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Bank Behavior and Social Capital

Marcia Millon Cornetta*, Kristina Minnick a, Patrick J. Schornob, Hassan Tehranianc*

a Department of Finance, Bentley University, Waltham, MA 02452 USA

b Ally Financial, Charlotte, NC 28202 c Carroll School of Management, Boston College, Chestnut Hill, MA 02467 USA

January 2018

Abstract

This paper examines the performance and policies of banks located in high social capital areas.

Results show that higher levels of social capital are associated with safer, more profitable, yet less

capitalized banks. Additionally, banks in areas with higher social capital display lower likelihoods

of default and failure, yet higher likelihoods of receiving capital under the TARP Capital Purchase

Program. Examining the relationship between social capital and bank/consumer relations, we find

that banks in higher social capital areas pay more interest and charge fewer fees on deposits, and

charge lower rates and fees on loans. The results suggest that banks located in higher social capital

areas take actions that promote trust and therefore enable working relationships that have

productive benefits for both banks and consumers.

Keywords: Financial institutions, social capital

JEL Classification: A13, G01, G21, D14, D71, Z13

* Corresponding author. Tel.: +1 617-552-3944.

E-mail addresses: mcornett@bentley.edu (M.M. Cornett), kminnick@bentley.com (K. Minnick),

patrick.schorno@ally.com (P.J. Schorno), hassan.tehranian@bc.edu (H. Tehranian).

The views expressed in this paper are those of the authors and do not necessarily reflect those of

Ally Financial.

Acknowledgements: The authors are grateful to Ian Appel, Otgo Erhemjamts, Ali Fatemi, Iraj

Fooladi, Pouyan Foroughi, Rawley Heimer, Saeid Hoseinzadeh, Qian Jun, Oguzhan

Karakas, Shahriar Khaksari, Samer Khalil, Len Kostovetsky, Vladimir Kotonin, Alan Marcus,

Hamid Mehran, Mahdi Mohseni, Cal Muckley, Ali Ebrahim Nejad, Jordan Nickerson, Vinh

Nguyen, Ronnie Sadka, Assem Safeeddine, Yao Shen, Phil Strahan, and Anand Venkateswaran

for their helpful comments.

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Bank Behavior and Social Capital

1. Introduction

Social capital can broadly be defined by the links, shared values, and understandings in

society that promote trust and therefore enable working relationships that have productive

benefits. Within the academic literature, much attention has been paid to the impact of social

capital on the performance of local and national governments (e.g., Putnam, 1993; Laporta et al.,

1997; Knack and Keefer, 1997). However, more recent literature has focused on the relationship

between social capital and financial transactions, with analysis indicating that there exists a bi-

directional relationship between borrowers and lenders driven by trust. The information

asymmetry inherent within the relationship between banks and their consumers can potentially

lead to moral hazard if bank decision-making and policy setting is driven in any part by

characteristics not directly associated with consumer creditworthiness. By studying the relation

between social capital and bank decision-making, we document the relation between social

capital and bank behavior, as measured by regulatory risk ratios, capitalization, bank default risk

and failure, loan performance, deposit rates and fee structures, and loan income.

We use two broad measures of social capital: the Putnam Index and Social Capital

County. The Putnam Index is computed using principal components analysis on a set of fourteen

different factors of associational activities (e.g., number of club memberships, amount of

volunteering and participation in Presidential elections, attendance at political meetings, and

participation in election campaigns (Putnam, 1993 and 1995)). Social Capital County is a county

level, survey-based measure of social capital from Rupasingha and Goetz (2006, 2008) which

includes variables representing membership organizations at the county level (e.g., civic

organizations, bowling centers, golf clubs) and associational activities (percent of the voting

eligible population in each county who voted in presidential elections, county-level response

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rates to Census Bureau’s decennial census, and per capita non-profit organizations). We also

include social controls for crime, education, and church attendance. Our bank data set includes

all U.S. banks with data available from 2000 through 2015. We first examine bank behavior

using risk measurement ratios used by federal regulators to assess bank risk. To control for

endogeneity, we use a two-stage least squares approach in which we first endogenize the social

capital measures (using distance of a bank’s headquarters from the Canadian border and voter

turnout as instruments) and then regress the dependent variable of interest on the fitted index and

controls for bank size, balance sheet composition, loan performance and reserves, and liquidity.

Our results suggest that higher levels of social capital are associated with safer, more

profitable, yet less capitalized banks. Additionally, banks in areas with higher social capital

display lower likelihoods of financial distress and failure. Aligned with lower capitalization

levels, we find a positive and significant relationship between social capital and the likelihood of

the receipt of capital under the TARP Capital Purchase Program, as well as the likelihood of

having a positive return on assets, particularly during the financial crisis. To complete the

analysis, we examine the relationship between social capital and bank loan rates and deposit

rates and fees. We find that banks in higher social capital areas pay more interest and charge

fewer fees on deposits and charge lower rates and fees on loans. The results suggest that banks

located in areas with higher social capital take actions that promote trust and therefore enable

working relationships that have productive benefits for both the banks and consumers.

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

and motivation. Section 3 discusses the data. Section 4 presents methodology and results.

Finally, Section 5 concludes the paper.

2. Related Literature and Motivation

The idea of quantifying social capital first started in the sociology literature. Jacobs

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(1961), Coleman (1990), and Burt et al. (2009) define social capital as social and network ties

that benefit an individual. Putnam (1993) widens the definitions to classify social capital as

“networks, norms, and trust that enable participants to act together more effectively to pursue

shared objectives.” Knack and Keefer (1997) and Guiso et al. (2004) use the definition of social

capital from Putnam (1993) and conclude that social capital based off trust is essential to well-

functioning societies and the economic progress of those societies. This literature suggests that

social trust and other social capital aspects play an important role in economic performance and

may potentially lead to socially efficient outcomes and reduced information asymmetry. One of

the mechanisms through which social capital impacts economic efficiency is by enhancing the

prevailing level of trust. For example, Guiso et al. (2004) find that the effect of social capital is

more pronounced among less educated people, who need to rely more on trust because of their

limited understanding of contracting mechanisms. In high social capital communities, people

may trust each other more because the networks in their community provide better opportunities

to punish deviants. At the same time, in these communities people may rely more on others

keeping their promises because of the moral attitude imprinted with education.

Research also examines mechanisms by which social capital generates trust needed for

financial transactions. For example, Guiso et al. (2004) suggest that a high level of social capital

promotes participation of individuals in financial transactions. Guiso et al. (2008) find that less

trusting individuals are less likely to buy stock and, conditional on buying stock, they buy less.

Allen et al. (2016) find that well-governed firms that suffer less from agency concerns engage

more in activities that improve social good. Carlin et al. (2009) find that when the value of social

capital is high, government regulation and trustfulness are substitutes. In this case, government

intervention may actually cause lower aggregate investment and decreased economic growth. In

contrast, when social capital is low, regulation and trustfulness may be complements. Finally,

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Philipp (2015) shows that investors respond strongly negatively to negative events concerned

with a firm's social activities and weakly negatively to positive events.

El-Attar and Poschke (2011) find that households with less trust invest more in housing

and less in risky financial assets. Georgarakos and Pasini (2011) show that specific trust in

advice given by financial institutions represents a prominent factor for stock investing compared

to other tangible features of the banking environment. Finally, Duarte et al. (2012) use

photographs of potential borrowers from a peer-to-peer lending site and find that borrowers who

appear more trustworthy have higher probabilities of having their loans funded, better credit

scores, and default less often. They conclude that impressions of trustworthiness matter in

financial transactions.

More recently, Gupta et al. (2018) examine U.S. firms and find evidence that the implied

cost of equity is lower for firms with headquarters in areas with high social capital. Ostergaard et

al. (2016) find that stakeholder-oriented savings banks located in communities with high social

capital have a higher probability of survival, but no similar effect exists for equity holder-owned

commercial banks. The results are a function of the level of trust savings banks engender and the

level of civic engagement to which they commit in the communities they serve. The authors also

find that social capital is positively related to altruistic bank behaviors. Jin et al. (2017) find that

banks in high social capital areas experience fewer failures and less financial trouble during the

2007-2010 financial crisis. Additionally, they find that banks in high social capital areas are

more stable, as indicated by decreased risk-taking and increased accounting transparency and

conservatism. Lins et al. (2017) find that firms that entered the financial crisis with higher social

capital earned higher stock returns and experienced higher margins, sales growth, and sales-per-

employee, relative to firms with lower social capital. During the financial crisis, a time

characterized by an erosion of trust in firms, markets, and institutions, a firm’s social capital, and

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the trust that it engenders, paid off. Finally, Hasan et al. (2017) find that banks headquartered in

U.S. counties with higher levels of social capital incur lower bank loan spreads and conclude that

social capital is perceived by debtholders as a means to constrain opportunistic behavior.

In contrast to papers that document beneficial results of high social capital, some research

finds that higher social capital and trust may provide a feeding ground for self-serving behavior.

Knack and Keefer (1997) provide evidence for conflicting influences of social capital and

economic performance. That is, higher social capital provides individuals a way to capture

private benefits at the expense of society in general. Similarly, Olson (1982) finds that networks

can hurt economic performance because groups can act as lobbyists for their own causes and

that, in general, may impose costs on society. Applying this finding to banks, there is the

possibility that banks may take advantage of high social capital to capture private benefits, which

may enhance the bank’s profits.

The main business of depository institutions is to accept deposits from the public and

create credit for a community. Since trust among members in a community is an important part

of social capital, social capital should affect both the behavior of borrowers and the behavior of

lenders, particularly with respect to their capital, loan rates, and deposit rate and fees decisions.

For example, credit unions are depository institutions which place strong emphasis on building

social capital and empowering both their customers (who are also owners) and the local

community in which they are based. Credit unions exist primarily to serve their members with

higher interest rates on deposits. They also charge lower interest rates than banks on different

consumer loans such as mortgages, auto loans, and home equity lines of credit. Indeed, in 2008

and 2009, industry net income was negative for commercial banks, whereas, industry return on

assets remained positive for credit unions.

Like credit unions, social capital may reduce the cost of financial contracts for

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commercial banks, which may increase banks’ profits. Putnam (1993) suggests social capital

helps build stakeholder trust and cooperation. According to Arrow (1972), activities that require

agents to rely on the future actions of others are accomplished at lower cost in higher trust

environments. Coleman (1990) and Spagnolo (1999) suggest that individuals in high social

capital areas make additional efforts to honor contracts because there is a high cost of violating

the contracts. This research suggests that borrowers in high social capital areas may be less likely

to default on loans, which would increase bank profit and may also reduce bank risk. As a result,

banks in areas with high social capital may be able to operate with lower capital ratios because

their borrowers are less likely to default. Likewise, because customers make additional efforts to

honor contracts, banks in areas with high social capital may be less prone to financial distress

and remain profitable during times of financial distress. High levels of social capital may also

induce bank managers to be less selfish and more publicly minded (i.e., pay higher interest rates

on deposits and charge lower interest rates on loans).

Given findings of previous research, we examine the degree to which banks in high social

capital areas pursue or fail to pursue policies that enhance social capital. Specifically, we

document the relation between social capital and bank behavior, measured through regulatory

risk measurement ratios, loan performance, bank risk, bank failure, loan rates, and deposit rate

and fee structures.

3. Data and Univariates

Data used in the analysis come from a number of sources. All variables used in the

analysis are defined in Appendix A. We build a quarterly panel data set for the period 2000

through 2015 that includes all commercial banks. Quarterly financial statement data for financial

institutions are obtained from the Consolidated Financial Statements for Bank Holding

Companies (FR Y-9C) database from Federal Financial Institutions Examination Council

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(FFIEC). Table 1 presents the sample breakdown of quarterly bank observations by state. We

find a relatively even distribution across states with Illinois having the largest percentage of

observations (8.36%) and only Alaska and Hawaii containing no observations.

As mentioned above, we use two measures of social capital: the Putnam Index and Social

Capital County. The Putnam Index uses 14 associational activity measures (including number of

club memberships, amount of volunteering and participation in Presidential elections, attendance

at political meetings, and participation in election campaigns) to produce a state-level composite

index of social capital in the United States. Putnam’s (1993) principal component analysis

constructs an index as a weighted sum of each of the components; calculating weights on each of

the components that maximize the total sum of the squared correlations between the composite

variable and the components. Thus, higher weights are given to components that are more highly

correlated with each other and outlier components get lower weights. We collect Putnam Index

data from the Bowling Alone database, www.bowlingalone.com. We match sample banks to the

Putnam Index based on the state in which the bank is headquartered.

Rupasingha et al. (2006) and Rupasingha and Goetz (2008) develop a county-based

model of social capital covering the entire United States. They use a data set from the County

Business Patterns (CBP), compiled by the Census Bureau, which includes an extensive and

comprehensive set of variables representing membership organizations at the county level (e.g.,

civic organizations, bowling centers, golf clubs). In addition to associational activities, they

include the percent of the voting eligible population in each county who voted in presidential

elections, county-level response rates to Census Bureau’s decennial census, and per capita non-

profit organizations from the National Center for Charitable Statistics. Based on principal

component analysis, they create overall social capital indices from these data for the years 1990,

1997, and 2005 (they later add 2009 to the database). The first principal component is interpreted

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as the index of social capital (hereafter called Social Capital County). We collect this data from

Penn State University’s Northeast Regional Center for Rural Development, http://aese.psu.edu/

nercrd.1 We match sample banks to the Social Capital County based on the county in which the

bank is headquartered.

Table 2 presents descriptive statistics for the social capital indexes (Putnam Index and

Social Capital County). The mean (median) value for the Putnam Index is -0.141 (-0.216) and

ranges from -1.15 to 1.29. The mean (median) value for Social Capital County is -0.173 (-0.230)

and ranges from -1.71 to 1.74. These values are similar to Gupta et al. (2018), although they are

slightly lower than Hasan et al. (2017). Higher values for both measures indicate higher levels of

social capital.

Social capital control variables include percent of population that attend church, percent

of population affected by reported crime in a given year, and percent of high school graduates—

all variables are measured as percent in the state in which a bank is headquartered. Putnam

(1993) states that communities and regions rich in social capital suffer less crime, educate their

children better, have higher church attendance, and have more smoothly functioning economies.

Previous research has documented religious engagement as one factor that contributes to overall

levels of social capital in a community (e.g., King and Furrow, 2004; Smith, 2003). Hilary and

Hui (2009) find that firms located in counties with higher levels of religiosity display lower

degrees of risk exposure, exhibit a lower investment rate, and have less growth, but generate a

more positive market reaction, when they announce new investments. More specific to banking,

Adhikari and Agrawal (2016) find that banks headquartered in more religious areas exhibit lower

stock return volatility, lower tail risk, and lower idiosyncratic risk. Akçomak and ter Weel (2012)

1 Social Capital County data are available for the years 1990, 1997, 2005, and 2009. We follow existing literature

and fill in gap years using the most recent values available.

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find that higher levels of social capital are associated with lower crime rates. In the context

of education, social capital in the forms of parental expectations, obligations, and social networks

that exist within the family, school, and community are important for student success (Helliwell

and Putnam, 1999). Pevzner et al. (2015) find that investor reactions to earnings announcements

are significantly higher in more trusting countries. They also find that the positive effect of

societal trust on investor reactions to earnings news is more pronounced when a country's

investor protection and disclosure requirements are weaker (suggesting that trust acts as a

substitute for formal institutions), and when a country's average education level is lower

(consistent with less educated people relying more on trust in making economic decisions).

We collect county-level data for percent of population that attend church from the

Association of Statisticians of American Religious Bodies, http://www.thearda.com, and percent

of population affected by reported crime in a given year from the Uniform Crime Reporting

Statistics, http://www.ucrdatatool.gov. State-level data on percent of high school graduates is

collected from the U.S. Census Bureau, http://www.census.gov. Table 2 presents descriptive

statistics on the variables. On average, 60.30% of the population attends church, 7.40% is

affected by crime, and 19.53% graduate high school. Table 3 presents a correlation matrix

among the social capital variables. Confirming previous research, there is a negative correlation

between social capital and reported crime (-39.53% using the Putnam Index and -34.53% using

Social Capital County, both significant at 1%) and a positive correlation between social capital

and education (26.98% using the Putnam Index, significant at 1%, and 1.90% using Social

Capital County, insignificant). Correlations between social capital and church attendance are

mixed: -1.35% for the Putnam Index and 3.45% for Social Capital County. However, neither is

significant.

FR Y-9C reports include data for risk measurement metrics: loan loss provision/total

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loans, total loan net charge-offs (NCOs)/total loans, pre-provision net revenue (PPNR), and Tier

1 risk-based capital (RBC) ratio. Loan loss provisions, pre-provision net revenue, and Tier 1

risk-based capital ratios are three of the five ‘risk measurement metrics’ now used by regulators

to determine capital adequacy (as part of the Comprehensive Capital Analysis and Review

(CCAR) program). 2 Loan loss provisions and total loan NCOs are components of loan losses

recorded by a bank in a given quarter. Loan loss provisions are the expected losses on the loan

portfolio that are recognized in the quarter, while NCOs are any additional losses or recoveries

received when a bad loan is finally removed from a bank’s balance sheet. The two measures

together are a major driver of bank losses each quarter and are used by regulators to project net

income. However, they are excluded from PPNR. Thus, we include them as dependent variables

for regression analysis. The risk measures are calculated from bank financial statements as:

1. Loan loss provisions/total loans

2. Total loan NCOs/total loans

3. PPNR/total assets = (net interest income + noninterest income – noninterest

expense)/total assets

4. Tier 1 RBC ratio = Tier 1 capital/risk-weighted assets

Data reported in Table 2 suggest fat-tailed distributions for the risk measurement metrics.

For example, the mean (median) loan loss provisions/total loans ratio for the sample is 0.31%

(0.10%), total loan NCOs/total loans ratio is 0.30% (0.10%), PPNR/total assets ratio is 8.30%

(4.20%), and Tier 1 risk-based capital (RBC) ratio is 12.80% (12.00%). The values in our sample

are aligned with regulatory expectations for capital adequacy: 12.00% Tier 1 RBC ratio.

To examine the relation between social capital and productive benefits for both banks and

consumers, we collect data on bank deposit rates and fees, and loan rates and fees from FR Y-

9C’s. Reported in Table 2, the mean (median) interest expense on core deposits/core deposits is

2 CCAR also includes the Tier 1 leverage ratio and total RBC ratio. The paper reports only results using the Tier 1

RBC ratio. Results using the other CCAR capital ratios are similar and lead to the identical conclusions.

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5.60% (2.44%). Deposit interest rate and fee variables in our sample include automated teller

machine (ATM) fees/core deposits and check income/core deposits. Table 2 shows means

(medians) for the two measures are 0.09% (0.00%) and 0.01% (0.00%), respectively. On the

lending side, we look at loan fee and interest income/total loans: the mean (median) value for the

sample is 4.36% (4.26%). Combining interest income and interest expense, we calculate net

interest income (total income on investment securities and loans minus total interest

expense)/total loans: the mean (median) value for the sample is 3.19% (3.00%).

An alternative source of income to consumer-oriented loan interest and fees is noninterest

income, which results from on- and off-balance-sheet activities. Noninterest income has become

increasingly important to banks as the ability to attract core deposits and high-quality loan

applicants becomes more difficult. Included in this category is income from fiduciary activities

(for example, earnings from operating a trust department), trading revenues (gains [losses] and

fees from trading marketable instruments and off-balance sheet (OBS) derivative instruments),

fees from other-than-banking activities (such as security brokerage, investment banking, and

insurance), servicing fees (from mortgages, credit cards, and other assets), and gains and losses

from the sale of investment securities. The mean (median) noninterest income/total income ratio

for the sample is 2.45% (0.67%).

Additionally, from FR Y-9C’s we collect data on bank-specific independent variables

used to control for operating differences between banks. These measures include total assets,

loan performance (nonperforming loans/total loans and loan loss reserve/total loans), portfolio

composition controls (percent of total loans for commercial and industrial, agricultural,

consumer, foreign government, real estate, and depository institution), and liquidity ratio (cash

and investment securities/total assets). Table 2 shows the descriptive statistics for the financial

statement variables. The average bank in the sample has approximately $9.10 billion in assets

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(ranging from $0.18 million to $2.57 trillion).

The mean value of nonperforming loans (which includes loans past due 90 days or more

and still accruing interest and loans in nonaccrual status)/total loans for the sample banks is

1.40% (0.70% median). Nonperforming loans are still listed on a bank’s balance sheet as an

asset. The reserve for loan losses is a contra asset account that serves as an estimate by the

bank’s management of the amount of gross loans that will not be repaid to the bank. The reserve

for loan losses is an accumulated reserve that is adjusted each period as management recognizes

the possibility of additional bad loans and makes appropriate provisions for such losses.

Although tax laws influence the maximum amount of the reserve, the bank’s management

actually sets the level based on loan growth and recent loan loss experience. The mean (median)

loan loss reserve/total loans ratio for the sample is 1.50% (1.30%).

The distribution of banks’ loan portfolio shows that real estate loans are the most

predominant (mean is 26.40%, ranging from 0.26% to 89.49%), followed by commercial and

industrial loans (mean 15.90%, ranging from 3.31% to 34.38%) and consumer loans (mean

7.50%, ranging from 0.29% to 22.53%). Finally, a bank’s ability to absorb losses is also affected

by the amount of liquid assets held. To reduce liquidity risk, banks hold cash and other liquid

assets as part of their overall management strategy. We include a liquidity ratio (cash and

investment securities/total assets) in our analysis. Table 2 shows that banks’ average liquidity

ratio is 23.20% over the sample period, ranging from 5.95% to 46.12%.

4. Methodology and Results

The econometric approach of this paper is two-fold. First, to understand which elements

of social capital may drive bank behavior, we estimate regressions which establish relationships

between dependent variables and measures of social capital. Specifically, we use OLS regression

analyses to examine regulatory risk measures as dependent variables and social capital variables

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and controls as independent variables, with bank-specific independent variables to control for

operating differences between banks. If social capital reduces the cost of financial contracts, and

thus increases banks’ profits, we expect banks in areas with higher social capital to have lower

loan losses, higher profit, and lower capital ratios.

We examine versions of the following regression:

𝑅𝑖𝑠𝑘 𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑖,𝑡 = ∝ +𝛽1 ∗ (𝑆𝑜𝑐𝑖𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖,𝑡) + 𝛽2 ∗ (𝑆𝑜𝑐𝑖𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡)

+ 𝛽3 ∗ (𝐵𝑎𝑛𝑘 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖,𝑡) + 𝜀 (1)

In all regressions in the paper, bank and year fixed effects control for unobserved heterogeneity

within the variables. Risk measures include loan loss provision/total loans, total loan NCOs/total

loans, PPNR/total assets, and Tier 1 RBC ratio. As noted above, loan loss provision/total loans,

PPNR/total assets, and Tier 1 RBC capital ratios are now used by regulators to determine capital

adequacy. Further, loan loss provisions and total loan NCOs are components of loan losses

recorded by a bank in a given quarter. However, they are excluded from PPNR. Thus, we include

them as dependent variables for regression analysis. Social capital variables include the state-

level Putnam Index and county-level Social Capital County. Social capital controls are percent of

population that attends church, percent of population affected by crime, and percent of the state

population that is educated. Bank control variables include bank size, nonperforming loans/total

loans, reserve for loan losses/total loans, composition of loan portfolio, and liquidity ratio.

Second, we use instrumental-variable, two-stage least squares regressions (2SLS) to

mitigate endogeneity concerns. First stage instruments used to assist in proper identification of

the fitted value of social capital measures are distance of the bank’s headquarters from the

Canadian border and voter turnout in the state in which the is bank headquartered. Ln(Canada) is

the log of the distance from the bank’s headquarters to the Canadian border, from https://www.

freemaptools.com/measure-distance.htm. Voter Turnout is percent of the voting eligible

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population in the state in which the bank is headquartered that voted for the highest office in a

given election year, obtained from www.electproject.org. The numerator is the number of people

who voted for the "highest office" in a given election. The denominator is the number of people

eligible to vote. The use of these instruments is supported by prior research. Hasan et al. (2017)

and Gupta et al. (2018) reference Putnam (2001) and argue that distance to the Canadian border

is the best single predictor of the level of social capital within the United States, where being

closer to the Canadian border means more social capital. Additionally, both participation in

social activities (Alesina and La Ferrara, 2000) and level of trustworthiness (Glaeser et al., 2000)

have been found to be lower in areas with lower voter turnout. Hasan et al. (2017) conclude that,

as there are no material incentives to vote, the public only engages in voting activity as a civic

responsibility, which should improve social capital. Thus, these two instrumental variables are

related to social capital. However, neither variable should influence bank behavior.

4.1. Risk Measurement Ratios

Table 4 presents OLS regression results, which suggest that high social capital is

associated with lower loan risk and improved operating performance. The coefficients on the

social capital variables (Putnam Index and Social Capital County3) in regressions 1 and 2 are all

-0.001, while the coefficients in regression 3 are 0.002 and 0.001, respectively (all significant at

1%). The negative relation between loan loss provision/total loans (and total loan NCOs/total

loans) and social capital suggests that high social capital is associated with lower risk loan

portfolios. The positive and significant relation between PPNR/total assets and social capital

suggests that higher levels of social capital (and the lower risk loan portfolio) enhance operating

3 In this table, we include both measures of social capital in the regressions and find that they remain negative and

significant despite the significant positive correlation between the two variables. In the next set of tables,

instrumental-variable two-stage regressions require that we separate the two measures.

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performance. Finally, from Table 4, regression 4, high social capital is associated with lower Tier

1 RBC ratios (coefficients on Putnam Index and Social Capital County are -0.004 and -0.001,

respectively, significant at 1%). These findings suggest that borrowers in high social capital

areas are less likely to default on loans, which increases bank profit and reduces bank risk. As a

result, banks in areas with high social capital may be able to operate with lower capital ratios

because their borrowers are less likely to default. A test of economic significance shows moving

from the 25th to the 75th percentile value of social capital areas (measured by Putnam) decreases

loan loss provision/total assets by 16.97%.4 We see similar economically significant total loan

NCOs/total loans, PPNR/total assets, and Tier 1 RBC ratios (-12.47%, 5.04%, -6.82%,

respectively for a move from the 25th percentile to the 75th percentile values of Putnam social

capital, holding all other independent variables constant at their mean values).

From Table 4, we see that social capital controls are significantly related to the dependent

variables. Church Attendance is associated with lower loan loss provision/total loans, total

NCOs/total loans, and capital levels (coefficients in regressions 1, 2, and 4 are -0.001, -0.001,

and -0.002, respectively, all significant at 1%). Crime is associated with higher loan loss

provision/total loans and total loan NCOs/total loans, and lower capital levels (coefficients in

regressions 1, 2, and 4 are 0.006, 0.002, and -0.138 respectively, significant at 1%). Education is

associated with lower loan loss provision/total loans and total NCOs/total loans, and higher

capital levels (coefficients in regressions 1, 2, and 4 are -0.001, -0.001, and 0.001 respectively,

significant at better than 5%). The results are consistent with Jin et al. (2017) who find that banks

in high social capital areas are more stable, as indicated by decreased risk-taking and increased

4 If we hold all of the independent variables constant, and set the value of the Putnam Index to the 25th percentile

value, loan loss provision/total assets is 0.1921%. If we set Putnam to the 75th percentile value, loan loss

provisions/total assets are 0.1595%. This shows a 16.97% decrease in the value of loan loss provisions/total assets

((0.1595%/0.1921%)-1).

16

accounting transparency and conservatism.

Finally, in line with Cornett et al. (2017), results suggest that larger banks have riskier

loan portfolios, smaller PPNR/Total Assets, and hold less capital (e.g., coefficient on ln(Total

Assets) is 0.001 in regressions 1 and 2, -0.001 in regression 3, and -0.004 in regression 4, all are

significant at 1%). Further, banks with more commercial and industrial, consumer, and real

estate loans have larger loan loss provision to total loans (coefficients on C&I Loans/Total

Assets, Consumer Loans/Total Loans, and Real Estate Loans/Total Loans are 0.002, 0.003, and

0.000, respectively, in regression 1, significant at 1%). A similar trend is seen with total loan

NCOs/total loans in regression 2. Banks with higher liquidity on the balance sheet have higher

Tier 1 RBC ratios and less risky portfolios, yet lower operating profit (the coefficient on

Liquidity ratio in regression 4 is 0.129, in regressions 1 and 2 are -0.002 and -0.001, respectively,

and in regression 3 is -0.019, all significant at 1%).

Table 5 presents results from 2SLS estimations in which the social capital index is fitted

within stage one and used as an independent variable within stage two. For both the Putnam

Index and Social Capital County measures, the instruments (ln(Canada) and Voter Turnout)

satisfy the exclusion criterion based on the Hansen J-statistic and p-values corresponding to the

Sargan C-statistic (reported in the bottom two lines of the table) reject the null hypothesis that

the measures of social capital are exogenous. Additionally, coefficients on both instruments align

with intuition and prior literature (i.e., negative for ln(Canada) and positive for Voter Turnout).

The regressions confirm results of Table 4. Coefficients on social capital (Putnam Index

and Social Capital County) in regressions 3, 4, 7, and 8 are all -0.001, while coefficients in

regressions 5 and 9 are 0.002 and 0.001, respectively (all significant at better than 5%). Finally,

high social capital is associated with lower Tier 1 RBC ratios (coefficients on Putnam Index and

Social Capital County are -0.942 and -0.231 in regressions 6 and 10, respectively, significant at

17

5%). A test of economic significance shows that moving from the 25th to the 75th percentile

values of social capital measured by Putnam, loan loss provisions decrease by 14.15%.We see

similar economically significant total loan NCOs/total loans, PPNR/total assets, and Tier 1 RBC

ratios (changes of -6.96%, 6.16%, and -18.12% respectively). It is important to note that the

coefficient on Putnam rises sharply for the Tier 1 RBC 2SLS estimation in comparison to the

OLS estimation. This implies that examining the link between social capital and Tier 1 RBC

using OLS understates rather than overstates the effect of social capital. Overall, these findings

suggest that, controlling for endogeneity, borrowers in high social capital areas are less likely to

default on loans, which increases bank profit and reduces bank risk. As a result, banks in areas

with high social capital may be able to operate with lower capital ratios because their borrowers

are less likely to default. In Table 5, we also see that, in almost every case, bank control variables

have the same signs and significance levels.

4.2. Bank Financial Distress and Failure

Results from Tables 4 and 5 suggest that banks operating in high social capital areas are

less likely to experience loan defaults, which reduces bank risk and increases bank profit. As a

result, banks in areas with high social capital may be able to operate with lower capital ratios.

Given these differences, a natural follow-up is to examine whether there is a relation between

social capital and bank financial distress and subsequent failure.

We first analyze the relation between the risk of financial distress and social capital for

banks. To measure financial distress, we use the Z-score, initially developed by Roy (1952) and

subsequently advanced by Boyd and Graham (1986), Hannan and Hanweck (1988) and Boyd et

al. (1993), which relates a firm’s capital level to the variability in its returns to determine losses

that can be absorbed without the firm becoming insolvent. The variability in returns is typically

measured as the standard deviation of return on assets (ROA). Accordingly, we calculate

18

quarterly values of a bank’s Z-score as the sum of the equity capital ratio (common equity/total

assets) and ROA divided by the standard deviation of ROA, where standard deviation is the

quarterly deviation over the three prior years. This measure of financial distress links a bank’s

capitalization with its ROA and risk (volatility of ROA), and indicates the allowable drop in

ROA before the bank becomes insolvent. In other words, Z-score represents a bank’s distance

from insolvency. A higher value of Z-score indicates a lower probability of financial distress.

The average Z-score for the sample banks (reported in Table 2) is 12.22, which is in line with Z-

score estimates in Bouvatier et al. (2017).

We relate bank Z-scores to social capital and bank specific control variables5 using the

following 2SLS estimation:

𝑍 − 𝑠𝑐𝑜𝑟𝑒𝑖,𝑡 = ∝ +𝛽1 ∗ (𝑆𝑜𝑐𝑖𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖,𝑡) + 𝛽2 ∗ (𝐵𝑎𝑛𝑘 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡) + 𝜀 (2)

Results presented in Table 6 use the same instruments as Table 5 (the Putnam Index in regression

1 and Social Capital County in regression 2), with both estimations satisfying the Hansen and

Sargan tests for exclusion criterion and exogeneity of social capital. Results in Table 6 indicate

that high social capital areas are associated with higher Z-scores (coefficient on Putnam Index is

2.556 (regression 2) and on Social Capital County is 3.275 (regression 4), both are significant at

5% or better) and therefore lower financial distress. This result aligns with Adhikari and Agrawal

(2016), Ostergaard et al. (2016), and Jin et al. (2017) in that banks in high social capital are safer.

Next, we analyze the relation between bank failure and social capital using the following

2SLS estimation:

% 𝐹𝑎𝑖𝑙𝑒𝑑 𝐵𝑎𝑛𝑘𝑠𝑖,𝑡 =∝ +𝛽1 ∗ (𝑆𝑜𝑐𝑖𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠 𝑖,𝑡)

+ 𝛽2 ∗ (𝑀𝑒𝑑𝑖𝑎𝑛 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝐵𝑎𝑛𝑘 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖,𝑡) + 𝜀 (3)

5 To conserve space, we do not report coefficient results for bank control variables in the remaining tables. However,

in all cases, the signs and significance levels are similar to those reported in Tables 4 and 5.

19

% Failed Banks is calculated as the number of failed banks in the county (from the Federal

Reserve) divided by the total number of banks in that county. We use the median quarterly

values of bank-specific financial ratios in each quarter to control for operating differences

between banks in different counties.

Table 7 presents the regression results. Similar to Tables 5 and 6, we use instrument

variables to estimate Putnam Index in regression 1 and Social Capital County in regression 3. In

both regressions the Hansen and Sargan test results confirm appropriate exclusions and

exogeneity. Results in Table 7 indicate that not only are banks in high social capital areas safer

(Table 6), but they also fail at a lower rate (coefficient on Putnam Index is -0.002 (regression 2)

and on Social Capital County is -0.003 (regression 4), significant at 10% and 5%, respectively).

This is consistent with Jin et al. (2017) who find that banks in high social capital areas

experience fewer failures and less financial trouble during the 2007-2010 financial crisis.

4.3. Financial Crisis Performance

Related to financial distress and failure, we next look at the relation between social

capital and bank performance during the financial crisis. First, we estimate 2SLS regressions

similar to those in Table 5, but examine separately bank risk measures during (2008-2009)

versus outside (2000-2007 and 2010-2015) the financial crisis. Table 8 presents results from the

estimations in which the social capital index is fitted within stage one and used as an independent

variable within stage two. Panel A shows regressions in which social capital is measured using

the Putnam Index and Panel B shows regressions using Social Capital County. For both the

Putnam Index and Social Capital County measures, the instruments (ln(Canada) and Voter

Turnout) again satisfy the exclusion criterion based on the Hansen J-statistic and p-values

corresponding to the Sargan C-statistic (reported in the bottom two lines of the table) reject the

null hypothesis that the measure of social capital is exogenous.

20

Table 8 regression results confirm results of Table 5 showing that high social capital is

associated with lower loan risk and improved operating performance. Further, the results are

consistent both within and outside the financial crisis. Coefficients on the Putnam Index in

regressions 1 and 2 are both -0.001 (significant at 10% and 1%, respectively, and not

significantly different from each other), in regressions 3 and 4 are also both -0.001 (significant at

10% and 1%, respectively, and not significantly different from each other), and in regressions 5

and 6 are 0.001 and 0.002, respectively (significant at 1% and not significantly different from

each other). Further, high social capital is associated with lower Tier 1 RBC ratios in regressions

7 and 8 (coefficients on Putnam Index are -0.001 and -0.006 in regressions 7 and 8, respectively,

significant at 1% and not significantly different from each other). Identical results and

conclusions are found in Panel B using Social Capital County. These findings suggest that, both

during and outside the financial crisis years, borrowers in high social capital areas are less likely

to default on loans, which increases bank profit and reduces bank risk. As a result, banks in areas

with high social capital may be able to operate with lower capital ratios because their borrowers

are less likely to default.

We next analyze the association between the receipt of TARP Capital Purchase Program

(CPP) funds and social capital. For this test, we only look at the 2008-2009 period. We split this

sample according to whether a bank received TARP funds during the crisis. Using an

instrumental variables approach within a probit regression framework (IVPROBIT), we first

endogenize the social capital measure and then use that in the second stage as an independent

variable when regressed on a TARP indicator variable (one if the bank received TARP funds and

zero otherwise) as shown below:

𝑇𝐴𝑅𝑃𝑖,𝑡 =∝ +𝛽1 ∗ (𝑆𝑜𝑐𝑖𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠 𝑖,𝑡)

+ 𝛽2 ∗ (𝐵𝑎𝑛𝑘 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖,𝑡) + 𝜀 (4)

21

Results reported in Table 9 suggest that banks in high social capital areas are associated

with a greater likelihood of receiving TARP funds (coefficient on Putnam Index is 0.031 in

regression 2 and on Social Capital County is 0.358 in regression 4, both significant at 1%). In

evaluating the banks that would receive these funds, the U.S. Treasury intended to use TARP

CPP funds to inject capital into healthy banks as a way to stimulate lending and restore credit

flowing in the economy. Thus, the receipt of TARP funds is a sign that regulators expected these

banks to survive the financial crisis. Consistent with the results in Tables 6 and 7, we find that

these banks that are more likely to survive the crisis are in areas with higher social capital.

Finally, we examine the association between bank ROAs and social capital both overall

and during and outside the financial crisis. As mentioned above, credit unions are depository

institutions which place strong emphasis on building social capital and empowering their

customers and the local community in which they are based. As a result, during the financial

crisis, while industry average net income was negative for commercial banks, credit unions

return on assets remained positive. This test allows us to exam whether banks in high social

capital areas, as was the case with credit unions, are those more likely to have positive ROAs

both during and outside the financial crisis.

We split the sample according to whether a bank has a positive ROA for the quarter. In

addition to looking at the full sample period, we also examine separately bank periods during

(2008-2009) versus outside (2000-2007 and 2010-2015) the financial crisis. Using an

instrumental variables approach within a probit regression framework (IVPROBIT), we first

endogenize the social capital measure and then use that in the second stage as an independent

variable when regressed on by an ROA indicator variable (one if the bank’s ROA is greater than

0 for the quarter and zero otherwise) as shown below:

𝑅𝑂𝐴𝑖,𝑡 =∝ +𝛽1 ∗ (𝑆𝑜𝑐𝑖𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠 𝑖,𝑡)

22

+ 𝛽2 ∗ (𝐵𝑎𝑛𝑘 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖,𝑡) + 𝜀 (5)

Results presented in Table 10 suggest that banks in high social capital areas are

associated with a greater likelihood of having a positive ROA (coefficient on Putnam Index is

0.183 in regression 3 and on Social Capital County is 0.210 in regression 6, significant at 5% and

1%, respectively). Further, the results are more pronounced during the financial crisis. The

coefficient on Putnam Index is 0.038 in regression 4 (non-crisis years) and is 0.216 in regression

5 (crisis years), the difference is significant at 1%. Likewise, the coefficient on Social Capital

County is 0.013 in regression 7 (non-crisis years) and is 0.246 in regression 8 (crisis years), the

difference is significant at 1%. Thus, banks in high social capital areas see significantly larger

profit (ROA) during the financial crisis.

4.4. Social Capital and Bank Deposit Rates and Fees and Loan Income

Results thus far have highlighted the relation between social capital and overall bank risk

and performance. We next study the other half of the bi-directional relationship: that between

banks and consumers of banking products (borrowers and depositors). As mentioned above,

previous research documents beneficial results of high social capital. Ostergaard et al. (2016)

find that social capital is positively related to altruistic bank behaviors. Hasan et al. (2017) find

that banks headquartered in counties with higher levels of social capital incur lower bank loan

spreads and diminished opportunistic behavior. In contrast, Knack and Keefer (1997) find that

higher social capital provides way to capture private benefits at the expense of society in general.

This section examines the relation between social capital and consumer/bank relations (i.e.,

deposit rate and fee structures and loan income). We test whether social capital is positively

related to altruistic bank behaviors, or whether banks take advantage of high social capital to

capture private benefits, which may enhance profits.

23

We again first estimate ordinary least squares regressions with deposit interest and fees,

loan income, noninterest income, and net income as dependent variables on social capital index

variables, social capital controls, and bank-specific independent variables to control for operating

differences between banks. We then confirm results using a more robust 2SLS estimation

framework in which we endogenize the social capital indices and use the fitted values as

independent variables in the second stage. We use the following model for both the OLS and

2SLS approaches:

𝐼𝑛𝑐𝑜𝑚𝑒(𝐸𝑥𝑝𝑒𝑛𝑠𝑒)𝑖,𝑡 = ∝ +𝛽1 ∗ (𝑆𝑜𝑐𝑖𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖,𝑡)

+𝛽2 ∗ (𝑆𝑜𝑐𝑖𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡) + 𝛽3 ∗ (𝐵𝑎𝑛𝑘 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖,𝑡) + 𝜀 (6)

Income items include ATM Fees/Core Deposits, Check Income/Core Deposits, Fee and Interest

Income/Total Loans, and Total Noninterest Income/Total Income. Expense items include Interest

Expense on Core Deposits/Core Deposits. Combining income and expense, a final measure is

Net Interest Income/Total Loans.

Table 11 presents OLS regression results. Looking first at deposit trends, we see that

banks in high social capital areas charge significantly lower ATM and checking fees and pay

higher rates on core deposits (coefficients on Putnam Index in regressions 1 through 3 are -0.001,

-0.001, and 0.001, respectively, all significant at 1%). Signs and significance levels for Social

Capital County are similar. Further, regression 4 shows that banks in high social capital areas

charge significantly lower loan rates (coefficient on Putnam Index is -0.001 and on Social

County Capital is -0.001, both significant at 1%). Focusing on the Putnam Index, we find that

holding all other independent variables constant at their mean values, moving from the 25th to the

75th percentile value of Putnam Index leads to a 20.48% decline in ATM fees, a 63% decline in

checking fees, and a 10% increase in interest expense on core deposits.

24

Combining interest income and interest expense, Regression 5 shows that banks in high

social capital areas have significantly lower net return on loans (coefficient on Net Interest

Income/Total Loans is -0.163, significant at 1%). Moving from the 25th to 75th percentile value

for Putnam Index decrease the ratio of net interest income by 7.39%. This supports previous

findings that banks in higher social capital areas incur lower bank loan spreads and diminished

opportunistic behavior. However, the result appears to be inconsistent with results from Tables 4

and 5 where we see a positive and significant relation between PPNR/Total Assets and social

capital, suggesting improved operating performance. However, PPNR/Total Assets includes

noninterest income while Net Interest Income/Total Loans does not. Regression 6 shows that

banks in high social capital areas earn significantly more noninterest income (coefficient on

Putnam Index is 0.001 and on Social County Capital is 0.018, significant at 5% and 1%,

respectively). Moving from the 25th to 75th percentile value for Putnam Index increase the ratio

of net interest income by 5.48%. As noted above, this category includes such things as income

from fiduciary activities, trading revenues, fees from other-than-banking activities, servicing

fees, and gains and losses from the sale of investment securities, and not consumer oriented lines

of business. Thus, banks build social capital and empower their customers (borrowers and

depositors) and the local community in which operate. Banks in high social capital areas commit

to a high level of civic engagement in the communities they serve, which gives rise to increased

profits. The results suggest that banks located in areas with higher social capital take actions that

promote trust and therefore enable working relationships that have productive benefits for both

the banks and consumers.

Table 12 presents results from 2SLS regressions in which the social capital indices are

fitted within stage one and used as independent variables within stage two. Across both social

capital measures, the instruments (ln(Canada) and Voter Turnout) satisfy the exclusion criterion

25

based on the Hansen J-statistic and p-values corresponding to the Sargan C-statistic reject the

null hypothesis that the measure of social capital is exogenous. Further, both sets of results

confirm the results of Table 11: banks in areas with higher social capital pay more interest and

charge fewer fees on deposits and charge lower rates and fees on loans.

5. Conclusion

In this paper, we examine the degree to which banks in high social capital areas pursue or

fail to pursue policies that enhance social capital by studying the relationship between social

capital and bank decision-making, as measured by regulatory risk ratios, capitalization, bank

default risk and failure, loan performance, deposit rates and fee structures, and loan income. Our

results suggest that higher levels of social capital are associated with safer, more profitable, yet

less capitalized banks. Additionally, banks in areas with higher social capital values display

lower likelihoods of default and failure. Aligned with lower capitalization levels, we find a

positive and significant relationship between social capital and the likelihood of the receipt of

capital under the TARP Capital Purchase Program. To complete the analysis, we examine the

relation between social capital and bank loan rates and deposit rates and fees. We find that banks

in areas with higher social capital pay more interest and charge fewer fees on deposits, and

charge lower rates and fees on loans. The results suggest that banks located in areas with higher

social capital take actions that promote trust and therefore enable working relationships that have

productive benefits for both the banks and consumers.

26

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29

Table 1 Sample Breakout by State

This table presents the number of quarterly observations for the sample banks by state from 2000

through 2015 (79,448 total quarterly observations). Data for financial institutions are obtained

from the Consolidated Report of Condition and Income database.

State Number

of Obs.

Percent of

Sample

State Number

of Obs.

Percent of

Sample

AL 1,440 1.81 NC 1,886 2.37

AR 1,948 2.45 ND 585 0.74

AZ 143 0.18 NE 1,238 1.56

CA 3,640 4.58 NH 199 0.25

CO 1,091 1.37 NJ 1,602 2.02

CT 525 0.66 NM 469 0.59

DC 75 0.09 NV 263 0.33

DE 354 0.45 NY 2,933 3.69

FL 2,337 2.94 OH 2,172 2.73

GA 3,153 3.97 OK 1,557 1.96

IA 2,302 2.90 OR 525 0.66

ID 234 0.29 PA 3,895 4.90

IL 6,643 8.36 RI 197 0.25

IN 2,103 2.65 SC 1,103 1.39

KS 1,867 2.35 SD 660 0.83

KY 2,088 2.63 TN 2,431 3.06

LA 1,357 1.71 TX 5,527 6.96

MA 2,317 2.92 UT 422 0.53

MD 875 1.10 VA 2,090 2.63

ME 654 0.82 VT 321 0.40

MI 2,092 2.63 WA 1,443 1.82

MN 2,353 2.96 WI 2,554 3.21

MO 3,011 3.79 WV 661 0.83

MS 1,328 1.67 WY 275 0.35

MT 510 0.64 Total 79,448 100

30

Table 2 Descriptive Statistics

This table presents descriptive statistics for the sample banks over the period 2000-2015. Quarterly financial

statement data for financial institutions are obtained from the Consolidated Report of Condition and Income database

(79,448 bank quarters). Several variables are not available for all bank quarters including: ATM Fees, Check Income,

and Fee and Interest Income. For these variables, we require observations for at least half of the bank quarters to

ensure a robust sample. Putnam Index data are from the Bowling Alone database, Social Capital County from Penn

State University’s Northeast Regional Center for Rural Development, Church Attendance from the Association of

Statisticians of American Religious Bodies, Crime data from the Uniform Crime Reporting Statistics, and Education

data from the U.S. Census Bureau. ln(Canada) is the log of the distance from the bank’s headquarters to the Canadian

border, from https://www.freemaptools.com/measure-distance.htm. Voter Turnout data are collected from the United

States Elections Project database. The Z-score is calculated as the sum of the equity capital ratio and return on assets

divided by the standard deviation of return on assets, where the standard deviation is the quarterly deviation over the

three prior years. Failed Banks in County is the number of failed banks in a county (from the Federal Reserve)

divided by the total number of banks in that county. TARP Money is an indicator that is equal to one if the bank

received TARP money and zero otherwise. All variables are defined in Appendix A.

Variable Mean Median Std. Dev. Minimum Maximum

Putnam Index -0.141 -0.216 0.66 -1.15 1.29

Social Capital County -0.173 -0.230 1.06 -1.71 1.74

Church Attendance 60.30% 60.70% 16.13% 34.83% 85.80%

Crime 7.40% 7.50% 1.88% 4.58% 10.38%

Education 19.53% 18.94% 3.48% 13.63% 26.97%

Loan Loss Provision/Total Loans 0.31% 0.10% 0.84% 0.01% 1.95%

Total Loan NCOs/Total Loans 0.30% 0.10% 0.81% 0.00% 1.21%

PPNR/Total Assets 8.30% 4.20% 13.96% 1.16% 12.18%

Tier 1 RBC Ratio 12.80% 12.00% 29.00% 7.41% 22.53%

Total Assets (millions of $s) 9,098 573 87,300 181 2,570,000

ln(Total Assets) 13.51 13.26 1.39 12.11 21.67

Nonperforming Loans/Total Loans 1.40% 0.70% 2.33% 0.01% 5.57%

Loan Loss Reserve/Total Loans 1.50% 1.30% 0.97% 0.75% 2.89%

C&I Loans/Total Loans 15.90% 14.10% 10.19% 3.31% 34.38%

Agricultural Loans/Total Loans 2.90% 0.20% 6.14% 0.00% 15.95%

Consumer Loans/Total Loans 7.50% 4.70% 9.34% 0.29% 22.53%

Foreign Gov. Loans/Total Loans 0.01% 0.00% 0.10% 0.00% 0.05%

Real Estate Loans/Total Loans 26.40% 6.53% 33.39% 0.26% 89.49%

Depository Inst. Loans/Total Loans 0.10% 0.00% 1.55% 0.00% 0.21%

Liquidity Ratio 23.20% 21.50% 12.34% 5.95% 46.12%

ln(Canada) 5.77 6.29 1.52 4.19 6.98

Voter Turnout 17.92% 1.68% 24.73% 0.51% 56.26%

Z-score 12.22 4.13 168.11 0.11 25.30

Failed Banks in County 1.80% 0.00% 11.13% 0.00% 2.70%

TARP Money 3.70% 0.00% 18.95% 0.00 1.00

ATM Fees/Core Deposits 0.09% 0.00% 3.43% 0.00% 0.81%

Check Income/Core Deposits 0.01% 0.00% 0.02% 0.00% 0.08%

Interest Expense on Core Deposits/Core Deposits 5.60% 2.44% 309.88% 0.57% 12.69%

Fee and Interest Income/Total Loans 4.36% 4.26% 2.17% 1.53% 9.04%

Net Interest Income/Total Loans 3.19% 3.00% 2.77% 0.01% 9.06%

Noninterest Income/Total Income 2.45% 0.67% 64.45% 0.01% 11.29%

31

Table 3 Correlations of Social Capital Variables

This table presents correlations between social capital variables for the sample banks from 2000-

2015. Putnam index is created using principal component analysis on a set of fourteen different

factors (Putnam, 1993) and is collected from the Bowling Alone database. Social Capital County

data is a survey-based measure of social capital based on Rupasingha and Goetz (2008) and is

collected from Penn State University’s Northeast Regional Center for Rural Development

database. Church Attendance is the percent of the population in the state in which the bank is

headquartered that attends church, collected from the Association of Statisticians of American

Religious Bodies. Crime is the percent of the overall population in the state in which the bank is

headquartered that is affected by any reported crime in a given year, collected from the Uniform

Crime Reporting Statistics. Education is the percent of high school graduates in the state in which

the bank is headquartered, collected from the U.S. Census Bureau.

Putnam

Index

Social Capital

County

Church

Attendance Crime Education

Putnam Index 1 Social Capital County 0.5379 1 Church Attendance -0.0135 0.0345 1 Crime -0.3953 -0.3453 0.0007 1 Education 0.2698 0.0190 -0.0386 -0.1642 1

32

Table 4 Social Capital and Bank Risk Measures

This table presents OLS regression results in which we examine the relation between bank regulatory risk

measures and social capital with bank-specific independent variables to control for operating differences

between banks. Quarterly financial statement data for financial institutions are obtained from the

Consolidated Financial Statements for Bank Holding Companies database (79,448 bank quarters). Putnam

Index data are collected from the Bowling Alone database, Social Capital County data from Penn State

University’s Northeast Regional Center for Rural Development, Church Attendance data from the

Association of Statisticians of American Religious Bodies, Crime data from the Uniform Crime Reporting

Statistics, and Education data from the U.S. Census Bureau. All variables are defined in Appendix A. Bank

and year fixed effects are included within the estimations. p-values are shown in parenthesis below the

coefficient estimates. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. (1) (2) (3) (4)

Loan Loss

Provision/Total

Loans

Total Loan

NCOs/Total

Loans

PPNR/Total

Assets

Tier 1

RBC

Ratio

Putnam Index -0.001*** -0.001*** 0.002*** -0.004***

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

Social Capital County -0.001*** -0.001*** 0.001*** -0.001***

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

Church Attendance -0.001*** -0.001*** -0.002*** -0.002***

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

Crime 0.006*** 0.002*** 0.065*** -0.138***

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

Education -0.001*** -0.001*** 0.001*** 0.001**

(0.00) (0.00) (0.01) (0.03)

ln(Total Assets) 0.001*** 0.001*** -0.001*** -0.004***

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

Nonperforming Loans/Total Loans 0.023*** 0.033*** -0.030*** -0.312***

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

Loan Loss Reserve/Total Loans 0.020*** 0.006*** 0.194*** 0.415***

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

C&I Loans/Total Loans 0.002*** 0.001*** 0.007*** -0.038***

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

Agricultural Loans/Total Loans -0.000** -0.000** 0.005** -0.016***

(0.01) (0.04) (0.01) (0.00)

Consumer Loans/Total Loans 0.003*** 0.003*** 0.026*** 0.028***

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

Foreign Gov. Loans/Total Loans -0.011* 0.001 0.343*** 0.255**

(0.06) (0.81) (0.00) (0.02)

Real Estate Loans/Total Loans 0.000*** 0.000*** 0.001*** 0.019***

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

Depository Inst. Loans/Total Loans -0.002*** -0.000* -0.036*** -0.071***

(0.00) (0.07) (0.00) (0.00)

Liquidity Ratio -0.002*** -0.001*** -0.019*** 0.129***

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

Constant 0.000*** -0.001*** 0.040*** 0.159***

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

Observations 79,448 79,448 79,448 79,448

Adjusted R2 0.204 0.416 0.023 0.297

33

Table 5 Social Capital and Bank Risk Measures Controlling for Endogeneity This table presents 2SLS regression results in which we examine regulator risk measures as dependent variables with bank-specific independent variables

to control for operating differences between banks. Columns (1) and (2) report the coefficients of the first stage regressions, which are used to obtain the

fitted social capital variables. The dependent variables in the first stage regressions are Putnam Index (1) and Social Capital County (2). The instruments are

ln(Canada) and Voter Turnout. These instruments satisfy the exclusion criterion based on the Hansen J-statistic. The p-values corresponding to the Sargan

C statistic reject the null hypothesis (in all columns of Table 5) that the measure of social capital is exogenous. Bank and year fixed effects are included

within the estimations. p-values are shown in parenthesis below the coefficient estimates. ***,**, and * denote significance at the 1%, 5%, and 10% levels,

respectively. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

First Stage Second Stage

Putnam

Index

Social Capital

County

Loan Loss

Provision/

Total Loans

Total Loan

NCOs/

Total Loans

PPNR/

Total

Assets

Tier 1 RBC

Ratio

Loan Loss

Provision/

Total loans

Total Loan

NCOs/

Total Loans

PPNR/

Total

Assets

Tier 1

RBC

Ratio

ln(Canada) -0.227*** -0.215*** (0.00) (0.00)

Voter Turnout 0.001*** 0.009*** (0.00) (0.00)

Putnam Index -0.001*** -0.001** 0.002*** -0.942**

(0.00) (0.02) (0.00) (0.01)

Social Capital County -0.001*** -0.001** 0.001*** -0.231***

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

ln(Total Assets) 0.004*** 0.039*** 0.001*** 0.001*** -0.001*** -0.669*** 0.001*** 0.001*** -0.001*** -0.914*** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Nonperforming Loans/Total

Loans

-2.038*** -2.237*** 0.058*** 0.062*** -0.138*** -0.213 0.058*** 0.062*** -0.136*** -0.021

(0.00) (0.00) (0.00) (0.00) (0.00) (0.82) (0.00) (0.00) (0.00) (0.84)

Loan Loss Reserve/Total Loans 5.549*** 2.141*** 0.473*** 0.389*** 0.889*** 0.667 0.472*** 0.389*** 0.879*** 0.788

(0.00) (0.00) (0.00) (0.00) (0.00) (0.63) (0.00) (0.00) (0.00) (0.58)

C&I Loans/Total Loans 0.501*** 0.110*** 0.001*** 0.001 0.008*** -0.792** 0.001*** -0.001 0.007*** -0.383** (0.00) (0.00) (0.00) (0.93) (0.00) (0.02) (0.01) (0.87) (0.00) (0.02)

Agricultural Loans/Total Loans 3.792*** 5.030*** -0.001** -0.002*** -0.004 -0.057 -0.001 -0.001*** -0.005* -0.219

(0.00) (0.00) (0.03) (0.00) (0.14) (0.94) (0.28) (0.00) (0.07) (0.99)

Consumer Loans/Total Loans -0.376*** -0.295*** 0.008*** 0.010*** 0.076*** 0.675*** 0.008*** 0.010*** 0.077*** 0.063***

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

Foreign Gov. Loans/Total Loans 0.318 49.188*** -0.001 0.017 0.034 0.450 0.019 0.024 0.100 0.285

(0.86) (0.00) (1.00) (0.49) (0.81) (0.20) (0.45) (0.33) (0.49) (0.19)

Real Estate Loans/Total Loans 0.041*** 0.041*** -0.001 0.001*** 0.001 0.086 -0.001 0.001*** 0.001 0.122

(0.00) (0.00) (0.24) (0.00) (0.77) (0.17) (0.17) (0.00) (0.86) (0.18)

Depository Inst. Loans/Total

Loans

0.422*** 1.473*** -0.001 0.003** 0.109*** -0.125*** 0.001 0.003** 0.110*** -0.840***

(0.00) (0.00) (0.88) (0.03) (0.00) (0.00) (0.92) (0.03) (0.00) (0.00)

Liquidity Ratio -0.074*** 0.018 -0.007*** -0.004*** -0.020*** 0.513*** -0.007*** -0.004*** -0.019*** 0.408*** (0.00) (0.49) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Constant 0.883*** 0.224*** -0.005*** -0.007*** 0.038*** -0.054*** -0.006*** -0.007*** 0.038*** -0.583***

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

Observations 79,448 79,448 79,448 79,448 79,448 79,448 79,448 79,448 79,448 79,448

R2 0.425 0.238 0.398 0.347 0.280 0.202 0.398 0.347 0.278 0.202

Hansen 0.127 0.316 0.202 0.698 0.3 0.372 0.738 0.577

Sargan 0.002 0.003 0.000 0.005 0.002 0.003 0.001 0.007

34

Table 6 Social Capital and Financial Distress

This table presents 2SLS regression results in which we examine social capital and bank financial

distress using bank specific Z-scores as the dependent variable with bank-specific independent

variables to control for operating differences between banks. The Z-score is calculated as the sum

of the equity capital ratio and return on assets divided by the standard deviation of return on assets,

where the standard deviation is the quarterly deviation over the three prior years. Columns (1) and

(3) report coefficients of the first stage regressions, which are used to obtain the fitted social capital

variables. The dependent variable in the first stage regression in column (1) is the Putnam Index

and in column (3) is Social Capital County. The instruments are ln(Canada) and Voter Turnout.

All variables are defined in Appendix A. Bank and year fixed effects are included within the

estimations. p-values are shown in parenthesis below the coefficient estimates. ***, **, and *

denote significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4)

Putnam

Index Z-score

Social Capital

County Z-Score

ln(Canada) -0.227*** -0.215***

(0.00) (0.00) Voter Turnout 0.001*** 0.009***

(0.00) (0.00) Putnam Index 2.556***

(0.00) Social Capital County 3.275**

(0.02)

Constant 0.883*** 5.857*** 0.224*** 4.017***

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

Bank Control Variables Yes Yes Yes Yes

Bank Fixed Effects Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes

Observations 79,448 79,448 79,448 79,448

R2 0.425 0.202 0.238 0.206

Hansen 0.411 0.293

Sargan 0.001 0.001

35

Table 7 Social Capital and County Level Bank Failures

This table presents 2SLS regression results in which we examine the percent of banks that fail in

a county (from the Federal Reserve) with median quarterly values of bank-specific independent

variables in each county to control for operating differences between banks in different counties.

Failed Banks in County is the number of failed banks in a county divided by the total number of

banks in that county. Columns (1) and (3) report coefficients of first stage regressions, which are

used to obtain the fitted social capital variables. The dependent variables in the first stage

regression are Putnam Index (1) and Social Capital County (3). The instruments are ln(Canada)

and Voter Turnout. All variables are defined in Appendix A. Bank and year fixed effects are

included within the estimations. p-values are shown in parenthesis below the coefficient estimates.

***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4)

Putnam

Index

Failed Banks

in County

Social Capital

County

Failed Banks

in County

ln(Canada) -0.240*** -0.214***

(0.00) (0.00) 0.001*** 0.007***

Voter Turnout (0.00) (0.00) Putnam Index -0.002*

(0.07) Social Capital County -0.003**

(0.02)

Constant 0.807*** 0.043*** 0.339*** 0.041***

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

Bank Control Variables Yes Yes Yes Yes

Bank Fixed Effects Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes

Observations 12,398 12,398 12,398 12,398

R2 0.469 0.203 0.249 0.203

Hansen 0.818 0.881

Sargan 0.001 0.001

36

Table 8 Social Capital and Bank Risk Ratios During versus Outside the Financial Crisis

This table presents 2SLS regression results in which we examine bank regulatory risk measures both during (2008-2009) and

outside (2000-2007 and 2010-2015) the financial crisis as dependent variables with bank-specific independent variables to control

for operating differences between banks. The dependent variables in the first stage regressions are Putnam Index (1) and Social

Capital County (2). The instruments are ln(Canada) and Voter Turnout. We use predicted values in the second stage and report

Sargan C and Hansen-J 2SLS test statistics. These instruments satisfy the exclusion criterion based on the Hansen J-statistic. p-

values corresponding to the Sargan C statistic reject the null hypothesis (in all columns) that the measure of social capital is

exogenous. All variables are defined in Appendix A. Bank and year fixed effects are included within the estimations. p-values

are shown in parenthesis below the coefficient estimates. ***, **, and * denote significance at the 1%, 5%, and 10% levels,

respectively.

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

Second Stage

Non-Crisis Crisis Non-Crisis Crisis Non-Crisis Crisis Non-Crisis Crisis

Loan Loss Provision

/Total Loans

Total Loan NCOs/

Total Loans

PPNR/Total Assets

Tier 1 RBC Ratio

Panel A: Putnam Index

Putnam Index -0.001* -0.001*** -0.001*** -0.001* 0.001*** 0.002*** -0.001*** -0.006***

(0.06) (0.00) (0.00) (0.06) (0.00) (0.00) (0.00) (0.00)

Constant -0.003*** -0.011*** -0.005*** -0.007** 0.042*** 0.166*** 0.093* 0.216***

(0.00) (0.00) (0.00) (0.01) (0.00) (0.00) (0.10) (0.00)

Bank Control Variables Yes Yes Yes Yes Yes Yes Yes Yes

Bank Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes

Observations 71,598 7,850 71,598 7,850 71,598 7,850 71,598 7,850

R2 0.377 0.549 0.316 0.459 0.084 0.134 0.101 0.157

Hansen 0.177 0.131 0.316 0.352 0.363 0.357 0.215 0.776

Sargan 0.001 0.006 0.004 0.001 0.001 0.001 0.001 0.001

Panel B: Social Capital County

Social Capital County -0.001** -0.001*** -0.001*** -0.002*** 0.001* 0.002*** -0.002** -0.005***

(0.02) (0.00) (0.00) (0.01) (0.09) (0.00) (0.01) (0.00)

Constant -0.003*** -0.011*** -0.005*** -0.007** 0.040*** 0.153*** 0.060* 0.207***

(0.00) (0.00) (0.00) (0.01) (0.00) (0.00) (0.06) (0.00)

Bank Control Variables Yes Yes Yes Yes Yes Yes Yes Yes

Bank Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes

Observations 71,598 7,850 71,598 7,850 71,598 7,850 71,598 7,850

R2 0.363 0.543 0.316 0.453 0.469 0.221 0.613 0.211

Hansen 0.155 0.161 0.616 0.656 0.666 0.655 0.615 0.556

Sargan 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001

37

Table 9 Social Capital and TARP Money during the Financial Crisis

This table presents IVPROBIT regression results in which we examine whether a bank received

TARP money as the dependent variable with bank-specific independent variables to control for

operating differences between banks. The dependent variable, TARP Money, is an indicator equal

to one if the bank received TARP money and zero otherwise. We look only at the 2008-2009

period. Columns (1) and (3) report coefficients of first stage regressions, which are used to obtain

the fitted social capital variables. The dependent variable in the first stage regression in column

(1) is the Putnam Index and in column (3) is Social Capital County. The instruments are

ln(Canada) and Voter Turnout. All variables are defined in Appendix A. Bank and year fixed

effects are included within the estimations. p-values are shown in parenthesis below the coefficient

estimates. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4)

Putnam

TARP

Money

Social Capital

County

TARP

Money

ln(Canada) -0.211*** -0.183***

(0.00) (0.00) Voter Turnout 0.001** 0.011***

(0.02) (0.00) Putnam Index

0.031***

(0.00) Social Capital County 0.358***

(0.00)

Constant 1.359*** -0.843*** 1.336*** -0.840***

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

Bank Control Variables Yes Yes Yes Yes

Bank Fixed Effects Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes

Observations 7,850 7,850 7,850 7,850

R2 0.622 0.240 0.343 0.260

Hansen 0.436 0.996

Sargan 0.003 0.007

38

Table 10 Social Capital and Return on Assets – Crisis and Non-Crisis

This table presents the IVPROBIT results in which we examine an indicator variable for positive

or negative ROA (indicator equal to one for positive ROA and zero otherwise) with bank-specific

independent variables to control for operating differences between banks. Columns (1) and (2)

report coefficients of first stage regressions, which are used to obtain the fitted social capital

variables. The dependent variable in the first stage regression is the Putnam Index (1) and Social

County Capital (2). The instruments are ln(Canada) and Voter Turnout. These instruments satisfy

the exclusion criterion based on the Hansen J-statistic. p-values corresponding to the Sargan C

statistic reject the null hypothesis (in all columns) that the measure of social capital is exogenous.

All variables are defined in Appendix A. Bank and year fixed effects are included within the

estimations. p-values are shown in parenthesis below the coefficient estimates. ***,**, and *

denote significance at the 1%, 5%, and 10% levels, respectively.

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

First Stage Second Stage

Putnam

Social

Capital

County ROA

All Years Non-Crisis Crisis All Years Non-Crisis Crisis

ln(Canada) -0.227*** -0.215***

(0.00) (0.00) Voter Turnout 0.001*** 0.009***

(0.00) (0.00) Putnam

0.183** 0.038*** 0.216**

(0.04) (0.01) (0.04) Social Capital County

0.210*** 0.013*** 0.246***

(0.01) (0.01) (0.01)

Constant 0.883*** 0.224*** 1.901*** 0.546 2.065*** 1.792*** 0.552 1.926***

(0.00) (0.00) (0.00) (0.34) (0.00) (0.00) (0.33) (0.00)

Bank Control Variables Yes Yes Yes Yes Yes Yes Yes Yes

Bank Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes

Observations 79,448 79,448 79,448 71,598 7,850 79,448 71,598 7,850

R2 0.425 0.238 0.201 0.201 0.222 0.201 0.201 0.222

Hansen

0.664 0.651 0.446 0.713 0.736 0.447

Sargan 0.048 0.036 0.005 0.000 0.013 0.013

39

Table 11 Social Capital and Bank Deposit Rate and Fees and Loan Income

This table presents OLS regression results in which we examine bank deposit rate and fee structures, and

loan income as dependent variables with bank-specific independent variables to control for operating

differences between banks. Quarterly financial statement data for financial institutions are obtained from

the Consolidated Report of Condition and Income database (79,448 bank quarters). Putnam Index data

are collected from the Bowling Alone database, Social Capital County data from Penn State University’s

Northeast Regional Center for Rural Development, Church Attendance data from the Association of

Statisticians of American Religious Bodies, Crime data from the Uniform Crime Reporting Statistics,

and Education data from the U.S. Census Bureau. All variables are defined in Appendix A. Bank and

year fixed effects are included within the estimations. p-values are shown in parenthesis below the

coefficient estimates. ***,**, and * denote significance at the 1%, 5%, and 10% levels, respectively.

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

ATM

Fees/Core

Deposits

Check

Income/Core

Deposits

Interest Expense

on Core

Deposits/Core

Deposits

Fee and

Interest

Income/Total

Loans

Net Interest

Income/Total

Loans

Noninterest

Income/Total

Income

Putnam Index -0.001*** -0.001*** 0.001*** -0.001*** -0.163*** 0.001**

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

Social Capital County -0.001** -0.001*** 0.001*** -0.001*** -0.001*** 0.018***

(0.03) (0.00) (0.00) (0.00) (0.00) (0.00)

Church Attendance -0.001 -0.001*** -0.002*** 0.001 -0.001 -0.001***

(0.37) (0.00) (0.00) (0.15) (0.40) (0.00)

Crime -0.006 0.001*** 0.030*** 0.024*** 0.045*** 0.020***

(0.51) (0.00) (0.00) (0.00) (0.00) (0.00)

Education -0.001** -0.001*** 0.001*** -0.001 -0.001*** 0.001***

(0.05) (0.00) (0.00) (0.32) (0.00) (0.00)

Constant -0.003* -0.001* 0.029*** 0.052*** 0.028*** -0.006***

(0.07) (0.07) (0.00) (0.00) (0.00) (0.00)

Bank Control Variables Yes Yes Yes Yes Yes Yes

Bank Fixed Effects Yes Yes Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes Yes Yes

Observations 63,537 63,398 79,448 48,747 79,448 79,448

Adjusted R2 0.202 0.228 0.130 0.253 0.258 0.273

40

Table 12 Social Capital and Bank Fees Controlling for Endogeneity

This table presents 2SLS regression results in which we examine bank fee structures as dependent variables with bank-specific independent variables

to control for operating differences between banks. Columns (1) and (2) report the coefficients of first stage regressions, which are used to obtain the

fitted social capital variables. The dependent variables in the first stage regression are the Putnam Index and Social Capital County. The instruments

are ln(Canada) and Voter Turnout. These instruments satisfy the exclusion criterion based on the Hansen J-statistic. The p-values corresponding to

the Sargan C statistic reject the null hypothesis (in all columns of Table 10) that the measure of social capital is exogenous. Bank and year fixed

effects are included within the estimations. p-values are shown in parenthesis below the coefficient estimates. ***, **, and * denote significance at

the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)

First Stage Second Stage

Putnam

Social

Capital County

ATM

Fees/Core Deposits

Check

Income/Core Deposits

Interest Expense

on Core

Deposits/Core Deposits

Fee and

Interest

Income/ Total Loans

Net Interest

Income/Total Loans

Noninterest

Income/Total Income

ATM

Fees/Core Deposits

Check

Income/Core Deposits

Interest Expense

on Core

Deposits/Core Deposits

Fee and

Interest

Income/Total Loans

Net Interest

Income/

Total Loans

Noninterest

Income/Total Income

ln(Canada) -0.227*** -0.215*** (0.00) (0.00)

Voter Turnout 0.001*** 0.009*** (0.00) (0.00)

Putnam

-0.001*** -0.001*** 0.018** -0.001** -0.001*** 0.012*

(0.00) (0.00) (0.05) (0.04) (0.00) (0.06) Social Capital

County

-0.001** -0.001*** 0.011*** -0.001** -0.001** 0.018***

(0.03) (0.00) (0.01) (0.01) (0.02) (0.00)

Constant 0.883*** 0.224*** -0.002 -0.001*** -0.679*** 0.054*** 0.027*** -0.141*** -0.002* -0.001*** -0.662*** 0.054*** 0.027*** -0.152*** (0.00) (0.00) (0.13) (0.00) (0.00) (0.00) (0.00) (0.00) (0.09) (0.00) (0.00) (0.00) (0.00) (0.00)

Bank Control

Variables Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Bank Fixed

Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Year Fixed

Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 79,448 79,448 63537 63,398 79,448 48,747 79,448 79,448 63,537 63,398 79,448 48,747 79,448 79,448

R2 0.425 0.238 0.202 0.225 0.201 0.252 0.243 0.220 0.201 0.215 0.201 0.252 0.244 0.219 Hansen 0.757 0.282 0.551 0.595 0.342 0.703 0.894 0.754 0.551 0.338 0.980 0.176

Sargan 0.001 0.001 0.001 0.001 0.001 0.003 0.001 0.001 0.001 0.001 0.001 0.001

41

Appendix A

This appendix provides definitions and sources of variables used in the analysis.

Variable Definition Data Source

Putnam Index

A county level social capital index based on 14

different social capital indicators and available

from Bowling Alone database. www.bowlingalone.com

Social Capital County

A survey-based measure of social capital based on

Rupasingha and Goetz (2008). It is constructed

using principal component analysis based on social

capital indicators at the county level and available

from Penn State University’s Northeast Regional

Center for Rural Development. http://aese.psu.edu/nercrd

Church Attendance

Percent of the population in the state in which the

bank is headquartered that attends church, collected

from the Association of Statisticians of American

Religious Bodies. http://www.thearda.com

Crime

Percent of the overall population the state in which

the bank is headquartered that is affected by any

reported crime in a given year, collected from the

Uniform Crime Reporting Statistics.

Uniform Crime Reporting Statistics

http://www.ucrdatatool.gov/

Education

Percent of high school graduates in the state in

which the bank is headquartered, collected from the

U.S. Census Bureau. U.S. Census Bureau

Total Assets Quarterly total assets of the bank Call report

ln(Total Assets) Natural log of total assets Call report

Loan Loss Provision/Total Loans Ratio of loan loss provision to total loans Call report

PPNR/Total Assets

Ratio of (net interest income + noninterest income

– noninterest expense) to total assets Call report

Tier 1 RBC Ratio Ratio of Tier 1 capital to risk-weighted assets Call report

Total Loan NCOs/Total Loans Ratio of total loan net charge-offs/Total loans Call report

Nonperforming Loans/Total Loans

Ratio of loans past due 90 days or more and still

accruing interest and loans in nonaccrual

status/Total loans Call report

Loan Loss Reserve/Total Loans Ratio of reserve for loan losses/Total loans Call report

C&I Loans/Total Loans Ratio of commercial loans to total loans Call report

Agricultural Loans/Total Loans Ratio of agricultural loans to total loans Call report

Consumer Loan/Total Loans Ratio of consumer loans to total loans Call report

Foreign Gov. Loans/Total Loans Ratio of foreign government loans to total loans Call report

Real Estate Loans/Total Loans Ratio of real estate loans to total loans Call report

Depository Inst. Loans/Total Loans

Ratio of loans to depository institutions to total

loans Call report

Liquidity Ratio

Ratio of cash and investment securities to total

assets Call report

ln(Canada)

The log of the distance from the bank's

headquarters to the Canadian border

https://www.freemaptools.com/measure-

distance.htm

Voter Turnout

Percent of voting eligible population in the state in

which the bank is headquartered that voted for the

highest office in a given election year. The

numerator is the number of people who voted for www.electproject.org/home

42

the "highest office" in a given election. The

denominator is the voting eligible population,

defined as the number of people eligible to vote.

Z-score

Sum of the equity capital ratio and return on assets

divided by the standard deviation of the return on

assets, where the standard deviation is the quarterly

deviation over the three prior years. Call report

Failed Banks in County

Number of failed banks in a county divided by the

total number of banks in that county. Federal reserve

TARP Money

Indicator equal to one if the bank took TARP

money and zero otherwise. Federal Reserve

ROA Net income to total assets Call report

ATM Fees/Core Deposits Ratio of ATM fees to core deposits Call report

Check Income/Core Deposits Ratio of total income from checks to core deposits Call report

Interest Expense on Core

Deposits/Core Deposits

Ratio of interest income expense on deposits to

core deposits Call report

Net Interest Income/Total Loans Ratio of net interest income to total loans Call report

Noninterest Income/Total Income Ratio of non-interest income to total income Call report

Fee and Interest Income/Total

Loans Ratio of fee and interest income to total loans Call report