WORKING PAPER SERIES

Poor Performance and CEO Turnover in Community 

Banks: The Role of Gender in Managerial Successions

Ajay Palvia

Federal Deposit Insurance Corporation

Alireza Ebrahim

Office of the Comptroller of the Currency 

Emilia Vähämaa 

Hanken School of Economics 

Sami Vähämaa

University of Vaasa

August 2023

FDIC CFR WP 2023-02 

fdic.gov/cfr 

The Center for Financial Research (CFR) Working Paper Series allows CFR staff and their coauthors to circulate 

preliminary research findings to stimulate discussion and critical comment. Views and opinions expressed in CFR Working 

Papers reflect those of the authors and do not necessarily reflect those of the FDIC or the United States. Comments and 

suggestions are welcome and should be directed to the authors. References should cite this research as a “FDIC CFR 

Working Paper” and should note that findings and conclusions in working papers may be preliminary and subject to 

revision.
Poor Performance and CEO Turnover in Community Banks: 

The Role of Gender in Managerial Successions 

Alireza Ebrahima,*, Ajay Palviab,**, Emilia Vähämaac,***, Sami Vähämaad,****

a Office of the Comptroller of the Currency, U.S. Department of the Treasury 

b Federal Deposit Insurance Corporation, Division of Insurance and Research 

c Hanken School of Economics, Department of Finance and Economics 

d University of Vaasa, School of Accounting and Finance 

August 18, 2023 

Abstract

We examine the association between community bank financial performance and CEO 

turnover and assess the impact of CEO gender on succession and post-succession actions and 

outcomes. Using a large sample of U.S. community banks between the years 2008 and 2017, 

we document that poor performance has a causal impact on CEO turnover. Although poor 

financial performance is a key determinant of CEO turnovers, it is neither linked to the gender 

of the bank’s dismissed nor incoming CEO. However, we find strong evidence of asymmetric 

post-turnover operational and balance-sheet adjustments depending on the gender of the 

incoming CEO, especially for banks undergoing CEO turnover amidst periods of poor 

performance. These adjustments suggest differential attempts at reducing leverage and risk 

for banks transitioning to female leadership. We find limited evidence that transitions to 

female leadership decrease realized risk or improve bank performance post-turnover.

JEL classification: G20, G21, G30, G32, M12, M14 

Keywords: banks, community banks, CEO turnovers, CEO gender, female CEOs 

 

 We thank Anna Agapova, Rosalind Bennett, Allen Berger, Chris Blickley, Rebel Cole, Chacko George, Yosh 

Halberstam, Henry Jarva, Roni Kisin, Vesa Pursiainen, Natalya Schenck, Laima Spokeviciute, Alvaro Taboada, Alex 

Ufier, and workshop and seminar participants at Harvard University, Florida Atlantic University, the Office of the 

Comptroller of the Currency, the Federal Deposit Insurance Corporation, and the 3rd University of Vaasa Banking 

Research Workshop for valuable comments and suggestions. E. and S. Vähämaa gratefully acknowledge the financial 

support of the OP Group Research Foundation, the Foundation for Economic Education, Emil Aaltonen Foundation, 

Marcus Wallenberg Foundation, Niilo Helander Foundation, the Finnish Foundation for Share Promotion, and the 

Foundation for the Advancement of Finnish Securities Markets. Part of this paper was written while E. Vähämaa was 

visiting Harvard University and S. Vähämaa was visiting Bentley University. The views expressed in this paper are 

those of the authors and do not reflect the views of the Office of the Comptroller of the Currency (OCC), the Federal 

Deposit Insurance Corporation (FDIC), the U.S. Department of the Treasury, or the United States.

* Address: Office of the Comptroller of the Currency, 400 7th Street SW, Washington, D.C. 20219, USA; E-mail 

address: alireza.ebrahim@occ.treas.gov ** Address: Federal Deposit Insurance Corporation, Division of Insurance and Research, 550 17th Street NW, 

Washington, D.C. 20429, USA; E-mail address: apalvia@fdic.gov *** Address: Hanken School of Economics, Department of Finance and Economics, P.O. Box 287, FI-65101 Vaasa, 

Finland; E-mail address: emilia.vahamaa@hanken.fi **** Address: University of Vaasa, School of Accounting and Finance, P.O. Box 700, FI-65101 Vaasa, Finland; Email address: sami@uwasa.fi 
2

1. Introduction

This paper studies the impact of poor financial performance on community bank CEO 

turnover and considers the role CEO gender plays both in succession and in subsequent bank 

actions and outcomes. The antecedents, consequences, and underlying mechanisms of CEO 

turnover have been extensively examined in the literature over the last four decades. In general, 

previous studies have documented that a firm’s financial performance, level of risk-taking and 

financial distress, and governance mechanisms are among the key factors that influence CEO 

turnover (see e.g., Coughlan and Schmidt, 1985; Gilson, 1989; Murphy and Zimmerman, 1993; 

Farrell and Whidbee, 2003; Arthaud-Day et al., 2006; Bushman, Dai and Wang, 2010; Jenter and 

Kanaan, 2015; Jenter and Lewellen, 2021; Burns, Minnick and Starks, 2023). Prior literature also 

suggests that CEO turnover may significantly affect firm’s strategic direction, financial and 

investment policies, and other corporate outcomes, including the potential triggers of CEO 

turnover such as poor performance or financial distress (e.g., Beatty and Zajac, 1987; Kesner and 

Dalton, 1994; Weisbach, 1995; Shen and Cannella, 2002; Huson, Malatesta and Parrino, 2004; 

Fiordelisi and Ricci, 2014; Gao, Harford and Li, 2017; Lin et al., 2020). 

While a vast body of literature has examined CEO turnover in public nonfinancial firms, 

comparatively less attention has been devoted to CEO turnover in banks and in particular 

community banks, which are fundamentally different from nonfinancial firms in terms of their 

business models, opaqueness, exposure to regulations and supervision, and societal importance.

Furthermore, despite the extensive literature on CEO turnovers, only a few studies have considered 

the role of CEO gender as a potential factor in influencing managerial turnovers and post-turnover 
3

corporate decisions and outcomes. (see e.g., Adams, Gupta and Leeth, 2009; Elsaid and Ursel, 

2011; Rigolini, Gabaldon and Le Bruyn Goldeng, 2021; Ma, 2022). 

This paper contributes to the extant literature by considering three interrelated questions

associated with CEO turnover in community banks. First, we investigate whether poor financial 

performance is associated with CEO turnover in community banks and also assess whether such 

an association is potentially affected by the gender of the incumbent CEO. Second, we examine

whether the association between poor financial performance and CEO turnover depends on the 

gender of the outgoing or incoming CEO. Finally, we explore whether the gender of the incoming 

CEO, and especially a change in CEO gender, influences the bank’s policy decisions, financial 

performance, and risk-taking in the aftermath of the turnover. In our analysis, we exploit unique 

data on CEO turnovers covering nearly all U.S. community banks between the years 2008 and 

2017 to address these questions.

While a linkage between CEO turnover and poor performance is critical to affirming a degree 

of managerial discipline in community banks, the question about incoming CEO gender is more 

timely and considers whether troubled community banks turn to female leadership specifically 

because they are in trouble, consistent with the so-called glass cliff hypothesis proposed by Ryan 

and Haslam (2005, 2007). Our empirical findings indicate that poor financial performance has a 

causal impact on CEO turnover in community banks. In addition, we find that although poor

performance is a key determinant of CEO turnovers, it is not linked to the gender of the bank’s 

dismissed nor the incoming CEO. This suggests that troubled, poorly performing banks are not 

more likely to retain their incumbent female CEOs or to replace male CEOs with a female. 
4

After documenting a positive causal linkage between poor performance and CEO turnovers

in community banks, we proceed to examine whether the gender of the incoming CEO affects bank

actions and outcomes in the aftermath of executive turnovers. To the extent that the gender of the 

incoming CEO may influence bank risk-taking preferences and policies, we expect female CEOs 

to pursue more conservative and less risky strategies based on the well-documented gender-based 

differences in risk preferences and tolerance of individuals (see e.g., Levin, Snyder and Chapman, 

1988; Johnson and Powell, 1994; Powell and Ansic, 1997; Jianakoplos and Bernasek, 1998; 

Sunden and Surette, 1998; Barber and Odean, 2001; Agnew, Balduzzi and Sunden, 2003; Watson 

and McNaughton, 2007; Halko, Kaustia and Alanko, 2012). 

Consistent with this presumption, we document that a transition from a male CEO to a female 

CEO leads to post-turnover deleveraging and derisking actions. Among the community banks with 

executive turnovers, the banks with incoming female CEOs take actions to reduce both assets and 

liabilities while banks with male CEOs do not. In contrast, when the incoming CEO is male, we 

observe increases in the amounts of loans and risky assets. These gender-related asymmetries in 

bank actions following leadership changes are most pronounced for banks in which CEO turnover

occurs amidst poor performance. Specifically, our findings indicate that poorly performing 

community banks with incoming female CEOs pursue reductions in assets, risk-weighted-assets,

and the number of employees and bank branches. These banks also exhibit reductions in liabilities, 

deposits, and brokered deposits in the aftermath of CEO turnover. Poorly performing community 

banks that do not experience a transition to female leadership either do not have reductions or 

comparable levels of reductions in their assets and liabilities. 
5

We also examine the role of incoming CEO gender in influencing the bank’s financial 

performance and riskiness after changes in leadership. If the gender of the incoming CEO 

influences the bank’s strategic decisions with respect to asset and liability growth, such decisions 

could affect post-turnover performance and the level of risk. We find that banks with CEO turnover 

experience reductions in default risk and earnings volatility, especially if the turnover is 

concomitant with poor performance. These reductions tend to be greater after a transition from a 

male CEO to a female CEO. However, the differences are not always economically different from 

comparable changes in banks that do not undergo a transition to female leadership. In addition, we 

find no evidence that transitions to female leadership would lead to post-turnover improvements 

in capital levels or profitability. Our findings suggest that attempts to reduce leverage or risk by 

poorly performing banks transitioning to female leadership are effective in improving outcomes

but may not be consistently superior to other strategies. In other words, the post-turnover strategies 

used by banks that do not transition to female leadership are often as effective.

Our paper contributes to several strands of literature. First, we extend the extensive literature 

on CEO turnovers which generally suggests that poor financial performance increases the 

likelihood of CEO turnover (see e.g., Coughlan and Schmidt, 1985; Gilson, 1989; Murphy and

Zimmerman, 1993; Jenter and Kanaan, 2015; Jenter and Lewellen, 2021). Most previous work

examines publicly-listed nonfinancial firms, and the few exceptions focusing on CEO turnovers in 

banks are the studies by Schaeck et al. (2012), Bornemann et al. (2015), Srivastav et al. (2017), 

Chen and Ebrahim (2018), Sarkar, Subramanian and Tantri (2019), and Bunkanwanicha, Di Giuli 

and Salvade (2022). In brief, these studies suggest that CEO turnovers in banks are positively 

related to poor performance, the degree of risk-taking and financial distress, and the stringency of 
6

board and regulatory monitoring. Overall, as contended by Becht, Bolton and Roell (2011), Adams 

and Mehran (2012), de Haan and Vlahu (2016), and Palvia, Vähämaa and Vähämaa (2020), among 

others, banks are fundamentally different from nonfinancial firms in terms of their business 

models, governance structures, and supervision and regulation, and consequently, additional 

research on the antecedents and consequences of CEO turnovers in the banking industry is 

warranted. 

Second, our paper contributes to the scant literature on community bank governance and, 

more specifically, on CEO turnovers and the effects of these turnovers on community bank actions

and outcomes. While banks, in general, are different from nonfinancial firms, community banks 

have a unique set of characteristics relative to large, publicly-traded financial institutions, 

including their business models, risk management strategies, and ownership and governance 

structures. In community banks, CEOs often have closer ties with the board members, employees, 

and shareholders, and their governance structures are influenced by personal relationships between 

the different stakeholders.

1 These characteristics make community banks an interesting setting to 

examine CEO turnovers.

The only previous studies on CEO turnovers in community banks we are aware of are those 

of Schaeck et al. (2012), Palvia (2012), and Dahl, Milchanowski and Coster (2018). In terms of 

factors triggering turnovers, Palvia (2012) documents a linkage between regulatory monitoring 

and CEO turnovers, while the findings of Schaeck et al. (2012) indicate that increased default risk 

increases the likelihood of CEO dismissal. Schaeck et al. (2012) also examine the effect of CEO 

 1 For example, a single influential executive could potentially have an outsized role as community banks often have 

limited numbers of executives and board members.
7

turnovers on community banks’ financial performance and risk profile and find no evidence that 

leadership changes would improve bank performance. Finally, Dahl et al. (2018) focus on the 

impact of CEO turnovers on regulatory-assessed managerial performance. Their findings suggest 

that CEO turnovers neither weaken nor improve management performance ratings. Our study is 

the first to examine the potential role of CEO gender, and specifically the impact of appointing 

female CEOs, in influencing community bank actions and outcomes after leadership successions. 

The third related stream of literature investigates female CEOs and the effects of female 

leadership on corporate decisions and outcomes. This body of literature shows that firms led by

female executives make less risky financing choices and investment decisions, and are more 

conservative with respect to financial reporting practices (e.g., Peni and Vähämaa, 2010; Huang 

and Kisgen, 2013; Francis et al., 2015; Faccio, Marchica and Mura, 2016; Adhikaria, Agrawal and 

Malm, 2019; Hrazdil et al., 2020; Peltomäki et al., 2021). Hence, the existing empirical evidence 

generally suggests that the behavioral differences between female and male executives are 

reflected in corporate-level outcomes. In the banking context, the implications of female leadership 

have been previously examined by Berger, Kick, and Schaeck (2014), Palvia, Vähämaa, and 

Vähämaa (2015), Skala end Weill (2018), Fan et al. (2019), and Palvia, Vähämaa and Vähämaa 

(2020). Collectively, the findings of these studies are consistent with the view that female 

executives and directors may promote more conservative and less risky business strategies and 

financial decisions in the banking industry. We extend this literature by documenting that a 

transition to female leadership in community banks leads to post-turnover deleveraging and 

derisking actions.
8

Finally, our study contributes to the small body of literature about the role of CEO gender in 

managerial turnovers. Elsaid and Ursel (2011) and Rigolini, Gabaldon and Le Bruyn Goldeng 

(2021) document that a transition to a female CEO is associated with a decrease in firm risk, while 

Ma (2022) finds that female CEOs are more likely than their male counterparts to be dismissed 

after performance declines. Ryan and Haslam (2005, 2007) argue that women are more likely than 

men to be appointed to risky leadership positions which they coin the “glass cliff” form of gender 

discrimination. Consistent with this view, Cook and Glass (2014a) and Elsaid and Ursel (2018) 

document that companies are more likely to appoint female CEOs after experiencing poor financial 

performance. On the other hand, the findings of Adams, Gupta and Leeth (2009), Elsaid and Ursel 

(2011), and Cook and Glass (2014b) suggest that poor performance or pre-turnover financial 

distress are not associated with the appointment of female CEOs. Given the mixed empirical 

evidence, it is of interest to examine the role of incoming CEO gender in poorly performing 

community banks. We document that poorly performing community banks are not more likely to 

appoint female CEOs, and thereby our empirical findings do not provide support for the glass cliff 

hypothesis.

The remainder of the paper is organized as follows. Section 2 reviews the related literature 

and presents our research hypotheses. Section 3 describes the data on U.S. community banks and 

presents the empirical framework used in our analysis. The empirical findings on the impact of 

poor financial performance on community bank CEO turnovers and the role of CEO gender in 

executive turnovers are reported in Section 4. Finally, the last section summarizes our findings and 

concludes the paper.
9

2. Background and hypotheses

2.1. Related literature

The two broad strands of literature our study builds upon focus on the antecedents and 

consequences of CEO turnovers and the influence of CEO gender on firm-level financial decisions 

and outcomes. The extensive extant literature on CEO turnovers indicates that poor or declining 

financial performance and increasing levels of riskiness and financial distress increase the 

likelihood of CEO turnover (e.g., Coughlan and Schmidt, 1985; Gilson, 1989; Murphy and 

Zimmerman, 1993; Jenter and Kanaan, 2015; Jenter and Lewellen, 2021). As noted by ArthaudDay et al. (2006), when firms are in trouble, the replacement of the incumbent CEO may be an 

attractive and powerful means of legitimacy restoration when the firm aims to make a strategic 

change to recover from a critical situation. In a recent study, Jenter and Lewellen (2021) document 

that 38 to 55 percent of CEO turnovers are performance driven. The prior literature also 

demonstrates that CEO turnovers often lead to changes in the firm’s strategic direction, financial 

and investment decisions, and various other outcomes (e.g., Beatty and Zajac, 1987; Kesner and 

Dalton, 1994; Shen and Cannella, 2002; Huson et al., 2004; Gao et al., 2017; Lin et al., 2020).

CEO turnovers in the banking industry have been previously examined by Palvia (2012), 

Schaeck et al. (2012), Bornemann et al. (2015), Srivastav et al. (2017), Chen and Ebrahim (2018), 

Dahl, Milchanowski and Coster (2018), Sarkar et al. (2019), and Bunkanwanicha, Di Giuli and 

Salvade (2022). Broadly consistent with the studies based on nonfinancial firms, these studies 

suggest that CEO turnovers in banks are positively related to weak profitability and losses, the 

degree of risk-taking and financial distress, and the stringency of board and regulatory monitoring.

Collectively, the prior literature on CEO turnovers provides motivation to further explore the 
10

antecedents and consequences of CEO turnovers in community banks which are unique in terms 

of their business models, risk management strategies, and ownership and governance structures. 

The motivation for examining the role of CEO gender in managerial turnovers stems from 

the prior literature on gender-based behavioral differences between women and men and especially 

from previous studies that have linked CEO gender and female leadership to corporate decisions 

and outcomes. Over the last few decades, gender-based differences in overconfidence, 

conservatism, and risk preferences and tolerance of individuals have been extensively documented 

in the cognitive psychology and behavioral economics literature (see e.g., Levin, Snyder and 

Chapman, 1988; Feingold, 1994; Johnson and Powell, 1994; Powell and Ansic, 1997; Jianakoplos 

and Bernasek, 1998; Sunden and Surette, 1998; Fehr-Duda et al., 2006; Charness and Gneezy, 

2012).

The effects of female CEOs on firms’ business strategies, financial and investment decisions, 

risk profile and the level of risk-taking, and various other corporate outcomes have been examined, 

by Peni and Vähämaa (2010), Elsaid and Ursel (2011), Huang and Kisgen (2013), Khan and Vieito 

(2013), Faccio et al. (2016) Hrazdil et al. (2020), and Peltomäki et al. (2021), among others. 

Collectively, these studies provide evidence that the gender-based behavioral differences are

reflected in corporate decisions that the top executives make. This stream of literature shows that 

female-led firms make less risky financing choices and investment decisions, have lower risk 

profiles, are less likely to issue debt and conduct acquisitions, and are more conservative with 

respect to their financial reporting practices than male-led firms.

In the banking context, the implications of female CEOs have been previously examined by 

Palvia et al. (2015, 2020) and Skala and Weill (2018). Palvia et al. (2015) document that female-
11

led banks hold more conservative levels of equity capital and are less likely to fail after controlling 

for the bank’s asset risk and other attributes, and in a similar vein, Skala and Weill (2018) report 

that banks with female CEOs are associated with higher capital ratios. Palvia et al. (2020) examine 

the effect of real estate lending exposure and real estate shocks on bank performance and document 

that female-led banks have lower loan charge-offs and non-accrual loans relative to similar maleled banks. 

Finally, the role of CEO gender in managerial turnovers has been previously studied by Ryan 

and Haslam (2005, 2007), Adams et al. (2009), Elsaid and Ursel (2011, 2018), Cook and Glass 

(2014a, 2014b), Rigolini et al. (2021), and Ma (2022). The findings of Elsaid and Ursel (2011) and 

Rigolini et al. (2021) indicate that a transition to a female CEO is associated with a decrease in 

firm risk, while Ma (2022) finds that female CEOs are more likely than their male counterparts to 

be dismissed in response to poor financial performance. Cook and Glass (2014a) and Elsaid and 

Ursel (2018) document that companies are more likely to appoint female CEOs after experiencing 

poor financial performance, while the findings of Adams et al. (2009), Elsaid and Ursel (2011), 

and Cook and Glass (2014b) suggest that poor performance or pre-turnover financial distress are 

not associated with the appointment of female CEOs.

2.2. Hypotheses

Our hypotheses are based on the aforementioned strands of literature. Consistent with the 

prior CEO turnover literature, we expect that poor financial performance will increase the 

propensity of CEO turnover. We also explore the possibility of a “glass cliff” given the literature 

suggesting that women are more likely than men to find themselves in leadership positions of firms 

with weaker prospects.
12

Hypothesis 1a: Poor performance causes CEO turnover.

Hypothesis 1b: For banks with CEO turnover, poor performance increases the likelihood of 

female CEOs replacing male CEOs. 

Hypothesis 1c: Poor performance has a weaker impact on CEO turnover if the incumbent

CEO is female. 

We argue that if Hypothesis 1a holds, it suggests community bank CEOs may leave either 

due to firing or quitting in response to poor performance. Hypothesis 1b considers the “gender 

cliff” view. If the gender cliff view holds, we expect that female CEOs are appointed when the 

bank failure risk is high, thereby setting them up for failure. Hypothesis 1c considers an alternative 

manifestation of the gender cliff view. Under this view, we expect distressed banks with existing 

female CEOs to have a reduced propensity for CEO turnover. Specifically, appointing females to 

leadership positions at times of bank distress is conceptually analogous to the bank being less 

likely to dismiss an incumbent female CEO when the bank failure risk is high. In the latter case, 

the female CEO is retained because the bank has a diminished chance of survival, which could 

adversely affect success record of female CEOs, in aggregate.

Our hypotheses also draw upon the literature documenting gender-based behavioral 

differences in risk preferences and risk tolerance of individuals suggesting that women are 

generally more cautious and risk-averse than men regarding their financial decisions. Consistent 

with previous studies, we expect gender-based differences in conservatism and risk aversion to 

persist at the executive level and that individual executives may impact banks’ financial decisions. 

We build upon the streams of literature on female executives and managerial successions by 

examining the following research hypotheses:
13

Hypothesis 2a: The gender of the incoming CEO has an impact on the bank’s deleveraging 

and de-risking actions, especially following periods of poor performance. 

Hypothesis 2b: The gender of the incoming CEO has an impact on the bank’s realized risk 

and performance outcomes, especially following periods of poor performance. 

We contend that if incoming female CEOs are more risk-averse, then hypothesis 2a should 

hold and we should expect banks with incoming female CEOs to be more likely to take actions to 

reduce leverage and risk relative to banks with incoming male CEOs. We expect these actions 

could result in adjustments that reduce assets and favor less risky assets and reduce liabilities while 

favoring less volatile liabilities. We expect that such divergences should be the strongest in banks 

whose CEOs changed amidst poor bank financial performance (e.g., very low profitability or high 

losses). 

While conservative actions should unambiguously affect the risk-taking propensity of banks 

post-turnover, it is unclear if such actions will necessarily result in a lower realized risk profile or 

improved performance. If incoming female CEOs are more risk averse and conservatism is the 

best strategy for the bank given its operating environment, competition, etc., a lower risk profile 

and better performance should result. For example, conservatism might allow a bank suffering 

high losses and limited lending opportunities a better chance to reduce risk and better improve its 

financial health. Thus, our null hypothesis is that there is a favorable impact on outcomes. On the 

other hand, an overly cautious approach could miss out on improvement in economic times and 

result in less robust performance relative to a strategy of staying the course or even increasing risk.

Consequently, while we expect gender-based differences to result in risk-reducing actions 
14

(hypothesis 2a), the extent of improvements in the bank’s risk-profile and financial performance 

is an empirical question, our null for hypothesis 2b notwithstanding. 

3. The empirical setup

3.1. Data 

The study uses data on U.S. community banks which we define as commercial banks with 

total assets below $10 billion following the Federal Reserve.

2

 We obtain balance sheet and income 

statement data for community banks from the bank Call Reports through the Federal Financial 

Institutions Examination Council (FFIEC). The data on CEO turnovers and the genders of the 

bank’s dismissed and incoming CEOs are constructed from SNL Financial available between 2008 

and 2017. Our CEO turnover data covers virtually the entire population of community banks with

missing information affecting only about 3.2 percent of bank-quarters. After excluding commercial 

banks above the size threshold and removing bank-quarters with missing CEO, gender, or other 

data, we obtain a sample of 6,832 individual community banks and an unbalanced panel of 52,504 

bank-year observations over the sample period of 2008 to 2017. The final sample used in our 

empirical analysis essentially includes all community banks with available data in the U.S., and 

on average, there are about 5,200 individual banks in the sample in any given year. 

Following Palvia et al. (2015, 2020), we deduce the genders of community bank CEOs based 

on the names of these individuals as reported in SNL Financial. At a given point in time, SNL 

Financial provides the names of the incumbent bank CEOs. Because historical data on CEO names 

are unavailable from SNL Financial, we use historical snapshots of the data taken at the end of 

 

2 See https://www.federalreserve.gov/supervisionreg/community-and-regional-financial-institutions.htm for more 

details. Our main findings remain unchanged if a sticter threshold of $1 billion in total assets is used. 
15

June of each individual year included in our sample. For each community bank and for each fiscal 

year, we manually determine the gender of the bank’s CEO based on their first names. In the case 

of unisex names, we require that at least 80 percent of the name holders belong to a particular 

gender to determine the gender of a given CEO.3 For ambiguous first names, we performed an 

internet search to determine CEO gender. Any unclear cases that could not be gender assigned 

based on these searches were excluded from the final sample.

3.2. Empirical tests 

We begin our empirical analysis by examining the association between CEO turnover and 

poor financial performance. Specifically, we estimate the following regression specification to test 

the hypothesis that poor performance leads to CEO turnover in community banks (H1a): 

CEO turnoveri,t = β0 + β1Poor performancei,t-1 + α1 (Bank-specific controls)

i,t-1

 +α2(Other controls)

i,t-1 + ∑ ωy

2017

y=2009 Yeari

y + εi,t (1)

The dependent variable CEO turnoveri,t is an indicator variable that equals one if there was 

a CEO turnover in bank i during year t and; β1 represents the main coefficient of interest and α1

and α2 respectively denote coefficient vectors for the bank-specific and other controls. We define 

CEO turnover as any change in the CEO in the given year, unlike previous studies that typically

classify CEO turnovers into forced and unforced turnovers based on various ad-hoc rules. We do 

so because we do not have access to data to implement such rules for CEO turnovers in community 

 

3 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 percent of individuals named Pat are males and 60.3 percent are females. This 

doesn’t meet the 80 percent threshold and, consequently, CEOs named Pat were excluded from the sample. 
16

banks which are small and generally private.

4 However, as argued by Jenter and Lewellen (2021), 

the commonly used approaches of classifying forced and unforced CEO turnovers by age and other 

attributes may lead to downwards biased estimates with respect to the linkage between CEO 

turnover and firm performance. In addition, given that unforced turnovers are unlikely to be 

correlated with firm performance as suggested by Jenter and Lewellen (2021), the inclusion of 

unforced CEO turnovers in our sample is likely to generate noise in the estimations rather than 

bias. 

Hypothesis 1a predicts that the coefficient estimate for Poor performance will be positive 

(i.e., β1 > 0). Hypotheses 1b and 1c are then tested with a similar regression specification but with 

various adjustments to the dependent variable and the sample as required in each case. In 

particular, based on the genders of the bank’s outgoing and incoming CEOs, the turnovers are 

categorized into the following three indicator variables for testing hypothesis 1b: (i) CEO Change 

FM denoting female-to-male CEO turnover, (ii) CEO Change MA denoting male-to-female CEO 

turnover, and (iii) CEO Change NG representing same gender CEO turnover. 

The main variable of interest in Equation 1 for testing Hypothesis 1a, 1b, and 1c is Poor 

performance. We use two alternative measures of poor financial performance in our regressions: 

(i) Low ROA is defined as a dummy variable which equals one for bank-year observations with 

return on assets in the bottom decile, and (ii) High Chg-Off is a dummy variable which equals one 

for bank-year observations with the ratio of loan charge-offs to total assets in the top decile.

 4 For example, criteria such as departing CEO age or usage of certain keywords as an expressed reason for the turnover 

in news reports are sometimes used to determine turnover reason. Governance characteritics are typically only 

available for large publicly traded financial institutions and news reports also generally focus on larger publicly traded 

firms.
17

We include a number of control variables in the regressions to account for the potentially 

confounding effects on CEO turnovers. The bank-specific control variables used in Equation (1) 

include various governance characteristics and bank structural indicators. A central governance 

control is Female CEO, which is a dummy variable set to one if the bank’s incumbent CEO in year 

t is a female; a non-zero value for the respective coefficient would provide preliminary evidence 

that CEO turnover is linked to outgoing CEO gender. Interactions of poor performance with this 

indicator form the basis of our tests for hypothesis 1c. We exclude Female CEO from 

specifications testing hypothesis 1b as the dependent variables for those specifications effectively 

incorporate the gender of both the incoming and outgoing CEOs.

We include other key governance controls in all regressions including (i) CEO Duality,

which is a dummy variable set to one if the same individual is the bank’s CEO and the chairperson 

of the board, (ii) Female Chair, which denotes that the board chair is female, and (iii) Related 

Chair, which proxies for family control and ownership and is a dummy variable which equals one 

if the bank’s CEO and chairperson of the board are different individuals but have the same last 

names.

Our structural controls include (i) Log Assets denotes the logarithm of total assets and measures 

bank size, (ii) Assets > $1B is a dummy variable which equals one for community banks with total 

assets in excess of $1 billion, (iii) Public is a dummy variable for publicly traded banks, (iv) 

Subchapter S is a dummy variable assigned to one for closely held banks that are organized under 

the subchapter-S, (v) MBHC is a dummy variable which equals one for banks that are affiliated 

with a multibank holding company, (vi) Log Bank Age denotes the logarithm of the age of the 

bank, (vii) Organizational Change is a dummy variable which equals one for banks that 
18

experienced any kind of a change in holding company structure, and (viii) Merger activity is a 

dummy variable for banks that were involved in a merger during the year preceding CEO turnover. 

Other controls, which are derived from state-level or other market level information, include: (i) 

Number of states is the number of states the bank operates in, (ii) HHI is the Herfindahl-Hirschman 

Index measuring deposit market concentration in the combined statistical area (CSA) or county, 

(iii) State Unemp Rate is the state unemployment rate, and (iv) State Real PCI is the real per-capita 

income in the state. We also include year fixed-effects in the regressions to account for any 

systematic variation in the amount of CEO turnovers over time. The definitions of all the variables 

used in the regressions are provided in Table 1.

After examining the linkage between poor bank performance and CEO turnover, we proceed 

by studying community bank actions and outcomes following the turnovers and especially the role 

of the gender of the incoming CEO. To test hypotheses 2a and 2b, we regress changes in bank 

assets and liabilities as well as various performance and risk outcome measures on three different 

CEO turnover variables that are constructed based on the genders of the bank’s outgoing and 

incoming CEOs. Specifically, we estimate various alternative versions of the following regression 

specification:

∆yi,t = β0 + β1Poor performancei,t-1 + β2Male-to-female CEO turnoveri,t-1

 + β3Female-to-male CEO turnoverii,tt-1 + β4Same gender CEO turnoveri,t-1

 + α1(Bank-specific controls)

i,t-1+α2(Other controls)i,t-1 (2)
19

+�ωb

N-1

b=1

Bankb + � ωy

2017

y=2009

Ye 

εi,t

The dependent variable Δyi,t represents the annual change in a specific balance sheet 

indicator, measuring bank assets or liabilities, or a specific financial outcome measure, measuring

default risk or performance. Our tests effectively consider the one-year evolution in these balance

sheet and outcome measures to gauge bank actions and financial outcomes following turnover.

The four different bank asset measures used in the regressions are (i) Total Assets, (ii) Loans, (iii) 

Employees, and (iv) Branches; the four bank liability measures include (i) Total Liabilities, (ii) 

Deposits, (iii) Brokered Deposits, and (iv) Non-Deposit Liabilities. All the asset and liability 

measures are measured in logs and form the dependent variable, y, in equation (2). The various 

outcome measures include three measures of realized default or asset risk, (i) Z-score, (ii) 

Equity/Assets, and (iii) return on assets (ROA) and earnings volatility (ROA Volatility) as a measure 

of performance.

Similar to Equation (1), poor performance is measured either with Low ROA or High ChgOff Ratio. The variables of interest in Equation (2) for testing Hypotheses 2a and 2b are (i) CEO 

Change FM or male-to-female CEO turnover,(ii) CEO Change MA or female-to-male CEO 

turnover, and (iii) CEO Change NG or same gender CEO turnover which are dummy variables 

constructed based on the genders of the bank’s outgoing and incoming CEOs. Thus, β1- β4

represent the main coefficients of interest and α1 and α2, as in equation 1, respectively denote 

coefficient vectors for the bank-specific and other controls.
20

In addition to the bank and other control variables included in Equation (1), we augment the 

set of controls depending on the specifications.5 For specifications considering the evolution of 

assets, we include three additional controls representing bank liquidity or asset risk. These are

Loans/Assets, Cash Balances/Total Assets, and RWA/Assets. For specifications considering the 

evolution of liabilities, we include the following three additional controls representing bank 

funding risk: Deposits/Liabilities, Brokered Deposits/Liabilities, Avg Dep Interest Rate. For 

specifications considering the evolution of outcomes, which depict realized risk or performance 

for the bank as a whole, we include all six of these controls. Lastly, we include bank fixed-effects 

and year fixed-effects in Equation (2) to control for unobserved heterogeneity across banks and 

any systematic variation in the different dependent variables over time. 

(Insert Table 1 about here)

3.3. Descriptive statistics

The descriptive statistics for the variables used in the empirical analysis are presented in 

Table 2. As can be noted from Panel A, our sample of 52,504 bank-year observations includes 

5,270 community bank CEO turnovers. In most of these turnovers, the genders of the bank’s 

outgoing and incoming CEOs are the same. Our sample includes 330 CEO turnovers (6.3 percent 

of the turnover observations) in which a male is replaced by a female (i.e., CEO Change FM) and 

214 turnovers (4.1 percent of turnovers) in which a female CEO is replaced by a male CEO (i.e.,

CEO Change MA). 

 5 Similar to tests of hypothesis 1b, all tests based on equation (2) exclude Female CEO as the key explanatory variables, 

Male-to-female CEO turnover (CEO Change FM), Female-to-male CEO turnover (CEO Change MA), and Same 

gender CEO turnover (CEO Change NG), effectively incorporate both the incoming and outgoing CEO gender.
21

The descriptive statistics in Panel B of Table 2 indicate that there is considerable dispersion 

in our sample with respect to bank performance as measured by return on assets (ROA) and loan 

charge-offs. The mean value of ROA is 0.7 percent with the 10th to 90th percentile range being

from –0.03 percent to 1.7 percent. The 10th percentile Chg-Off Ratio is essentially zero and the 90th

percentile is about 0.2 percent. Regarding the control variables, it can be noted from Table 2 that 

there is a wide variation in our bank-years in terms of size, funding and asset structure, growth, 

and financial performance. The logarithm of total assets has a mean of 12.10, which implies total 

assets of $200 Million; similarly, the 5th percentile to 95th percentile range varies from 10.34 to 

14.20, or $31 Million to $1.47 Billion.

6 The average bank in a given year holds about 70 percent

of its loan portfolio in residential real estate loans, thereby suggesting a very substantial exposure 

to real-estate price shocks. The statistics also show substantial variation in our distributions for the 

various balance sheet controls for assets (Loans/Assets, Cash Balances/Total Assets, and 

RWA/Assets) and liabilities (Deposits/Liabilities, Brokered Deposits/Liabilities, Avg Dep Interest 

Rate).

In approximately 35 percent of our sample, the positions of the CEO and board chair are 

held by the same individual. Interestingly, in 6 percent of the sample, the board Chair is related to 

the CEO of the bank (i.e., they have the same last name). Finally, the descriptive statistics indicate 

that 18 percent of the bank-years in our sample are publicly traded, about 37 percent are 

subchapter-S banks, and approximately 15 percent of the banks are affiliated with a multibank 

holding company. Lastly, the overwhelming majority of banks operate in a single state with the 

mean being 1.12 and the 75th percentile being 1.

 6 Data from Call Reports are reported in thousands.
22

(insert Table 2 about here)

4. Results

4.1. Poor performance and CEO turnovers

We begin our analysis by looking at the extent of CEO turnover across community banks. 

Figure 1a shows that CEO turnover ranges from about 13 percent of total annual observations in 

2008 to less than 8 percent in 2017; this suggests more turnover during the financial crisis, where 

bank performance is expected to be the worst on average. We then classify banks as being poorly

performing based on the 10th percentile of ROA and the 90th percentile of Chg-Off Ratio, 

respectively. We focus on these non-continuous measures as we are interested in identifying very 

poor performance rather than the entire distribution of our performance measures. Figures 1b and 

1c show that turnover rates were substantially higher in the subpopulations of banks with poor

versus not poor performance and the divergence remains throughout the 10 years of the sample

and not just during the crisis and its immediate aftermath. 

(Insert Figure 1 about here.)

Our formal analysis utilizes panel regressions of CEO changes. We regress bank leadership

(i.e. CEO) changes from t-1 to t on indicators of poor bank performance and a set of control 

variables at time t-1. We analyze model specification (1) without Poor Performance in the model, 

whereas the other model specifications include one of the performance indicators, i.e. Low ROA

(i.e. 10th Percentile ROA) or High Chg-Off Ratio (90th Percentile Chg-Off Ratio), which 

corresponds to our two measures of poor performance. Following our univariate analysis, we focus 
23

on poor performance rather than the continuous measures as we are focused on CEO turnover 

following adverse performance. These results are reported in Table 3.

(insert Table 3 about here)

As can be seen from the table, our financial stress indicator variables, Low ROA and High

Chg-Off Ratio are positive and are highly statistically significant in all the model specifications 

where they are included, thereby indicating that poorly performing banks are more likely to change 

CEOs than their peers. The coefficient of Female CEO is near zero suggesting that incoming CEO 

gender does not affect CEO turnover. CEO Duality, Related Chair, Bank Age, and size (Log 

Assets) have statistically significant negative coefficients, thereby suggesting that these variables 

are negatively associated with the CEO change. We also find no evidence that c hair gender is 

linked to CEO turnover.

The coefficients of CEO Duality and Related Chair suggest greater CEO power vis-à-vis the 

board is linked to a reduced turnover likelihood. The signs of Bank Age suggest more stable or 

established banks are less likely to have turnover. The variables MBHC, Organizational Change, 

Unemployment, Assets > $ 1B, and Log Assets are positively associated with the CEO change. The 

coefficients on Log Assets and Assets >$1B suggest a non-monotonic relation between bank size 

and CEO turnover; larger banks are less likely to experience CEO turnover but when a bank 

becomes a large enough community bank (i.e. assets > $1 Billion), the CEO turnover is becomes 

more probable. 

4.2. Endogeneity
24

Although our analysis considers the link between community bank CEO turnover and 

adverse bank performance controls for various bank-specific characteristics as well as other 

controls including state-based economic indicators, it is possible that we have omitted correlated 

variables or some unobservable bank characteristics that simultaneously affect both the level of 

financial distress and the appointment of CEOs. We attempt to alleviate possible endogeneity 

concerns by performing instrumental variable analyses. 

Our strategy relies on exploiting exogenous geographically and time-varying housing price 

shocks, based on a state-based housing price index (HPI) obtained from the FHFA, for 

identification. We employ two alternative versions of a shock to HPI as instrumental variables for 

poor performance in our two-stage regressions: HPI 10 percent Shock is defined as a year-overyear decline of at least 10 percent in the HPI during the two years prior to the leadership change 

in the regions in which the bank operates and HPI 20 percent Shock is a similar measure but with 

at least 20 percent decrease in the HPI.

7 We presume that HPI shocks should be positively 

associated with poor bank performance while not being directly correlated with the CEO changes 

in community banks. The first stage of the IV analysis is not tabulated for the sake of brevity. 

However, tests for under-identification and weak identification are rejected in all model

specifications, thereby confirming the validity of the chosen instrument.

8 The second stage of the 

IV analysis is depicted in Table 4; the specifications are identical to the models presented in Table 

3, but utilize our instrument for poor performance.

 7 A bank is classified as having been exposed to a 20 percent or a 10 percent HPI shock if the state in which a bank

operates suffers a decline in HPI by these amounts. For banks operating in multiple states, we use a deposit-weighted 

HPI based on the states from where the bank has deposits to estimate bank-level HPI. 8 Reported underindentification and weak identification tests stats in Table 4 are Klieibergern-Paap rank LM and 

Cragg-Donald Wald F-Statistics, respectively.
25

(insert Table 4 about here)

Columns (1), (3), (5), and (7) of Table 4 present results for both performance measures and 

both IVs. Columns (2), (4), (6), and (8) report results based on the subset of bank observations 

with higher RE exposure (75th percentile of residential real estate loans ratio just before the 

beginning of the RE shock, i.e. 3 years out). We use low ROA and high Chg-Off Ratio in columns 

from (1) to (4) and (5) to (8) respectively as our measures of poor performance. Results are 

estimated using the IV based on a 10 percent HPI decline (IV1) in columns (1), (2), (5), and (6) 

and using the IV based on a 20 percent HPI decline (IV2) in the other columns.

As can be seen from the table, the instrumented poor performance variables have statistically 

significant positive coefficients in all the model specifications. The results show an economically 

strong relation between poor performance and CEO changes. Columns (1) and (5) suggest that 

poor performance leads to an 11 percent and 9 percent increase in the likelihood of CEO change, 

respectively. This is roughly as high as the unconditional mean CEO change of 10 percent reported 

in the descriptive statistics for the total sample. Note that the IV estimates for the poor performance 

are somewhat higher but in the same general range as the OLS estimates. The estimates using the 

subsample of banks more exposed to the real estate (RE) price shocks, i.e. those with higher shares 

of ex ante residential RE loans, have substantially higher coefficients. Overall, the IV analyses 

confirm the findings of the main regressions reported in Table 3, thereby indicating that there is a 

causal relationship between bank performance and CEO turnover. 

4.3. The role of gender in CEO turnover
26

We further analyze the relationship between CEO changes and poor performance by 

examining if the incoming CEO gender depends on performance. Previous literature has suggested 

the possibility of a “glass cliff” whereby women are more likely than men to be appointed to 

leadership positions that are risky which would then set them up for failure (Ryan and Haslam, 

2005 & 2007). As a precursor to considering the effect of gender on bank activities following CEO 

changes, we consider whether incoming CEO gender transitions are themselves potentially driven 

by poor performance. We focus on the subsample of banks for which the CEO changed during the 

sample period (n = 5,270). Figure 2 panels A and B show unconditional means of CEO changes 

in subpopulations of poor and non-poor performance. The proportions of CEOs in each 

subcategory are similar in subsamples of new male incoming CEO, new female incoming CEO, 

and new incoming CEO with no gender change. 

(Insert Figure 2a and 2b about here.)

Regression analysis assessing the possibility of a “glass cliff” is conducted and the results 

are tabulated in Table 5 panels A and B. In panel A of the table the analysis is restricted to bankyears where we observe a CEO turnover. As can be seen from the table, changes in the gender of 

the new CEO are notstatistically related to bank performance. Columns (3)-(6) present OLS results 

suggesting no link between poor performance and a change in incoming CEO gender, conditional 

on succession; similarly, columns (7)-(12) present second-stage IV results further documenting no 

association between bank performance and incoming gender, regardless of a change in gender of 

outgoing and incoming CEOs. Furthermore, the gender of the new CEO seems unrelated to most 

factors beyond the prior CEO’s gender. However, banks with female chairs are more likely to 

replace CEOs with an opposite gender and as part of such change, they are more likely to replace 
27

a female CEO with a male CEO. Also, it appears smaller community banks are less likely to have 

incoming CEOs of a different gender than outgoing CEOs.

Importantly, the results reported in Panel A of Table 5 suggest that poor performance of 

banks is not a major factor in the gender choice of a new CEO. In general, there is no statistical 

association between our performance measures and incoming CEO gender with the exception of 

column (1) suggesting a lack of change in incoming CEO gender is associated with poor

performance; however, this result is not robust as it does not hold up in the corresponding IV 

regression (column 7). Thus, our analysis does not support the gender cliff view. Moreover, the 

results indicate that the gender of the incoming CEO is random, i.e. that it is not related to prior 

bank performance.

We also consider a further manifestation of the gender cliff theory by estimating the 

likelihood of CEO turnover conditional on the existing CEO being female. The results, shown in 

Table 5 Panel C, show that banks with female CEOs are not more or less likely to have a CEO

turnover. Importantly, the interaction term of Poor Performance and Female CEO is small and not 

statistically significant. While the coefficient is negative in both columns (2) and (4), denoting 

interactions with Low ROA and High Chg-Off Ratio respectively, the insignificant negative relation 

between the interaction term and CEO turnover is contrary to hypothesis 1c which suggests female 

CEOs are not less likely to depart amidst poor performance. Thus the results in Table 5 Panel C 

are also inconsistent with the gender-cliff view. 

(Insert Table 5 about here.)

4.4. Bank actions after CEO turnovers
28

To the extent poor performance causes CEO turnover, it is important to understand the 

degree to which incoming CEO characteristics influence subsequent bank deleveraging and 

derisking. Specifically, our focus is the extent to which the incoming CEO gender is a factor in the 

evolution of bank balance sheets towards a reduced and less risky asset and liability composition. 

We analyze bank actions after the leadership changes with univariate and multivariate analyses. 

We begin with univariate analyses depicted in Figures 3A and 3B. These figures clearly show that

CEO changes resulting in female CEOs are associated with a decrease both in asset growth and in

liability growth. For example, Figure 3b shows the unconditional mean for employee and office 

growth is negative for changes to female leadership, (CEO Change FM) while these are positive 

for all other categories—No CEO change, CEO Change NG, and CEO Change MA; asset and loan 

growth also appear to be lower for banks with changes to female leadership. Similar patterns can 

be seen in Figure 3b; for example, total liabilities and deposit growth are smaller for banks with 

changes to female leadership.

(insert Figure 3 about here)

We report our more formal multivariate analysis in Table 6. These results further depict how 

assets and liabilities evolve for banks with a CEO turnover with and without gender changes. As 

can be seen from Panel A of Table 6, CEO changes in which a male CEO is replaced by a female 

are associated with lower asset growth than the CEO changes resulting in male CEOs.

(insert Table 6 about here)

Moreover, as can be seen from Panel A of Table 6, the banks where a female CEO replaces 

a male are associated with decreased risk-weighted assets, number of employees, and number of 

branches. These results are both statistically and economically significant. For example, column 
29

(1) suggests that a change to a female CEO is associated with asset declines of about 1.7 percent; 

this number represents about 1/10th of one standard deviation and about half of the median change 

in log assets. The impact on assets and liabilities of CEO changes where the gender of the CEO 

remains the same and changes where a female CEO is replaced by a male appear to be lower in 

magnitude and not statistically significant.

Next, we examine the impact of CEO changes further by considering subsequent changes in 

bank liabilities and deposits. These findings are reported in Panel B of Table 6. As can be seen 

from the table, the banks where a male CEO was replaced by a female are associated with lower

growth in liabilities and deposits. The relation is statistically significant and economically 

meaningful; column (1), for example, shows female leadership changes associated with a 2.4

percent decline in liabilities which corresponds to about 1/10th of one standard deviation and more 

than half of the median for liabilities growth. We do not find any statistical relation with brokered 

deposits or non-deposit liabilities. 

Next, we consider the impact of poor performance on incoming CEO deleveraging actions. 

Figures 4a–d present the impact of CEO change on asset and liabilities’ evolution depending on

the performance prior to CEO turnover. The differences are striking; for example, in the poor

performance scenarios, changes to female leadership are shown to be associated with large 

declines in all sub-categories of assets whereas male CEO changes are related to increases in asset 

subcategories. For total assets, there is a substantially smaller increase in total assets when 

leadership changes to a female CEO than in the cases of incoming male CEOs. As can be seen 

from Figures 4c and 4d, a similar story is evident for liabilities. There is a large decline in brokered 
30

deposits and non-deposit liabilities for banks that change to female leadership but not for the banks

changing to male leadership.

(Insert Figures 4a-d about here.)

Next, we evaluate bank actions following leadership changes amidst poor performance in a 

regression framework. We re-estimate the results presented in Table 6 while interacting the CEO 

change indicators with binary indicators of poor performance and not poor performance. Tables

7a and 7b report these results based on the two alternative measures of poor performance. In model 

specifications (1) to (5), we define poor performance by Low ROA, while in model specifications 

(6) to (10) the poor performance is proxied by High Chg-Off Ratio.

The results reported in Panels A and B of Table 7 indicate that new female CEOs are likely 

to take more substantial actions to reduce their assets and liabilities, especially riskier assets and 

liabilities when the bank is in more severe financial trouble. This finding also holds when the CEO 

gender does not change using the High Chg-Off Ratio measure of poor performance in Panel A of 

Table 7 and for both poor performance measures in some specifications in Panel B of Table 7. The 

magnitude of the coefficients and the number of specifications with statistically significant results, 

however, is generally higher in the subset of banks with incoming female CEOs; this is especially 

true for assets (Panel A of Table 7).

In summary, we find that poorly performing banks with incoming female CEOs have 

significantly lower asset growth, loan growth, risk-weighted-asset growth, employee growth, and 

branch growth. Similarly, we find that poor-performing banks with incoming female CEOs have 

lower liabilities growth, deposit growth, and brokered deposit growth. Importantly, we find no 
31

evidence of reductions in assets and liabilities in banks with incoming male CEOs for either 

performance measure.

(Insert Table 7 about here.)

4.5. Risk profile and performance changes after CEO turnovers

Finally, we examine the evolution of risk and performance after leadership changes at one 

and two year horizons following turnover. Panels A and B of Table 8 show the change in bank risk 

profiles for a one-year period after the CEO change. We measure realized risk using measures of 

default risk (Z-score), solvency (Equity/Assets), asset risk (ROA Volatility), and performance 

(ROA). We regress 1-year changes in these measures (year t to t+1) on indicators of bank CEO

changes from t-1 to t and controls at time t. Panel A and B differ in that the latter includes

interactions between the CEO change indicators and poor performance measures. Panel A shows 

that while poorly performing banks subsequently perform better, likely due to a variety of factors 

including mean reversion, banks with new incoming female CEOs perform even better, at least 

with respect to improvements in Z-score and reduction in earnings volatility, whereas there is no 

discernible positive impact for banks with new incoming male CEOs.

Panel B shows that the impact of a male CEO being replaced by a female is generally more 

prominent in poorly performing banks; these coefficients are both statistically and economically 

significant in most specifications. In column (1) of panel B of Table 8, for example, we see the 

relation between poor-performing bank Female CEO change and non-poor-performing bank

Female CEO change to be 0.87; the improvements are qualitatively similar for incoming Male 

CEO banks and incoming no CEO gender change banks (0.66 and 0.71). Similar observations can 

be made in column (5) for the other measure of poor performance but with a somewhat larger 
32

difference between male and female incoming CEO coefficients. We also observe similar results 

with our measure of realized asset risk (earnings volatility) and performance. We do not find 

gender-based differences in the propensity to increase capital ratios irrespective of distress.

(Insert Table 8 about here.)

To examine the risk profile changes further, we also run similar regressions using a longer 

window, two years, to examine if the reported findings result from a short-term phenomenon or if 

the risk profile changes persist over a longer time period. These results are similar to the results 

reported for the 1-year period in Table 8 but with larger and more significant coefficients. The 

additional results are reported in panels A and B of Table 9. 

(Insert Table 9 about here.)

Table 9 panel A provides evidence that CEO changes are related to improvement in risk

profile and performance regardless of incoming CEO gender even as the results are somewhat 

stronger for incoming female CEO banks. Banks with changes to female CEOs have higher Zscores, and lower ROA volatility whereas banks with changes to male CEOs also have higher Zscores but not lower ROA volatility. 

Taken together, the results of Tables 6-7 and 8-9 suggest that, while female-led banks clearly 

take actions to reduce risk when a new CEO takes the helm, the actual impact on financial 

outcomes arising from gender-based differences in risk-taking are mixed and possibly short-lived. 

The lack of consistently superior reductions in risk or performance improvements for banks for 

incoming female CEOs relative to banks with CEO turnover more generally suggests that 

conservatism is not necessarily a superior strategy for banks facing financial difficulties. 
33

4.6. Limitations

We acknowledge several limitations in our empirical analyses that should be considered 

when interpreting our results. To address endogeneity of a central variable in our analysis, poor

performance, we utilize instrumental variable analysis. In addition, because CEO gender 

assignment following turnover is shown to be random in Table 5, we expect the endogeneity biases 

arising from our gender-based CEO turnover indicators in the second part of our analysis to be 

substantially mitigated. Nevertheless, similar to most empirical corporate governance studies, our 

analysis is still subject to endogeneity concerns arising from other variables.

In our regressions, we use lagged independent variables to mitigate endogeneity concerns 

further. We also control for a number of bank-specific characteristics that are known to affect bank 

performance and CEO turnover likelihood. However, due to data unavailability, we are unable to 

fully control for banks’ corporate governance attributes such as board size and independence, 

ownership structure, and managerial compensation incentives in our regressions; unlike studies of 

public firms, our data allows for limited governance variables. In our regressions, we have 

attempted to control for differences in ownership and governance structures by including dummy 

variables for CEO Duality, CEO/Chair family linkages (Related Chair), chair gender, publicly 

traded banks (Public), closely-held banks (Subchapter-S), and multibank holding companies 

(MBHC).

Finally, we acknowledge that our sample of U.S. commercial banks is severely unbalanced 

towards male-led banks as female-led banks comprise only about 6 percent of the observations. 

This low proportion of banks with changes in CEO gender is an evident limitation of this study. It 

is also important to recognize that female CEOs are more common in smaller, privately-held banks 
34

(see e.g. Palvia et al., 2020) which may have different business strategies and face less stringent 

regulatory oversight compared to large banks. Due to these limitations, our findings should be 

viewed as suggestive and causal interpretations, for analysis considering actions following 

leadership changes, should be made cautiously.

5. Conclusions

This paper studies the impact of poor financial performance on community bank CEO 

turnovers and the role of CEO gender in these successions. Specifically, we examine three 

interrelated questions. First, we investigate whether poor financial performance is associated with 

CEO turnover in community banks and also assess whether such an association is potentially 

affected by the gender of the incumbent CEO. Second, we examine whether the association 

between poor financial performance and CEO turnover depends on the gender of the outgoing or 

incoming or outgoing CEO. Finally, we explore the role of the incoming CEO gender in 

influencing the bank’s policy decisions, financial performance, and risk-taking in the aftermath of 

the turnover. In our empirical analysis, we use unique CEO turnover data that covers nearly all 

U.S. community banks over the period 2008-2017. 

Our empirical findings indicate that poor financial performance has a causal impact on CEO 

turnover in community banks. Although weak performance is a key determinant of CEO turnovers, 

it is not linked to the gender of the bank’s dismissed nor the incoming CEO. This suggests that 

troubled, weakly performing banks are not more likely to retain their incumbent female CEOs or 
35

to replace male CEOs with a female. Furthermore, we document that a transition to female 

leadership leads to post-turnover deleveraging and derisking actions. Banks with incoming female 

CEOs take actions to reduce both assets and liabilities while male-led banks do not. In contrast, 

when the incoming CEO is male, we observe increases in the amounts of loans and risky assets. 

These gender-related asymmetries in bank actions following leadership changes are most 

pronounced for banks in which CEO turnover occurs amidst periods of poor performance. Our 

findings also indicate that managerial successions are associated with reductions in default risk 

and earnings volatility, especially if CEO turnover is concomitant with poor performance. We find 

limited evidence that transitions to female leadership would ultimately lead to greater postturnover reductions in realized risk or improvements in bank performance. Overall, our findings 

suggest that CEO gender meaningfully influences community bank decisions in the aftermath of 

executive turnovers.
36

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43

FIGURE 1a. CEO turnover by year

FIGURE 1b. CEO turnover by poor performance - ROA

FIGURE 1c. CEO turnover by poor performance – Chg-Off Ratio

The figures describe CEO turnover variations over the sample period of 2008-2017. Figure1a depicts all 

CEO turnover variations and figures 1b and 1c show turnover variations depending on classification of

poor performance based on Low ROA and High Chg-Off Ratio respectively. .06 .08 .1 .12 .14 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 .05 .1 .15 .2 .25

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Low ROA Not Low ROA
44

FIGURE 2a. Performance by CEO change type - ROA

FIGURE 2b. Performance by CEO change type – Chg-Off Ratio

The figures highlight performance depending on gender of the incoming CEO for banks with CEO 

turnovers. Figure 2a shows the percentage of banks with low ROA and not Low ROA depending on the 

incoming CEO gender; figure 2b presents the same information using High Chg-Off Ratio as the 

measure of poor performance. 

0 .2 .4 .6 .8 1

No CEO Change CEO Change (NG) CEO Change (FM) CEO Change (MA)

Low ROA Not Low ROA
45

FIGURE 3a. Asset evolution for all banks

FIGURE 3b. Liabilities’ evolution for all banks

Figures 3a and 3b show how assets and liabilities evolve for banks depending on CEO turnover with and 

without gender changes. 

0 .02 .04 .06 0 .02 .04 .06

No CEO Change CEO Change (NG)

CEO Change (FM) CEO Change (MA)

Log Assets Growth (t,t+1) Log Loan Growth (t,t+1)

Log RWA Growth (t,t+1) Log Employee Growth (t,t+1)

Log Office Growth (t,t+1)

-.1 -.05 0 .05 -.1 -.05 0 .05

No CEO Change CEO Change (NG)

CEO Change (FM) CEO Change (MA)

Log Liab Growth(t,t+1) Log Dep Growth(t,t+1) 

Log Brokered Deposit Growth(t,t+1) Log Non-Deposit Liabilities Growth(t,t+1) 
46

FIGURE 4a. Impact of CEO change on assets – Low ROA banks

FIGURE 4b. Impact of CEO change on assets – High Chg-Off ratio banks 

Figures 4a and 4b show how assets evolve for banks depending on CEO turnover with and without gender 

changes for the subset of banks with poor performance as defined as low ROA and high Chg-Off ratio, 

respectively.

 

-.05 0 .05 .1 -.05 0 .05 .1

No CEO Change CEO Change (NG)

CEO Change (FM) CEO Change (MA)

Log Assets Growth (t,t+1) Log Loan Growth (t,t+1)

Log RWA Growth (t,t+1) Log Employee Growth(t,t+1) 

Log Office Growth (t,t+1) 

-.05 0 .05 .1 -.05 0 .05 .1

No CEO Change CEO Change (NG)

CEO Change (FM) CEO Change (MA)

Log Assets Growth (t,t+1) Log Loan Growth (t,t+1)

Log RWA Growth (t,t+1) Log Employee Growth(t,t+1) 

Log Office Growth (t,t+1) 
47

FIGURE 4c. Impact of CEO change on liabilities – Low ROA banks

FIGURE 4d. Impact of CEO change on liabilities – High Chg-Off ratio banks 

Figures 4c and 4d show how liabilities evolve for banks depending on CEO turnover with and without 

gender changes for the subset of banks with poor performance as defined as low ROA and high Chg-Off

ratio respectively.

-1 -.5 0 .5 -1 -.5 0 .5

No CEO Change CEO Change (NG)

CEO Change (FM) CEO Change (MA)

Log Liab Growth (t,t+1) Log Dep Growth (t,t+1 

Log Brokered Deposits Growth (t,t+1) Log Non-Deposit Liabilities Growth (t,t+1) 

 

-.6 -.4 -.2 0 .2 -.6 -.4 -.2 0 .2

No CEO Change CEO Change (NG)

CEO Change (FM) CEO Change (MA)

Log Liab Growth (t,t+1) Log Dep Growth (t,t+1) 

Log Brokered Deposits Growth (t,t+1) Log Non-Deposit Liabilities Growth (t,t+1) 

 
48

TABLE 1. Variable definitions 

Variable Definition

CEO Change NG Denotes CEO change NOT resulting in change in gender

CEO Change FM Denotes CEO change resulting in change in gender to female

CEO Change MA Denotes CEO change resulting in change in gender to male

Change in Log(Assets) Assets = ASSET*

Change in Log(Loans

andLeases)

Loans and Leases = LNLS*

Change in Log(RWA) RWA (Risk Weighted Assets) = RWAW*

Change in Log(Employees) Employees = NUMEMP*

Change in Log(Offices) Offices = OFFSOD*

Change in Log(Liab) Liabilities = LIAB*

Change in Log(Deposits) Deposits = DEP*

Change in Log(Brokered 

Deposits)

Brokered Deposits = BRO*

Change in Log(Non-Deposit 

Liabilities)

Non-Deposit Liabilities=LIAB* - DEP*

Change in Log(Z-Score) Z-Score =(ROA + EQ*/ASSET*)/SD(ROA); Standard Deviation (SD) 

measured over 4 quarters. 

Change in Equity/Assets Equity/Assets=EQ*/ASSET*

Change in ROA ROA=Annualized(QNETINC*)/ASSET*

Change in ROA Volatility ROA Volatility = SD (ROA); Standard Deviation (SD) measured over 

4-quarters

Low ROA Return on Assets below 10th percentile of sample bank ROAs

High Chg-Off

Log Assets 

Charge-offs to assets above 90th percentile of sample banks charge-offs 

Assets=ASSET*

Assets > $ 1B Assets=ASSET*

Loans/Assets Loans=LNLS*, Assets=ASSET*

Cash Balances/Total Assets Cash Balances=CHBAL*, Total Assets=ASSET*

RWA/Assets RWA (Risk Weighted Assets)=RWAW, Assets=ASSET*

Deposits/Liabilities Deposits=DEP*, Liabilities = LIAB*

Brokered 

Deposits/Liabilities

Brokered Deposits=BRO*, Liabilities =LIAB*

Avg Dep Interest Rate Annualized Quarterly EINTEXP*/ DEP*

Female CEO CEO Flagged as Female

Female Chair Chair Flagged as Female

CEO Duality CEO and Chair same person

Related Chair CEO and Chair have same last name

Public Bank Flagged as public based on FEDNY PERMCO RSSD Match 

Data9

Sub-Chapter-S Subchapter S Selection = SUBCHAPS*

MBHC Multi-Bank Holding Company (MBHC) = More than 1 Institution 

under RSSDHCR using Call Report data

 

9 CRSP-FRB LinkFEDERAL RESERVE BANK of NEW YORK 

(https://www.newyorkfed.org/research/banking_research/crsp-frb)
49

Bank Age Years since bank established date

Organizational Change A change in top-level holding company status of any kind 

Merger Activity A dummy variable which equals one if the bank was involved in any 

merger during the year.

Number of States The number of states the bank operates in

HHI The Herfindahl-Hirschman Index measures deposit market 

concentration 

State Unemp Rate Unemployment rate in state(s) where bank operates.

State Real PCI Per-Capita Income in state(s) where bank operates

Residential RE Loan Share Share of residential real estate loans:( LNRE*-LNCOMRE*)/LNLS*

The table reports variable definitions. Data for variable construction are from various sources; most 

variables are derived from Call Report data obtained from the FDIC Research Information System (RIS) 

and, where applicable, RIS variable names are referenced with an asterisk. The link 

https://www7.fdic.gov/DICT/app/templates/Index.html#!/Main provides more details on the RIS 

variables. 
50

TABLE 2. Panel A. Descriptive statistics – CEO turnover data

N mean count mean*

CEO Change 52504 0.100 5270 1.000

CEO Change NG 52504 0.090 4726 0.897

CEO Change FM 52504 0.006 330 0.063

CEO Change MA 52504 0.004 214 0.041

The table reports the descriptive statistics for the variables. The variables are defined in Table 1. Mean* 

denotes mean conditional on a CEO change occurring. Count denotes the number of banks with indicator 

variable equal to 1.
51

TABLE 2. Panel B. Descriptive statistics – all other variables

N mean sd p10 p25 p50 p75 p90

ROA 52504 0.007 0.011 -0.003 0.004 0.009 0.012 0.017

Chg-Off Ratio 52504 0.001 0.001 0.000 0.000 0.000 0.001 0.002

Change in Log(Assets) 50301 0.046 0.125 -0.051 -0.006 0.033 0.080 0.145

Change in Log(Loans and Leases) 50289 0.042 0.157 -0.089 -0.024 0.035 0.094 0.167

Change in Log(RWA) 50301 0.042 0.144 -0.085 -0.020 0.035 0.092 0.163

Change in Log(Employees) 50283 0.016 0.141 -0.087 -0.034 0.000 0.049 0.119

Change in Log(Offices) 50301 0.018 0.142 0.000 0.000 0.000 0.000 0.020

Change in Log(Liab) 50301 0.045 0.135 -0.058 -0.010 0.033 0.082 0.152

Change in Log(Deposits) 50301 0.050 0.151 -0.054 -0.008 0.036 0.086 0.159

Change in Log(Brokered Deposits) 50301 -0.047 2.197 -0.662 0.000 0.000 0.000 0.432

Change in Log(Non-Deposit 

Liabilities) 50290 -0.040 0.842 -0.769 -0.287 -0.023 0.201 0.659

Change in Log(Z-Score) 50134 -0.005 1.123 -1.355 -0.631 0.011 0.641 1.310

Change in Equity/Assets 50301 0.000 0.017 -0.012 -0.005 0.001 0.006 0.012

Change in ROA 50301 0.000 0.012 -0.005 -0.002 0.000 0.002 0.006

Change in ROA Volatility 50301 0.000 0.015 -0.006 -0.002 0.000 0.002 0.006

Log Assets 52504 12.104 1.163 10.702 11.315 12.014 12.779 13.632

Assets > $ 1B 52504 0.079 0.269 0.000 0.000 0.000 0.000 0.000

Loans/Assets 52504 0.621 0.158 0.399 0.524 0.644 0.739 0.806

Cash Balances/Total Assets 52503 0.089 0.085 0.020 0.033 0.062 0.116 0.191

RWA/Assets 52504 0.678 0.134 0.494 0.593 0.689 0.773 0.840

Deposits/Liabilities 52504 0.945 0.062 0.864 0.917 0.966 0.992 0.997

Brokered Deposits/Liabilities 52504 0.035 0.083 0.000 0.000 0.000 0.034 0.111

Avg Dep Interest Rate 52504 0.002 0.002 0.001 0.001 0.002 0.003 0.005

Female CEO 52504 0.059 0.236 0.000 0.000 0.000 0.000 0.000

Female Chair 52504 0.058 0.233 0.000 0.000 0.000 0.000 0.000

CEO Duality 52504 0.351 0.477 0.000 0.000 0.000 1.000 1.000

Related Chair 52504 0.060 0.238 0.000 0.000 0.000 0.000 0.000

Public 52504 0.175 0.380 0.000 0.000 0.000 0.000 1.000

Sub-Chapter-S 52504 0.372 0.483 0.000 0.000 0.000 1.000 1.000

MBHC 52504 0.148 0.356 0.000 0.000 0.000 0.000 1.000

Bank Age 52504 3.935 1.028 2.251 3.359 4.421 4.677 4.804

Organizational Change 52504 0.069 0.254 0.000 0.000 0.000 0.000 0.000

Merger Activity 52504 0.080 0.271 0.000 0.000 0.000 0.000 0.000

Number of States 52504 1.117 0.569 1.000 1.000 1.000 1.000 1.000

HHI 52504 0.234 0.127 0.113 0.147 0.204 0.285 0.394
52

State Unemp Rate 52504 0.064 0.022 0.038 0.046 0.061 0.081 0.095

State Real PCI 52504 0.043 0.006 0.036 0.039 0.043 0.046 0.051

Residential RE Loan Share 52504 0.699 0.185 0.437 0.598 0.737 0.834 0.900

The table reports the descriptive statistics for the variables. The variables are defined in Table 1.
53

TABLE 3. Impact of poor performance on community bank CEO turnover

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

CEO CEO CEO CEO CEO CEO

Change Change Change Change Change Change

Low ROA 0.112*** 0.108***

(0.01) (0.01)

High Chg-Off Ratio 0.084*** 0.082***

(0.01) (0.01)

Female CEO 0.001 -0.000 -0.000

(0.01) (0.01) (0.01)

CEO Duality -0.019*** -0.019*** -0.019***

(0.00) (0.00) (0.00)

Female Chair 0.006 0.005 0.005

(0.01) (0.01) (0.01)

Related Chair -0.039*** -0.039*** -0.040***

(0.00) (0.00) (0.00)

Public -0.001 -0.003 -0.002

(0.00) (0.00) (0.00)

Sub-chapter-S -0.004 -0.003 -0.004

(0.00) (0.00) (0.00)

MBHC 0.024*** 0.025*** 0.024***

(0.00) (0.00) (0.00)

Log Bank Age -0.007*** -0.000 -0.005***

(0.00) (0.00) (0.00)

Organizational

Change 0.061*** 0.056*** 0.061***

(0.01) (0.01) (0.01)

Merger Activity -0.005 -0.005 -0.003

(0.01) (0.01) (0.01)

Number of States 0.004 0.003 0.003 0.003 0.002 0.002

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

HHI -0.013 0.007 -0.005 0.001 0.009 0.005

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

State Unemp Rate 0.771*** 0.396*** 0.586*** 0.584*** 0.344*** 0.439***

(0.10) (0.10) (0.10) (0.10) (0.10) (0.10)

State Real PCI -0.251 -0.249 -0.113 -0.251 -0.186 -0.093

(0.27) (0.26) (0.26) (0.26) (0.26) (0.26)

Log Assets -0.002 0.000 -0.003** -0.004** -0.002 -0.005***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Assets > $ 1B 0.015** 0.014** 0.015** 0.017** 0.015** 0.017**

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

Time FE YES YES YES YES YES YES

N 52504 52504 52504 52504 52504 52504

R^2A 0.0048 0.0158 0.0111 0.0107 0.0205 0.0167

F 15.501 32.317 25.673 19.089 28.163 24.657
54

This table shows bank CEO changes from (t,t+1) regressed on indicators of distress and controls at time 

t. The variables are defined in Table 1. ***, **, and * denote significance at the 0.01, 0.05, and 0.10 

levels, respectively.
55

TABLE 4. Impact of poor performance on community bank CEO turnover: IV

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

CEO CEO CEO CEO CEO CEO CEO CEO

Change Change Change Change Change Change Change Change

IV-1 IV-1 IV-2 IV-2 IV-1 IV-1 IV-2 IV-2

All

Sig RE 

Exp All

Sig RE 

Exp All

Sig RE 

Exp All

Sig RE 

Exp

Low ROA 0.112** 0.198** 0.087** 0.148**

(0.05) (0.08) (0.04) (0.06)

High Chg-Off Ratio 0.090** 0.159** 0.083** 0.166**

(0.04) (0.06) (0.03) (0.07)

Female CEO 0.005 -0.021* 0.005 -0.019* 0.005 -0.019 0.005 -0.019

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

CEO Duality 0.020*** 0.018*** 0.019*** 0.018*** 0.020*** 0.016*** 0.019*** -0.016**

(0.00) (0.01) (0.00) (0.01) (0.00) (0.01) (0.00) (0.01)

Female Chair -0.000 -0.001 -0.000 -0.001 -0.001 -0.000 -0.000 -0.001

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

Related Chair 0.039*** -0.014 0.039*** -0.015 0.040*** -0.013 0.040*** -0.013

(0.00) (0.01) (0.00) (0.01) (0.00) (0.01) (0.00) (0.01)

Public -0.003 -0.007 -0.003 -0.007 -0.002 -0.005 -0.002 -0.005

(0.00) (0.01) (0.00) (0.01) (0.00) (0.01) (0.00) (0.01)

Sub-chapter-S -0.003 -0.011 -0.003 -0.012 -0.004 -0.015** -0.004 -0.015**

(0.00) (0.01) (0.00) (0.01) (0.00) (0.01) (0.00) (0.01)

MBHC 0.024*** 0.031*** 0.024*** 0.031*** 0.023*** 0.028*** 0.023*** 0.027***

(0.00) (0.01) (0.00) (0.01) (0.00) (0.01) (0.00) (0.01)

Log Bank Age -0.001 0.005 -0.002 0.003 -0.004** -0.002 -0.005** -0.002

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Organizational Change 0.056*** 0.052*** 0.057*** 0.053*** 0.060*** 0.054*** 0.060*** 0.054***
56

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

Merger Activity -0.004 -0.004 -0.005 -0.005 -0.003 -0.002 -0.003 -0.002

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

Number of States 0.002 0.003 0.002 0.003 0.002 0.004 0.002 0.004

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

HHI 0.008 0.062** 0.007 0.057** 0.005 0.059** 0.005 0.059**

(0.01) (0.03) (0.01) (0.03) (0.01) (0.03) (0.01) (0.03)

State Unemp Rate 0.316** 0.223 0.378*** 0.374 0.396*** 0.360 0.412*** 0.339

(0.15) (0.34) (0.13) (0.30) (0.13) (0.30) (0.12) (0.32)

State Real PCI -0.164 0.087 -0.179 -0.022 -0.077 0.222 -0.089 0.248

(0.26) (0.48) (0.26) (0.45) (0.27) (0.50) (0.26) (0.50)

Log Assets -0.003 -0.008** -0.003 -0.009** 0.006*** 0.019*** 0.006*** 0.019***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Assets > $ 1B 0.016** 0.027** 0.016** 0.028** 0.017*** 0.040*** 0.017*** 0.040***

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

Time FE YES YES YES YES YES YES YES YES

N 52502 13368 52502 13368 52502 13368 52502 13368

R^2A 0.0160 0.0042 0.0166 0.0141 0.0131 0.0084 0.0134 0.0071

F 19.100 5.262 19.022 5.347 18.726 5.147 18.694 5.139

RMSE 0.30 0.31 0.30 0.31 0.30 0.31 0.30 0.31

Underidentification 

(Kleibergen-Paap) 272.74 86.51 324.40 108.88 392.66 146.63 356.39 97.16

Weak Identification 

(Cragg-Donald) 645.55 167.06 1279.89 300.92 974.10 295.16 1366.15 271.61

The second stage of the IV analysis is depicted in this table. We employ RE shock as an instrumental variable in our two-stage instrumental variable regressions. 

RE shock is defined as a decline of at least 10 or 20 percent (depending on the model) in the House Price Index (HPI) during the two years prior to CEO turnover

in the regions in which the bank operates. IV1 denotes an HPI 10 percent Shock over 2 years in year prior to turnover and IV2 denotes an HPI 20 percent Shock 

over 2 years in year prior to turnover. Columns 2, 4, 6, and 8 of the table include the subset of observations with higher RE exposure (75th percentile of 
57

residential real estate loans just before the beginning of the RE shock, i.e. 3 years out). The variables are defined in Table 1. ***, **, and * denote significance 

at the 0.01, 0.05, and 0.10 levels, respectively.
58

TABLE 5. Panel A. Incoming CEO gender and performance - OLS regressions.

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

No change in CEO gender Incoming female CEO Incoming male CEO

Low ROA 0.025** -0.014 -0.012

(0.01) (0.01) (0.01)

High Chg-Off Ratio 0.006 -0.005 -0.001

(0.01) (0.01) (0.01)

CEO Duality 0.008 0.007 0.001 0.001 -0.009 -0.009

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

Female Chair -0.194*** -0.194*** 0.026 0.026 0.168*** 0.168***

(0.03) (0.03) (0.02) (0.02) (0.02) (0.02)

Related Chair 0.010 0.010 -0.009 -0.009 -0.001 -0.001

(0.02) (0.02) (0.02) (0.02) (0.02) (0.02)

Other controls YES YES YES YES YES YES

Time FE YES YES YES YES YES YES

N 5270 5270 5270 5270 5270 5270

R^2A 0.0339 0.0331 0.0054 0.0050 0.0441 0.0437

F 5.146 4.955 2.209 2.095 5.066 5.032

TABLE 5. Panel B. Incoming CEO gender and performance - IV regressions.

(7) (8) (9) (10) (11) (12)

No change in CEO gender Incoming female CEO Incoming male CEO

Low ROA 0.091 -0.055 -0.036

(0.15) (0.11) (0.09)

High Chg-Off Ratio 0.073 -0.045 -0.029

(0.12) (0.09) (0.07)

CEO Duality 0.009 0.008 0.001 0.001 -0.009 -0.009

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

Female Chair -0.197*** -0.195*** 0.028 0.027 0.169*** 0.168***

(0.03) (0.03) (0.02) (0.02) (0.02) (0.02)

Related Chair 0.011 0.011 -0.010 -0.010 -0.001 -0.001

(0.02) (0.02) (0.02) (0.02) (0.02) (0.02)

Other controls YES YES YES YES YES YES

Time FE YES YES YES YES YES YES

N 5269 5269 5269 5269 5269 5269

R^2A 0.0281 0.0270 0.0017 0.0017 0.0423 0.0412

F 4.938 4.887 2.078 2.060 5.021 4.988
59

TABLE 5. Panel C. CEO turnovers, outgoing CEO gender, and performance

(1) (2) (3) (4)

CEO CEO CEO CEO

Change Change Change Change

Low ROA 0.108*** 0.110***

(0.01) (0.01)

High Chg-Off Ratio 0.082*** 0.083***

(0.01) (0.01)

Female CEO 0.005 0.008 0.005 0.007

(0.01) (0.01) (0.01) (0.01)

Low ROA X Female CEO -0.029

(0.02)

High Chg-Off Ratio X 

Female CEO -0.017

(0.02)

Other Controls YES YES YES YES

Time FE YES YES YES YES

N 52504 52504 52504 52504

R^2A 0.0205 0.0205 0.0167 0.0167

F 28.163 27.122 24.657 23.809

This table examines the impact of gender of incumbent CEO on turnover and gender of succeeding

CEO. Panel A considers the impact of poor performance on incoming CEO gender for the subsample 

of banks where CEO turnovers have occurred; panel B conducts the same regressions using our twostage IV approach which instruments for poor performance. Panel C focuses on the full sample and 

considers the link between outgoing CEO gender and CEO changes especially in the presense of poor 

performance. Untabulated controls include Female Chair, CEO Duality, Related Chair, Public, Subchapter-S, MBHC, Log Bank Age, Organizational Change, Merger Activity, Log Assets, Log Asset 

Change, Assets >1B, Loans/Asset, Cash/Assets, RWA/Assets, Number of States, HHI, State Unemp 

Rate, and State Real PCI. The variables are defined in Table 1. ***, **, and * denote significance at the 

0.01, 0.05, and 0.10 levels, respectively. 
60

TABLE 6, Panel A. Multivariate analysis: Bank actions after CEO turnovers -Role of 

Gender – Poor performance

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

Δ Log Assets Δ Log Loans Δ Log RWA

Δ Log # 

Employees

Δ Log # 

Branches

Low ROA -0.020*** -0.026*** -0.024*** -0.006 -0.015***

(0.00) (0.00) (0.00) (0.00) (0.00)

High Chg-Off Ratio

CEO Change NG -0.002 0.001 -0.002 -0.002 -0.002

(0.00) (0.00) (0.00) (0.00) (0.00)

CEO Change FM -0.017** -0.014 -0.018** -0.027*** -0.026***

(0.01) (0.01) (0.01) (0.01) (0.01)

CEO Change MA -0.005 -0.002 -0.004 -0.021 0.010

(0.01) (0.02) (0.02) (0.02) (0.01)

Female Chair -0.001 -0.013* -0.009 0.002 0.003

(0.01) (0.01) (0.01) (0.01) (0.01)

CEO Duality -0.004 -0.003 -0.003 0.001 0.002

(0.00) (0.00) (0.00) (0.00) (0.00)

Related Chair -0.007 -0.004 -0.009 -0.006 -0.002

(0.00) (0.01) (0.01) (0.00) (0.01)

Other controls YES YES YES YES YES

Bank FE YES YES YES YES YES

Time FE YES YES YES YES YES

N 50254 50242 50254 50237 50254

R^2A 0.158 0.155 0.147 0.047 0.048

R2W 0.158 0.155 0.147 0.048 0.049

45.333 91.039 81.155 15.947 13.982
61

TABLE 6, Panel A cont.

(6) (7) (8) (9) (10)

Δ Log Assets Δ Log Loans Δ Log RWA

Δ Log # 

Employees

Δ Log # 

Branches

Low ROA

High Chg-Off Ratio -0.021*** -0.027*** -0.026*** -0.007** -0.017***

(0.00) (0.00) (0.00) (0.00) (0.00)

CEO Change NG -0.002 0.001 -0.002 -0.002 -0.002

(0.00) (0.00) (0.00) (0.00) (0.00)

CEO Change FM -0.017** -0.014 -0.018** -0.027*** -0.026***

(0.01) (0.01) (0.01) (0.01) (0.01)

CEO Change MA -0.005 -0.002 -0.004 -0.021 0.010

(0.01) (0.02) (0.02) (0.02) (0.01)

Female Chair -0.001 -0.013* -0.008 0.002 0.003

(0.01) (0.01) (0.01) (0.01) (0.01)

CEO Duality -0.004 -0.003 -0.003 0.001 0.002

(0.00) (0.00) (0.00) (0.00) (0.00)

Related Chair -0.007 -0.004 -0.009 -0.006 -0.002

(0.00) (0.01) (0.01) (0.00) (0.01)

Other controls YES YES YES YES YES

Bank FE YES YES YES YES YES

Time FE YES YES YES YES YES

N 50254 50242 50254 50237 50254

R^2A 0.158 0.155 0.147 0.047 0.048

R2W 0.159 0.156 0.148 0.048 0.049

45.477 88.331 77.324 15.946 14.245

This table shows changes between (t, t+1) in Log Assets, Log Loans, Log Risk-Weighted Assets, Log 

Number of Employees, and Log Number of Branches regressed on indicators of bank leadership 

changes from (t-1, t) and controls at time t. Untabulated controls include Female Chair, CEO Duality, 

Related Chair, Public, Sub-chapter-S, MBHC, Log Bank Age, Organizational Change, Merger Activity, 

Log Assets, Log Asset Change, Assets >1B, Loans/Asset, Cash/Assets, RWA/Assets, Number of States, 

HHI, State Unemp Rate, and State Real PCI. The variables are defined in Table 1. ***, **, and * denote 

significance at the 0.01, 0.05, and 0.10 levels, respectively. 
62

TABLE 6, Panel B. Bank actions after CEO turnovers – The role of gender

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

Δ Log 

Liabilities

Δ Log 

Deposits

Δ Log 

Brokered 

Deposits

Δ Log Nondeposit 

Liabilities

Δ Log 

Liabilities

Δ Log 

Deposits

Δ Log 

Brokered 

Deposits

Δ Log Nondeposit 

Liabilities

Low ROA -0.027*** -0.025*** -0.417*** -0.053***

(0.003) (0.004) (0.052) (0.018)

High Chg-Off Ratio -0.030*** -0.029*** -0.338*** -0.068***

(0.003) (0.003) (0.048) (0.016)

CEO Change NG -0.003 -0.002 0.003 -0.010 -0.003 -0.002 -0.002 -0.010

(0.002) (0.002) (0.041) (0.014) (0.002) (0.002) (0.041) (0.014)

CEO Change FM -0.024*** -0.024*** 0.004 -0.025 -0.024*** -0.024*** -0.000 -0.025

(0.008) (0.009) (0.122) (0.050) (0.008) (0.009) (0.122) (0.050)

CEO Change MA -0.007 -0.008 -0.123 -0.082 -0.007 -0.008 -0.128 -0.082

(0.015) (0.015) (0.212) (0.079) (0.016) (0.015) (0.211) (0.079)

Female Chair -0.004 -0.001 -0.063 -0.009 -0.004 -0.001 -0.060 -0.009

(0.007) (0.007) (0.082) (0.034) (0.007) (0.007) (0.082) (0.034)

CEO Duality -0.004 -0.004 -0.040 -0.011 -0.004 -0.004 -0.040 -0.011

(0.003) (0.004) (0.048) (0.018) (0.003) (0.004) (0.048) (0.018)

Related Chair -0.007 -0.007 -0.042 0.011 -0.007 -0.007 -0.042 0.012

(0.005) (0.005) (0.079) (0.033) (0.005) (0.005) (0.079) (0.033)

Other controls YES YES YES YES YES YES YES YES

Bank FE YES YES YES YES YES YES YES YES

Time FE YES YES YES YES YES YES YES YES

N 50255 50255 50255 50244 50255 50255 50255 50244

R^2A 0.121 0.124 0.032 0.111 0.122 0.125 0.032 0.112

R2W 0.122 0.125 0.033 0.112 0.123 0.126 0.033 0.112

F 33.362 41.658 22.924 64.210 31.497 41.949 22.662 64.424
63

This table shows changes between (t, t+1) in Log Liabilities, Log Deposits, Log Brokered Deposits, and Log Non-deposits Liabilities regressed on indicators 

of bank leadership changes from (t-1, t) and controls at time t. Untabulated controls include Female Chair, CEO Duality, Related Chair, Public, Sub-chapter-S, 

MBHC, Log Bank Age, Organizational Change, Merger Activity, Log Assets, Log Asset Change, Assets >1B, Deposits/Liabilities, Brokered Deposits/Deposits, 

Deposit Interest Rate, Number of States, HHI, State Unemp Rate, and State Real PCI. The variables are defined in Table 1. ***, **, and * denote significance 

at the 0.01, 0.05, and 0.10 levels, respectively. 
64

Table 7 Panel A: Actions by banks following CEO turnovers -Role of Gender –

Poorperformance

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

Δ Log Assets Δ Log Loans

Δ Log 

RWA

Δ Log # 

Employees

Δ Log # 

Branches

Poor Performance= Low ROA

Poor Performance X CEO 

Change NG -0.011 * -0.008 -0.009 -0.001 -0.007

-(0.01) -(0.01) -(0.01) -(0.01) -(0.01)

Not Poor Performance X 

CEO Change NG -0.002 0.001 -0.001 -0.003 -0.002

(0.00) (0.00) (0.00) (0.00) (0.00)

Poor Performance X CEO 

Change FM -0.049 * -0.062 ** -0.058 ** -0.080 ** -0.058 **

-(0.03) -(0.03) -(0.03) -(0.03) -(0.03)

Not Poor Performance X 

Change FM -0.012 * -0.00589

-

0.01162 -0.018 * -0.021 **

-(0.01) -(0.01) -(0.01) -(0.01) -(0.01)

Poor Perfromance X CEO 

Change MA 0.023 0.023 0.014 -0.022 -0.026

-(0.06) -(0.07) -(0.07) -(0.05) -(0.04)

Not Poor Performance X 

Change MA -0.013 -0.010 -0.009 -0.021 0.016

-(0.01) -(0.02) -(0.01) -(0.02) -(0.01)

Controls YES YES YES YES YES

Bank FE YES YES YES YES YES

Time FE YES YES YES YES YES

N 50254 50242 50254 50237 50254

R^2A 0.156 0.153 0.145 0.047 0.048

R2W 0.157 0.154 0.146 0.048 0.048

F 41.983 82.874 72.381 14.968 12.979
65

Table 7 Panel A cont: Actions by banks following CEO turnovers -The role of gender 

(6) (7) (8) (9) (10)

Δ Log Assets Δ Log Loans Δ Log RWA

Δ Log # 

Employees

Δ Log # 

Branches

Poor Performance = High Chg-Off Ratio

Poor Performance X 

CEO Change NG -0.026 *** -0.026 *** -0.026 *** -0.015 ** -0.014 **

-(0.01) -(0.01) -(0.01) -(0.01) -(0.01)

Not Poor Performance 

X CEO Change NG 0.001 0.004 0.001 0.000 -0.001

(0.00) (0.00) (0.00) (0.00) (0.00)

Poor Performance X 

CEO Change FM -0.052 * -0.097 *** -0.077 *** -0.085 ** -0.058 *

-(0.03) -(0.04) -(0.03) -(0.03) -(0.03)

Not Poor Performance 

X Change FM -0.012 * -0.001 -0.009 -0.017 * -0.021 **

-(0.01) -(0.01) -(0.01) -(0.01) -(0.01)

Poor Perfromance X 

CEO Change MA 0.045 0.009 0.015 -0.027 0.010

-(0.06) -(0.07) -(0.06) -(0.05) -(0.04)

Not Poor Performance 

X Change MA -0.017 -0.007 -0.010 -0.020 0.008

-(0.01) -(0.02) -(0.01) -(0.02) -(0.01)

Controls YES YES YES YES YES

Bank FE YES YES YES YES YES

Time FE YES YES YES YES YES

N 50254 50242 50254 50237 50254

R^2A 0.157 0.154 0.145 0.047 0.048

R2W 0.157 0.154 0.146 0.048 0.048

F 42.425 82.670 72.380 15.175 12.860

This table shows changes between (t, t+1) in Log Assets, Log Loans, Log Risk-Weighted Assets, Log 

Number of Emloyees, and Log Number of Branches regressed on interaction variables of bank 

leadership changes from (t-1, t) and a distress measure. The control variables are measured at time t. 

Untabulated controls include Female Chair, CEO Duality, Related Chair, Public, Sub-chapter-S, 

MBHC, Log Bank Age, Organizational Change, Merger Activity, Log Assets, Log Asset Change, 

Assets >1B, Loans/Asset, Cash/Assets, RWA/Assets, Number of States, HHI, State Unemp Rate, and

State Real PCI.The variables are defined in Table 1. ***, **, and * denote significance at the 0.01, 0.05, 

and 0.10 levels, respectively. 
66

Table 7, Panel B. Actions by banks following CEO turnovers -Role of Gender – Poor performance

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

Δ Log Δ Log Δ Log Δ Log Δ Log Δ Log Δ Log Δ Log 

Liabilities Deposits Brokered

Nondeposit Liabilities Deposits BrokeredNondeposit

Deposits Liabilities Deposits Liabilities

Poor Performance = Low ROA

Poor Performance = High Chg-Off Ratio

Poor Performance X 

CEO Change NG -0.018** -0.013 -0.250** -0.117*** -0.037*** -0.036*** -0.374*** -0.096***

(0.01) (0.01) (0.12) (0.04) (0.01) (0.01) (0.11) (0.03)

Not Poor Performance 

X CEO Change NG -0.001 -0.001 0.032 0.009 0.002 0.003 0.050 0.003

(0.00) (0.00) (0.04) (0.02) (0.00) (0.00) (0.04) (0.02)

Poor Performance X 

CEO Change FM -0.049* -0.051* -0.412 -0.122 -0.062** -0.096** -0.255 -0.220**

(0.03) (0.03) (0.31) (0.13) (0.03) (0.04) (0.33) (0.10)

Not Poor Performance 

X Change FM

-

0.020*** -0.020** 0.062 -0.011 -0.018** -0.012* 0.028 0.006

(0.01) (0.01) (0.13) (0.05) (0.01) (0.01) (0.13) (0.06)

Poor Performance X 

CEO Change MA 0.021 0.012 -0.699 0.209 0.042 0.038 -0.532 0.124

(0.06) (0.06) (0.60) (0.19) (0.06) (0.05) (0.64) (0.19)

Not Poor Performance 

X Change MA -0.015 -0.015 -0.024 -0.153* -0.020 -0.021 -0.060 -0.134

(0.01) (0.01) (0.22) (0.09) (0.01) (0.01) (0.21) (0.09)

Controls YES YES YES YES YES YES YES YES

Bank FE YES YES YES YES YES YES YES YES

Time FE YES YES YES YES YES YES YES YES
67

N 50255 50255 50255 50244 50255 50255 50255 50244

R^2A 0.119 0.123 0.031 0.112 0.121 0.124 0.031 0.111

R2W 0.120 0.123 0.032 0.112 0.121 0.124 0.032 0.112

F 29.944 39.023 20.821 60.855 30.929 39.737 20.782 60.604

This table shows changes between (t, t+1) in Log Liabilities, Log Deposits, Log Brokered Deposits, and Log Non-deposits Liabilities regressed on interaction 

variables of bank leadership changes from (t-1, t) and a distress measure. The control variables are measured at time t. Untabulated controls include Female 

Chair, CEO Duality, Related Chair, Public, Sub-chapter-S, MBHC, Log Bank Age, Organizational Change, Merger Activity, Log Assets, Log Asset Change, 

Assets >1B, Deposits/Liabilities, Brokered Deposits/Deposits, Deposit Interest Rate, Number of States, HHI, State Unemp Rate, and State Real PCI. The 

variables are defined in Table 1. ***, **, and * denote significance at the 0.01, 0.05, and 0.10 levels, respectively. 
68

Table 8. Panel A. Bank outcomes following CEO turnovers: Risk profile changes (1-year horizon)

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

Δ Log Δ Equity Δ ROA Δ ROA Δ Log Δ Equity Δ ROA Δ ROA 

Z-score to assets volatility Z-score to assets volatility

Low ROA 0.823*** 0.003*** 0.006*** -0.011***

(0.03) (0.00) (0.00) (0.00)

High Chg-Off Ratio 0.588*** 0.003*** 0.004*** -0.008***

(0.03) (0.00) (0.00) (0.00)

CEO Change NG 0.077*** -0.000 0.000 -0.001* 0.089*** -0.000 0.000 -0.001**

(0.02) (0.00) (0.00) (0.00) (0.02) (0.00) (0.00) (0.00)

CEO Change FM 0.185*** 0.000 0.001 -0.003** 0.194*** 0.000 0.001 -0.003**

(0.07) (0.00) (0.00) (0.00) (0.07) (0.00) (0.00) (0.00)

CEO Change MA 0.134 0.001 0.004 -0.002 0.148 0.001 0.004 -0.002

(0.09) (0.00) (0.00) (0.00) (0.09) (0.00) (0.00) (0.00)

Controls YES YES YES YES YES YES YES YES

Bank FE YES YES YES YES YES YES YES YES

Time FE YES YES YES YES YES YES YES YES

N 50087 50254 50254 50254 50087 50254 50254 50254

R^2A 0.071 0.033 0.047 0.044 0.061 0.033 0.041 0.029

R2W 0.072 0.034 0.047 0.044 0.061 0.034 0.041 0.029

F 104.003 43.382 38.268 27.145 98.352 41.669 34.976 23.902

This table shows changes in Z-score, Equity to assets, ROA, and ROA volatility between t, and t+1 regressed on indicators of bank leadership changes from t-1 

to t and controls at time t. Untabulated controls include Female Chair, CEO Duality, Related Chair, Public, Sub-chapter-S, MBHC, Log Bank Age, 

Organizational Change, Merger Activity, Log Assets, Log Asset Change, Assets >1B, Loans/Assets, Cash/Assets, RWA/Assets, Deposits/Liabilities, Brokered 

Deposits/Deposits, Deposit Interest Rate, Number of States, HHI, State Unemp Rate, and State Real PCI. The variables are defined in Table 1. ***, **, and * 

denote significance at the 0.01, 0.05, and 0.10 levels, respectively.
69

Table 8. Panel B. Bank outcomes following CEO turnovers: Risk profile changes (1-year horizon) - interactions

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

Δ Log Δ Equity Δ ROA Δ ROA Δ Log Δ Equity Δ ROA Δ ROA 

Z-score to assets volatility Z-score to assets volatility

Poor Performance = Low ROA Poor Performance = High Chg-Off Ratio

Poor Performance X CEO 

Change (NG) 0.707*** 0.002 0.004*** -0.011*** 0.595*** 0.003*** 0.002** -0.009***

(0.06) (0.00) (0.00) (0.00) (0.06) (0.00) (0.00) (0.00)

Not Poor Performance X 

CEO Change (NG) -0.004 -0.001** -0.000 0.001*** 0.027

-

0.001*** 0.000 0.000

(0.02) (0.00) (0.00) (0.00) (0.02) (0.00) (0.00) (0.00)

Poor Performance X CEO 

Change (FM) 0.870*** -0.004 0.010 -0.018** 0.858*** 0.001 0.014* -0.019**

(0.22) (0.00) (0.01) (0.01) (0.22) (0.00) (0.01) (0.01)

Not Poor Performance X 

Change (FM) 0.095 0.001 -0.000 -0.001 0.103 0.000 -0.001 -0.001

(0.07) (0.00) (0.00) (0.00) (0.08) (0.00) (0.00) (0.00)

Poor Performance X CEO 

Change (MA) 0.664** 0.001 0.007** -0.015* 0.465 -0.001 0.010*** -0.007

(0.29) (0.01) (0.00) (0.01) (0.30) (0.00) (0.00) (0.01)

Not Poor Performance X 

Change (MA) 0.072 0.001 0.004 0.000 0.113 0.001 0.003 -0.001

(0.09) (0.00) (0.00) (0.00) (0.09) (0.00) (0.00) (0.00)

Controls YES YES YES YES YES YES YES YES

Bank FE YES YES YES YES YES YES YES YES

Time FE YES YES YES YES YES YES YES YES
70

N 50087 50254 50254 50254 50087 50254 50254 50254

R^2A 0.051 0.032 0.037 0.022 0.049 0.032 0.037 0.019

R2W 0.051 0.032 0.038 0.023 0.050 0.033 0.038 0.020

F 77.397 37.481 29.836 19.757 75.622 37.443 29.648 18.975

This table shows changes in Z-score, Equity to assets, ROA, and ROA volatility between t, and t+1 regressed on indicators of bank leadership changes from t-1 

to t and controls at time t. The variables are defined in Table 1. ***, **, and * denote significance at the 0.01, 0.05, and 0.10 levels, respectively. 
71

Table 9, Panel A. Long-term bank outcomes following CEO turnovers: Risk profile changes (2-year horizon).

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

Δ Log Δ Equity Δ ROA Δ ROA Δ Log Δ Equity Δ ROA Δ ROA 

Z-score to assets volatility Z-score to assets volatility

Low ROA 1.026*** 0.005*** 0.007*** -0.013***

(0.03) (0.00) (0.00) (0.00)

High Chg-Off Ratio 0.703*** 0.005*** 0.005*** -0.009***

(0.03) (0.00) (0.00) (0.00)

CEO Change (NG) 0.104*** -0.000 0.001** -0.001*** 0.119*** -0.000 0.001*** -0.001***

(0.02) (0.00) (0.00) (0.00) (0.02) (0.00) (0.00) (0.00)

CEO Change (FM) 0.156** -0.000 0.000 -0.003** 0.165** -0.000 0.001 -0.003**

(0.08) (0.00) (0.00) (0.00) (0.08) (0.00) (0.00) (0.00)

CEO Change (MA) 0.193* 0.000 0.004 -0.002 0.200* 0.000 0.004 -0.002

(0.10) (0.00) (0.00) (0.00) (0.11) (0.00) (0.00) (0.00)

Controls YES YES YES YES YES YES YES YES

Bank FE YES YES YES YES YES YES YES YES

Time FE YES YES YES YES YES YES YES YES

N 47947 48085 48085 48085 47947 48085 48085 48085

R^2A 0.100 0.048 0.065 0.069 0.084 0.047 0.054 0.049

R2W 0.100 0.049 0.066 0.070 0.084 0.048 0.055 0.050

F 103.979 51.420 39.239 31.630 92.686 49.044 37.098 28.252

This table shows changes in Z-score, Equity to assets, ROA, and ROA volatility between t, and t+2 regressed on indicators of bank leadership changes from t-1 

to t and controls at time t. Untabulated controls include Female Chair, CEO Duality, Related Chair, Public, Sub-chapter-S, MBHC, Log Bank Age, 

Organizational Change, Merger Activity, Log Assets, Log Asset Change, Assets >1B, Loans/Assets, Cash/Assets, RWA/Assets, Deposits/Liabilities, Brokered 

Deposits/Deposits, Deposit Interest Rate, Number of States, HHI, State Unemp Rate, and State Real PCI. The variables are defined in Table 1. ***, **, and * 

denote significance at the 0.01, 0.05, and 0.10 levels, respectively.
72

Table 9, Panel B. Long-term bank outcomes following CEO turnovers: Risk profile changes (2-year horizon) – Interactions

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

Δ Log Δ Equity Δ ROA Δ ROA Δ Log Δ Equity Δ ROA Δ ROA 

Z-score to assets volatility Z-score to assets volatility

Poor Performance = Low ROA Poor Performance = High Chg-Off Ratio

Poor Performance X CEO 

Change (NG) 0.799*** 0.003 0.007*** -0.011*** 0.669*** 0.004*** 0.004*** -0.010***

(0.07) (0.00) (0.00) (0.00) (0.07) (0.00) (0.00) (0.00)

Not Poor Performance X CEO 

Change (NG) 0.023 -0.000 -0.000 0.001* 0.056** -0.001 0.000 0.000

(0.02) (0.00) (0.00) (0.00) (0.02) (0.00) (0.00) (0.00)

Poor Performance X CEO 

Change (FM) 0.910*** 0.000 0.006* -0.023*** 1.072*** 0.007* 0.006** -0.023***

(0.22) (0.00) (0.00) (0.01) (0.21) (0.00) (0.00) (0.01)

Not Poor Performance X 

Change (FM) 0.060 0.000 -0.000 0.000 0.034 -0.001 -0.000 0.000

(0.08) (0.00) (0.00) (0.00) (0.08) (0.00) (0.00) (0.00)

Poor Performance X CEO 

Change (MA) 0.853** -0.004 0.007 -0.015* 0.664** -0.003 0.004 -0.011

(0.37) (0.01) (0.00) (0.01) (0.33) (0.01) (0.00) (0.01)

Not Poor Performance X 

Change (MA) 0.114 0.001 0.004 0.000 0.146 0.001 0.004 -0.000

(0.11) (0.00) (0.00) (0.00) (0.11) (0.00) (0.00) (0.00)

Controls YES YES YES YES YES YES YES YES

Bank FE YES YES YES YES YES YES YES YES

Time FE YES YES YES YES YES YES YES YES
73

N 47947 48085 48085 48085 47947 48085 48085 48085

R^2A 0.070 0.045 0.049 0.038 0.068 0.045 0.046 0.035

R2W 0.071 0.045 0.049 0.038 0.069 0.046 0.047 0.036

F 72.184 43.932 29.751 21.837 70.280 44.004 29.592 21.461

This table shows changes in Z-score, Equity to assets, ROA, and ROA volatility between t, and t+2 regressed on indicators of bank leadership changes from t-1 

to t and controls at time t. The variables are defined in Table 1. ***, **, and * denote significance at the 0.01, 0.05, and 0.10 levels, respective.