Abstract
This paper examines whether bank capital ratios and default risk are associated with the gender of the bank’s Chief Executive Officer (CEO) and Chairperson of the board. Given the documented gender-based differences in conservatism and risk tolerance, we postulate that female CEOs and board Chairs should assess risks more conservatively, and thereby hold higher levels of equity capital and reduce the likelihood of bank failure during periods of market stress. Using a large panel of U.S. commercial banks, we document that banks with female CEOs hold more conservative levels of capital after controlling for the bank’s asset risk and other attributes. Furthermore, while neither CEO nor Chair gender is related to bank failure in general, we find strong evidence that smaller banks with female CEOs and board Chairs were less likely to fail during the financial crisis. Overall, our findings are consistent with the view that gender-based behavioral differences may affect corporate decisions.
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Notes
We focus on CEOs and Chairpersons of the board because they are arguably the most powerful individuals within a firm and may have substantial influence on the firm’s decision-making process. The CEO and the board Chair are responsible for making and approving the major strategic decisions of the firm. While the CEO is responsible for decision management, the board Chair can be considered as the firm’s principal decision control agent who also has an important role in strategic decision-making. In the U.S., both of these leadership positions are often held by the same individual, and in most firms with CEO-Chair separation, the board Chair is a former CEO or other top executive of the firm (see e.g., Brickley et al. 1997; Adams et al. 2005).
Bertrand and Schoar (2003) provide a comprehensive discussion on why individual managers may matter for corporate financial decisions.
The underlying premise in our empirical analysis is that gender-based differences in conservatism and risk tolerance persist in a professional setting and the preferences of individual managers influence the firm’s financial decisions.
Croson and Gneezy (2009) provide a review of gender differences in economic experiments.
Several studies have recently examined the relationship between firm performance and gender diversity on the board of directors. The empirical evidence on the effects of female directors is mixed. While Carter et al. (2003), Erhardt et al. (2003), Campbell and Minguez-Vera (2008), Anderson et al. (2011), and Joecks et al. (2013) document that gender diversity may have positive effects on profitability and market valuation, the results of Rose (2007), Adams and Ferreira (2009), Ahern and Dittmar (2012), and Chapple and Humphrey (2014) indicate that effect of female directors on firm performance is insignificant, or even negative.
It should be noted that these studies focus on large, publicly traded banks. A vast majority of the banks in our sample are privately-owned banks for which data on governance structures and ownership is not publicly available. Given the data restrictions, we are unfortunately unable to control for governance attributes and ownership structure.
These snapshots of SNL Financial data are available only from 2007 onwards.
The unclear names were coded to females and males based on http://www.genderchecker.com and http://www.nameplayground.com. The latter website provides percentages for the popularity of a given name in the U.S. in both genders. For instance, 39.7 % of individuals named Pat are males and 60.3 % are females, and consequently, CEOs and board Chairs named Pat were excluded from the sample.
Given this definition, we use bank failures from January 2008 to December 2011 in our failure prediction regressions.
As a robustness check, we have used additional asset risk proxies including earnings volatility (proxied by the standard deviation of ROA) and asset distribution (proxied by the loan share of commercial loans and the loan share of real estate loans) as additional control variables in the regressions. The estimation results of these regressions are consistent with our main analysis.
We are unable to estimate regression specifications with bank fixed-effects because our female dummy variables remain unchanged over time for most banks, thereby leading to almost perfect collinearity with bank fixed-effects.
The gender and financial data cover years 2007–2010 and we use bank failures during years 2008-2011 in our tests.
For brevity, we do not tabulate the correlation coefficients. The correlation matrix is available from the authors upon request.
For brevity, we do not tabulate the results of the t-tests. These results are available from the authors upon request.
The subsample of large banks consists of banks with above median total assets and the subsample of small banks contains banks with below median total assets. Berger and Bouwman (2013) argue that size is a source of economic strength along with equity capital, and therefore, higher capital buffers are more beneficial for smaller banks.
In our robustness checks, we further address endogeneity concerns by utilizing propensity score matching technique to identify male-led banks that are statistically indistinguishable from female-led banks in terms of size, growth, liquidity, deposit base and other observable bank characteristics.
In calculating this commonness measure, we exclude the bank in consideration to ensure that the gender of that particular bank’s CEO or board Chair does not affect the computation of the instrument. Hence, if there are in total f female-led banks and m male-led banks in a state, the commonness measure equals (f − 1)/(f − 1 + m).
It should be noted that it is very difficult to find suitable instruments. In addition to the gender status equality and the commonness of female-led banks in a given state, we also tried to use several other state-level variables, such as the supply of educated women and the level of female participation in the labor force as instruments, but these variables appeared uncorrelated with the female dummies.
The coefficient estimates of the control variables are not tabulated in Table 4 for brevity.
As a robustness check, we have estimated the first-stage regressions using lagged bank-specific control variables and also without the state-specific control variables. The estimates of these regressions are consistent with the estimates reported in Table 4.
The results of the t-tests are not tabulated for brevity, but are naturally available upon request.
In our sample, the failure rates of large and small banks are 1.6 and 0.7 %, respectively.
Specifically, we estimate failure prediction regressions with bank size interactions of the following form: Failure j,t+1 = α + β 1Female j,t × Small bank j,t + β 2Female j,t × Large bank j,t + β 3Capital j,t + β 4Size j,t + β 5Loan growth j,t + β 6Core deposits j,t + β 7Insured deposits j,t + β 8Delinquent loans j,t + β 9ROA j,t + β 10Liquidity j,t + β 11Public j,t + β 12Subchapter S j,t + β 13MBHC j,t + β 14Dual j,t + β 15Unemployment j,t + β 16PCI j,t + β 17–19 (Year dummies) j,t + ε j,t .
It can be argued that CEOs and board Chairs may have a stronger influence on corporate decisions-making in smaller, privately-owned firms. Moreover, as noted by Holod (2012), the CEOs of smaller, private banks are more likely to hold large ownership stakes, and may therefore have very different incentives than the CEOs of large publicly traded banks.
Although propensity score matching is often used as a tool for alleviating endogeneity concerns, it is important to note that the matching is based on the observable firm characteristics. Consequently, our empirical findings may still suffer from endogeneity biases caused by omitted variables or unobservable bank characteristics.
Under this definition, a bank is well-capitalized if the ratio of Tier-1 capital to total assets (Tier-1 leverage ratio) is at least 5 %, the ratio of Tier-1 capital to risk-weighted assets (Tier-1 risk-based ratio) is at least 6 %, and the ratio of the sum of Tier-1 and Tier-2 capital to risk-weighted assets (total risk-based ratio) is at least 10 %.
It should be noted that Eq. (2) is a failure prediction regression in which future bank failures are predicted with variables that are currently observable. In this type of prediction setup, there cannot be simultaneity issues and reverse causality would essentially require that a failure at a future point in time causes the gender of the bank’s CEO and board Chair at the present time.
Holod (2012) argues that the CEOs of smaller, private banks are more likely to hold larger ownership stakes of those banks, while the CEOs of larger, publicly traded banks are more likely to be hired agents with relatively small ownership stakes.
A minority-owned financial institution is defined as a bank that is at least 51 % owned by minorities, such as African Americans, Hispanic Americans, or women.
Anecdotal evidence suggests that firms in which the CEO and the Chairperson of the board are relatives (i.e., spouses, siblings or descendants) tend to be family controlled. Thus, we presume that a common last name of the CEO and board Chair can be used as a proxy for controlling family ownership.
The evidence regarding the effects of executive education on risk-taking is mixed. While Bertrand and Schoar (2003) find that executives with MBA degrees follow more aggressive business strategies, the results of Berger et al. (2014) suggest that executives with higher levels of education are more conservative. Ahern and Dittmar (2012) and Huang and Kisgen (2013) document that female executives and directors are more likely to hold an MBA degree.
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Acknowledgments
We wish to thank two anonymous referees, David Aristei, Allen N. Berger, Gerald P. Dwyer, Jason Park, Seppo Pynnönen, Daniel A. Rogers, and seminar participants at Stockholm University, Hanken School of Economics, Pablo de Olavide University, the Office of the Comptroller of the Currency, the 53rd Southern Finance Association Meeting, the 49th Eastern Finance Association Meeting, the 25th Australasian Finance and Banking Conference, and the 16th International Conference on Macroeconomic Analysis and International Finance for insightful comments and discussions. E. and S. Vähämaa gratefully acknowledge the financial support of the Academy of Finland, the Foundation for Economic Education, the Marcus Wallenberg Foundation, and the NASDAQ OMX Nordic Foundation. All views expressed in this paper are those of the authors alone and do not necessarily reflect those of the Office of the Comptroller of the Currency or the U.S. Department of the Treasury.
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Palvia, A., Vähämaa, E. & Vähämaa, S. Are Female CEOs and Chairwomen More Conservative and Risk Averse? Evidence from the Banking Industry During the Financial Crisis. J Bus Ethics 131, 577–594 (2015). https://doi.org/10.1007/s10551-014-2288-3
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DOI: https://doi.org/10.1007/s10551-014-2288-3