DBJ Discussion Paper Series, No. 1902

Do Social Networks Encourage Risk-taking?
Evidence from Bank CEOs

Yiwei Fang
（Stuart School of Business）
Iftekhar Hasan（Fordham University）
LiuLing Liu（College of Business）
Haizhi Wang
（Stuart School of Business）

April 25, 2019

Discussion Papers are a series of preliminary materials in their draft form. No quotations,reproductions or circulations should be made without the written consent of the authors in order toprotect the tentative characters of these papers. Any opinions, findings, conclusions or
recommendations expressed in these papers are those of the authors and do not reflect the views of
the Institute.

Do Social Networks Encourage Risk-taking? Evidence from Bank CEOs

Yiwei Fang
Stuart School of Business
Illinois Institute of Technology
565 W. Adams Street
Chicago, IL 60661
Telephone: 312-906-6559
E-mail: yfang15@stuart.iit.edu
Iftekhar Hasan
Fordham University
45 Columbus Avenue, 5th Floor
New York, NY 10023
Telephone: 646-312-8278
E-mail: ihasan@fordham.edu
LiuLing Liu
College of Business
Bowling Green State University
216 Business Administration
Bowling Green, OH 43403
Telephone: 419 372 0267
E-mail: liulinl@bgsu.edu
Haizhi Wang
Stuart School of Business
Illinois Institute of Technology
10 W 35th Street
Chicago, IL 60616
Telephone: 312-906-6557
E-mail: hwang23@stuart.iit.edu

April 25, 2019

Note: Part of this work was completed by Hasan while he was a Shimomura Fellow at theDevelopment Bank of Japan.
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Do social networks encourage risk-taking? Evidence from bank CEOs

Abstract

This paper investigates the impact of CEO’s social network on bank risk and observes a significant
positive association. Adopting a difference-in-difference approach, using deaths and retirements
within networks, it confirms that the findings are causal. It also reports that well-connected bankCEOs take more risk when more of their social ties are linked to informationally opaque firms andwhen the labor market offers fewer employment options. In addition, diversity of social ties
(professional and educational) helps to mitigate the impact on risk. Finally, it reveals an inefficient
trade-off between bank risk and return, suggesting that executive social networks lead to excessivebank risk.

Key Words: Risk-taking, social networks, CEOsJEL Classification: L14, G21, G31.

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1. Introduction
The tumultuous events surrounding bank failures during the recent financial crisis have raised
significant concerns about the high degree of interconnectedness in the financial system (Allen
and Babus, 2008).1 The substantial costs associated with the too-connected-to-fail (TCTF) havecalled for regulatory reforms that consider interconnectedness among banking institutions incapital charges.2 To promote a safe and sound banking system, it is therefore crucial to understandthe economic consequences when financial institutions are well connected.
In particular, the interconnectedness of CEOs is quite common and prevalent in thecorporate world. Motivated by the increasing importance of financial networks to bank stability(e.g., Allen and Gale, 2000; Leitner, 2005; Hautsch et al., 2014), this paper empirically investigates
the risk implications of social connections among bank CEOs. Specifically, we ask whether andto what extent the interconnectedness of bank CEOs in social networks among the corporate elite
affects bank risk-taking.
Large financial institutions are often viewed as dominating the business world and forging
the makings of a “ruling class” in the United States both economically and politically (Mizruchi,
1982; 2004). Bank executives frequently have personal and professional connections with a wide
variety of people and occupy central positions in social networks of the corporate elite. In line witha large amount of sociology literature (e.g., Coleman, 1988; Ellison and Fudenberg 1993; 1995),
we propose three channels through which social ties may affect executives’ decision-making: the1

For instance, the demise of Lehman Brothers immediately led to increased volatility and liquidity shortages for
American International Group (AIG). Because AIG is a central player with connections to many other financial
institutions, the U.S. government had to bail out AIG the next day for $85 billion to prevent the domino effect of
AIG’s credit derivative exposure on the rest of the financial system.
2
In response, the Bank for International Settlements (BIS) considered interconnectedness as a key feature to identify
global systemically important banks (G-SIBs) and imposed an additional common equity Tier I surcharge as a capitalrequirement in Basel III. Common equity Tier I surcharges range from 1% to 3.5%. The goal is to reduce theprobability of failure of a G-SIB and limit the extent of a G-SIB’s failure on the financial system.

3soft information channel, the job market insurance channel, and the groupthink channel. Personalconnections provide an effective channel for information exchange, allowing soft information to
be spread in social networks (e.g., Burt 1992; Jackson, 2011; Cohen et al., 2008; Cai et al., 2016).
Hence, a well-connected bank CEO may be in a prime position to obtain privileged informationfrom her network contacts to aid in decision-making. Such informational advantage reduces
uncertainties ex-ante and is likely to make bank CEOs feel more comfortable to take high-risk
investment projects (though perhaps not excessively risky projects).
Social networks are also a major source of job opportunities (Holzer, 1987; Granovetter,
1995; Calvo-Armengol and Jackson, 2004). Should a manager lose her job due to a failed risky
project, having strong social connections significantly facilitates the search for the next job
opportunity (Liu, 2014; Faleye et al., 2014). Hence, well-networked bank CEOs may be more risk
tolerant because they face less personal downside from risk-taking and enjoy greater upside if theirrisky projects succeed.
In addition, social network theory suggests that the actions of others affect individual
preferences and decisions (e.g., Buskens, 2002). The diffusion of similar ideas can be more
pronounced when there is strong homophily (the tendency of individuals to associate and bondwith similar people) in social networks (McPherson et al., 2001; Lee et al., 2014). However,homophily may also induce social conformity and “groupthink” that can lead to inefficientdecision-making (e.g., Asch, 1951; Janis, 1982; Ishii and Xuan, 2014; Lee et al., 2014; Gompers
et al., 2015). Because our sample period from 2000 to 2012 includes the era of high risk-taking bybanks leading up to the financial crisis of 2007-2009, it is plausible that bank CEOs take greaterrisks because they are more closely connected to similar peers and tend to act with a groupthinkmindset.

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Using a sample of 4,077 bank-year observations for 481 U.S. public banks between 2000
and 2012, we empirically examine how social connections among bank CEOs affect bank risktaking behavior. We track the employment history of bank CEOs and identify their professionalties to top executives or board members in other companies (including both banks and nonbanks)
through overlapping past working relationships. We identify educational ties if a bank CEO andan executive or board member of another company attended the same school and studied in thesame degree program (within three years of each other). Following Faleye et al. (2014), we thenconstruct an aggregate measure of connectedness by summing professional and educational ties
for each bank CEO.
The main finding of our study is that bank risk increases with the number of CEO social
connections. Specifically, a one-standard-deviation increase in CEO social connections translates
into a 10 percent decrease in Z-score. Moreover, CEOs’ professional ties to other top executives
and board members drive bank risk-taking. Social networks do not only influence Z-score, but also
the key components of the Z-score. Using alternative measures of bank risk, such as volatility of
return on assets (ROA), volatility of return on equity (ROE), and idiosyncratic risk, we reportconsistent results. To ensure robustness of our findings and draw causal inferences, we adopt
several methods to address potential endogeneity issue. First, we include bank fixed effects tocontrol for time-invariant and unobservable bank heterogeneity, and our results still hold. Second,
omitted variables capturing factors such as corporate governance, CEO incentive alignment, andmanagerial ability can potentially lead to a spurious correlation between CEO social connections
and risk-taking. Therefore, we rerun our main regression model controlling for bank corporate
governance, CEO ownership, compensation sensitivity to stock-price volatility, and generalmanagerial ability. Including these additional controls has no material effect on our main findings.

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Third, we use the deaths or retirements of network members as an exogenous shock to the size ofan individual bank CEO’s network (Fracassi and Tate, 2012; Lee et al., 2015). We employ adifference-in-difference estimator (DID hereafter) and find that banks demonstrate lower risk
following exogenous severance of ties among their CEOs.
In the next step, we explore the three abovementioned channels through which CEO
connections may affect bank risk-taking. To test the soft information channel, we calculate thepercentage of CEO connections to opaque firms. We posit that such connections allow banks toobtain more hard-to-collect information and reduce CEOs’ ex-ante risk aversion. In line with our
prediction, we show that the ability to obtain soft information leads to greater risk. To test the jobmarket insurance channel, we examine how CEO connections affect risk-taking during adverse
CEO labor market conditions. We find that when the overall labor market presents fewer
opportunities, CEOs on average take less risk to avoid losing their jobs. Nonetheless, even if theexternal labor market is poor, well-connected CEOs take more risk than those who do not havestrong social networks. The results lend support to the job-insurance channel (Faleye et al., 2014).
Last, we show that the positive relation between CEO social networks and bank risk can be
mitigated when social networks are more heterogeneous. This suggests that groupthink, driven byhomophily in social networks, exerts a positive influence on bank risk.
Although we find evidence that all three channels link CEO social networks to bank risktaking, the implications of the three channels on bank performance are quite different. For example,
the soft information channel suggests that privileged information percolating through social
networks allows bank CEOs to make informed decisions and results in superior bank performance.
However, the job-market insurance channel suggests that CEOs have incentives to take excessive
risk, which can deteriorate bank performance. According to the groupthink channel, bankers

6simply assimilate with their network peers’ decisions when the outcome is unclear, which may
have ambiguous implications for bank performance. Therefore, we explore whether higher bankrisk-taking translates into better bank performance or higher returns. We adopt two bankperformance measures, ROA and profit margin, to examine empirically the performance
implications of bank CEOs’ social networks. In general, our findings reveal that the higher risks
associated with CEO social networks do not produce higher returns. This inefficient risk-return
tradeoff reveals a “dark side” of social networking. It also indicates that the job-security channel
is most helpful in explaining bank risk-taking and performance.
The rest of this paper is organized as follows. Section 2 provides a review of related
literature and develops the hypotheses. Section 3 details the data, sample, and measures. Section
4 reports the empirical results. Section 5 summarizes and concludes the paper.

2. Related literature and hypotheses development
2.1. Soft information channel
When social agents make decisions without knowledge of the consequences, they often rely on
information from their personal relationships (Ellison and Fudenberg, 1993; 1995). The ability for
information to flow through a social network has been well documented in finance field. For
instance, Hochberg et al. (2007) find that venture capital firms enjoy more deal flows and better
performance when they have larger networks of partners. In the bank loan market, Engelberg et al.
(2012) show that personal connections between a borrower and a lender result in larger loan
amounts, lower interest rates, and less restrictive covenants. In the mergers and acquisitions setting,Cai and Sevilir (2012) find that social ties with target firms facilitate information acquisition byacquirers, which benefits the latter with lower takeover premiums and higher value creation.

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Larker et al. (2013) demonstrate that valuable information circulates through networks of board
directors. Also, Cohen et al. (2010) find that sell-side analysts perform better if they share an almamater with key executives of covered firms, which supports the information sharing role of social
networks.
According to the information role of social networks, CEOs with more connections are atbetter position to acquire soft information that is hard to obtain otherwise. For banking businessthat relies on relationship, soft information is crucial to make risky investment decisions, such as
lending to small business and foreign markets. Hence, with more connections, bank CEOs are
more informed and can become less risk-averse. More important, information from trustworthy
people allows bank CEOs to make decisions more confidently (Borgatti and Cross, 2003). Thesearguments could suggest that ex-ante, having access to information through social networks may
reduce CEOs’ risk aversion and encourage them to take more risks. Banks with greater connections
may find it attractive to invest in higher-risk investments because of CEO’s risk appetites.
However, enhanced information may enable CEOs to better evaluate the risk and thus limit
excessive risk taking. In either situations, the information channel of social networks would
suggest higher risk-adjusted performance.

2.2. Job-market insurance channelCEOs are responsible for making important decisions and bear the negative consequences of
unsuccessful investments, such as dismissal (Lehn and Zhao, 2006). However, the existing
literature suggests that social networks play an important role in CEO job searches andreemployment. In particular, because information relating to a candidate’s abilities and reputation
is often subtle and tends to be difficult to verify, prospective employers often avoid using publicly

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available information sources, preferring to rely on information gleaned from social ties to learn
about an individual’s qualities (Holzer, 1987; Granovetter, 1995; Calvo-Armengol and Jackson,2004).
CEOs are connected to other executives and directors through various activities, such as
past co-working experience, as well as school or club ties. These connections provide information
about executive or board vacancies at other firms and reflect outside employment options (Liu,
2014). Mazerolle and Singh (2004), for example, show that personal connections improve the odds
of reemployment following labor market displacement. Berger et al. (2011) find that the numberof social ties is a strong predictor of a bank executive’s outside appointments. Similarly, Faleye etal. (2014) demonstrate that CEOs’ social ties increase tolerance for failed innovation because of
the job security provided by social networks. Therefore, social connections may be safety nets thatenhance the probability of reemployment should an individual lose his/her job. Applying these
insights in our context, it is plausible that a bank CEO can readily access information about outside
job vacancies through personal connections. Consequently, ceteris paribus, a well-connected bank
CEO may be less worried about the personal consequences of undertaking risky investment
projects and thus be less risk averse.

2.3. Groupthink channel
Social capital theory predicts that individuals tend to establish social ties on the basis of homophily.
That is, individuals tend to socialize or partner with others who share common characteristics, suchas ethnicity, age, gender, education, or social status. This phenomenon occurs frequently in social
networks (McPherson et al., 2001; Lee et al., 2014), and it is likely to lead to “herd mentality” andgroupthink (Asch, 1951; Janis, 1982).

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The existing literature documents evidence of such a groupthink mentality. For example,
Fracassi (2012) reports that managers in similar industries tend to make similar investment
decisions if they are well connected. In addition, Gompers et al. (2015) show that venture
capitalists are more likely to form syndicates for joint investments if they share similar ethnic,
educational, or career backgrounds. Lee et al. (2014) document that political alignment between
executives and directors increase agency problems among managers, which implies that diversity
among corporate leaders can benefit shareholders. Houston, Lee and Suntheim (2018) find thatboard connections in the financial industry foster a groupthink mentality that contributes to higher
systemic risk. Likewise, we hypothesize that bank CEOs who share a groupthink mentality as aresult of their social network connections tend to undertake risk without challenging each other.
Leading up to the crisis of 2008, given many large banks were undertaking security investment
without understanding the underlying risk, it is plausible that such activities cause herding
behavior among well-connected CEOs and lead to higher risk for those banks. Thus, through thechannel of groupthink mentality, more connections with risky firms cause CEOs to take evenhigher risks. We thus expect, conversely, that diverse social ties mitigate the effect of homophilyon bank risk.

3. Data, sample, and measures
3.1. Sample selection
We obtain information on the social network connections of CEOs from the BoardEx database ofManagement Diagnostics Ltd (BoardEx). We obtain other financial information for our sample
banks from bank call reports. Focusing on publicly traded banks in the U.S., we match the BoardEx

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dataset with call reports to construct our sample. Our procedure yields 4,077 bank-year
observations from 2000 to 2012 for 481 unique public banks in the United States.

3.2. Measures
3.2.1. Measures of bank risk
The main dependent measure of our regression analysis is the Z-score, which empirical research
widely uses as an indicator of financial stability (Roy, 1952; Laeven and Levine, 2009; Houston
et al., 2010). Specifically, 𝑍𝑠𝑐𝑜𝑟𝑒 = (𝑅𝑂𝐴 + 𝐸/𝐴)/(𝑅𝑂𝐴) where ROA, E/A, and (ROA) are
the mean return on assets, the mean equity-to-assets ratio, and the standard deviation of ROA,
respectively, in the three-year window. The Z-score has an inverse relationship with bankinsolvency risk and captures the likelihood that a bank will go out of business due to insufficient
capital to compensate for a decrease in asset value (i.e., E/A < -ROA) (Roy, 1952). Following
Laeven and Levine (2009), we take the natural logarithm of the Z-score to normalize the
distribution.

3.2.2. Measures of CEO social networks
We obtain each CEO’s professional and educational background from the BoardEx database. The
information includes firms in which those CEOs have worked for, their roles in those firms, yearsof employment, the universities and degree programs they attended, and when they graduated.
Following Fracassi (2016), we map each CEO’s professional connections by carefully examining
his/her employment history. We consider a connection as a professional tie if a bank CEO andanother executive or board member (in another company) worked at the same company prior to11their current employment and both were top executives or directors at the previous company.3
Similarly, we consider a connection as an educational tie if a bank CEO and another executive or
director (from another company) graduated from the same school and the same degree program
within three years of each other (Cohen et al., 2008). We then count the total number ofprofessional ties, the total number of educational ties, and the total number of professional andeducational ties for each bank CEO. In addition, we take the natural logarithms of all threemeasures to normalize the distribution.

3.2.3. Control variables
Following the existing literature (Laeven and Levine, 2009; Pathan, 2009), we include a set ofcontrols that captures various bank characteristics. Log(bank size) is the natural logarithm of banktotal assets; Capital ratio is bank equity divided by bank total assets; Deposit ratio is total deposits
divided by bank total assets; Not rated is a dummy variable that equals 1 if a bank does not havelong-term bond ratings and is zero otherwise. We also control for a set of variables capturing
various CEO characteristics (Coles et al., 2006; Berger et al., 2014). Chairman is a dummy variable
that equals 1 if a CEO is also chairman of the board. CEO age is the natural logarithm of CEO age
in years. CEO tenure is the number of years that a CEO has been working for a particular bank.

3.3. Summary statistics
Table 1 reports the summary statistics of variables used in our regression analyses. The mean valueof Log(Z-score) is 2.86, which is comparable to numbers reported in prior studies of U.S. banks(e.g., Pathan, 2009). The mean of Total ties is about 10. Note that we take a conservative measure

To mitigate the concern that a CEO’s connections might include contacts at his or her current job, we exclude
connections at the CEO’s current employer (Fracassi, 2016).
3

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of CEO networks because we restrict social ties to top executives and board members of othercompanies. This approach is consistent with Fracassi (2016). The mean values of Log(Bank size),
Capital ratio, and Deposit ratio are 14.68, 0.9, and 0.74, respectively. In our sample, 84 percentof the banks do not have bond ratings, and 44 percent of the CEOs are also chairmen. The averagebank CEO in our sample is 57 and has an average tenure of approximately nine years.

[Insert Table 1 about here]

4. Empirical results
4.1. Baseline regression results
In table 2, we report our baseline regression results relating CEO social networks to bank risk.
Specifically, we model Log(Z-score) as a function of CEO social-network measures, along withvariables capturing various bank and CEO characteristics. In addition, we include year fixed
effects to absorb economy-wide shocks and timely trends that could affect bank risk. In all
regression models, we adopt cluster standard errors to control for the dependence of residuals overtime for the same bank (Petersen, 2009). In column 1 of table 2, we document a significantly
negative coefficient (-0.041) of Total ties (logged). To interpret the economic significance, itmeans that a 1 percent increase in total ties can lead to 0.041 percent decrease in Z-score.
We further decompose bank CEOs’ total network connections into Professional ties andEducational ties, and we report our results in column 2. We investigate whether one type of
network drives the negative relationship between CEO social networks and bank risk. We find asignificantly negative coefficient of Professional ties and an insignificant coefficient of13

Educational ties. The results thus indicate that professional connections convey valuable
information that encourages bank CEOs to take risks.
CEOs’ professional ties to other top executives and board members drive much of bankrisk. In column 3 of table 2, we include bank fixed effects to control for any time-invariant andunobservable heterogeneity across banks. Nonetheless, this alternative model specification does
not alter our results in a material way, and we still find a significant and negative coefficient for
Total ties.
With regard to other control variables, we find that banks with greater assets, higher capital,and higher deposit ratios are more stable. However, we also find that CEO age can affect risktaking decisions in different ways, and therefore the effects of CEO age and tenure on bank risk
are mixed (Berger et al., 1997; Coles et al., 2006). Our results based on the model specification
including bank fixed effects are more in line with Coles et al. (2006). Specifically, we find that
CEO age and tenure are indeed negatively associated with Z-score.

[Insert Table 2 about here]

4.2. Robustness checks
4.2.1. Alternative measures of bank risk
To ensure the robustness of our main findings, we employ several alternative measures of bankrisk and report our results in table 3. We measure Volatility of ROA as the standard deviation of
return on assets on a yearly basis using quarterly data. Similarly, we measure Volatility of ROE.
Following Houston et al. (2010), we calculate Idiosyncratic risk as the standard deviation of theresiduals from a single-index market model using yearly data. In columns 1–4 of table 3, we report14

our results using Volatility of ROA, Volatility of ROE, and Idiosyncratic risk as dependentmeasures, respectively. In all three model specifications, we include the same set of control
variables along with year fixed effects and bank fixed effects. Consistent with table 2, we find apositive and significant relationship between total social ties and bank risk.

[Insert Table 3 about here]

4.2.2. Alternative measures of social networks
In this section, we gauge each CEO’s social network centrality by using three standard measures,
namely degree centrality (number of direct ties one has), closeness (how close one is to others in
the network), and betweenness (the chance of linking any two people in the network). In addition
to the meaning of interconnectedness, network centrality indicates social status in the network.
Higher centrality measures (all three above) suggest more connections and greater power in a status
hierarchy (Freeman 1979; Burt, 1982).
According to graph theory, Degree centrality is the most straightforward measure ofwhether a node is in the central position — “in the thick of things” (Scott, 2000). It counts thenumber of direct ties a CEO has and then normalizes by dividing the maximum possible degrees
in an N-actor network in our sample. Betweenness captures the extent to which a node lies between
others and makes pass-through communications within the network. Hence, a person with highbetweenness is an important intermediary who connects other people as a “broker” or “gatekeeper”,
and is thereby central to the network. Closeness captures the degree to which an individual is near
all other individuals in a network. It is the inverse of the sum of the shortest distance between each
person and every other person in the network.

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Table 4 reports baseline regressions using three centrality measures as our independent
variables of interest and Log(Z-score) as the dependent variable. We find that all three centrality
measures have a negative and significant effect on Z-score, which suggests that centrally locatedCEOs are associated with lower bank stability and higher risk. This is consistent with our baseline
results, which use total ties as the measure of social networks.

[Insert Table 4 about here]

4.2.3. Alternative explanations
We perform several additional tests to rule out alternative explanations for our results. For instance,
Berger et al., (2014) find that banks with poor governance tend to take greater risks. Fracassi andTate (2012) find that social networks are associated with weak corporate governance. Therefore,
it is possible that our measures of social networks capture weak corporate governance. To address
this concern, we add a governance index (Gompers et al., 2003) in column 1 of table 5 and reestimate the regression.
Saunders et al. (1990) find that banks with larger management ownership exhibit higher
risk-taking behavior. We thus include Management holding, measured as percentage of shares heldby a bank CEO, as an additional explanatory variable in column 2 of table 5. In addition,
managerial compensation is an important mechanism for aligning managers’ incentives with thoseof shareholders to maximize shareholder value (Murphy, 1999). We thus add Vega, whichmeasures how sensitive bank CEO compensation is to stock-price volatility, in column 3 of table
5 to control for the possibility that CEO compensation packages drive bank risk-taking. It is also
possible that our measures of CEO social networks capture management quality (i.e., better CEOs16

have more social connections). Consequently, we add an index of general managerial ability(Demerjian et al., 2012) in column 4 of table 5. Including these additional controls based onalternative explanations has no material effect on our main findings.

[Insert Table 5 about here]

4.2.4. Difference-in-difference analysis
Although we include bank fixed effects in our baseline estimation to control for the potential
omitted-variable problem, an endogeneity issue arises from other possibilities. For example, timevariant unobservables may drive both CEO social networks and bank risk. In addition, given theinherent risk in the lending business, CEOs with more social connections may take more risks invarious bank activities in order to expand their networks. In other words, the existence of apotential feedback effect (i.e., reverse causality) prevents us from making causal inferences.
Therefore, following existing literature (Fracassi and Tate, 2012), we use the deaths andretirements of executives in bank CEOs’ networks as exogenous shocks to perform a differencein-difference (DID) analysis. The logic is that the size of a bank CEO’s network drops for reasons
not necessarily related to bank risk when a person connected to a bank CEO dies or retires in agiven year.
We identify a treatment group of banks affected by such events; unaffected banks compose
our control group. We first collect death or retirement information from BoardEx for all people inthe bank CEOs’ social networks, if any. We then match this information with our main sample.
This process allows us to identify whether any death or retirement events occurred in a CEO’s
network in a given year.

17Affected bank is a dummy variable that equals 1 if a bank is affected by a death or retirement
event and its CEO’s network size is reduced exogenously; the variable equals zero otherwise. We
then identify a matching bank for each treatment bank using a propensity-score-matching method(Irani and Oesch, 2014; Hasan et al., 2014). We run logistic regressions for each matching year
(i.e., death or retirement year), where the dependent variable is Affected bank and the independent
variables include all control variables in the baseline model.
According to the propensity score calculated from the logistic regression, we match eachbank in the treatment group with a bank in the control group without replacement. Following Hasan
et al. (2014), we use the caliper-matching method, where caliper refers to the difference in thepredicted probabilities between the treatment and matching banks. Using matching within a caliperof 10 percent, we identify 246 pairs of matched banks. We use a dummy variable, Post, whichequals 1 for each full fiscal year after the death or retirement of executives (or board members) ina bank CEO’s network, and zero otherwise. We use the standard DID model (see equation 1) togauge the effect of network shrink on subsequent bank risk:
Log(Z-score)i,t= β0+β1 Affected bank + β2 Post + β3 Affected bank× Post +Σβ (Bankcharacteristics and CEO characteristics)t-1 +εi,t(1)

In this DID model, the variable of interest is the interaction term Affected bank×Post. The
coefficient on this interaction term captures the difference-in-difference estimate in bank risk
between treatment and matching banks across the pre- and post-event sample periods. Given thattreatment banks have smaller networks in the post-event periods, we predict a positive coefficienton this interaction term, which indicates that reducing the size of social networks leads to decreasedbank risk (or increased bank stability).

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In table 6, we report our results using a bank-year sample spanning two one-year periods(column 1) before and after the event (i.e., a two-year window). For a robustness check, we also
provide results for four- and six-year windows in columns 2 and 3, respectively. We find asignificantly positive coefficient of 0.294 on the interaction variable in column 1. This finding
suggests that shrinking CEO social networks reduces bank risk in post-event periods. We findsimilar results when we use four- and six-year windows.

[Insert Table 6 about here]

4.3. Testing channels linking CEO social networks and bank risk-taking
4.3.1. Soft information channel
According to our information-based hypothesis, social networks provide private/soft informationthat may reduce the perceived uncertainty of investment projects, thus reducing CEOs’ risk
aversion and encouraging them to take more risks. If this were true, connections with more opaquefirms would provide bank CEOs with more soft information that is hard to collect in the market
and hence reduces their risk aversion to a larger extent.
To test this conjecture, we use two proxies to capture the information opacity of theconnected firms. The first proxy, % opaque connections_bid-ask spread, is constructed using bidask spread. In particular, we first identify the firms to which a focal bank CEO is connected; wethen obtain those firms’ stock bid-ask spreads from CRSP. A firm is opaque if its average daily
bid-ask spread over a year is higher than the sample median (Amihud et al., 1986). Based on thebid-ask spread definition, we calculate the proportion of firms in a CEO’s network that are opaque.

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Our second proxy, % opaque connections_forecast dispersion, is related to analyst forecast
dispersion, calculated as the standard deviation of EPS forecasts among analysts following a firmusing IBES data. A firm is opaque if its analyst forecast dispersion in a year is higher than thesample median. We then calculate the proportion of firms in a CEO’s network that are opaque
based on the analyst forecast dispersion. We expect that a higher proportion of opaque firms in a
network provides more soft and private information to CEOs.
In table 7, we replace our total network ties with these two proxies that capture the featuresof the connecting ties in our main regression. The negative and statistically significant coefficientsof those two proxies suggest that having a higher percentage of opaque firms in bank CEOs’
networks is associated with a higher level of bank risk. The findings support our hypothesis that
CEOs obtain valuable information through their social connections, which reduces their risk
aversion and allows them to make informed decisions.

[Insert Table 7 about here]

4.3.2. Job-market insurance channel
The job-market insurance hypothesis argues that CEOs take greater risks because their social
networks reduce their job-security concerns (Faleye et al., 2014). If this is true, then the positive
relationship between social networks and bank risk should be more pronounced in a worsening job
market. To test this conjecture, we use two proxies for CEOs’ alternative job opportunities. The
first is High turnover, which equals 1 if the CEO forced-turnover rate, obtained from Eisfeldt andKuhnen (2013), in a given year is higher than the sample median, and zero otherwise. A highforced-turnover rate among CEOs is often due to bad economic performance at both the firm and20

industry levels, which creates a tougher job market for CEOs. The second proxy is Highnoncompete. This measure captures the variations in the enforceability of noncompete agreementsthat restrict workers from joining or forming rival companies (Garmaise, 2009). We collect thenoncompetition index from Garmaise (2009). A higher value indicates fewer job alternatives. We
construct High noncompete, which equals 1 if the banks’ headquarters are in states where
noncompetition indices are higher (i.e., they are more restrictive) than the sample median, and zerootherwise.
In table 8, we interact our proxies with Log (Total ties). In both models, the first-ordereffects of Total ties on bank risk remain negative and significant. In addition, we consistently
document negative and statistically significant coefficients of the interaction terms between CEOsocial networks and our proxies for outside job options. The results suggest that CEO social
networks increase bank risk-taking more when the job market worsens and job-security insurance
benefits of networks increase, thus supporting the job-market insurance channel.

[Insert Table 8 about here]

4.3.3. Groupthink mentality channel
As discussed, social network connections may lead to groupthink and induce bankers to engage in
herd behavior regarding trendy investments without understanding the inherent risks. For thisreason, we expect CEOs with heterogeneous social networks to be less susceptible to groupthink.
We use two measures to proxy for the heterogeneity of social ties. The first is Low het_prof,
which equals 1 if professional heterogeneity is smaller than the sample median. Professional
heterogeneity is measured as the inverse Herfindahl index and based on different industries and21

different management expertise of individuals in the network. The second proxy is Low het_edu,
which equals 1 if educational heterogeneity is lower than the sample median. Educational
heterogeneity is measured as the inverse Herfindahl index, based on different educational
backgrounds in the network. Overall, a lower heterogeneity score indicates greater homophily.
We expect that the effect of social networks on bank risk is more pronounced when networkties are more homogeneous. To test this, we include two interaction terms, Log(Total tie) ×Low
het_prof and Log(Total ties) ×Low het_edu, in table 9. Across both columns, the coefficients on
Log(Total ties) remain significant and negative. In line with our expectations, we find that bothinteraction terms enter a significant and negative coefficient, which indicates that well-connected
CEOs are more likely to take risks when their network connections are homogeneous.

[Insert Table 9 about here]

4.4. The effects of social networks on bank performance
We adopt two measures to gauge bank performance: ROA and Profit margin. ROA is return on
assets, which captures accounting-based bank performance for a given year. Profit margin is net
income divided by total operating income and captures banks’ ability to pay expenses and generate
net income. The effect of social networks on bank performance also faces endogeneity concerns.
To mitigate omitted-variable bias and tease out the reverse effect of performance on social
networks, we run difference-in-difference estimations on bank performance. Our difference-indifference model specification is the same as equation (1), except that we use bank performancemeasures as dependent variables instead of Z-score. The event variable to examine before and afteris the death and retirement of executives in bank CEOs’ networks (Fracassi and Tate, 2012).

22

Affected bank is a dummy variable that equals 1 if a bank is affected by the death or retirement
event and its CEO’s network size is reduced exogenously; the variable equals zero otherwise. One
treatment group has exogenous shocks (Affected bank =1). The variable of interest is the interaction
term between Affected bank and Post. Post is equal to 1 for the years after the event. It is captured
using three alternative windows: [-1,1], [-2,2], and [-3,3] years.
Table 10 reports the difference-in-difference analysis results for bank performance. We
find that the coefficients on the interaction term Affected bank×Post across all models are positiveand statistically significant, which suggests that bank performance improves after the exogenous
size reduction of its CEO’s social network. Combining this finding with our previous finding onbank risk, we conclude that well-connected bank CEOs take greater risks but fail to generate good
returns (i.e., they demonstrate an inefficient risk-return trade-off). The result supports theperformance implications of the job-market insurance channel, which encourages CEOs to makeunsuccessful risky decisions when they have fewer concerns about job security.

[Insert Table 10 about here]

5. Summary and conclusions
Bank risk-taking behavior has long been the focus of academics, policy makers, and practitioners.
To promote a sound and safe banking system, regulators have implemented numerous measures
to encourage prudent bank practices. Academic researchers also strive to understand the causes
and consequences of bank risk-taking behavior. In this study, we use bank CEOs’ social networks
to explain variations in bank risk-taking behaviors and their associated financial consequences.
Our findings indicate that social connectedness encourages bank CEOs to take higher risks and23

leads to poor performance. Our results are robust to the inclusion of bank fixed effects, analysis
based on a difference-in-difference approach, and the adoption of various bank-risk measures. We
also perform tests to ensure that alternative explanations such as corporate governance, managerial
ownership, compensation, or CEO ability do not drive our main findings.
We explore several channels that link CEO social networks to bank risk. When we evaluate
the ways in which social networks affect bank risk, we find that the positive relationship between
CEO social networking and bank risk is particularly pronounced for CEOs who are connected withmore informationally opaque firms, homogeneous networks with higher odds of groupthinkmentality, and in worsening job markets. These findings are consistent with existing studies thatshow social networks promote the spread of private information and provide network members
more reemployment opportunities (Cohen et al., 2008; Liu, 2014).
In general, we present evidence that the higher risks associated with CEO social networks
do not translate into higher returns. This inefficient risk-return tradeoff reveals a “dark side” ofsocial networking. We find evidence that connections with a diverse group of individuals
significantly alleviate the dark side of social networking for CEOs who are too connected. Overall,
our findings suggest markets and regulators may underestimate bank risk if they do not consider
the social connections of bank CEOs.
Our study contributes to the literature in the following ways. First, we expand the literatureon the determinants of bank risk. Early research focuses on the role of industry competition (Boydand De Nicolo, 2005), monetary policies (Borio and Zhu, 2012), regulatory policies (Keeley, 1990),
ownership structure (Saunders et al., 1990), CEO compensation, and corporate governance(Houston and James, 1995; Fahlenbrach and Stulz, 2011; Berger et al., 2014). Also, Cai et al.
(2016) examine bank-loan syndication networks and find that interconnectedness is positively

24

correlated with systemic risk, highlighting the importance of understanding interbank connections.
Nonetheless, evidence is still scant regarding the effects of one of the socioeconomic
characteristics of bank executives on bank risk-taking. We add to this line of research by
identifying a robust positive relationship between CEO social networks and bank risk.
Second, our study provides new insight on the costs and benefits of corporate executive
connections. Researchers find that personal connections among managers and firms affect various
business activities and outcomes (e.g., Lee et al., 2014; Faleye et al., 2014; Engelberg et al., 2012;
Larcker et al., 2013). It is noteworthy that Houston, Lee and Suntheim (2018) find that bankdirectors’ networks facilitate the formation of joint syndicated loan deals and contribute tosystemic risks in the banking system. Different from their research design focusing on thesystemic-risk-contagion perspective, our study focuses on the individual bank risk driven by bankCEOs’ exposure to network information, employment options, and peer influence. Therefore, we
provide novel evidence that social networks play a significant role in explaining bank risk-taking
behavior.
Finally, our evidence on the inefficient trade-off between risk and return reveals the darkside of interconnectedness of financial firms. Prior studies explore the dark side of social
networking by examining CEO compensation and board independence in industrial sectors (e.g.,
Fracassi and Tate, 2012; Hwang and Kim; 2009). Our findings imply that the labor market
advantage provided by CEO social networks imposes a cost on shareholders because CEOs withlots of connections take greater risks but do not deliver better profitability. Echoing regulatory
concerns over firms “too connected to fail,” we emphasize the adverse effect of interconnectedness
among bank CEOs.

25

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Appendix A. Variable definitions and sources of data
Variable nameDefinition

Data source

Logarithm of Z-score, Z-score is computed as the sum of ROA and equity-to-assets
divided by the standard deviation of ROA of each bank over past three years.

Call report

The standard deviation of return on assets on a yearly basis using quarterly data.
The standard deviation of return on equity on a yearly basis using quarterly data.
The standard deviation of residuals from a single-index market model with weekly returnas the dependent variable and weekly market return as the independent variable (Houston
et al., 2010).
Return (net income) on assets.
Net income divided by total operating income.

Call report
Call report

Log(Total tie)
Log(Work tie)
Log(Education tie)
C. Bank characteristics

Logarithm of total number of social connections.
Logarithm of total number of social connections with past coworkers.
Logarithm of total number of social connections with schoolmates.

BoardEx
BoardEx
BoardEx

Log(Bank size)

Logarithm of total assets.

Call report

Capital ratio

Equity divided by total assets.

Call report

Deposit ratio

Deposit divided by total assets.
A dummy variable that equals 1 if the bank does not have a long-term bond rating, and 0
otherwise.

Call report

Chairman
CEO age
CEO tenure

A dummy variable that equals 1 if the CEO is also the chairman of the board.
Logarithm of CEO age.
Number of years the CEO has worked for this bank.

BoardEx
BoardEx
BoardEx

E. Alternative explanations
GindexManagerial holding
Vega

The Gompers et al. (2003) measure for the strength of shareholder rights.
Percentage of shares held by the bank CEO.
Sensitivity of CEO total compensation to stock price volatility.

General ability

Demerjian et al. (2012)'s measure of managers' general managerial ability.

A. Bank risk and return
Log(Z-score)
Volatility of ROA
Volatility of ROE
Idiosyncratic risk

ROA
Profit margin

CRSP
Call report
Call report

B. Network measures

Not rated
D. CEO characteristics

F. Variables for interaction terms% opaqueWe first identify the firms to which a focal bank CEO is connected. We then obtain thoseconnections_bid-ask
firms’ stock bid-ask spreads. A firm is firm if its average daily bid-ask spread over a year
spreadis higher than the sample median. We calculate the proportion of firms in a CEO’s networkthat are opaque based on the bid-ask spread definition.
% opaqueWe first identify the firms to which a focal bank CEO is connected. We then obtain thoseconnections_forecast
firms’ analyst forecast dispersions, calculated as the standard deviation of EPS forecasts
among analysts following a firm. A firm is opaque if its analyst forecast dispersion in ayear is higher than the sample median. We then calculate the proportion of firms in a
CEO’s network that have high analyst forecast dispersion.
High turnover
Equals 1 if the average CEO forced turnover rate in a given year is higher than the sample
median, and 0 otherwise.
High noncompete
Equals 1 if the state in which the bank is headquartered allows stricter noncompeteagreements than the sample median, and 0 otherwise.
Low het_prof
Equals 1 if professional heterogeneity is smaller than the sample median. Professional
heterogeneity is the inverse Herfindahl index based on different industries and different
management expertise of individuals in the network.
Low het_edu
Equals 1 if educational heterogeneity is smaller than the sample median. Educational
heterogeneity is the inverse Herfindahl index based on different educational backgrounds
in the network.

30

Risk Metrics
ExecuComp
ExecuComp
Demerjian et al.
(2012)CRSP

IBES
Risk Metrics
ThomsonFinancial 13 F
BoardEx
BoardEx
BoardExTable 1. Summary statistics
This table reports the summary statistics of bank risk measures, network measures, bank financial characteristics, CEOcompensation, and governance characteristics. Variable definitions are reported in appendix A.

Variable
Total ties
Professional ties
Education ties
Log(Z-score)Log(Bank size)
Capital ratio
Deposit ratio
Not rated
Chairman
CEO age
CEO tenure

N
4,077
4,077
4,077
4,077
4,077
4,077
4,077
4,077
4,077
4,077
4,077

Mean
9.95
3.46
2.39
2.86
14.68
0.09
0.74
0.84
0.44
56.96
8.52

Median
3.00
2.00
0.00
2.90
14.31
0.09
0.77
1.00
0.00
57
8.5

31

Standard deviation
24.58
4.72
14.1
1.06
1.63
0.04
0.13
0.36
0.50
7.24
7.28

P5
1
0.00
0.00
1.12
12.79
0.06
0.53
0.00
0.00
46.00
2.50

P95
39
11.00
7.00
4.46
17.93
0.14
0.88
1.00
1.00
68.00
19.50

Table 2. Baseline regression
The dependent variable of three model specifications is Log(Z-score). The variables are defined in appendix A. We
estimate models 1 and 2 with OLS; model 3 uses bank fixed effects. Robust standard errors are corrected for banklevel clustering and heteroskedasticity. T-statistics are in parentheses. The *, **, *** marks denote statisticalsignificance at the 10%, 5%, and 1% levels, respectively.
Intendent variables
Dependent variable: Log(Z-score)
(1)
(2)
(3)
Log(Total ties)-0.041**(-2.090)-0.053**(-2.032)

Log(Professional ties)-0.056**(-2.404)
0.002(0.103)0.049***(3.021)
3.477***(6.461)
0.521***(3.424)-0.091(-1.480)0.084**(2.449)0.053***(2.993)0.053***(3.004)-1.886(-1.531)
4,077
0.114
Y
N

Log(Educational ties)
Log(Bank size)
Capital ratio
Deposit ratio
Not rated
Chairman
CEO age
CEO tenure
Constant
Observations
Adjusted R-squaredYear fixed effect
Bank fixed effect0.048***(2.940)3.447***(6.402)0.509***(3.369)-0.094(-1.534)0.087**(2.552)
0.053***(2.994)0.053***(3.004)-1.859(-1.511)
4,077
0.114
Y
N

32

0.027
(0.576)4.915***(8.448)0.304(1.284)0.072(0.762)0.039(0.831)-0.046***(-2.808)-0.042***(-2.616)
4.259***(3.171)
4,077
0.452
Y
Y

Table 3. Alternative measurement of bank risk
The dependent variables are volatility of ROA, volatility of ROE, and idiosyncratic risk for the three model
specifications, respectively. The variables are defined in appendix A. We estimate all regressions with bank fixed
effects. We correct robust standard errors for bank-level clustering and heteroskedasticity. T-statistics are inparentheses. The *, **, *** marks denote statistical significance at the 10%, 5%, and 1% levels, respectively.
Variables
Log(Total ties)
Log(Bank size)
Capital ratio
Deposit ratio
Not rated
Chairman
CEO age
CEO tenure
Constant
Observations
Adjusted R-squaredYear fixed effect
Bank fixed effect(1)
Volatility of ROA(2)Volatility of ROE(3)
Idiosyncratic risk

0.004**(1.982)0.015***(2.991)-0.304***(-5.037)0.027(1.093)-0.016*(-1.684)-0.003(-0.631)0.019***(11.202)0.019***(11.275)-1.411***(-10.156)
4,077
0.814
Y
Y

0.011***(2.840)0.016*(1.789)-1.512***(-13.884)
0.030(0.679)0.017(0.980)-0.006(-0.627)
0.003(1.115)0.002(0.719)-0.289(-1.152)
4,077
0.326
Y
Y

0.012**(2.212)-0.023*(-1.858)
3.024***(19.426)-0.301***(-4.778)-0.017(-0.691)-0.006(-0.500)-0.004(-0.919)-0.004(-0.835)0.220(0.621)
4,077
0.669
Y
Y

33

Table 4. Alternative measurement of social networks
The independent variables are professional network’s degree centrality, closeness, and betweenness. The dependent
variable is Log(Z-score) for the three model specifications. The variables are defined in appendix A. We estimate all
regressions with OLS. We correct robust standard errors for bank-level clustering and heteroskedasticity. T-statistics
are in parentheses. The *, **, *** marks denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Variables
Degree(1)
Log(Z-score)(2)
Log(Z-score)-191.043**(-2.029)

Closeness-0.508*(-1.886)

Betweenness
Log(Bank size)
Capital ratio
Deposit ratio
Not rated
Chairman
CEO age
CEO tenure
Constant
Observations
Adjusted R-squaredYear effect
Bank fixed effect(3)
Log(Z-score)

0.054***(3.106)
3.158***(5.978)0.478***(3.052)-0.108*(-1.707)0.082**(2.362)0.038**(2.135)0.038**(2.146)-0.894(-0.723)
3,990
0.114
Y
N

0.053***(3.127)3.239***(6.142)
0.526***(3.333)-0.096(-1.519)0.082**(2.364)0.036**(2.067)0.037**(2.092)-0.836(-0.687)
3,990
0.114
Y
N

34

-206.826**(-2.049)
0.045***(2.815)
3.195***(6.023)0.516***(3.279)-0.105*(-1.663)0.086**(2.471)0.040**(2.237)0.040**(2.243)-0.965(-0.779)
3,990
0.114
Y
N

Table 5. Alternative explanations
The dependent variable is Log(Z-score) for the four model specifications. The variables are defined in appendix A.
We estimate all regressions with OLS. We correct robust standard errors for bank-level clustering andheteroskedasticity. T-statistics are in parentheses. The *, **, *** marks denote statistical significance at the 10%, 5%,
and 1% levels, respectively.
Variables
Log(Total ties)
Gindex(1)
Log(Z-score)(2)
Log(Z-score)(3)
Log(Z-score)(4)
Log(Z-score)-0.058**(-2.000)
0.003(0.306)-0.065**(-1.969)-0.066**(-2.017)-0.149*(-1.735)

Management holding-0.072***(-5.718)

Vega

0.0001(1.343)General ability
Log(Bank size)
Capital ratio
Deposit ratio
Not rated
Chairman
CEO age
CEO tenure
Constant
Observations
Adjusted R-squaredYear fixed effect
Bank fixed effect

0.060**(2.052)
1.121(0.948)0.232(0.830)-0.044(-0.597)0.243***(3.992)0.006(0.330)0.014(0.780)7.482***(4.159)
1,150
0.122
Y
N

0.096***(2.833)0.508(0.374)0.496(1.644)-0.136(-1.604)
0.269***(3.752)-0.008(-0.443)0.004(0.222)1.352(0.935)
972
0.122
Y
N

35

0.083**(2.154)0.747(0.529)0.488(1.608)-0.145*(-1.684)
0.262***(3.584)-0.009(-0.459)
0.005(0.248)1.518(1.021)
966
0.104
Y
N

0.244*(1.942)-0.270(-1.100)
2.810(0.581)0.148(0.141)-0.925***(-4.070)0.251*(1.711)-0.005(-0.154)-0.002(-0.061)
8.349**(2.004)
499
0.259
Y
N

Table 6. Difference-in-difference on bank riskThis table presents the results of DID tests on how exogenous shocks to social networks affect bank risk. The
dependent variable is Log (Z-score). The variables are defined in appendix A. We estimate all regressions with OLS
with difference-in-difference specifications. We correct robust standard errors for bank-level clustering andheteroskedasticity. T-statistics are in parentheses. The *, **, *** marks denote statistical significance at the 10%, 5%,
and 1% levels, respectively.
(1)
(2)
(3)
Variables
Two-Year Window
Four-Year Window
Six-Year Window
Affected firm*Post
Affected bank
Post
Log(Bank size)
Capital ratio
Deposit ratio
Not rated
Chairman
CEO age
CEO tenure
Constant
Observations
Adjusted R-squaredYear fixed effect
Bank fixed effect0.294**(2.184)-0.167*(-1.697)-0.208**(-2.075)-0.049(-1.282)
2.112(1.166)0.065(0.198)-0.312**(-2.378)
0.216***(2.803)0.053(0.878)0.060(0.994)-0.353(-0.088)
492
0.043
Y
N

36

0.166*(1.718)-0.011(-0.164)-0.160**(-2.328)-0.012(-0.469)2.070***(3.452)0.135(0.682)-0.164*(-1.794)0.161***(2.994)0.075(1.521)0.076(1.558)-2.399(-0.737)
1,044
0.080
Y
N

0.149*(1.784)-0.045(-0.787)-0.190***(-3.130)-0.014(-0.633)
1.815***(3.469)0.113(0.672)-0.123(-1.608)0.171***(3.747)0.072**(2.278)0.076**(2.435)-1.989(-0.946)
1,454
0.086
Y
N

Table 7. Information channel
The dependent variable is Log(Z-score). The variables are defined in appendix A. We estimate all regressions withbank fixed effects. We correct robust standard errors for bank-level clustering and heteroskedasticity. T-statistics arein parentheses. The *, **, *** marks denote statistical significance at the 10%, 5%, and 1% levels, respectively.
(1)
Log(Z-score)

Variables% opaque connections_bid-ask spread(2)
Log(Z-score)-0.066*(-1.868)% opaque connections_forecast dispersion
Log(Bank size)
Capital ratio
Deposit ratio
Not rated
Chairman
CEO age
CEO tenure
Constant

Observations
Adjusted R-squaredYear effect
Bank fixed effect

37

0.028
(0.587)4.904***(8.430)0.284(1.195)0.077(0.809)0.043(0.900)-0.046***(-2.785)-0.042***(-2.611)
4.239***(3.158)-0.127***(-4.102)0.026(0.557)5.044***(8.672)0.319(1.347)0.046(0.486)0.046(0.978)-0.045***(-2.741)-0.042**(-2.572)
4.253***(3.176)

4,077
0.453
Y
Y

4,077
0.455
Y
Y

Table 8. Job-security channel
The dependent variable is Log(Z-score). The variables are defined in appendix A. We estimate all regressions withbank fixed effects. We correct robust standard errors for bank-level clustering and heteroskedasticity. T-statistics arein parentheses. The *, **, *** marks denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Variables
Log(Total ties)
Log(Total ties) × High turnoverHigh turnover(1)
Log(Z-score)(2)
Log(Z-score)-0.052**(-1.997)-0.049**(-2.020)0.170***(3.108)-0.051*(-1.945)

Log(Total ties) × High noncompete
High noncompete
Log(Bank size)

0.029(0.618)4.940***(8.493)0.298(1.258)0.067(0.709)0.039(0.834)-0.046***(-2.783)-0.042***(-2.581)4.050***(3.019)
4,077
0.453
Y
Y

Capital ratio
Deposit ratio
Not rated
Chairman
CEO age
CEO tenure
Constant
Observations
Adjusted R-squaredYear effect
Bank fixed effect38-0.057*(-1.859)
0.066(0.840)0.029(0.616)4.933***(8.478)0.314(1.323)0.070(0.739)0.038(0.799)-0.045***(-2.720)-0.041**(-2.514)4.132***(3.073)
4,077
0.453
Y
Y

Table 9. Group-think channel
The dependent variable is Log(Z-score). The variables are defined in appendix A. We estimate all regressions withbank fixed effects. We correct robust standard errors for bank-level clustering and heteroskedasticity. T-statistics arein parentheses. The *, **, *** marks denote statistical significance at the 10%, 5%, and 1% levels, respectively.
Variables
Log(Total ties)
Log(Total ties) × Low het_prof
Low het_prof(1)
Log(Z-score)(2)
Log(Z-score)-0.072*(-1.846)-0.175***(-2.657)
0.051(0.915)-0.068*(-1.716)

Log(Total ties) × Low het_edu
Low het_edu
Log(Bank size)

0.063(0.852)9.288***(10.173)
0.341(0.920)-0.159(-1.159)
0.104(1.453)-0.037(-1.641)-0.029(-1.322)3.212*(1.647)
3,075
0.349
Y
Y

Capital ratio
Deposit ratio
Not rated
Chairman
CEO age
CEO tenure
Constant
Observations
Adjusted R-squaredYear fixed effect
Bank fixed effect39-0.025(-0.402)-0.088(-1.570)
0.095(1.373)8.934***(10.276)
0.347(1.005)-0.124(-0.917)0.040(0.594)-0.030(-1.316)-0.024(-1.050)
2.285(1.195)
3,470
0.351
Y
Y

Table 10. Difference-in-difference on bank performance
The dependent variables are bank ROA and profit margin, respectively. The variables are defined in appendix A. We
estimate all regressions with OLS with difference-in-difference specifications. We correct robust standard errors forbank-level clustering and heteroskedasticity. T-statistics are in parentheses. The *, **, *** marks denote statisticalsignificance at the 10%, 5%, and 1% levels, respectively.
(1)
ROA

(2)
ROA

(3)
ROA

Variables

Two-yearwindow

Four-year
window

Affected firm*Post

0.004*(1.901)-0.003(-1.465)-0.001(-0.449)-0.001(-1.567)
0.168***(4.861)-0.013***(-2.731)-0.004*(-1.932)
0.002(1.090)0.000(0.226)0.000(0.335)-0.006(-0.071)
493
0.182
Y

0.004**(2.115)-0.006***(-3.943)-0.002(-1.299)-0.001***(-2.700)
0.179***(10.117)-0.013***(-4.263)-0.006***(-3.687)
0.002**(2.117)-0.000(-0.166)-0.000(-0.081)0.030(0.437)
1,044
0.250
Y

Post
Affected firm
Log(Bank size)
Capital ratio
Deposit ratio
Not rated
Chairman
CEO age
CEO tenure
Constant
Observations
Adjusted R-squaredYear effectSix-yearwindow(4)
ProfitmarginTwo-yearwindow(5)
ProfitmarginFour-year
window(6)
ProfitmarginSix-yearwindow

0.003**(2.272)-0.006***(-5.270)-0.001(-1.490)-0.001***(-3.414)0.198***(10.404)-0.013***(-4.996)-0.006***(-4.587)
0.002**(2.126)-0.000(-0.393)-0.000(-0.313)0.039(0.775)
1,455
0.375
Y

0.106**(2.191)-0.052*(-1.704)-0.010(-0.356)-0.022**(-2.042)3.019***(3.444)-0.312***(-2.596)-0.044(-1.224)0.053*(1.809)0.007(0.354)0.010(0.526)-0.126(-0.098)
493
0.125
Y

0.105***(2.902)-0.114***(-4.248)-0.024(-1.134)-0.013*(-1.663)1.861**(2.528)-0.157**(-2.187)-0.066**(-2.478)0.049**(2.412)0.024(0.921)
0.025(0.979)-1.326(-0.776)
1,044
0.127
Y

0.090***(3.147)-0.111***(-5.175)-0.015(-1.035)-0.008(-1.387)
1.232***(2.955)-0.094*(-1.873)-0.056***(-2.729)
0.036**(2.337)0.009(0.617)0.010(0.666)-0.428(-0.430)
1,455
0.130
Y

40