

normal times.

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2 Gross Total Assets or GTA equals total assets plus the allowance for loan and lease losses and the allocated transfer risk reserve (a reserve for certain foreign loans). Total assets on Call Reports deduct these two reserves, which are held to cover potential credit losses. We add these reserves back to measure the full value of the loans financed. Section 5.4 shows results based on alternative cutoffs between medium and large banks.

We regress the percentage change in market share during the

crisis on the bank’s average pre-crisis capital ratio (interacted with the banking crisis, market crisis, and

3 Robustness checks yield comparable results based on gross total assets market share (see Section 5.1).

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normal times dummies mentioned above) and a set of control variables. Our results support the hypothesis

that capital helps to increase market shares for banks of all sizes during banking crises and normal times, and

for small banks also during market crises.

To test our profitability hypotheses, we run regressions that are similar to the market share regressions

except that we use the change in return on equity (ROE) during a crisis as the dependent variable. ROE is an

appropriate profitability measure since both net income (the numerator) and equity (the denominator) reflect

all of the bank’s on- and off-balance sheet activities.4

We perform a variety of robustness checks. First, to check the sensitivity of the results to our

definitions of various performance measures, we use alternative definitions of survival, market share, and

profitability. Second, we use regulatory capital ratios instead of the equity-to-assets ratio to define capital.

Third, we drop banks that are “Too-Big-To-Fail” from the large-bank sample to see if our large-bank

conclusions are driven by the dominance of a few very large banks. Fourth, we use alternative cutoffs to

separate medium and large banks to examine the sensitivity of our results to the manner in which banks are

classified by size. Fifth, we measure pre-crisis capital ratios averaged over the four quarters before the crisis

or one quarter before the crisis starts rather than averaging them over the eight quarters before the crisis. The

purpose is to determine if the time period over which pre-crisis capital is defined affects our results. Sixth,

while the theories suggest a causal relationship from capital to performance, we recognize that in practice both

may be jointly determined. Although our main regression analyses use lagged capital to mitigate this potential

endogeneity problem, we also address it more directly using an instrumental variable approach.

Our results support the hypothesis that capital improves

profitability for small banks at all times, for medium banks during market crises, and for large banks during

banking and market crises.

While most of our analyses focus on the effect of book capital on the performance of individual banks,

we also perform analyses for listed entities (listed banks and listed bank holding companies). Specifically, we

examine how book and market capital ratios affect the performance of these organizations during crises and

normal times.

We can broadly summarize our findings as follows. First, capital is especially beneficial during

4 A robustness check based on return on assets (ROA), which divides net income by total assets, a measure of the bank’s on-balance sheet activities, shows similar results for small banks and weaker results for medium and large banks (see Section 5.1).

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banking crises. It helps banks of all sizes during such crises – higher pre-crisis capital increases the odds of

survival and enhances market share for banks of all sizes and it increases profitability for all but medium-sized

banks during such crises. Second, small banks benefit in all respects from higher capital during market crises

and normal times as well. For large and medium banks, higher capital improves only profitability during

market crises and only market share during normal times. While the survival and market share results are

highly robust, the profitability results are less so.

The remainder of this paper is organized as follows. Section 2 develops the empirical hypotheses.

Section 3 explains our empirical approach, describes the financial crises and normal times, describes all the

variables and the sample, and provides summary statistics. Section 4 discusses the results of our empirical

tests. Section 5 includes the robustness tests. Section 6 contains the additional analysis of listed entities.

Section 7 concludes.

2. Development of the empirical hypotheses

In this section, we review existing theories to formulate our empirical hypotheses about the effects of bank

capital on the survival probability, market share, and profitability of banks during crises and normal times.

2.1. Survival

Hypothesis 1a: Capital enhances the bank’s survival probability during crises and normal times.

Hypothesis 1b: Capital diminishes the bank’s survival probability during crises and normal times.

There are several reasons, grounded in existing theories, to believe that capital improves a bank’s survival

probability. First, there is a set of theories that emphasizes the role of capital as a buffer to absorb shocks to

earnings (e.g., Repullo 2004, Von Thadden 2004). If the bank’s portfolio, screening, and monitoring choices

are held fixed, then this buffer role immediately implies that higher capital increases the probability of the

bank’s survival.5

5 We know from various theories, however, that all of these choices are influenced by the bank’s capital structure.

We can view this as the “mechanical effect” of higher capital. Second, there is another set

of theories that focuses on the incentive effects of capital. This set includes theories based on monitoring,

screening, and asset-substitution-moral-hazard. The monitoring-based papers include Holmstrom and Tirole

(1997), Allen, Carletti, and Marquez (forthcoming), and Mehran and Thakor (forthcoming). A key result in

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these papers is that higher bank capital induces higher levels of borrower monitoring by the bank, thereby

reducing the probability of default or otherwise improving the bank’s survival odds indirectly by increasing

the surplus generated by the bank-borrower relationship.6

A different strand of the theoretical literature suggests that banks with higher capital may experience

lower survival odds. Calomiris and Kahn (1991) show that a capital structure with sufficiently high demand

deposits (and by implication lower equity) leads to more effective monitoring of bank managers by informed

depositors and hence a smaller likelihood of bad investment decisions. This suggests that a bank with higher

capital (and consequently lower deposits) may face a higher probability of bad loans and hence loan default,

which may result in a lower survival probability. This paper has spawned a sizeable literature on the market

discipline role of bank leverage (see Freixas and Rochet (2008) for an overview).

In their screening-based theory of banking with

behaviorally-biased agents, Coval and Thakor (2005) show that a minimum amount of capital may be essential

to the very viability of the bank. The asset-substitution-moral-hazard theories argue that government

guarantees cause shareholders to prefer low capital and excessive risk to increase the value of the deposit

insurance put option (Merton 1977), and that banks with more capital will optimally choose less risky

portfolios (see the overview of this literature in Freixas and Rochet 2008).

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Thus, some theories predict that higher bank capital should lead to a higher survival probability for the

bank, whereas others suggests that higher capital may worsen the portfolio choices and liquidity of banks and

hence lead to a lower survival likelihood.

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These papers do not focus on financial crises per se. However, since a bank’s likelihood of survival is

lower during a crisis, it follows that the effect of capital on survival may be more positive during a crisis,

particularly in light of regulatory discretion in closing banks, forcing them into assisted mergers, or otherwise

6 In Mehran and Thakor (forthcoming), the mechanical effect of capital also shows up – higher-capital banks are more likely to survive not just because they monitor more, but also because they have a greater capital cushion to absorb losses and avoid interim closure. 7 For example, Diamond and Rajan (2001) argue that replacing demandable deposits with capital weakens the bank’s incentive to collect repayments from borrowers, thereby reducing loan liquidity. In fact, in an all-equity bank, with no demand deposits to discipline the bank the loan would be completely illiquid because the bank cannot credibly pre-commit to collect repayment on behalf of others, so the loan could not be sold to another bank, and the bank could additionally credibly threaten to withhold its repayment collection technology. 8 The empirical literature on bank failure finds that capital reduces the probability of failure (e.g., Wheelock and Wilson 2000), but this literature does not focus on crises.

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resolving problem institutions based on their capital ratios.9

2.2. Market share

Hypothesis 2a: Capital enhances the bank’s market share during crises and normal times.

Hypothesis 2b: Capital diminishes the bank’s market share during crises and normal times.

The theories on the effect of capital on market share also produce opposing predictions. In the banking models

of Holmstrom and Tirole (1997), Allen and Gale (2004), Boot and Marinc (2008), Allen, Carletti, and

Marquez (forthcoming), and Mehran and Thakor (forthcoming), banks derive a competitive advantage from

higher capital. These papers imply that higher-capital banks will end up with higher market shares.

In contrast, there is also a literature that focuses on the relationship between leverage and market share

for nonfinancial firms (e.g., Brander and Lewis 1986, Lyandres 2006). This literature shows that more highly-

levered firms will be more aggressive in their product-market-expansion strategies and hence suggests that

capital and market share will be negatively correlated.

The literature discussed above does not focus on crises, so the predictions of these papers should be

viewed as applying during normal times. However, it is plausible to argue that the competitive advantage of

capital is more pronounced during crises, particularly during banking crises. There are several reasons for this.

First, the bank’s customers may be more cognizant of the bank’s capital during a crisis, making it easier for

better-capitalized banks to take customers away from lesser-capitalized peers. Second, banks with more

capital may have greater flexibility to make certain types of loans, offer credit lines and otherwise create

liquidity in various ways that may be unavailable to lower-capital banks that may feel particularly constrained,

possibly by regulators, during crises. Third, banking crises are generally associated with numerous bank

failures and near failures. Failing and near-failing banks tend to be bought by competitors and such

acquisitions have to be approved by bank regulators. Since regulatory approval depends in part on the

acquiring bank’s capital, banks with higher capital ratios are better positioned to buy their troubled brethren,

and hence improve their market share.

9 The benefits of higher capital may be weaker if regulators delay closure of troubled banks because of regulatory career concerns (Boot and Thakor 1993) or because regulators perceive a high value associated with avoiding bankruptcy as, for example, happened during the recent subprime lending crisis (Veronesi and Zingales 2010).

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2.3. Profitability

Hypothesis 3a: Capital enhances the profitability of the bank during crises and normal times.

Hypothesis 3b: Capital diminishes the profitability of the bank during crises and normal times.

The theoretical literature also offers conflicting predictions about how capital should affect bank profitability.

One strand of theory predicts that higher capital enhances profitability. As pointed out above, Holmstrom and

Tirole (1997), Allen, Carletti, and Marquez (forthcoming), and Mehran and Thakor (forthcoming) show that

high bank capital increases the total surplus generated in the bank-borrower relationship. Assuming that banks

keep a large enough portion of the surplus, higher capital will lead to higher bank profitability. Moreover, if

the ratio of the surplus generated by high- versus low-capital banks is higher during crises, it follows that high-

capital banks will be able to improve their profitability during crises relative to low-capital banks.10

In contrast, another strand of theory predicts that higher capital should lead to reduced profitability.

The most obvious argument here goes back to Modigliani and Miller (1963), who show that higher capital

mechanically leads to lower ROE. Moreover, the literature on the disciplining role of debt (e.g., Calomiris and

Kahn 1991) suggests that higher leverage improves the bank’s asset choice and hence its profitability. Thus,

banks with higher capital have lower-quality assets. If these assets deteriorate in value more during a crisis

than do higher-quality assets, then banks with higher capital may suffer bigger declines in profitability during

crises than their lower-capital counterparts.

2.4. Empirical implications of the theories

It is clear from the discussions above that the theories have opposing views on the effect of capital on

performance. Our empirical analyses attempt to assess the impact of capital on performance to determine

which theories have the strongest empirical support. Not finding support for a particular theory does not

constitute a rejection of that theory, but simply implies that the theory for which we do find support is more 10 A recent survey paper by Campello, Giambona, Graham, and Harvey (2009) sheds further light on why banks with high capital may improve their profitability during crises relative to banks with low capital. It shows that during the subprime lending crisis, banks renegotiated in their own favor the terms for lines of credit with borrowers, possibly by threatening to invoke the material-adverse-change clause. It may be that banks with more capital could do this more easily because their stronger reputation in the loan commitment market gives them greater bargaining power with their borrowers (see Boot, Greenbaum, and Thakor 1993), which would result in increased profitability for high-capital banks (relative to low-capital banks) during crises. Berger (1995) finds that the relationship between capital and earnings can be positive or negative, but does not differentiate between financial crises and normal times.

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dominant empirically. This is because what we may be picking up in the data is the net effect of opposing

forces identified by different theories.

3. Methodology

This section first explains our empirical approach and describes the financial crises and normal times. It then

explains the performance measures. Next, it discusses the key exogenous variables and the control variables.

Finally, it describes the sample and provides summary statistics.

In our empirical approach, we examine the effect of capital (and other bank conditions) measured prior

to a crisis on bank performance during a crisis. We measure capital before a crisis for two reasons. First,

since it is not known a priori when a crisis will strike, the interesting question is whether banks that have

higher capital going into a crisis benefit from these higher capital ratios during a crisis. That is, we want to

know whether higher pre-crisis capital ratios result in better performance during a crisis. Second, it mitigates

endogeneity concerns because lagged capital and current performance are less likely to be jointly determined.

3.1. Empirical approach and description of financial crises and normal times

Our analyses focus on crises that occurred between 1984:Q1 and 2009:Q4. They include two banking crises

(crises that originated in the banking sector) and three market crises (crises that originated outside banking in

the financial markets). The banking crises are the credit crunch of the early 1990s (1990:Q1 – 1992:Q4) and

the recent subprime lending crisis (2007:Q3 – 2009:Q4). The market crises are the 1987 stock market crash

(1987:Q4); the Russian debt crisis plus Long-Term Capital Management (LTCM) bailout of 1998 (1998:Q3 –

1998:Q4); and the bursting of the dot.com bubble and the September 11 terrorist attacks of the early 2000s

(2000:Q2 – 2002:Q3). Appendix I describes these crises in detail.

Our hypotheses focus on the effect of a bank’s (pre-crisis) capital on its performance (survival, market

share, and profitability) during a crisis. A key issue is how to measure pre-crisis capital. In all of our main

analyses, we average each bank’s capital ratio over the eight quarters before the crisis to reduce the impact of

outliers. In robustness checks, we alternatively define the pre-crisis period as the four quarters before a crisis

or the quarter before a crisis (see Section 5.5). Our survival analyses then link this average pre-crisis capital to

whether a bank survived a crisis (it was in the sample one quarter before the crisis and is still in the sample one

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quarter after the crisis; see Section 3.2). Our market share analyses link pre-crisis capital to the bank’s

percentage change in market share (see Section 3.3), defined as the bank’s average market share during a crisis

minus its average share over the eight quarters before the crisis, normalized by its average pre-crisis market

share and multiplied by 100. Similarly, our profitability analyses link pre-crisis capital to the bank’s change in

profitability (see Section 3.4), defined as the bank’s average ROE during a crisis minus its ROE over the eight

quarters before the crisis.

We expect the effect of capital to be more positive during financial crises than during normal times.

While we highlight above how we examine the effect of (average) pre-crisis capital on bank performance

during a crisis, we still have to address how we measure “normal times.” A naïve approach would be to

simply view all non-crisis quarters as such. However, if so, it is not clear then how to examine the effect of

capital on bank performance during normal times. To ensure that we analyze actual crises and normal times in

a comparable way, we create “fake” crises to represent normal times. In essence, these “fake” crises act as a

control group. To construct these “fake” crises, we use the two longest time periods between actual financial

crises over our entire sample period. These periods are between the credit crunch and the Russian debt crisis,

and between the bursting of the dot.com bubble and the subprime lending crisis. In each case, we take the

entire period between the crises, designate the first eight quarters as “pre-crisis” and the last eight quarters as

“post-crisis” and the remaining quarters in the middle as the “fake” crisis. This treatment of the first eight

quarters as “pre-crisis” is consistent with our analysis of the banking and market crises. We thus end up with a

six-quarter “fake” crisis period between the credit crunch and the Russian debt crisis (from 1995:Q1 to

1996:Q2) and a three-quarter “fake” crisis period between the dot.com bubble and the subprime lending crisis

(from 2004:Q4 to 2005:Q2).11

To analyze how capital affects banks’ ability to survive crisis and normal times, we pool the data of

the banking crises, the market crises, and the normal times.

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11 Results are qualitatively similar if we instead use five- or four-quarter crisis periods for the first “fake” crisis, and two- or one-quarter crisis periods for the second “fake” crisis.

Since we have two banking crises, three market

crises, and two normal time periods (the “fake” crises), we have up to seven observations per bank, i.e.,

. Using these data, we run the following logit regressions:

12 Our regressions group the two banking crises together and the three market crises together. This approach allows us to contrast the effect of capital during banking and market crises. To check the robustness of our findings, we also run our main regressions using individual crisis dummies instead, and obtain broadly consistent results (not shown for brevity).

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(1)

where measures whether bank i survived crisis or normal time period t (see Section 3.2).

is the bank’s average capital ratio over the eight quarters before crisis or normal time period t

(see Section 3.5). , , and are dummy variables that equal 1 if t is a

banking crisis, market crisis, or normal time period, respectively, and 0 otherwise. is a set of control

variables measured over the pre-crisis period (see Section 3.6).

To examine the impact of capital on a bank’s market share and profitability during banking crises,

market crises, and normal times, we use the following regression specifications:

(2)

(3)

where is the percentage change in bank i’s aggregate market share (see Section 3.3) and

is the change in bank i’s profitability (see Section 3.4). To mitigate the influence of outliers, both

variables are winsorized at the 3% level.13

Since each bank enters up to seven times in these regressions, all regressions are estimated with robust

standard errors, clustered by bank, to control for heteroskedasticity as well as possible correlation between

observations of the same bank in different years. The regressions also include individual crisis and normal

times dummies which act as time fixed effects.

is as defined above. is a set of control

variables over the pre-crisis period (see Section 3.6).

The literature documents differences by bank size in terms of portfolio composition (e.g., Kashyap,

Rajan, and Stein 2002, Berger, Miller, Petersen, Rajan, and Stein 2005) and the effect of capital on liquidity

creation which we use to construct our main market share variable (Berger and Bouwman 2009). We therefore

13 Unwinsorized data yielded changes in market share (profitability) that were roughly 54,600 (6,600) times the average change. Winsorization at the 1% level reduced this to 6.3 (23.6) times the average change, which still seemed too high for the change in profitability. Winsorization at the 3% level brought these numbers down to 2.0 and 7.2, respectively. Winsorization at the 1% level yields market share and profitability results that are very similar to the ones presented here, except that the market share results for medium banks and the profitability results for small banks are both weaker during banking crises.

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split the sample into small banks (gross total assets (GTA) up to $1 billion), medium banks (GTA exceeding

$1 billion and up to $3 billion), and large banks (GTA exceeding $3 billion) and run all regressions separately

for these three sets of banks. All dollar values are in 2009:Q4 terms. Our definition of small banks conforms

to the usual notion of “community banks.” The $3 billion cutoff for GTA divides the remaining observations

roughly in half.14

3.2. Definition of survival

To measure whether a bank survived a crisis, we require that the bank was not acquired and did not fail during

the crisis. Specifically, we use SURV, a dummy that equals 1 if the bank is in the sample one quarter before

such a crisis started and is still in the sample one quarter after the crisis, and 0 otherwise.15,16

3.3. Definition of market share

While most of the literature focuses on the role of capital in “traditional banks” that engage only in on-

balance-sheet activities, several papers highlight the importance of banks’ off-balance sheet activities (Boot,

Greenbaum, and Thakor 1993, Holmstrom and Tirole 1997, Kashyap, Rajan, and Stein 2002). We therefore

measure a bank’s competitive position as the bank’s market share of overall bank liquidity creation. Liquidity

creation is a superior measure of bank output since it is the only measure based on all the bank’s on- and off-

balance sheet activities.17

We calculate the dollar amount of liquidity created by each bank using Berger and Bouwman’s (2009)

preferred liquidity creation measure. The three-step procedure used to construct this measure is explained in

Appendix II. A bank’s liquidity creation market share is the dollar amount of liquidity creation by the bank

divided by the dollar amount of liquidity created by the industry.

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To establish whether banks improve their competitive positions during banking crises, market crises,

14 Berger and Bouwman (2009) also use these cutoffs. As shown in Section 5.4, broadly similar results are obtained when $5 billion and $10 billion cutoffs between medium and large banks are used instead. 15 Banks that were merged within a bank holding company are not classified as non-survivors since it unclear whether these consolidations occur because the bank is troubled or not. 16 Robustness checks using a slightly longer time window yield similar results (see Section 5.1). 17 As a robustness check, we rerun our main regressions using the market share of gross total assets, and obtain similar results (see Section 5.1). 18 Market shares are not merger adjusted because acquisitions are a key way for banks to increase their market shares, particularly during banking crises. In our analysis, a bank’s market share rises if it acquires another bank. Merger-adjusting market shares would take out this effect.

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and normal times, we define each bank’s percentage change in liquidity creation market share, %ΔLCSHARE,

as the bank’s average market share during a crisis minus its average market share over the eight quarters

before the crisis, normalized by its average pre-crisis market share and multiplied by 100.

3.4. Definition of profitability

We measure a bank’s profitability using the bank’s return on equity (ROE), i.e. net income divided by

stockholders equity.19 This is a comprehensive profitability measure, since banks may have substantial off-

balance sheet portfolios. Banks must allocate capital against every off-balance sheet activity in which they

engage. Hence, net income and equity both reflect the bank’s on- and off-balance sheet activities.20

To examine whether a bank improves its profitability during banking crises, market crises, and normal

times, we focus on the change in ROE (ΔROE), defined as the bank’s average profitability during these crises

minus the bank’s average ROE over the eight quarters before these crises.

3.5. Key exogenous variables

The key exogenous variables are three bank capital ratio-crisis interaction terms: EQRAT * BNKCRIS,

EQRAT * MKTCRIS, and EQRAT * NORMALTIME. EQRAT is the ratio of equity capital to gross total

assets, averaged over the eight quarters before the crisis.21

BNKCRIS, MKTCRIS, and NORMALTIME are

as defined in Section 3.1.

3.6. Control variables

The survival regressions contain X, a set of control variables which include: bank credit risk, bank size, bank

holding company membership, local market power, and profitability. The market share and profitability

regressions contain a slightly smaller set of controls Z, which excludes profitability from X. We discuss these

variables in turn. Each control variable is averaged over the eight-quarter pre-crisis period, except when noted 19 If a bank’s capital to GTA ratio is less than one percent, we calculate ROE as net income divided by one percent of GTA. For observations for which equity is between 0% and 1% of GTA, dividing by equity would result in extraordinarily high values. For observations for which equity is negative, the conventionally-defined ROE would not make economic sense. We considered the alternative of dropping negative-equity observations, but rejected it because these are the banks that are most likely to be informative of banks’ ability to survive crises. 20 Section 5.1 includes a robustness check in which we use return on assets (ROA) instead. 21 Similar results are obtained in robustness checks which use a bank’s regulatory capital ratio (see Section 5.2) or measure capital at alternative points before the crisis starts (see Section 5.5).

13

otherwise.

Credit risk, defined as the bank’s Basel I risk-weighted assets divided by gross total assets, is used as a

measure of bank risk taking (e.g., Logan 2001). Risk-weighted assets, a weighted average of the bank’s assets

and off-balance-sheet activities designed to measure credit risk, is the denominator in the Basel I risk-based

capital requirements. Since these requirements only became effective in December 1990 and, hence, were

only reported in Call Reports from that moment onward, we use a Federal Reserve Board program to construct

risk-weighted assets from the beginning of our sample period. Banks with riskier portfolios (i.e. higher risk-

weighted assets relative to gross total assets) may be less likely to survive crises. They may also find it harder

to improve their market shares and profitability during crises. We therefore interact the credit risk variable

with the three crisis dummies, and expect the coefficients on the banking and market crisis interaction terms to

be negative in all regressions.

Bank size is controlled for by including lnLC, the log of liquidity creation, in all regressions.22

To control for bank holding company status, we include D-BHC, a dummy variable that equals 1 if the

bank was part of a bank holding company (BHC) at any time in the eight quarters preceding the crisis. BHC

membership is expected to help a bank survive and strengthen its competitive position because the holding

company is required to act as a source of strength to all the banks it owns, and may also inject equity

voluntarily when needed. In addition, other banks in the holding company provide cross-guarantees. Houston,

James, and Marcus (1997) find that bank loan growth depends on BHC membership. Thus, the coefficient on

bank holding company status is expected to be positive and significant in the survival and market-share

regressions. BHC membership is also expected to reduce the cost of financing and enhance profitability

In

addition, we run regressions separately for small, medium, and large banks. Bank size is expected to have a

positive effect on the probability of survival, since it is well-known that larger banks have higher survival odds

than smaller banks. In contrast, the coefficient on bank size is expected to be negative and significant for all

size classes in the market share regressions, since the law of diminishing marginal returns suggests that it will

be more difficult for bigger banks (that already have larger market shares) to improve their market shares. It is

unclear whether bank size will have a significant effect on banks’ ability to improve their profitability

(measured as a bank’s ROE) during crises.

22 Robustness checks which instead include lnGTA, the log of gross total assets, yield similar results. These are not shown for brevity.

14

because it provides greater access to internal capital markets and provides potential protection against the

vagaries of market financing.

We control for local market power by including HHI, the bank-level Herfindahl-Hirschman index of

deposit concentration for the local markets in which the bank is present.23 From 1984-2004, we define the

local market as the Metropolitan Statistical Area (MSA) or non-MSA county in which the offices are located.24

After 2004, we use the new local market definitions based on Core Based Statistical Area (CBSA) and non-

CBSA county.25

The survival regressions also include a measure of profitability because banks that are more profitable

before the crisis may be more likely to survive crises. We use a bank’s return on assets, ROA, for this

purpose. The reason to use ROA instead of ROE is that we want to measure the effect of capital on banks’

ability to survive crises and avoid using control variables that include capital.

The larger is HHI, the greater is a bank’s market power. Since more market power should

help a bank survive, the coefficient on HHI is expected to be positive in the survival regressions for banks of

all sizes. More market power likely makes it harder to improve market share since regulatory approval for

acquisitions will be more difficult to obtain. The coefficient on HHI is therefore likely to be negative in the

market share regressions. Since higher market power increases the profitability of local loans and deposits,

more market power should make it easier to improve profitability. Thus, the coefficient on HHI in the

profitability regressions is expected to be positive.

3.7. Sample and summary statistics

For every bank in the U.S., we obtain quarterly Call Report data from 1984:Q1 to 2009:Q4. We keep a bank

in the sample if it: 1) has commercial real estate or commercial and industrial loans outstanding; 2) has

deposits; and 3) has gross total assets or GTA exceeding $25 million.

As indicated above, we split these banks into three size categories. Analyses that focus on the effect 23 While our focus is on the change in banks’ competitive positions measured in terms of their aggregate liquidity creation market shares, we control for “local market competition” measured as the bank-level Herfindahl-Hirschman index based on local market deposit shares. This is a standard measure of competition used in antitrust analysis in the U.S. Deposits are used for this purpose because deposits are the only bank output variable for which location is known. 24 When appropriate, we use New England County Metropolitan Areas (NECMAs) instead of MSAs, but refer to these as MSAs. 25 The term CBSA collectively refers to Metropolitan Statistical Areas and newly-created Micropolitan Statistical Areas. Areas based on these new standards were announced in June 2003. For recent years, the Summary of Deposits data needed to construct HHI is available on the FDIC’s website only based on the new definition. It is not possible to use the new definition for our entire sample period.

15

of capital on survival have 46,107 small-bank, 1,599 medium-bank, and 1,194 large-bank observations.

Analyses that focus on the effect of capital on market share and profitability have 52,107 small-bank, 1,911

medium-bank, and 1,382 large-bank observations.26

Table 1 contains summary statistics on the regression variables. The sample statistics are shown for

banking crises, market crises, and normal times. All financial values are put into real 2009:Q4 dollars (using

the implicit GDP price deflator) before size classes are constructed.

4. Main regression results

In this section, we discuss the main empirical results.

4.1. Does capital affect the bank’s ability to survive during crises and normal times?

Table 2 Panel A presents the survival findings for small, medium, and large banks. Two main results are

apparent. First, higher capital helps banks in all size classes to improve the probability of surviving banking

crises. Second, higher capital also helps small banks improve their odds of survival during market crises and

normal times. These results generally support the hypothesis that capital helps banks survive.

These findings have sensible economic interpretations. Capital is the main line of defense against

negative shocks for small banks, since they have limited (and relatively costly) access to the financial market

in the event of unanticipated needs. Hence, higher capital enhances the probability of survival for such banks

at all times. This finding is consistent with the earlier-discussed theoretical papers that predict that higher

capital increases a bank’s survival probability. Medium and large banks can rely on financial market access,

and correspondent and other interbank relationships as risk-mitigation sources in addition to their on-balance-

sheet capital to survive negative shocks. So, capital is less pivotal for survival during normal times for these

banks than it is for small banks. However, banking crises create stresses for all banks, and financial market

access and interbank relationships may offer inadequate protection against negative shocks for all but the very

largest banks. Hence, capital may be important for survival for medium and large banks during banking crises.

The market crisis results for medium and large banks suggest that such crises do not pose much of a survival

threat for these banks. Given the access that medium and large banks have to the interbank lending market 26 The survival regressions have fewer observations because we do not have data on the first quarter after the end of the subprime lending crisis and because we drop within-bank-holding-company consolidations from these regressions.

16

(e.g., federal funds) that itself is unlikely to be adversely impacted by a market crisis, capital may not be

critical for these banks to survive market crises.

To judge the economic significance of our findings, Table 2 Panels B, C, and D show the predicted

probabilities of non-survival. The top part of each panel shows the average capital ratio and the average

capital ratio plus or minus one standard deviation of small, medium, and large banks over the eight quarters

before banking crises, market crises, and normal times. The bottom part of each panel shows the predicted

probability of not surviving banking crises, market crises, and normal times at these capital ratios. A small,

medium, or large bank with an average capital ratio (10.00%, 8.76%, and 8.30%, respectively) had a

probability of not surviving banking crises of 0.61%, 0.55%, and 0.12%, respectively. Reducing capital by

one standard deviation more than doubles these probabilities of non-survival. Similarly, increasing capital by

one standard deviation reduces the probabilities of non-survival by more than one half for all size classes. The

corresponding probabilities for market crises and normal times are generally higher.27

Turning to the control variables, we find that the coefficients generally have the predicted signs. As

expected, being part of a bank holding company, greater market power, and higher profitability helps banks

survive. Banks that held more credit risk before banking and market crises are less likely to survive

(significant for small banks; for medium banks only before banking crises). Bank size has no significant effect

on medium and large banks’ ability to survive. Among small banks, larger banks are less likely to survive,

which is surprising.

4.2. Does capital affect the market shares of banks during crises and normal times?

Table 3 summarizes the results from regressing the percentage change in a bank’s market share

(%ΔLCSHARE) during crises on the bank’s pre-crisis capital ratio plus control variables. As before, results

are shown separately for small, medium, and large banks. The t-statistics are based on robust standard errors

clustered by bank.

We find two main results. First, higher capital helps banks of all sizes improve their market shares

during banking crises and normal times. Second, higher capital also helps small banks to improve their market

27 The survival probabilities of medium banks actually decrease slightly when capital is higher during market crises and normal times. This is consistent with the negative but insignificant coefficients on EQRAT for these banks shown in Table 2 Panel A.

17

shares during market crises.28

We can again interpret these results in the context of the theories discussed earlier. Note that capital is

essential to survival for small banks, as discussed earlier. Moreover, these banks engage largely in

relationship lending, and long-lasting bank-borrower relationships are crucial for relationship banking to create

value.

Capital does not appear to produce such benefits during market crises for

medium and large banks. Somewhat surprisingly, medium banks with higher capital ratios seem to lose

market share during market crises. These results generally support the hypothesis that capital helps banks to

improve their market shares.

29 This means that relationship borrowers will tend to gravitate toward high-capital banks, since higher

capital leads to a higher survival probability for small banks at all times (see Section 4.1).30

To judge the economic significance of these results, focus first on the effect of capital during banking

crises. The coefficients on the EQRAT * BNKCRIS interaction terms imply that a one standard deviation

increase in EQRAT would lead to a 0.373, 0.114, and 0.216 standard deviation change in for

small, medium, and large banks, respectively, during such crises (not shown for brevity). The corresponding

figures for EQRAT * MKTCRIS and EQRAT * NORMALTIME are (0.310, -0.152, and -0.040) and (0.231,

0.185, and 0.250), respectively. These results seem economically significant for banks of all sizes during

banking crises and for small banks also during market crises and normal times.

Thus, it is not

surprising that higher capital benefits small banks in terms of gaining market share at all times. During

banking crises and normal times, capital also helps improve the market shares of medium and large banks. In

contrast, during market crises, capital seems to hurt the market shares of medium banks (no obvious

interpretation) and does not significantly affect large banks, possibly because it is relatively easy for these

banks to turn to the discount window and the interbank market – both of which may not experience stress

during market crises – to ensure unruptured relationships with their borrowers during such crises. This means

that while capital may offer large banks a benefit during market crises, this benefit may be no greater than that

before such crises.

28 The positive effect of capital on large banks’ market shares during normal times disappears when we use a $5 billion or $10 billion cutoff between medium and large banks, suggesting that our main result is driven by smaller large banks (see Section 5.4). 29 See, e.g., Ongena and Smith (2001) Bae, Kang, and Lim (2002), and Bharath, Dahiya, Saunders, and Srinivasan (2007) for empirical evidence. 30 This argument is consistent with the intuition in Song and Thakor (2007) who show that banks that make relationship loans will prefer to finance with “stable funds” because this increases the likelihood that a relationship loan will not need to be terminated prematurely.

18

Turning to the control variables, we find that banks with higher pre-crisis credit risk are generally less

likely to improve their market shares during crises. The reason may be similar to the argument given above

for capital: safer banks create more surplus and therefore are more likely to increase their market shares.

Among banks of all size classes, the coefficient on bank size is negative and significant, likely because it is

harder for larger banks to increase their percentage market share. Being part of a bank holding company helps

to improve market share (significant for small and medium banks). Among small banks, the ones with greater

market power increase their market shares less, probably because these banks tend to operate locally and

regulators do not approve mergers that increase their market shares significantly. This does not hold for

medium and large banks, since they operate more on a national or international basis.

4.3. Does capital affect bank profitability during crises and normal times?

Table 4 contains the results of regressing the change in profitability (ΔROE) during crises on the bank’s pre-

crisis capital ratio plus control variables. The setup of the table is similar to the previous one. As before, t-

statistics are based on robust standard errors clustered by bank.

We again find two main results. First, high-capital banks of all sizes improve their profitability during

banking crises (not significant for medium banks) and market crises. Second, capital also enhances the

profitability of small banks during normal times. These results generally support the hypothesis that capital

improves bank profitability.

The reason for these results can be found in our earlier discussion of the central importance of capital

for small banks at all times. Because small banks with higher capital have higher survival probabilities and

can offer higher-valued relationships, it is intuitive that higher-capital small banks improve their profitability

at all times. It may seem surprising, however, that capital helps to improve profitability for medium and large

banks during market crises, since higher capital improves neither the market share nor the survival probability

for these banks during such crises. One possible explanation is that these banks continue to have access to

uninsured sources of funding during market crises, but there may be a “flight to quality” phenomenon,

whereby the cost of such funds becomes more sensitive to the amount of capital the bank has.

To judge the economic significance of these results, focus first on the effect of capital during banking

crises. The coefficients on the EQRAT * BNKCRIS interaction terms imply that a one standard deviation

19

increase in EQRAT would lead to a 0.041, 0.061, and 0.125 standard deviation change in for small,

medium, and large banks, respectively, during such crises (not shown for brevity). The corresponding figures

for EQRAT * MKTCRIS and EQRAT * NORMALTIME are (0.076, 0.078, and 0.184) and (0.085, 0.026, and

0.011), respectively. These results seem less economically significant than the survival and market share

results.

The control variables generally have the expected signs. Banks that operate with higher credit risk

before banking and market crises find it harder to improve their profitability during such crises. Bank size has

no significant impact on banks’ ability to improve their profitability. Bank holding company status increases

profitability for small banks only. Higher HHI increases profitability not just for small banks but also for

medium banks.

5. Robustness checks

This section presents a number of checks to establish the robustness of our results. First, we use alternative

specifications of survival, market share, and profitability. Second, we use regulatory capital ratios instead of

the equity-to-assets ratio. Third, we drop Too-Big-To-Fail banks from the large-bank sample. Fourth, we use

alternative cutoffs separating medium and large banks. Fifth, we measure pre-crisis capital ratios alternatively

one quarter before the crisis starts or averaged over the four quarters before the crisis. Sixth, we deal with the

potential endogeneity issues related to capital by using an instrumental variable approach. Our survival and

market share findings are generally qualitatively similar to the main results. The profitability results are less

robust.

To preserve space, we only present the coefficients on the variables of interest, the pre-crisis capital

ratio interacted with the crisis dummies. The same control variables as used in the main regression

specifications are included with some exceptions noted below.

5.1. Use alternative specifications of survival, market share, and profitability

Our main survival results are based on SURV, survival until one quarter after a crisis. As a robustness check,

we now use a slightly longer time window. Specifically, we use SURV_alt, a dummy that equals 1 if the bank

is still in the sample four quarters after the crisis.

20

Table 5 Panel A1 shows the results. As can be seen, the coefficients and significance levels are

similar to those shown in Table 2, except that based on this alternative definition, the effect of capital on large

banks’ ability to survive banking crises is no longer significant. Thus, our survival results do not seem to be

driven appreciably by the choice of time window.

Our main competitive position results are based on %ΔMKTSHARE, a bank’s liquidity creation market

share. As a robustness check, we now use %ΔMKTSHARE_alt, a bank’s market share of aggregate gross total

assets (GTA). GTA is a traditional measure of size that focuses on the bank’s on-balance sheet activities.

GTA market share is calculated as the bank’s gross total assets divided by the industry’s gross total assets.

The main shortcoming of this measure is that it ignores off-balance sheet activities and treats all assets

identically, i.e., it neglects the qualitative asset transformation nature of the bank’s activities (e.g.,

Bhattacharya and Thakor 1993, Kashyap, Rajan, and Stein 2002). For consistency, we also use lnGTA, the

log of GTA, instead of lnLC as a size control in these regressions.

Table 5 Panel A2 shows that based on GTA market share, small banks are able to improve their

market shares at all times, while medium and large banks improve their market shares only during banking

crises. The difficult-to-explain finding that medium banks with higher capital ratios lose market share during

market crises is still present, but the effect of capital on market share during normal times for medium and

large banks has disappeared. Thus, the results confirm the small-bank results and the banking crisis (not

market crisis) results for medium and large banks shown in Table 3.

Our main profitability results focus on ΔPROF, the bank’s change in ROE. As a robustness check, we

now use ΔPROF_alt, the change in ROA. This measure divides net income, generated based on all the bank’s

on- and off-balance sheet activities, by GTA, a measure of the bank’s on-balance sheet activities. The change

in ROA is meaningful for small banks since most of their activities are on the balance sheet. It is less

appropriate for medium and large banks, since these banks engage in more off-balance sheet activities.

Table 5 Panel A3 shows the results. As before, capital helps small banks improve their profitability at

all times. Not surprisingly, the medium and large bank results are generally not significant. Thus, the results

confirm the main profitability results shown in Table 4 for small banks, and are understandably weaker for

medium and large banks.

21

5.2. Use regulatory capital ratios

The main results highlight the benefits of higher capital ratios. One may wonder to what extent these results

are specific to our choice of the definition of capital, EQRAT, the ratio of equity capital to assets. To examine

this issue, we rerun our regressions using regulatory capital ratios.

Basel I introduced two risk-based capital ratios, the Tier 1 and Total risk-based capital ratios. Since

these ratios only became fully effective as of December 1990, we use each bank’s ratio of equity capital to

GTA before this date and its Tier 1 risk-based capital ratio from that moment onward. We obtain very similar

results when we use the Total risk-based capital ratio instead of the Tier 1 capital ratio. Prior to 1996, all

banks with assets over $1 billion had to report this information, but small banks only had to report their risk-

based capital positions if they believed that their capital was less than eight percent of adjusted total assets. In

all these cases, we use a Federal Reserve Bank program to reconstruct what those banks’ risk-based capital

ratios are based on (publicly-available) Call Report data.

Table 5 Panel B shows the results.31

We also ran all the robustness checks presented in the rest of this section using regulatory capital and

obtain results that are very similar to the main survival, market share, and profitability results. Nonetheless, we

believe that our choice of using EQRAT in the main regressions is appropriate. The regulatory ratios mix

capital with credit risk which is already controlled for in our main regressions. We prefer to focus on the

effect of capital per se.

Clearly, for small and large banks, we obtain significance in

exactly the same cases as before (see Tables 2 – 4). For medium banks, the survival and profitability results

are similar, but the market share results are less significant (capital does not help these banks improve their

market shares during banking crises).

5.3. Exclude Too-Big-To-Fail banks

Very large banks are often considered Too-Big-To-Fail (TBTF). For these banks, capital may not provide the

benefits we highlight in this paper since such banks know they will be bailed out and supported if needed. To

make sure that our large-bank results are not overly influenced by TBTF banks, we rerun our main analyses 31 While most of the control variables used before should clearly be included in these regressions, the risk interaction terms may not belong. The reason is that the numerator of the risk variable (Basel I risk-weighted assets) is identical to the denominator of the regulatory capital ratio. We present results here excluding the risk interaction terms, but results are comparable if we instead include them (not shown for brevity).

22

for large banks while excluding the TBTF banks. Since no official definition of TBTF exists, we use two

alternative definitions. First, in every quarter, we deem all banks with GTA exceeding $50 billion to be TBTF.

Second, we classify the 19 largest banks in each period as TBTF. This definition is inspired by the

government’s disclosure in April 2009 that the 19 largest banks had to undergo stress tests, and would be

assisted with capital injections if they could not raise capital on their own. This effectively made them TBTF.

Table 5 Panels C1 and C2 contain the results for large banks excluding the TBTF banks based on the

two alternative definitions. The coefficients are bigger than those presented in the main tests in all but one

case and significance is found in similar cases. Both sets of results suggest that the presence of TBTF banks

weakened our main results to some extent, but leave our main conclusion unchanged.

5.4. Use alternative size cutoffs

In our main analyses, small banks have GTA up to $1 billion; medium banks have GTA between $1 and $3

billion; large banks have GTA exceeding $3 billion. As explained above, our small-bank definition captures

community banks, while the remaining observations were split roughly in half by choosing a $3 billion cutoff

(see also Berger and Bouwman 2009). One may wonder to what extent the medium and large bank results are

driven by our choice of the $3 billion cutoff. We reran our analyses using $5 billion and $10 billion cutoffs

between medium and large banks, which reclassify some large banks as medium banks.

Table 5 Panels D1 and D2 show the results for medium and large banks using these alternative

cutoffs. The survival results indicate that, as before, capital helps medium and large banks survive banking

crises only.

As before, capital helps medium and large banks expand their market shares during banking crises, but

now, the effect is also significant for large banks during market crises (based on a $10 billion cutoff only).

The reclassification of some large banks as medium banks makes the effect of capital on market share stronger

for medium banks and not significant for large banks during normal times.

Finally, while our main results suggest that capital helps large banks improve their profitability during

banking crises, the reclassification of some large banks as medium banks causes this effect to now only be

significant for medium banks. This suggests that the positive effect of capital on profitability occurs mainly

for banks that are smaller than $5 billion.

23

5.5. Measure capital ratios at alternative times before the crisis

Our main results are based on regression specifications which include every bank’s capital ratio averaged over

the eight quarters before a crisis. The advantage of using eight-quarter averages is that such averages are not

very sensitive to the effects of outliers. As robustness checks, we rerun our regressions while measuring bank

capital averaged over the four quarters before a crisis or measuring it the quarter before a crisis.

Table 5 Panels E1 and E2 contain the results. Clearly, based on these two alternative capital

measures, the survival and market share results are similar to the main results. The profitability results,

however, are somewhat weaker. When capital is averaged over the four quarters before the crisis, capital helps

to improve small banks’ profitability during banking crises, but the effect is no longer significant (t-statistic

1.52). When capital is measured at the end of the quarter before the crisis, high-capital small banks are not

able to improve their profitability relative to low-capital banks at any time, and high-capital large banks do not

improve their profitability during banking crises.32

5.6. Use an instrumental variable analysis

The analyses presented so far suggest that capital helps banks of all sizes during banking crises, and improves

the performance of small banks during market crises and normal times as well. However, a potential

endogeneity issue clouds the interpretation of our results. The theories suggest a causal link from capital to

performance. But we know that in practice, capital is an endogenous choice variable for a bank, so the bank’s

market share, profitability, and even its likelihood of survival may impact the bank’s capital choice. This issue

is addressed partly in our analysis because capital is lagged relative to the dependent variables. However, this

may not be enough because of intertemporal rigidities in some of these variables. It may therefore be difficult

to know if we have detected causality or mere correlation. To address this concern, we now use an

instrumental variable approach.

We have three potentially endogenous variables, capital interacted with the three crisis dummies

(EQRAT * BNKCRIS, EQRAT * MKTCRIS, and EQRAT * NORMALTIME), so we need three

32 The reason for the weaker profitability results, especially for the small banks, may be that the beneficial effects of higher capital that are manifested through greater monitoring may be reflected in the bank’s profitability only with a significant lag. When pre-crisis capital is measured relatively close to the crisis, we may not fully capture the effect of higher capital.

24

instruments.33

This instrumentation strategy assumes that SENIORS and EFF-TAX are correlated with the amount of

capital, but do not directly affect performance. Both variables, used by Berger and Bouwman (2009) as

instruments for capital, seem to meet these conditions. To see why, consider SENIORS first. Seniors own

larger equity portfolios than the average family. Furthermore, using U.S. data, Coval and Moskowitz (1999)

document that investors have a strong preference for investing close to home. They find that this preference is

greater for firms that are smaller, more highly levered, and those that produce goods that are not traded

internationally. In combination, this evidence suggests that banks – particularly small and medium banks –

that operate in markets with more seniors have easier access to equity financing and hence, will operate with

higher capital ratios (see Berger and Bouwman 2009 for empirical evidence). We calculate the fraction of

seniors using county- and MSA-level population data from the 1990 and 2000 decennial Census.

We employ different instruments for banks of different sizes for reasons explained below.

Specifically, we use SENIORS, the fraction of seniors (people aged 65 and over) in the market in which a

bank is active, interacted with the three crisis dummies as instruments for medium and small banks; and EFF-

TAX, the effective state income tax rate a bank has to pay, interacted with the three crisis dummies as

instruments for large banks.

While SENIORS can be used for small and medium banks, it is not as attractive for large banks

because these banks are unlikely to be locally-based when it comes to raising equity. For this reason, we use

EFF-TAX for large banks. Since interest on debt is tax-deductible while dividend payments are not, banks

that operate in states with higher income tax rates are expected to have lower equity ratios, keeping all else

equal (see Ashcraft 2008 and Berger and Bouwman 2009 for empirical evidence). Similar to Ashcraft (2008),

we define EFF-TAX as the effective income tax rate to be paid on $1 million in pretax income. If a bank

operates in multiple states, we use the bank’s weighted-average state income tax rate, calculated using the

share of deposits in each state (relative to the bank’s total deposits) as weights.

Since ordinary least squares is preferred to instrumental variable regressions if we do not have an

endogeneity problem, we perform a Hausman test for endogeneity. As shown in Table 5 Panel F1, we do not

reject the null that the three potentially endogenous variables (the EQRAT interaction terms) are exogenous

33 It is not correct to view EQRAT as the endogenous right-hand-side variable, create a predicted value of EQRAT in the first stage and then interact it with the three crisis dummies. Wooldridge (2002, p. 236) calls this the “forbidden regression.”

25

for medium and large banks, suggesting that our original analyses were appropriate for these banks. However,

we do find evidence of endogeneity for small banks and therefore perform instrumental variable regressions

only for those banks.

Since we have three endogenous variables, we run three first-stage regressions. In each, we regress

EQRAT interacted with one of the three crisis dummies on all the exogenous variables used before plus the

three instruments (SENIORS interacted with the three crisis dummies). Importantly, when EQRAT *

BNKCRIS is the endogenous variable, the coefficient on the corresponding instrument (SENIORS *

BNKCRIS) is positive and highly significant, and we obtain similar results for the other endogenous variables

(not shown for brevity).

In the second-stage regressions, we regress bank performance on all the exogenous variables and the

predicted values from the first stage. Table 5 Panel F2 shows the second-stage instrumental variable

regressions for small banks. Using an instrument for small banks’ capital, we find that capital helps these

banks survive banking crises only, improve their market shares at all times (not significant during normal

times: t-statistic 1.59), and improve their profitability during market crises and normal times (surprisingly, a

negative effect during banking crises). Thus, most of our earlier results hold up in our instrumental variable

analysis, and the analysis broadly confirms our main results.

6. Comparing the effects of book and market capital during crises and normal times

The analyses above focus on the effect of book capital on the performance of individual banks. A subset of

these banks is independently listed or part of a listed BHC. This allows us to compare the effects of book and

market capital on performance for this set of banking organizations (jointly referred to here as “listed entities”

for ease of exposition). This is interesting because regulators focus on book capital ratios, which merely

measure the amount of capital at a particular point in time, while market capital ratios are forward-looking and

capture market participants’ beliefs about future performance.34

We expect the book-capital listed-entity results to be similar to our large-bank results (“higher capital

34 There are well-known difficulties with book equity because many assets, particularly loans, are not marked-to-market. While the loan and lease loss reserves are subtracted from book equity to account for credit losses on loans and leases, these may be imprecise and they leave out gains and losses from interest rate risks on fixed-rate loans. In addition, the book value of equity may be significantly overstated for failing institutions, given that the FDIC usually takes considerable losses when selling off the assets of these institutions.

26

helps especially during banking crises”), except that the relationship between book capital and listed entity

survival is likely weaker since many of the listed entities may be Too-Big-To-Fail. The listed entity results are

likely stronger based on market capital (than on book capital) because of their forward-looking nature.

For this analysis, we include listed banks and listed one-bank-holding companies, and we aggregate

the data of all the banks in a listed multi-bank-holding company. Book capital for the listed entity,

EQRAT_listed, is calculated as before.35

Table 6 shows the results based on book and market capital. As expected, our listed-entity results

support our large-bank findings in that capital helps listed entities in particular during banking crises.

Specifically, we find that those with higher capital ratios are more likely to survive banking crises (based on

MKTRAT_listed only; as expected, this result is not significant based on EQRAT_listed); increase their

market shares during banking crises (based on both capital measures; also significant during market crises

based on EQRAT_listed); and increase their profitability during banking crises (based on MKTRAT_listed

only) and market crises (based on both capital measures).

Each listed entity’s pre-crisis market capital ratio, MKTRAT_listed,

is calculated as the listed entity’s market value of equity (i.e., the number of shares outstanding multiplied by

the share price) divided by its assets in market value terms (i.e., the book value of liabilities plus the market

value of equity), averaged over the eight quarters before the crisis. We rerun regressions (1) – (3) using listed-

entity variables (except that we leave out the BHC dummy because almost all of these entities are BCHs).

Also as expected, the results are strongest based on market capital ratios. Nonetheless, we present the

book value analysis as the main approach in our paper for several reasons. First, most of the policy proposals

to raise capital in the wake of the recent financial crisis aim to directly strengthen the book value of capital.

While this may also increase the market value, a book value analysis may be more relevant for policy

purposes. Second, market values are only available for the relatively small subset of publicly-traded banks /

BHCs, so we would have an analysis that is not representative of small and medium banks. Thus, only a book

value analysis allows us to investigate the effects of capital on all sizes of banks in the industry. Finally,

market values of equity are largely based on the market’s expectation of future performance. To the extent

that the market is prescient, then regressing current performance on lagged market values of capital may create

35 For example, each multi-BHC’s pre-crisis book capital ratio, EQRAT_listed,, is calculated as the ratio of equity capital (summed over all banks in the BHC) to gross total assets (summed over all banks in the BHC), averaged over the eight quarters before the crisis.

27

a significant bias in favor of finding favorable effects of capital. By presenting both book-value and market-

value analyses, we can provide greater confidence in the robustness of our results.

7. Conclusion

This paper aims to help a recent debate on whether banks should hold more capital. Existing theories produce

conflicting predictions regarding the effect of capital on bank performance during normal times and have little

to say about the effect during financial crises. We formulate and test hypotheses regarding the effect of bank

capital on bank performance (survival probability, market share, and profitability) during financial crises and

normal times. Our two main results are as follows. First, capital enhances the performance of all sizes of

banks during banking crises. Second, during normal times and market crises, capital helps only small banks

unambiguously in all performance dimensions; it helps medium and large banks improve only profitability

during market crises and only market share during normal times. Our survival and market share findings are

generally robust, and our profitability results are less so.36

36 Given our findings, it may seem surprising that banks often resist calls for higher capital (e.g., Mishkin 2000), although most banks are found to hold capital well in excess of regulatory minimums (e.g., Berger, DeYoung, Flannery, Lee, and Oztekin 2008). While outside the scope of our paper, there may be several reasons for resistance: managerial hubris (e.g. Roll 1986) which may include underestimating the probability of banking crises, especially when times are good; private benefits related to the government safety net; and forced departure from privately-optimal capital structure choices (e.g., Mehran and Thakor forthcoming).

28

Appendix I: Description of the financial crises

This Appendix describes the two banking crises and the three market crises that occurred in the U.S between

1984:Q1 and 2008:Q4.

Two banking crises

• Credit crunch (1990:Q1 – 1992:Q4): During the first three years of the 1990s, bank commercial and industrial

lending declined in real terms, particularly for small banks and for small loans. The ascribed causes of the

credit crunch include a fall in bank capital from the loan loss experiences of the late 1980s (e.g., Peek and

Rosengren 1995), the increases in bank leverage requirements and implementation of Basel I risk-based capital

standards during this time period (e.g., Hancock, Laing, and Wilcox 1995, Thakor 1996), an increase in

supervisory toughness evidenced in worse examination ratings for a given bank condition, and reduced loan

demand because of macroeconomic and regional recessions (e.g., Bernanke and Lown 1991). The existing

research provides some support for each of these hypotheses.

• Subprime lending crisis (2007:Q3 – 2009:Q4): The subprime lending crisis has been characterized by turmoil

in financial markets as banks have experienced difficulty in selling loans in the syndicated loan market and in

securitizing loans. The supply of liquidity by banks dried up, as did the provision of liquidity in the interbank

market. Many banks experienced substantial losses in capital. Massive loan losses at Countrywide resulted in

a takeover by Bank of America. Bear Stearns suffered a fatal loss of confidence among its financiers and was

sold at a fire-sale price to J.P. Morgan Chase, with the Federal Reserve guaranteeing $29 billion in potential

losses. Washington Mutual, the sixth-largest bank, became the biggest bank failure in the U.S. financial

history. J.P. Morgan Chase purchased the banking business while the rest of the organization filed for

bankruptcy. IndyMac Bank was seized by the FDIC after it suffered substantial losses and depositors had

started to run on the bank. The FDIC sold all deposits and most of the assets to OneWest Bank, FSB, and

incurred an estimated loss of about $10 billion. The Federal Reserve also intervened in some unprecedented

ways in the market. It extended its safety-net privileges to investment banks and one insurance company

(AIG), intervened in the commercial paper market, and began holding mortgage-backed securities and lending

directly to investment banks. The Treasury initially set aside $250 billion out of its $700 billion bailout

package (TARP program) to enhance capital ratios of selected banks. This included $125 billion in $10 billion

and $25 billion increments to each of nine large banking organizations. Some of these banks used these funds

29

to acquire lesser-capitalized peers. For example, PNC Bank used TARP funds to acquire National City Bank,

and Bank of America bought Merrill Lynch. In all, the Treasury invested over $300 billion in almost 700

financial institutions (as well as over $80 billion in the automobile industry). During 2009, 140 U.S. banks

failed, and the FDIC Bank Insurance Fund fell into a deficit position. By the first quarter of 2010, much of the

TARP funds invested in financial institutions had been repaid, and order had been restored to most of the

financial markets, although small banks continued to fail at a high rate.

Three market crises

• Stock market crash (1987:Q4): On Monday, October 19, 1987, the stock market crashed, with the S&P500

index falling about 20%. During the years before the crash, the level of the stock market had increased

dramatically, causing some concern that the market had become overvalued.37

• Russian debt crisis / LTCM bailout (1998:Q3 – 1998:Q4): Since its inception in March 1994, hedge fund

Long-Term Capital Management (“LTCM”) followed an arbitrage strategy that was avowedly “market

neutral,” designed to make money regardless of whether prices were rising or falling. When Russia defaulted

on its sovereign debt on August 17, 1998, investors fled from other government paper to the safe haven of U.S.

treasuries. This flight to liquidity caused an unexpected widening of spreads on supposedly low-risk portfolios.

By the end of August 1998, LTCM’s capital had dropped to $2.3 billion, less than 50% of its December 1997

value, with assets standing at $126 billion. In the first three weeks of September, LTCM’s capital dropped

further to $600 million without shrinking the portfolio. Banks began to doubt its ability to meet margin calls.

To prevent a potential systemic meltdown triggered by the collapse of the world’s largest hedge fund, the

Federal Reserve Bank of New York organized a $3.5 billion bail-out by LTCM’s major creditors on September

23, 1998. In 1998:Q4, several large banks had to take substantial write-offs as a result of losses on their

investments.

A few days before the crash,

two events occurred that may have helped precipitate the crash: legislation was enacted to eliminate certain tax

benefits associated with financing mergers; and information was released that the trade deficit was above

expectations. Both events seemed to have added to the selling pressure and a record trading volume on Oct. 19,

in part caused by program trading, overwhelmed many systems.

37 E.g., “Raging bull, stock market’s surge is puzzling investors: When will it end?” on page 1 of the Wall Street Journal, Jan. 19, 1987.

30

• Bursting of the dot.com bubble and Sept. 11 terrorist attack (2000:Q2 – 2002:Q3): The dot.com bubble was a

speculative stock price bubble that was built up during the mid- to late-1990s. During this period, many

internet-based companies, commonly referred to as “dot.coms,” were founded. Rapidly increasing stock prices

and widely available venture capital created an environment in which many of these companies seemed to

focus largely on increasing market share. At the height of the boom, many dot.com’s were able to go public

and raise substantial amounts of money even if they had never earned any profits, and in some cases had not

even earned any revenues. On March 10, 2000, the Nasdaq composite index peaked at more than double its

value just a year before. After the bursting of the bubble, many dot.com’s ran out of capital and were acquired

or filed for bankruptcy (examples of the latter include WorldCom and Pets.com). The U.S. economy started to

slow down and business investments began falling. The September 11, 2001 terrorist attacks may have

exacerbated the stock market downturn by adversely affecting investor sentiment. By 2002:Q3, the Nasdaq

index had fallen by 78%, wiping out $5 trillion in market value of mostly technology firms.

31

Appendix II: Construction of bank liquidity creation (Berger and Bouwman 2009)

We calculate a bank’s dollar amount of liquidity creation using a three-step procedure, which is discussed below

and illustrated in Table A-1.

Step 1: All bank activities (assets, liabilities, equity, and off-balance sheet activities) are classified as liquid,

semi-liquid, or illiquid. For assets, this is done based on the ease, cost, and time for banks to dispose of their

obligations in order to meet liquidity demands. For liabilities and equity, this is done based on the ease, cost, and

time for customers to obtain liquid funds from the bank. We follow a similar approach for off-balance sheet

activities, classifying them based on functionally similar on-balance sheet activities. For all activities other than

loans, this classification process uses information on both product category and maturity. Due to data restrictions,

loans are classified entirely by category.

Step 2: Weights are assigned to all the bank activities classified in Step 1. The weights are consistent with

liquidity creation theory, which argues that banks create liquidity on the balance sheet when they transform illiquid

assets into liquid liabilities. Positive weights are therefore applied to illiquid assets and liquid liabilities. Following

similar logic, negative weights are applied to liquid assets and illiquid liabilities and equity, since banks destroy

liquidity when they use illiquid liabilities to finance liquid assets. Weights of ½ and -½ are used because liquidity

creation is only half attributable to the source or use of funds alone.38

Step 3: The activities as classified in Step 1 and as weighted in Step 2 are combined to construct Berger and

Bouwman’s (2009) preferred liquidity creation measure. This measure classifies loans by category, while all

activities other than loans are classified using information on product category and maturity, and includes off-

balance sheet activities.

An intermediate weight of 0 is applied to

semi-liquid assets and liabilities. Weights for off-balance sheet activities are assigned using the same principles.

39

To obtain the dollar amount of liquidity creation at a particular bank, we multiply the

weights of ½, -½, or 0, respectively, times the dollar amounts of the corresponding bank activities and add the

weighted dollar amounts.

38 The following examples illustrate this principle. Maximum liquidity is created when $1 of liquid liabilities is used to finance $1 in illiquid assets: ½ * $1 + ½ * $1 = $1. When $1 of liquid liabilities is used to finance $1 in liquid assets, no liquidity is created (½ * $1 + -½ * $1 = $0) because the bank holds items of approximately the same liquidity as those it gives to the nonbank public. Maximum liquidity is destroyed when $1 of illiquid liabilities or equity is used to finance $1 of liquid assets: -½ * $1 + -½ * $1 = -$1. 39 Berger and Bouwman (2009) construct four liquidity creation measures by alternatively classifying loans by category or maturity, and by alternatively including or excluding off-balance sheet activities. However, they argue that the measure we use here is the preferred measure since for liquidity creation, banks’ ability to securitize or sell loans is more important than loan maturity, and banks do create liquidity both on the balance sheet and off the balance sheet.

32

Table A-1: Liquidity classification of bank activities and construction of the liquidity creation measure This table explains the Berger and Bouwman (2009) methodology to construct their preferred liquidity creation measure that classifies loans by category and includes off-balance sheet activities in three steps.

Step 1: Classify all bank activities as liquid, semi-liquid, or illiquid. For activities other than loans, information on product category and maturity are combined. Due to data limitations, loans are classified entirely by product category.

Step 2: Assign weights to the activities classified in Step 1.

ASSETS:

Illiquid assets (weight = ½) Semi-liquid assets (weight = 0) Liquid assets (weight = - ½) Commercial real estate loans (CRE) Residential real estate loans (RRE) Cash and due from other institutions Loans to finance agricultural production Consumer loans All securities (regardless of maturity) Commercial and industrial loans (C&I) Loans to depository institutions Trading assets Other loans and lease financing receivables Loans to state and local governments Fed funds sold Other real estate owned (OREO) Loans to foreign governments Investment in unconsolidated subsidiaries Intangible assets Premises Other assets

LIABILITIES PLUS EQUITY:

Liquid liabilities (weight = ½) Semi-liquid liabilities (weight = 0) Illiquid liabilities plus equity (weight = - ½) Transactions deposits Time deposits Subordinated debt Savings deposits Other borrowed money Other liabilities Overnight federal funds purchased Equity Trading liabilities

OFF-BALANCE SHEET GUARANTEES (notional values):

Illiquid guarantees (weight = ½) Semi-liquid guarantees (weight = 0) Liquid guarantees (weight = - ½) Unused commitments Net credit derivatives Net participations acquired Net standby letters of credit Net securities lent Commercial and similar letters of credit All other off-balance sheet liabilities

OFF-BALANCE SHEET DERIVATIVES (gross fair values):

Liquid derivatives (weight = -½) Interest rate derivatives Foreign exchange derivatives Equity and commodity derivatives

Step 3: Combine bank activities as classified in Step 1 and as weighted in Step 2 to construct the liquidity creation (LC) measure.

LC = + ½ * illiquid assets + 0 * semi-liquid assets – ½ * liquid assets + ½ * liquid liabilities + 0 * semi-liquid liabilities – ½ * illiquid liabilities – ½ * equity + ½ * illiquid guarantees + 0 * semi-liquid guarantees – ½ * liquid guarantees – ½ * liquid derivatives

33

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Table 1: Summary statistics on the regression variables This table contains means and standard deviations (in parentheses) on all the regression variables used to examine the effect of pre-crisis capital ratios on banks’ ability to survive crises, and improve their competitive positions and profitability during such crises. We distinguish between banking crises (the credit crunch of the early 1990s and the recent subprime lending crisis), market crises (the 1987 stock market crash, the Russian debt crisis plus LTCM bailout in 1998, and the bursting of the dot.com bubble plus September 11), and normal times (see Section 3).

SURV, survival, is a dummy that equals 1 if the bank is in the sample one quarter before such a crisis started and is still in the sample one quarter after the crisis, and 0 otherwise. %ΔMKTSHARE, the change in market share, is measured as the bank’s average market share during a crisis minus its average market share over the eight quarters before the crisis, normalized by its pre-crisis market share and multiplied by 100. Market share is the bank’s liquidity creation (LC) as a fraction of total LC. ΔPROF, the change in profitability, is measured as the bank’s average profitability during a crisis minus its average profitability over the eight quarters before the crisis. Profitability is ROE, net income divided by equity capital.

All independent variables are measured as averages over the eight quarters prior to a crisis (except as noted). EQRAT is the equity capital ratio, calculated as equity capital as a proportion of GTA. GTA equals total assets plus the allowance for loan and the lease losses and the allocated transfer risk reserve (a reserve for certain foreign loans). CREDRISK, credit risk, is defined as the bank’s Basel I risk-weighted assets divided by GTA. lnLC is the log of liquidity creation. D-BHC is a dummy variable that equals 1 if the bank has been part of a bank holding company over the eight quarters before the crisis. HHI is a bank-level Herfindahl index based on bank and thrift deposits (the only variable for which geographic location is publicly available). We first establish the Herfindahl index of the local markets in which the bank has deposits and then weight these market indices by the proportion of the bank’s deposits in each of these markets. ROA is net income divided by total assets. All dollar values are expressed in real 2009:Q4 dollars using the implicit GDP price deflator.

Banking crises Market crises Normal times

Small banks

Medium banks

Large banks

Small banks

Medium banks

Large banks

Small banks

Medium banks

Large banks

Dependent variables: SURV 0.941 0.866 0.909 0.976 0.956 0.976 0.972 0.958 0.965 (0.236) (0.342) (0.288) (0.154) (0.206) (0.155) (0.165) (0.200) (0.185) %ΔMKTSHARE 0.255 0.183 0.192 0.213 0.212 0.166 0.183 0.136 0.161 (0.760) (0.554) (0.605) (0.666) (0.531) (0.471) (0.603) (0.434) (0.487) ΔPROF -0.024 -0.071 -0.077 -0.014 -0.015 -0.020 0.001 -0.005 -0.006 (0.080) (0.094) (0.101) (0.078) (0.070) (0.080) (0.048) (0.050) (0.057)

Independent variables: EQRAT 0.100 0.088 0.083 0.097 0.083 0.076 0.101 0.093 0.089 (0.051) (0.045) (0.044) (0.038) (0.040) (0.028) (0.035) (0.044) (0.036) CREDRISK 0.633 0.745 0.795 0.607 0.684 0.768 0.614 0.680 0.704 (0.135) (0.144) (0.178) (0.118) (0.136) (0.178) (0.131) (0.163) (0.168) lnLC 9.755 13.088 14.960 9.544 12.814 15.019 9.861 12.946 14.969 (1.544) (0.801) (1.418) (1.419) (0.871) (1.420) (1.411) (0.824) (1.494) D-BHC 0.709 0.854 0.927 0.706 0.883 0.945 0.725 0.845 0.935 (0.447) (0.348) (0.255) (0.443) (0.309) (0.223) (0.440) (0.357) (0.240) HHI 0.235 0.174 0.164 0.216 0.169 0.164 0.210 0.163 0.162 (0.171) (0.113) (0.089) (0.130) (0.070) (0.078) (0.135) (0.080) (0.080) ROA 0.008 0.010 0.010 0.009 0.011 0.011 0.011 0.012 0.013 (0.010) (0.010) (0.008) (0.011) (0.010) (0.011) (0.008) (0.008) (0.010)

Obs 16206 600 422 25873 788 573 15522 578 426

36

Table 2: The effect of the bank’s pre-crisis capital ratio on its ability to survive crises and normal times Panel A shows the results of logit regressions which examine how pre-crisis capital ratios affect banks’ ability to survive banking crises (BNKCRIS: the credit crunch of the early 1990s and the recent subprime lending crisis), market crises (MKTCRIS: the 1987 stock market crash, the Russian debt crisis plus LTCM bailout in 1998, and the bursting of the dot.com bubble plus September 11), and normal times (NORMALTIME) (see Section 3). In Panel A, the dependent variable is , where SURV is a dummy that equals 1 if the bank is in the sample one quarter before the crisis started and is still in the sample one quarter after the crisis, and 0 otherwise. Panels B – D contain the predicted probability of surviving banking crises, market crises, and normal times, respectively, at different capital ratios. Results are shown for small banks (GTA up to $1 billion), medium banks (GTA exceeding $1 billion and up to $3 billion), and large banks (GTA exceeding $3 billion). GTA equals total assets plus the allowance for loan and the lease losses and the allocated transfer risk reserve (a reserve for certain foreign loans). The key exogenous variables (EQRAT * BNKCRIS, EQRAT * MKTCRIS, and EQRAT * NORMALTIME) and control variables are averaged over the eight quarters before a crisis (except as noted). EQRAT is the equity capital ratio, calculated as equity capital as a proportion of GTA. GTA equals total assets plus the allowance for loan and the lease losses and the allocated transfer risk reserve (a reserve for certain foreign loans). CREDRISK, credit risk, is defined as the bank’s Basel I risk-weighted assets divided by GTA. lnLC is the log of liquidity creation. D-BHC is a dummy variable that equals 1 if the bank has been part of a bank holding company over the eight quarters before the crisis. HHI is a bank-level Herfindahl index based on bank and thrift deposits (the only variable for which geographic location is publicly available). We first establish the Herfindahl index of the local markets in which the bank has deposits and then weight these market indices by the proportion of the bank’s deposits in each of these markets. ROA is net income divided by total assets. All dollar values are expressed in real 2009:Q4 dollars using the implicit GDP price deflator.

t-statistics are in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively. Panel A: The effect of the bank’s pre-crisis capital ratio on its ability to survive

SURV Small banks Medium banks Large banks

EQRAT * BNKCRIS 15.839 23.257 38.825 (8.70)*** (2.22)** (2.11)** EQRAT * MKTCRIS 12.864 -0.623 10.898 (8.28)*** (-0.10) (0.76) EQRAT * NORMALTIME 9.264 -3.947 8.627 (4.79)*** (-0.89) (0.62) CREDRISK * BNKCRIS -4.658 -2.849 -0.554 (-9.16)*** (-2.20)** (-0.32) CREDRISK * MKTCRIS -1.401 2.266 -1.163 (-3.05)*** (1.21) (-0.53) CREDRISK * NORMALTIME 0.259 -1.146 0.001 (0.52) (-0.62) (0.00) lnLC -0.047 0.147 0.109 (-1.86)* (0.87) (0.73) D-BHC 0.136 0.688 1.064 (2.25)** (2.32)** (2.07)** HHI 1.634 5.042 7.577 (6.04)*** (2.74)*** (2.63)*** ROA (50.26) (77.20) (28.38) (19.87)*** (4.97)*** (1.66)* Constant 1.704 0.064 -1.738 (4.31)*** (0.030) (-0.780) Obs 46017 1599 1194

37

Panel B: Predicted probability of surviving banking crises at different capital ratios

Small banks Medium banks Large banks Capital ratio: Average minus 1 standard deviation 4.9% 4.3% 3.9% Average 10.0% 8.8% 8.3% Average plus 1 standard deviation 15.1% 13.3% 12.7% Predicted survival probability when capital is at: Average minus 1 standard deviation 92.8% 82.5% 88.1% Average 96.7% 93.0% 97.2% Average plus 1 standard deviation 98.5% 97.4% 99.4% Panel C: Predicted probability of surviving market crises at different capital ratios

Small banks Medium banks Large banks Capital ratio: Average minus 1 standard deviation 5.9% 4.3% 4.7% Average 9.7% 8.3% 7.6% Average plus 1 standard deviation 13.5% 12.4% 10.4% Predicted survival probability when capital is at: Average minus 1 standard deviation 97.2% 98.3% 99.0% Average 98.1% 98.2% 99.3% Average plus 1 standard deviation 98.8% 98.1% 99.5% Panel D: Predicted probability of surviving normal times at different capital ratios

Small banks Medium banks Large banks Capital ratio: Average minus 1 standard deviation 6.5% 4.9% 5.2% Average 10.1% 9.3% 8.9% Average plus 1 standard deviation 13.6% 13.6% 12.5% Predicted survival probability when capital is at: Average minus 1 standard deviation 97.2% 97.3% 97.3% Average 97.8% 96.9% 97.6% Average plus 1 standard deviation 98.4% 96.4% 97.8%

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Table 3: The effect of the bank’s pre-crisis capital ratio on its market share during crises and normal times This table shows how pre-crisis bank capital ratios affect banks’ competitive positions during banking crises (BNKCRIS: the credit crunch of the early 1990s and the recent subprime lending crisis), market crises (MKTCRIS: the 1987 stock market crash, the Russian debt crisis plus LTCM bailout in 1998, and the bursting of the dot.com bubble plus September 11), and normal times (NORMALTIME) (see Section 3). Results are shown for small banks (GTA up to $1 billion), medium banks (GTA exceeding $1 billion and up to $3 billion), and large banks (GTA exceeding $3 billion). GTA equals total assets plus the allowance for loan and the lease losses and the allocated transfer risk reserve (a reserve for certain foreign loans). The dependent variable is %ΔMKTSHARE, the percentage change in the bank’s liquidity creation market share measured as the bank’s average market share during a crisis minus its average market share over the eight quarters before the crisis, divided by its pre-crisis market share and multiplied by 100. The key exogenous variables (EQRAT * BNKCRIS, EQRAT * MKTCRIS, and EQRAT * NORMALTIME) and control variables are averaged over the eight quarters before a crisis. EQRAT is the equity capital ratio, calculated as equity capital as a proportion of GTA. GTA equals total assets plus the allowance for loan and the lease losses and the allocated transfer risk reserve (a reserve for certain foreign loans). CREDRISK, credit risk, is defined as the bank’s Basel I risk-weighted assets divided by GTA. lnLC is the log of liquidity creation. D-BHC is a dummy variable that equals 1 if the bank has been part of a bank holding company over the eight quarters before the crisis. HHI is a bank-level Herfindahl index based on bank and thrift deposits (the only variable for which geographic location is publicly available). We first establish the Herfindahl index of the local markets in which the bank has deposits and then weight these market indices by the proportion of the bank’s deposits in each of these markets. All dollar values are expressed in real 2009:Q4 dollars using the implicit GDP price deflator. t-statistics based on robust standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively. %ΔMKTSHARE

Small banks Medium banks Large banks EQRAT * BNKCRIS 5.124 1.305 2.448 (28.29)*** (2.24)** (3.34)*** EQRAT * MKTCRIS 4.072 -1.640 -0.492 (20.52)*** (-2.79)*** (-0.43) EQRAT * NORMALTIME 3.382 1.988 2.841 (14.27)*** (1.74)* (2.43)** CREDRISK * BNKCRIS -0.266 -0.213 -0.519 (-3.98)*** (-1.00) (-2.37)** CREDRISK * MKTCRIS -0.572 0.517 -0.055 (-10.50)*** (2.77)*** (-0.30) CREDRISK * NORMALTIME -0.734 0.554 -0.035 (-12.49)*** (2.83)*** (-0.17) lnLC -0.072 -0.218 -0.071 (-21.77)*** (-7.31)*** (-4.56)*** D-BHC 0.013 0.222 0.005 (1.79)* (5.72)*** (0.05) HHI -0.216 0.226 -0.017 (-10.04)*** (1.600) (-0.120) Constant 0.885 2.526 1.258 (23.82)*** (7.33)*** (5.19)*** Obs 52107 1911 1382 Adjusted R2 0.13 0.14 0.09

39

Table 4: The effect of the bank’s pre-crisis capital ratio on its profitability during crises and normal times This table shows how pre-crisis bank capital ratios affect banks’ profitability during banking crises (BNKCRIS: the credit crunch of the early 1990s and the recent subprime lending crisis), market crises (MKTCRIS: the 1987 stock market crash, the Russian debt crisis plus LTCM bailout in 1998, and the bursting of the dot.com bubble plus September 11), and normal times (NORMALTIME) (see Section 3). Results are shown for small banks (GTA up to $1 billion), medium banks (GTA exceeding $1 billion and up to $3 billion), and large banks (GTA exceeding $3 billion). GTA equals total assets plus the allowance for loan and the lease losses and the allocated transfer risk reserve (a reserve for certain foreign loans). The dependent variable is ΔPROF, the change in profitability measured as the bank’s average ROE (net income divided by GTA) during a crisis minus its average ROE over the eight quarters before the crisis. The key exogenous variables (EQRAT * BNKCRIS, EQRAT * MKTCRIS, and EQRAT * NORMALTIME) and control variables are averaged over the eight quarters before a crisis. EQRAT is the equity capital ratio, calculated as equity capital as a proportion of GTA. GTA equals total assets plus the allowance for loan and the lease losses and the allocated transfer risk reserve (a reserve for certain foreign loans). CREDRISK, credit risk, is defined as the bank’s Basel I risk-weighted assets divided by GTA. lnLC is the log of liquidity creation. D-BHC is a dummy variable that equals 1 if the bank has been part of a bank holding company over the eight quarters before the crisis. HHI is a bank-level Herfindahl index based on bank and thrift deposits (the only variable for which geographic location is publicly available). We first establish the Herfindahl index of the local markets in which the bank has deposits and then weight these market indices by the proportion of the bank’s deposits in each of these markets. All dollar values are expressed in real 2009:Q4 dollars using the implicit GDP price deflator. t-statistics based on robust standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively. ΔPROF

Small banks Medium banks Large banks EQRAT * BNKCRIS 0.061 0.109 0.239 (3.41)*** (0.70) (2.71)*** EQRAT * MKTCRIS 0.108 0.132 0.388 (6.39)*** (2.00)** (2.33)** EQRAT * NORMALTIME 0.135 0.044 0.020 (7.88)*** (0.64) (0.20) CREDRISK * BNKCRIS -0.201 -0.224 -0.151 (-30.18)*** (-4.76)*** (-5.24)*** CREDRISK * MKTCRIS -0.049 -0.066 -0.030 (-8.24)*** (-2.65)*** (-1.24) CREDRISK * NORMALTIME 0.029 -0.008 -0.041 (6.12)*** (-0.37) (-1.70)* lnLC 0.000 0.004 -0.001 (0.44) (1.48) (-0.81) D-BHC 0.004 0.001 -0.003 (5.20)*** (0.12) (-0.40) HHI 0.009 0.036 0.031 (4.52)*** (1.91)* (1.240) Constant 0.017 -0.025 0.022 (6.08)*** (-0.850) (0.960) Obs 52107 1911 1382 Adjusted R2 0.09 0.22 0.18

40

Table 5: Robustness This table presents the results of six checks to establish the robustness of our results. Panel A uses alternative specifications of survival, competitive position, and profitability. Panel B uses regulatory capital ratios instead of the equity-to-assets ratio. Panel C drops Too-Big-To-Fail banks from the large-bank sample. Panel D uses alternative cutoffs separating medium and large banks. Panel E measures pre-crisis capital ratios alternatively one quarter before the crisis starts or averaged over the four quarters before the crisis. Panel F deals with the potential endogeneity issues related to capital by using instrumental variable regressions. The crises include banking crises (BNKCRIS: the credit crunch of the early 1990s and the recent subprime lending crisis), market crises (MKTCRIS: the 1987 stock market crash, the Russian debt crisis plus LTCM bailout in 1998, and the bursting of the dot.com bubble plus September 11), and normal times (NORMALTIME) (see Section 3). Results are shown for small, medium, and large banks – the cutoffs are GTA of $1 billion and $3 billion, respectively, unless otherwise noted. GTA equals total assets plus the allowance for loan and the lease losses and the allocated transfer risk reserve (a reserve for certain foreign loans). SURV, survival, is a dummy that equals 1 if the bank is in the sample one quarter before such a crisis started and is still in the sample one quarter after the crisis, and 0 otherwise. %ΔMKTSHARE, the change in market share, is measured as the bank’s average market share during a crisis minus its average market share over the eight quarters before the crisis, normalized by its pre-crisis market share and multiplied by 100. Market share is the bank’s liquidity creation (LC) as a fraction of total LC. ΔPROF, the change in profitability, is measured as the bank’s average profitability during a crisis minus its average profitability over the eight quarters before the crisis. Profitability is ROE, net income divided by equity capital. To preserve space, we only present the coefficients on the key exogenous variables although all the control variables (see Tables 2 – 4) are generally included in the regressions. Exception: the risk interaction terms are not included in Panel B because the numerator of the risk variable (Basel I risk-weighted assets) is identical to the denominator of the regulatory capital ratio. t-statistics based on robust standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively. Panel A: Robustness – use alternative definitions of survival, market share, and profitability A1: SURV_alt

Until Q4 (instead of Q1) after crisis A2: %ΔMKTSHARE_alt

Based on GTA (instead of LC) A3: ΔPROF_alt

ROA (instead of ROE)

Small banks

Medium banks

Large banks

Small banks

Medium banks

Large banks

Small banks

Medium banks

Large banks

EQRAT * BNKCRIS 10.648 22.777 9.170 1.593 0.653 1.158 0.013 -0.014 -0.004 (7.40)*** (2.53)** (0.71) (31.27)*** (1.97)** (4.20)*** (6.93)*** (-0.67) (-0.57) EQRAT * MKTCRIS 12.414 -1.814 4.681 0.939 -0.280 0.502 0.004 -0.004 -0.003 (10.91)*** (-0.57) (0.58) (18.06)*** (-2.15)** (1.40) (2.09)** (-0.68) (-0.16) EQRAT * NORMALTIME 7.796 -1.303 -3.656 0.618 0.117 0.653 0.016 -0.015 -0.008 (5.85)*** (-0.33) (-0.70) (8.59)*** (0.31) (1.61) (7.94)*** (-1.73)* (-0.71) Obs 46017 1599 1194 52107 1911 1382 52107 1911 1382 Adj R2 0.14 0.06 0.04 0.08 0.24 0.19

41

Panel B: Robustness – use regulatory capital ratios SURV %ΔMKTSHARE ΔPROF

Small banks

Medium banks

Large banks

Small banks

Medium banks

Large banks

Small banks

Medium banks

Large banks

TIER1RAT * BNKCRIS 16.707 24.316 40.914 2.761 1.015 3.487 0.098 0.282 0.445 (8.91)*** (2.27)** (2.23)** (20.92)*** (1.43) (3.36)*** (8.32)*** (2.51)** (3.69)*** TIER1RAT * MKTCRIS 5.324 -1.282 4.954 2.575 -0.384 -0.314 0.047 0.049 0.143 (6.66)*** (-0.80) (0.74) (22.23)*** (-0.93) (-0.48) (5.89)*** (2.18)** (2.03)** TIER1RAT * NORMALTIME 2.942 -0.853 3.383 2.213 0.868 1.727 0.000 0.038 0.071 (3.52)*** (-0.44) (0.53) (18.52)*** (1.99)** (2.10)** (0.00) (1.23) (1.26) Obs 46017 1599 1194 52107 1911 1382 52107 1911 1382 Adj R2 0.13 0.12 0.09 0.07 0.18 0.16 Panel C: Robustness – exclude Too-Big-To-Fail banks C1: Drop banks with GTA > $50 billion C2: Drop 19 largest banks each quarter

SURV %ΔMKT SHARE ΔPROF SURV

%ΔMKT SHARE ΔPROF

EQRAT * BNKCRIS 42.959 2.707 0.267 47.774 2.700 0.285 (2.15)** (3.17)*** (2.68)*** (2.32)** (3.14)*** (2.83)*** EQRAT * MKTCRIS 9.947 -0.510 0.417 10.689 -0.548 0.324 (0.69) (-0.42) (2.44)** (0.73) (-0.43) (2.02)** EQRAT * NORMALTIME 6.068 3.025 0.006 6.816 3.040 0.012 (0.43) (2.55)** (0.05) (0.46) (2.58)** (0.12) Obs 1106 1273 1273 1088 1260 1260 Adj R2 0.10 0.19 0.10 0.19

42

Panel D: Robustness – use alternative cutoffs between medium and large banks of $5 billion and $10 billion D1: $5 billion cutoff SURV %ΔMKTSHARE ΔPROF

Medium

banks Large

banks Medium

banks Large banks

Medium banks

Large banks

EQRAT * BNKCRIS 29.779 48.853 1.756 2.502 0.206 0.103 (2.97)*** (1.86)* (3.05)*** (2.11)** (2.24)** (0.72) EQRAT * MKTCRIS 3.309 8.839 -1.392 -0.586 0.156 0.521 (0.48) (0.42) (-2.37)** (-0.34) (2.19)** (1.94)* EQRAT * NORMALTIME -3.751 11.747 2.304 1.886 0.028 0.011 (-0.86) (0.72) (2.45)** (1.11) (0.48) (0.06) Obs 1942 851 2312 981 2312 981 Adj R2 0.12 0.09 0.21 0.16 D2: $10 billion cutoff SURV %ΔMKTSHARE ΔPROF

Medium

banks Large banks

Medium banks

Large banks

Medium banks

Large banks

EQRAT * BNKCRIS 25.939 68.809 1.694 2.746 0.211 0.113 (2.68)*** (1.89)* (2.96)*** (1.75)* (2.41)** (0.72) EQRAT * MKTCRIS 3.448 47.787 -1.370 4.032 0.164 0.498 (0.51) (1.00) (-2.56)** (1.95)* (2.07)** (1.73)* EQRAT * NORMALTIME -1.078 5.419 2.464 -0.251 0.043 -0.166 (-0.22) (0.21) (2.71)*** (-0.16) (0.75) (-0.84) Obs 2315 239 2739 554 2739 554 Adj R2 0.10 0.10 0.21 0.16

43

Panel E: Robustness – measure pre-crisis capital ratios at different points before a crisis E1: Use the average capital ratio over 4 (instead of 8) quarters before the crisis SURV %ΔMKTSHARE ΔPROF

Small banks

Medium banks

Large banks

Small banks

Medium banks

Large banks

Small banks

Medium banks

Large banks

EQRATave4Q * BNKCRIS 19.164 19.646 29.526 4.787 1.326 2.450 0.029 0.070 0.167 (10.35)*** (2.07)** (1.71)* (24.07)*** (2.22)** (3.18)*** (1.52) (0.42) (1.80)* EQRATave4Q * MKTCRIS 12.863 -1.030 9.398 3.448 -1.991 -0.404 0.070 0.182 0.533 (8.36)*** (-0.19) (0.63) (17.29)*** (-4.34)*** (-0.32) (3.99)*** (2.21)** (2.72)*** EQRATave4Q * NORMALTIME 8.482 -4.197 3.762 2.704 1.493 2.569 0.069 -0.005 -0.040 (4.41)*** (-0.96) (0.32) (10.92)*** (1.39) (2.20)** (4.03)*** (-0.08) (-0.42) Obs 45947 1599 1194 52029 1911 1382 52029 1911 1382 Adj R2 0.12 0.14 0.08 0.09 0.22 0.18 E2: Measure capital 1 quarter before the crisis (instead of the average capital ratio over 8 quarters before the crisis) SURV %ΔMKTSHARE ΔPROF

Small banks

Medium banks

Large banks

Small banks

Medium banks

Large banks

Small banks

Medium banks

Large banks

EQRATQ1 * BNKCRIS 24.208 23.558 39.415 4.225 1.512 2.288 0.004 0.091 0.136 (12.94)*** (2.46)** (2.47)** (16.25)*** (2.29)** (2.72)*** (0.17) (0.58) (1.26) EQRATQ1 * MKTCRIS 13.144 0.506 7.574 2.299 -1.821 0.039 0.009 0.168 0.389 (8.29)*** (0.09) (0.53) (11.18)*** (-3.18)*** (0.03) (0.45) (2.15)** (1.91)* EQRATQ1 * NORMALTIME 6.930 -4.077 -2.044 1.647 0.273 2.298 -0.008 -0.125 -0.117 (3.53)*** (-0.95) (-0.22) (6.40)*** (0.49) (2.11)** (-0.44) (-2.53)** (-1.40) Obs 45751 1599 1194 51809 1911 1382 51809 1911 1382 Adj R2 0.10 0.13 0.08 0.09 0.22 0.17

44

Panel F: Robustness – instrumental variable analysis F1: Hausman test for endogeneity SURV %ΔMKTSHARE ΔPROF

Small banks

Medium banks

Large banks

Small banks

Medium banks

Large banks

Small banks

Medium banks

Large banks

Hausman endogeneity test (p-value) 0.27 0.98 0.33 0.00*** 0.16 0.18 0.00*** 0.95 0.68 F2: Second-stage IV regressions for

small banks

SURV %ΔMKT SHARE ΔPROF

EQRAT * BNKCRIS 2.531 8.640 -1.267 (1.79)* (2.43)** (-2.06)** EQRAT * MKTCRIS 1.071 24.341 2.956 (1.10) (5.71)*** (4.87)*** EQRAT * NORMALTIME -2.471 19.933 5.213 (-0.58) (1.59) (1.93)* Obs 23575 26671 26671

45

Table 6: Comparing the effects of book and market capital ratios during crises and normal times This table examines how book and market capital ratios affect the performance of listed entities during crises and normal times. For this analysis, we include listed banks and listed one-bank-holding companies, and we aggregate the data of all the banks in a listed multi-bank-holding company. The crises include banking crises (BNKCRIS: the credit crunch of the early 1990s and the recent subprime lending crisis), market crises (MKTCRIS: the 1987 stock market crash, the Russian debt crisis plus LTCM bailout in 1998, and the bursting of the dot.com bubble plus September 11), and normal times (NORMALTIME) (see Section 3). Results are shown for small, medium, and large banks – the cutoffs are GTA of $1 billion and $3 billion, respectively, unless otherwise noted. GTA equals total assets plus the allowance for loan and the lease losses and the allocated transfer risk reserve (a reserve for certain foreign loans). SURV, survival, is a dummy that equals 1 if the bank is in the sample one quarter before such a crisis started and is still in the sample one quarter after the crisis, and 0 otherwise. %ΔMKTSHARE, the change in market share, is measured as the bank’s average market share during a crisis minus its average market share over the eight quarters before the crisis, normalized by its pre-crisis market share and multiplied by 100. Market share is the bank’s liquidity creation (LC) as a fraction of total LC. ΔPROF, the change in profitability, is measured as the bank’s average profitability during a crisis minus its average profitability over the eight quarters before the crisis. Profitability is ROE, net income divided by equity capital. To preserve space, we only present the coefficients on the key exogenous variables although all the control variables (see Tables 2 – 4) except for the BHC dummy are included in the regressions. EQRAT_listed, a listed entity’s pre-crisis book capital ratio, is the listed entity’s book equity capital as a proportion of its GTA. MKTRAT_listed, a listed entity’s pre-crisis market capital ratio, is the listed entity’s market value of equity (i.e., the number of shares outstanding multiplied by the share price) divided by its assets in market value terms (i.e., the book value of liabilities plus the market value of equity), averaged over the eight quarters before the crisis. t-statistics based on robust standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively. Listed entity book capital ratios Listed entity market capital ratios

SURV %ΔMKT SHARE ΔPROF SURV

%ΔMKT SHARE ΔPROF

EQRAT_listed * BNKCRIS -17.363 1.642 -0.349 (-0.80) (2.06)** (-1.20) EQRAT_listed * MKTCRIS -3.020 1.370 0.242 (-0.45) (1.86)* (1.89)* EQRAT_listed * NORMALTIME 12.003 0.676 -0.042 (1.17) (1.08) (-0.37) MKTRAT_listed * BNKCRIS 22.173 0.579 0.339 (1.65)* (4.04)*** (1.93)* MKTRAT_listed * MKTCRIS 4.528 0.282 0.158 (1.56) (1.10) (2.70)*** MKTRAT_listed * NORMALTIME -0.135 0.253 0.052 (-0.06) (1.37) (1.56) Obs 1619 1889 1889 1619 1889 1889 Adj R2 0.08 0.31 0.08 0.24