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A Comparative analysis on the stability of US Community

and Non-Community banks

Vasileios Pappas · Athina Petropoulou

University of Bath, Bath, BA2 7AY, UK

December 2017

Abstract

In this paper, we assess the financial stability of Community and Non-Community banks in

the US over the period 1976 – 2013. We use the most recent definition of Community banks

published by the FDIC that does not rely solely upon bank size, but takes into account the

business model differences of Community banks. We find that Community banks are

financially stronger than Non-Community banks. Our results show that the financial stability

of either bank is equally sensitive to capitalisation changes; however, the financial stability of

Community banks is more robust to asset quality changes in their loan portfolios. Adverse

macroeconomic conditions and financial crises affect Community banks more than their Non-

Community counterparts.

Keywords: Community banks, Failure risk, Panel Data, Z-score

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1. Introduction

The banking system in the US has undergone considerable transformations. In particular, the

Global Financial Crisis (GFC) of 2007 has caused the number of commercial banks in the US

to shrink, in part through mergers and acquisitions but also through failures. According to the

FDIC 13 banks merged with other institution under the assistance of the FDIC, while 322

banks were given failure stature over the 2007 – 2010 period. The severity of the GFC has

renewed the interest of academics, bank managers and regulators in banking stability. The

importance of banking stability within the banking literature is reflected on the literature that

seeks to identify the determinants of banking failure and/or design early warning systems that

could signal a potential banking failure (James, 1991; Whalen, 1991; Cole & Gunther, 1998;

Kolari et al., 2002).

A peculiarity of the US banking system is that it spans from a few, large systemically

important financial institutions (SIFIs) to many, small Community banks (CB). On the one

hand, the importance of SIFIs to the stability of the banking system has been pronounced in

the recent years as evident by the special provisions in Basel III.1 On the other hand,

Community banks play a crucial role in providing credit and services to their local

communities by maintaining close relations with local businesses. Their importance is

associated with their special business model, in which they engage in traditional deposit-

taking, loan-making activities, i.e. they gather deposits locally and make loans to individuals

and local businesses. However, changes in the US financial sector during the past decades

have reduced the size of the community banking sector. The long-standing tradition of

Community banks in the US dates back to the prohibition of interstate banking (McFadden

1927 Act), which inflated the number of small, local (i.e., Community) banks. It was not until

the mid-nineties when such prohibitions were repelled (Riegle-Neal Act & Branching Act

1994); since then the number of Community banks has fallen sharply. According to the FDIC

(2012) the share of US banking assets held by Community banks declined from 38 percent in

1984 to 14 percent in 2011. Despite these structural changes, Community banks continue to

play a key role in the US economy as they accounted for 92 percent of FDIC-insured banks

and 95 percent of US banking organizations in 2011, suggesting that they are the most

prevalent type of FDIC-insured institutions.

1 See for example, http://www.bis.org/bcbs/basel3/b3summarytable.pdf

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Community banks face substantial challenges to their survival. The technological and legal

changes that have occurred in recent years place challenges for the long-term survival of

these banks. The response from this sector has mainly been through mergers and acquisitions

or bank failures. In particular, bank failures have become prominent during the global

financial crisis. The banking crisis of late 1980s and early 1990s and the financial crisis of

2007 caused 2,555 banks and thrifts to fail (FDIC 2012). Community banks are exposed to

the same market conditions as Non-Community banks. However, the business model of

Community banks differs substantially from that of Non-Community banks. There exists

important characteristics that distinguish Community banks from other commercial banks,

which could lead to potential differences in their stability profile. Given the different business

model of Community banks, we would expect that the two bank types have distinct

sensitivities to failure risk. The community banking sector has proven to be resilient during

the recent crisis. Alton Gilbert et al. (2013) suggest that well-run Community banks

weathered the financial crisis effectively and those banks that are committed to maintaining

high risk control standards are likely to continue prosper in the future.

The aim of this paper is to compare Community and Non-Community banks in terms of their

financial stability. Our sample covers US Community and Non-Community banks over the

period 1973 to 2013. To assess the financial stability of the two bank types, we rely on the Z-

score, which is a well-established measure for bank soundness, see for example Cihák &

Hesse (2010), Abedifar et al., (2013), Imbierowicz and Rauch (2014). Our control variables

capture bank-specific differences, such as size, capitalisation, profitability, liquidity, asset

quality and business orientation. In line with the literature we allow for key macroeconomic

variables to affect financial stability (see for example, Cihak and Hesse, 2010). Furthermore,

we compare and contrast the sensitivity of financial stability to bank-specific and

macroeconomic indicators between the two bank types. The impact of financial crises on

financial stability of Community and Non-Community banks is also addressed.

Our findings reveal that Community banks tend to be more financially stable than Non-

Community banks. Higher concentration on Net Loans and more Loan Loss Reserves are

associated with lower stability for both bank types, however the effect is less pronounced for

Community banks. Inflation growth significantly affects the stability of Community banks

but it does not have a significant effect on Non-Community. When the loan portfolio is more

concentrated towards Commercial and Industrial Loans stability for Community banks

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decreases. During periods of banking crisis, larger Community banks are more financially

sound than smaller ones.

In line with the literature, we find that bank-specific characteristics carry most of the

explanatory power in our models (Lane et al., 1986; Cole and Gunther, 1995; Cole and

Gunther, 1998; Cole and White, 2012).

Our contribution to the literature is twofold. First, we compare the financial stability of

Community and Non-Community banks, thereby contributing to the literature on the

comparative performance of these bank types. Second, we compare and contrast the

sensitivity of these banks’ financial risk profile to key bank-specific and macroeconomic

indicators. To our knowledge this is the first study to embark on such task.

The structure of this paper is as follows: In Section 2 we discuss the specifics of Community

banks and provide a brief literature review. Section 3 discusses our methodology and

introduces the variables and the data used. In section 4, we provide our empirical results.

Section 5 offers a brief conclusion.

2. Literature Review

Community banks retain a unique identity which is associated with the way they conduct

business and the services they provide. Community banks are small in terms of asset size and

they carry out the traditional loan- giving and deposit- taking functions on a local scale. They

are considered to be relationship lenders characterized by local ownership and local control.

This section provides an overview of the literature related to those key characteristics of the

Community banking model and how they have been found to influence the bank’s soundness.

2.1 Previous studies addressing bank-size related issues

Because of their narrow focus, Community banks are generally small in size. In fact, most

studies have used an upper threshold of $1 billion in assets in order to define the Community

banks (Nakamura, 1994; Berger and Bowman, 2013). Empirical evidence suggests that large

banks may be less likely to fail as they are better in diversifying credit risk, reap economies

of scale and have better access to capital markets (Shaffer 1989,1994).

Small bank size is often associated with inefficiency. Smaller banks have always been

struggling with intrinsically higher costs and increasing competition in the banking industry.

It has been suggested that banks with assets below $25 million are on average less efficient

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and a critical asset size of at least $50 million is required for the bank to be efficient (Shaffer

1989). Inefficient banks are less able to cover their costs when faced with adverse shocks, so

they tend to be more likely to fail. This can offer a justification for the fact that for the period

1984 to 1988 the largest percentage of failing banks had assets below $25 million (Shaffer,

1989). However, small banks can build protection for themselves by specializing in market

niches. For example, Čihák & Hesse (2010) finds that small Islamic banks face lower

insolvency risk than similar sized commercial banks.

Larger asset size is often associated with higher profitability (Berger et al., 1987; Bikker and

Hu, 2002). Profitability is considered an ex ante measure of asset risk and is inversely related

to the probability of a bank failing. Larger banks are able to diversify better their loan

portfolio, thus reduce their asset risk. So, large banks should have lower failure risk

(Calomiris & Mason, 2000). Lower profitability makes it more difficult for the bank to

increase its capital base and enhance its viability (Arena 2008). However, Basset and Brandy

(2001) suggest that during the period 1985 to 2001, banks with less than $1 billion assets

managed to outperform larger banks in terms of profitability. They increased the interest rates

offered on deposits in order to attract more funding. This increased cost reflects the relative

higher rate of return that small banks earned on their loans.

With regards to failure, under the “too-big-to-fail” doctrine, a large bank failing is more

feared than a small bank failing since it is more likely to be followed by macroeconomic

externalities. Big banks cannot default on their debt because their liabilities are de facto

guaranteed (Boyd & Runkle, 1993). The failure of large banks can pose significant risks to

other financial institutions and the financial system as a whole. So, in case of financial

distress they will receive government support. Since the creditors of such banks expect

government protection, large banks may take excessive risk because they exploit a

guaranteed safety net.

2.2 Geographically concentrated business model

The features of Community banks go beyond size. Community banks play a key role in the

economy by providing financial services in their local communities. They are prevalent in

small towns and rural areas which make up for most of the country. It is three times more

likely for Community banks to hold a banking office outside a metro area (FDIC 2012).

Community banks have a limited number of offices and level of deposits that they hold. The

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fact that Community banks operate within a limited geographic scope suggests that they hold

less diversified portfolios, so we would expect that the bank is heavily exposed to local

economic downturns. Nonetheless evidence from the literature is mixed. On the one hand,

Liang and Rhoades (1988) find evidence that geographic diversification can potentially

reduce bank risk. On the other hand, Meyer and Yeager (2001) find little correlation between

the geographic concentration of the bank offices and its vulnerability to local economic

shocks. Alton Gilbert et al. (2013) suggest that Community banks that thrived during the

Global Financial Crisis were not necessarily concentrated in areas with strong economic

growth but were geographically concentrated to areas that are relatively prosperous in

agriculture and energy. A plausible explanation for this, is that these sectors remain relatively

untouched during the Global Financial Crisis. Yeager (2004) compare the performance of

banks located in counties that went through economic shocks in the 1990s with banks in other

counties and find that Community banks are not systematically vulnerable to local market

risk. Community banks hold more diversifiable credit risk than their commercial counterparts

because they conduct geographically concentrated business (local market risk) and they are

smaller in size (idiosyncratic risk). Emmons et al. (2004) look at the ability of Community

banks to diversify default risk through geographic diversification and size growth and they

find that more risk reduction benefits are achieved by increasing the bank’s size, whereas

local market risk is less severe. Similarly, evidence from US banks during the period 2007-

2010 suggests that extensive branching across counties does not seem to be associated with

lower probability of default (Aubuchon & Wheelock, 2010).

2.3 Relationship banking approach

Community Banks are considered to be “relationship bankers” rather than “transactional

bankers”. Under “relationship lending” approach, credit decisions are based primarily on

“soft information”. “Soft information” includes nonstandard data and qualitative information

that is related to human interaction. “Transaction banking”, on the other hand, suggests that

any financial decision is based on quantifiable information, such as financial statements and

credit history. Due diligence is conducted based on hard information that can be easily

verifiable.

Relationship lending is more important for Community banks than other commercial banks.

They invest in long-term relationships with their customers and they have an advantage in

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offering personalized services. Literature on relationship banking is more focused on the

availability of credit and loan pricing and not so much on stability. The bank establishes close

ties with the borrower by being in a constant interaction with him and accumulate

information. These ties create an informational privilege for the bank. Proximity between the

bank and its borrowers has been shown to facilitate monitoring and screening and to resolve

problems of asymmetric information, since it establishes an improved information flow

between the two parties (Boot, 2000). Cornée et al. (2012) claim that borrowers’ repayment

rate is significantly higher when a bilateral relationship between the borrower and the lender

exists. This is in line with the notion of “reciprocity”, according to which borrowers that

consider themselves personally fairly treated undertake investments with low risk in order to

reciprocate the bank’s gesture hence lower their probability of default (Cornée & Szafarz,

2013). Hence, we would expect to find evidence of fewer non- performing loans in

Community banks’ portfolio. The quality of the bank’s loan portfolio is an important source

of shocks that can lead to bank insolvency (Wicker, 1996).

However, potential threats arise for the bank from engaging into relationship banking

approach. The soft budget constrain problem is related to the fact that the bank may lack the

toughness in enforcing credit contracts. The firm benefits from building close ties with the

lender in the form of increased financing (Petersen & Rajan, 1994). The bank may choose to

provide further credit to a borrower that is close to default in the hope of recovering a

previous loan. In line with this reasoning are the findings of Elsas & Krahnen (1998) who

suggest that Housebanks in Germany provide liquidity insurance to the borrowers in cases

where his rating deteriorates unexpectedly.

Research often recognises the link between small business lending and the community

banking sector and studies focus on the role that Community and Non-Community banks

play in the availability of small business credit. Small banks are the main source of credit for

SMEs. They have a comparative advantage at lending to small firms because they are better

able to monitor the loans (Nakamura, 1994). Berger et al. (1995) argue that larger banking

organizations tend to lend to large borrowers whereas small banking organizations are more

specialized to lending to small and medium size borrowers. Multi office banks tend to lend

less to small businesses than other banks (Keeton, 1995). Avery & Samolyk (2004) find that

from 1994 to 1997 mergers among Community banks and a greater presence of Community

banks in the banking industry is associated with higher small business lending. These

findings suggest that SMEs depend heavily on Community banks for obtaining finance.

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Previous research has shown that larger banks reduce the supply of credit to small businesses

(Berger & Udell, 1995). When a bank gets larger, its focus shifts to larger and more

standardised types of loans (Berger & Udell, 1998). Under this scope, the effects of a

Community bank failing go beyond its owners and the FDIC insurance fund and have direct

effect in the local businesses and customers. When a merger replaces a Community bank with

a larger regional bank the credit supply to small businesses is likely to decline. Households

that get certain types of financial services from community banks will be left with limited

options in case the local bank fails (Amel & Prager, 2013).

2.4 Ownership Structure

Community banks are typically privately owned and not widely traded. Thus, the ownership

of these banks is concentrated in the hands of few stockholders who are actively involved in

the management of the bank. In order for relationship banking approach to work, loan

officers should be given the ability to act independently on any set of information, without

the approval of numerous others. This implies localized decision making (Hein et al., 2005).

According to Brickley et al. (2003) managers of small banks have more decision authority. In

line with this reasoning is the finding of Berger and Udell (1995), who claim that

stockholders of larger banks are less eager to allow local managers the authority of decision-

making and instead rely on procedures that are more bureaucratic. The fact that owners are

actively involved in the bank’s management mitigates agency problems. When the ownership

structure of the bank is more concentrated, the owners are able to monitor the managers

closely and discipline their risk appetite. Thus, we would expect managers of Community

banks to be more risk averse. Cooperative banks in Europe which are typically owned by

their members are found to be more stable than other banks (Cihák & Hesse, 2007). They

focus on preserving the capital and preventing losses in the portfolio and they have a stable

basis of deposits and customers which could act as protection in periods of distress

(Fonteyne, 2007).

2.5 Traditional banking approach of Community Banks

Community banks carry out the traditional banking functions of lending and deposit

gathering on a local scale. Because of their traditional focus they derive most of their revenue

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from net interest income. On the other hand, Non-Community banks are able to generate

noninterest income from a variety of sources such as trading, venture capital and investment

banking that are not part of the community banking model. Non-traditional banking activities

are found to increase the likelihood that a healthy bank becomes financially troubled while

interest generating activities are no more or less associated with the bank becoming insolvent

(Torna, 2010). When a bank is given more investment opportunities it may decide to incur

more risk, so higher involvement in non-traditional banking activities is often associated with

lower stability (DeYoung & Torna, 2013). Boyd and Graham (1986) find that from 1971 to

1977 bank holding companies in the US that engaged into non-traditional banking activities

faced greater risk of failure. Similarly, Lepetit et al. (2008) show that European banks that

expand into non-interest income activities face higher insolvency risk than banks which

mainly give out loans. Alton Gilbert et al. (2013) after conducting interviews with managers

of well-run Community banks, find that these banks were committed to conservative lending

strategies and more tight lending standards. Those practices are by far different from the

subprime banking practices that led to the Global Financial Crisis. Moreover, Community

banks that thrive during the recent financial crisis appears to have adopted a business plan

that was tailored to their local communities.

The US banking system has attracted significant academic attention, however only few

studies provide insights into the Community banking sector specifically. The FDIC 2012

study reveals that Community banks had a lower propensity to fail than Non-Community

banks in almost every year from 1985 to 2011. Deyoung & Hunter (2002) examine the

performance of large and small banks in the US in the new environment of technological

change and suggest that even though the number of Community banks will almost certainly

decline, the more progressive and well managed ones are likely to survive and flourish.

Similarly, Alton Gilbert et al. (2013) claim that well-run Community banks are likely to

prosper in the future.

3. Methodology and Data

3.1 Measuring bank’s stability

A measure widely used as proxy for bank failure risk is the Z-score (Boyd & Runkle 1993;

Cihák & Hesse 2007; Lepetit et al. 2008; Cihák & Hesse 2010; Abedifar et al., 2013). The Z-

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score calculates the number of standard deviations that the bank’s return on assets (ROA)

must fall below its mean in order to deplete equity, under the assumption of normal bank

returns. It can be computed as Z= . The Z-score is inversely related to the

probability of a bank being insolvent. A bank becomes insolvent when its assets’ value drops

below its debt. Therefore, higher values of the Z-score are associated with more stable banks

and lower probability of default.

Several approaches have been applied when modelling bank failures. Many studies have used

multivariate probit or logit models (Martin, 1977; Demirgüc-Kunt and Detragiache, 1998;

Poghosyan and Cihak, 2009; Wong, Wong and Leung, 2010; Betz et al., 2014). These are

non-linear models that rely on the actual event of failures. The dependent variable is a

dummy which takes the value one if the bank fails or zero otherwise. Duration models (also

called hazard models) are commonly used to analyse bank failures as well (Lane at al., 1986;

Wheelock and Wilson, 1995; Wheelock and Wilson, 2000; Whalen, 1991; Molina, 2002;

Mannasoo and Mayes, 2009; Brown and Dinc, 2010). These models place the interest in an

event that ends a period of time. The duration time is the time until the bank becomes

insolvent. For models that rely on actual failure events and not proxies, there needs to be

specified what constitutes a failure.

Altman (1968) is the first study that uses Z-score to predict firm failures. The Z-score has

been widely used both in academic literature and by practitioners as a measure of financial

distress. It can be simply calculated using ROA and capital-asset ratios. Moreover, it is an

objective measure of stability across different bank types. For this reason we believe it is

applicable to our study. It can apply to both Community and Non-Community banks since

they face the same risk of insolvency in case they run out of capital. Another popular measure

of insolvency risk is the Distance-To-Default, which uses stock price data to measure the

volatility in the economic capital of the bank. (see e.g., Danmark Nationalbank, 2004).

However, most Community banks are not listed in equity markets so market price data are

not available. For this reason, this paper relies on the Z-score that uses accounting data.

3.2 Regression analysis

To assess financial stability between Community and Non-Community banks, we use a set of

Z-score regressions with bank type, bank-specific and macroeconomic explanatory variables.

In the analysis that follows three models are employed. The first one contains all banks, the

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second only Community banks and the third only Non-Community. Following Pappas et al.

(2016), each model has four variants, one that includes only bank specific variables and is

termed as “micro”, another that includes both bank specific and macroeconomic conditions

and is termed as “macro”, as well as two additional variants that also include interaction

terms between the Community bank dummy and the bank-specific and macroeconomic

variables respectively. Thus, the panel models are of the form:

Micro: , , , , (1)

Macro: , , , , , (2)

Micro with

interactions:

, , , , ,

, (3)

Macro with

interactions:

, , , , , ,

, , , (4)

Where the dependent variable is the z-score , for bank at period ; , is a dummy

variable that takes the value of 1 if the bank is a community bank at time t and 0 otherwise.

For instance, if Community banks are more stable than Non-Community, the dummy variable

will have a positive sign; , is a matrix of bank-specific independent variables; , is a

matrix of variables capturing the macroeconomic conditions in the US; , , is the

interaction between bank-specific variables and the community bank dummy; , ,

is the interaction between macroeconomic variables and the community bank dummy; is

the unobserved random effect that varies across banks; , is an idiosyncratic error term. The

equation is estimated using robust standard errors.

To assess in greater depth the robustness of our results with respect to bank size, we estimate

the same regressions separately for small Non-Community banks.

3.3 Data

The data used are quarterly, beginning from the first quarter of 1976 and going through the

fourth quarter of 2013. The sample consists of 22,419 banks, 5,671 of which are listed in the

FDIC as Community banks and 16,748 as Non-Community.

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Previous research has simply established an upper assets size threshold-typically $1 billion-

in order to define Community banks. However, this measure needs to be adjusted over time

to account for factors such as inflation, economic growth or size of the banking industry.

Applying a single size criterion will fail to take into consideration larger Community banks or

small Non-community banks. Moreover, the characteristics associated with Community

banks are not only size-related. Therefore, a closer look into the business model and office

structure of the bank must be taken. Hence, FDIC has developed a new research definition for

community banks which goes beyond the size criterion. The Community bank dummy is

constructed based on that definition. In order to arrive to that definition five steps are

followed. The first step is to collect bank level data and combine them to the level of a single

bank organization. Community banks’ data are reported at bank level. However, they operate

at organizational level. When a banking organization is reported as a Community bank, every

bank operating under that organization is considered a Community bank. The second step is

to remove specialty organizations. The categories of specialty organizations that are not

included in the definition process of Community bank are credit card specialists, consumer

nonbank banks, industrial loan companies, trust companies, banker’s banks and banking

organizations that hold at least 10 percent of their total assets in foreign offices. If 50% or

more of the bank’s assets belong within a specialty organization, then the bank is considered

a specialty bank and is not included in the Community bank definition. Thirdly, the

organizations that focus on traditional lending and deposit gathering activities are included.

In order to do so, minimum thresholds are imposed for the Loans/Assets ratio (33%) and for

the Core Deposits/Assets (50%). The forth step is to impose certain thresholds in order to

make sure that the organization operates within a limited geographic scope. This enables the

bank to engage in relationship lending activity. First, the organization must have at least one

office but fewer than a specific number of offices. This number is adjusted over time. It was

75 in 2010. Second, the bank should have offices in no more than three countries and no

more than two large metropolitan areas. This ensures the bank’s ability to engage in

relationship lending. Last, a maximum level of deposits that the organization can gather in

any one banking office is imposed. The threshold changes over time. It was 5 billion dollars

in 2010. Finally, an asset size limit is established. Below this threshold, the limitations from

the previous two steps are ignored. The asset size limit was $1 billion in 2010.

Some transformations had to be made to the data. Banks with missing or zero assets, equity,

loans, deposits and other non-commercial banks are excluded from the original dataset. Also,

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to avoid any distortion in the ratios, for the banks that had equity below 1% of the assets, the

equity value is set as 1% of the assets. Z-score is highly skewed so we use the natural

logarithm of the z-score which is normally distributed (Laeven and Levine, 2009). In order to

control for possible outliers in the data, each variable is winsorized at the 1st and 99th

percentile, following Beck et al. (2013). The time varying bank-specific factors and

macroeconomic factors are lagged one quarter to capture the possible past effects of these

variables on bank’s Z-score.

Our calculations are based on bank-specific data drawn from the FDIC and the FFIEC reports

provided by the WRDS database. Community bank status is taken from the FDIC and, using

the FDIC certificate number, the data from the two sources are merged, resulting in the final

dataset. The macroeconomic variables are collected from the Datastream.

3.4 Dependent and Independent Variables

Empirical literature has shown that most of the financial stability explanatory power comes

from bank-specific and macroeconomic variables. We consider some key financial ratios

which are in line with the CAMEL(S) framework to control for capitalization, asset quality,

earnings quality, cost efficiency and liquidity. CAMEL(S) proxies are found to be important

determinants of bank failures (Lane et al., 1986; Gajewski, 1989; Whalen, 1991; Demirguc-

Kunt, 1991; Thomson, 1992; Cole and Gunther, 1995; Cole and Gunther, 1998). It is

established in the literature that banks with high leverage and operating costs, illiquid and

with poor quality assets and little capital are more likely to fail (Wheelock and Wilson,

2000).

Capitalization is measured by the ratio of Equity/Assets. In order for a bank to be able to

constantly meet its borrowers’ credit needs, it has to maintain a sound capital base. Capital is

generally measured with respect to a bank’s assets and risk exposure. As Equity is a

protection against Asset impairment, this ratio captures the amount of protection offered to

the bank by its Equity. Asset quality is approximated by the ratio of Loan Loss

Allowance/Total Loans. This ratio indicates how much the bank sets aside for potential bad

loans. We control for earnings quality by using the Return on bank Assets (ROA). This ratio

looks at the returns generated from the bank’s Assets and serves as a measure for operating

efficiency as strong earnings enable banks to boost capital. The Non Interest Income to Total

Income ratio is included. A bank may lose its focus on traditional lending activity as it moves

towards Non-interest income activities. The Cost/Income ratio is a proxy for cost efficiency.

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Poorly run banks have greater incentive for risk-taking (Kwan & Eisenbeis, 1997). In terms

of liquidity, the Net Loans/Total Assets ratio is included in the model. This ratio indicates the

percentage of Assets that is given out as Loans. Loans are the least liquid of the bank’s

Assets, thus the greater the percentage of Assets tied up in loans, the greater the likelihood of

failure. The natural logarithm of assets is included to account for bank size.

Community banks are characterized by their lending specialty. Since 1984 Community

banks’ portfolios shifted away from retail focus and towards commercial focus. The five

major loan categories are Residential Mortgages, Agricultural Loans, Commercial and

Industrial Loans (C&I), Consumer Loans or Loans to Individuals and Commercial Real

Estate Loans (CRE). Construction and Development Loans (C&D) represent an important

subcomponent of CRE loans and for this reason is reported separately (FDIC, 2012). We

want to test whether concentration in certain categories of loan type affects the probability of

a bank failing. For this reason, we run additional regressions that include the ratio of each of

the loan categories to the total number of Assets. According to Bhattacharyya and

Purnanandam (2010), loans associated with inflated Assets such as Residential Mortgages

may expose the bank to higher failure risk than Loans of other categories. Real Estate Loans

are found to play a critical role in determining which banks fail and which not (Cole and

White, 2012). Multi-family Mortgages are associated with higher probability of a bank being

insolvent, whereas Single-family Mortgages are either neutral or associated with a lower

probability of default.

On macroeconomic level, to control for the impact of the general macroeconomic cycle, GDP

growth and CPI growth is included. Evidence from the literature shows that high GDP

growth improves the bank’s survival probability, whereas increasing inflation has an adverse

effect on bank’s stability (Detragiache and Demirgüç-Kunt, 1999). Sectoral shocks such as

the oil price shock that occurred in 1986 can have a severe impact on the banking system.

During this time, states with oil dependent economies exhibited higher rates of failures (Cole

and Gunther, 1995). In 1980s only a small percentage of bank assets were exposed to real

estate. However, by 2008 that percentage had more than doubled (Krainer, 2009). Since

banks hold marketable assets in their portfolio they become exposed to fluctuations in the

market prices. Real estate is used as collateral so changes in the real estate prices are likely to

affect bank performance. In addition, as banks get involved in trading operations and take

market positions using derivatives, their exposure to real estate price risk is likely to increase.

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Table 1 identifies the explanatory variables that appear in the model and offers a brief

definition for each of them. Table 2 gives the main descriptive statistics for those variables.

[Table 1 here]

[Table 2 here]

4. Empirical Results

4.1 Preliminary Data Analysis

As a preliminary analysis between the two bank types, the bank-specific variables are

reported. The mean difference t-tests for each category are shown in Table 3. Figure 1

demonstrates how the means for the two bank types have evolved over the study period.

[Table 3 here]

[Figure 1 here]

First, the two groups are compared to each other in terms of Asset size. As expected,

Community banks are smaller than Non-Community as measured by their Assets (128,770.4

billion against 249,868.1 billion). Non-Community banks were on average 5 times larger than

community banks in 1976, by 2013 they had become almost 36 times as large.

Based on the capitalization ratio, Community banks are better capitalized than Non-

community banks, as they maintain higher levels of capital (10% against 9%). Banks with

higher capital are likely to suffer less from debt overhang problems (Myers, 1977) and can

respond better to adverse shocks (Beltratti and Stulz, 2012), therefore they have lower

probability of default. As shown by Figure 1(b) Community banks consistently maintained

higher capital levels than Non-community over the period 1976-2013. Capital level for both

bank types increased in the middle 1990s as the industry recovered from a banking crisis and

banks raised their capital in order to be in line with the suggestions of Basel I.

Community banks are defined in large part by their focus on traditional lending and deposit

gathering activities. From Table 3, we can draw the conclusion that the quality of loan

portfolios is superior for Non-community banks, as suggested by the lower Loan Loss

Allowance/Total Loans ratio (1.3% against 1.4%). Community banks set aside more capital

for potential losses even though they have maintained lower credit losses over the period

1985 to 2011 according to the FDIC (2012). This can be traced to the fact that most

16

problematic loans at Community banks are secured. Also Community banks may do a better

job in underwriting loans than Non-community banks. After 2005 both Community and Non-

community banks have increased their allowance for potential losses, however Non-

community banks more sharply.

A comparison of ROA reveals that Community banks have been more profitable (0.6%

against 0.5%). During the whole study period Community banks have outperformed Non-

Community. The earnings gap has been more notable during the period 2008-2012 and can

be attributed to the lower credit losses that Community banks experienced. The ratio of

Cost/Income for Community banks is lower than that of conventional banks (41% against

44%). Community banks have been more efficient during the study period- that is they have

incurred less overhead expenses per dollar of income. The efficiency gap has widened

considerably after 2007. Historically, Non-Community banks have been more successful than

Community in generating Non-interest income (FDIC, 2012). The ratio of Non-Interest

Income/Total Income is lower for community banks (9.2% against 9.8%). Since Community

banks focus on traditional lending and deposit activities, net interest income accounts for the

main part of their revenue. Recently, the U.S. economy has been experiencing low interest

rates. Large banks have been able to compensate for that since they can possibly subside this

revenue from revenues from the capital markets or from fee-based products. However,

Community banks have been unable to do the same and they face decreased interest rate

margins. Non-Community banks get involved in trading, investment banking and venture

capital activities which generate non-interest income for them. Throughout the period 1976 to

2013 both Community and Non-Community banks have raised their holdings of non-interest

income to total income. This can be attributed to the decrease in the net interest margin for

both bank types.

For Community banks, the ratio of Net loans/Total Assets is slightly lower (55% against

56%) which indicates that they are more liquid than Non-Community banks. Unlike the Non-

Community banks, many Community banks’ ability to tap funding sources is limited so they

need to maintain higher liquidity buffers. Community banks have increased the share of Net

Loans to Total Assets from 48% in 1976 to a peak of 65% at 2008. The same pattern was

followed by Non-Community banks as well. They increased the ratio from 49% in 1976 to a

peak of 68% in 2008.

17

From a preliminary data analysis, we see that Community banks have significantly higher z-

score (22.6 against 18.4) which suggests that they may be more stable than Non-Community

banks. The difference is significant at 1%, 5% and 10% level. Following 1981 Community

banks have been constantly exhibiting higher z-score which suggests a lower propensity to

fail.

To assess the robustness of our findings we estimate the descriptive statistics separately for

small and large Non-Community banks. A threshold commonly used in the literature is $1

billion in assets (Cihák & Hesse, 2007; Čihák & Hesse, 2010). Table 4 presents the t-test for

the means for the bank-specific variables. Here we differentiate between Community banks,

all banks and small and large Non-Community banks. Community banks have significantly

higher Z-score than both small and large Non-Community.

[Table 4 here]

4.2 Empirical approach of the performance of z-scores

The results of the random effects panel estimations are presented in Table 5.

The table reports the estimated coefficients and the standard errors in parentheses. The micro

model which considers only bank-specific variables and the macro model that considers both

bank-specific and macroeconomic variables are repeated three times. One including all banks

(Columns 1 and 2), one including only Community banks (Columns 5 and 6) and one

including only Non-Community (Columns 7 and 8). Columns 3 and 4 report the coefficients

from the micro and the macro model that includes the interaction with the Community bank

dummy. The goodness of the fit statistics are shown at the bottom of the table. The average of

the overall R2 is 24.9325% for the micro model and 24.9675% for the macro which suggests

that the micro model carries most of the explanatory power. This is in line with the findings

of Cole and Wu (2009) that suggests that bank-specific characteristics are more essential in

predicting bank failures. The results show that fundamental of CAMEL(S) and

macroeconomic indicators have explanatory power in determining the values of z-score. We

are interested in capturing the differences in the sensitivity of the Z-score to bank-specific

and macroeconomic conditions. A positive sign of the coefficient indicates that an increase in

the relevant variable is associated with an increase in the Z-score.

18

Our primary interest focusses on the positive and significant coefficient of Community bank

binary variable. This verifies the results that we got from the preliminary analysis that

suggests that Community banks have higher z-scores than Non-community, indicating higher

stability for Community banks. The estimated positive coefficient is found significant after

controlling for both bank-specific and macroeconomic environments. This verifies the results

from the FDIC (2012), which suggests that Community banks have had a lower propensity to

fail than Non-community banks during the period 1991 to 2010.

Assets come with a positive and statistically significant coefficient. Empirical results suggest

that the larger the bank in terms of assets the less likely to fail. This offers support for the too-

big-to-fail doctrine. The interaction of Community bank dummy with asset size, however,

shows statistically significant reduction in the effect of asset size on stability for Community

banks. This means that Community banks benefit from a larger asset base but the effect is

less pronounced comparing to Non-Community banks. The larger the institution, the more

difficult it becomes to deliver the same level of relationship- based services that give

Community banks a comparative advantage in the first place. The capitalization ratio comes

with a positive coefficient. Banks with higher Equity as percentage of Assets should be less

likely to fail since they are more resilient to shocks. The more Equity the bank has, the more

protected it is against losses in the loan portfolio or drops in its Assets’ value. The interaction

of the Community bank dummy with the ratio of Equity to Assets enters the regression with a

statistically insignificant coefficient. Higher capitalization does not significantly enhances

Community banks’ stability. The ratio of Loan Loss Allowance/Total Loans indicates how

much the bank sets aside for potential bad loans. It enters the regression with a negative sign

for both Community and Non-Community banks. This could be suggesting that banks set

aside more provisions when the quality of their portfolio is poor. However, for Community

banks the effect is less pronounced. Since Community banks are focused on providing

traditional lending services, their exposure to credit risk is relative bigger so setting aside

more reserves could provide them with additional protection and stability.

The liquidity of assets is a factor affecting the risk of bank failure, since liquid assets allow

banks to withstand unexpected deposit withdrawals (Calomiris & Mason, 2000). The ratio of

Net Loans to Total Assets enters the regression with a negative coefficient for both bank

types, indicating that the more concentrated the bank’s portfolio is on loans the greater the

likelihood of failure, a result that has been previously verified by Wheelock and Wilson

(2000). Banks with higher loans to assets ratio may be more likely to face problems with non-

19

performing loans and thus be riskier. The interaction of Community banks dummy with Net

Loans to Assets ratio shows statistically significant increases in the effects of loan

concentration on stability. Concentration on loans does not lower stability for Community

banks as much as it does for Non-Community. This reflects the specialized focus that

Community banks have on traditional lending activity. Community banks do not normally

hold securities in their portfolio so they are more focused on loans in order to draw liquidity.

Higher Return on Assets signifies that the bank makes more efficient use of its asset base and

as such, it is associated with higher values of z-score and lower probability of default (Cole

and White, 2012; Lane et al., 1986; Lepetit et al., 2008). Nonetheless, the effect of ROA is

significantly less pronounced for Community banks. The objective of Community banks is

not so heavily oriented towards profitability as it is for Non-Community. In that sense, one

could argue that the objectives of Community banks partly resemble those of Social Banks.

Social banks are financial intermediaries that pay attention to non-economic criteria (e.g.

social) as well and they have corporate social responsibility (Cornée & Szafarz, 2013). Cost

efficiency increases stability for all banks as suggested by the negative and statistically

significant coefficient of Cost to Income ratio. The concentration on Non-Interest Income is

found to increase stability for both bank types, without a significantly different effect

between the two bank types. Non-Community banks have a comparative advantage in

creating higher volume of non-interest income since they have access to a wider range of

sources such as trading products or investment banking activities.

With respect to the macroeconomic environment, the effect of Inflation growth is

significantly positive for Community banks whereas it is insignificant for Non-Community.

When Inflation slows down, real interest rates get strengthened. This triggers credit risk for

Community banks since they are more heavily influenced by movements of the interest rates.

Non-community banks are able to hedge their exposure better and for this reason the

coefficient is insignificant. Higher levels of GDP growth improve the bank’s probability of

survival (Demirgüç-Kunt and Huizinga, 2010). Rise in the GDP levels causes reduction in the

probability of default for both bank types, however the effect of GDP growth on the stability

of Community banks is much less pronounced. Appreciation of the Oil Price Index comes

with a positive sign for Community banks but negative for Non-community. Non-community

banks were hurt by the Energy Recession when there was a falling demand for crude oil. This

verifies the findings of Cole and Gunther (1995) which suggest that oil price shock is a

significant determinant of the bank’s survival probability. Oklahoma, Texas, Wyoming,

20

North Dakota, Alaska and Louisiana are states that are heavily oil dependent. At these states

there is a dense concentration of Non-Community banks. House price appreciation is positive

for both Community and Non-Community banks. However, the effect for Community banks

is significantly less pronounced. This suggests that real estate downturns represent a

significant risk for Non-community banks but not so much for Community. Community

banks shifted their focus away from Retail Loans (1-4 residential real estate loans) and

towards Commercial Loans while Non-Community banks were doing the opposite. In

addition to Residential Real Estate Mortgages, Non-Community banks hold in their portfolios

trading products that are inflated with house prices. 2

[Table 5 here]

4.3 Loan specialty and Financial Stability

Lending strategy is an important factor in determining Community bank success. In this

section it is being investigated whether concentration on different types of loan has different

effect on the bank’s Z-score. Table 7 reports the estimated coefficients and standard errors for

the key loan specialties. In the first four columns, all banks are pooled together, in the fifth

and the sixth only Community banks are included and in the last two columns only Non-

Community banks. As previously, each panel has a micro and macro variant. In addition, the

interactions of Community bank variable with the loan specialty variables are included

(Columns 3 and 4). All other explanatory variables are included in the regression but they are

not reported. The F-test shows that the coefficients on the model are different than 0. The

average of the overall R2 is 28.205% for the micro model and 28.227 % for the macro.

The sensitivity of the Z-score to the bank’s loan specialty is different for Community and

Non-Community banks. Commercial and Industrial Loan specialty is entering the regression

with a highly significant coefficient for Community banks but not for Non-community banks.

Similarly, Lane et al. (1986) find this loan specialization variable highly significant when

applying Cox proportional hazards model to predict bank failures. The negative coefficient

implies that this type of credit risk exposure reduces the stability of Community banks.

Kupiec and Lee (2012) found that banks that were characterisized as Commercial and

2 We re-estimate the model for Community banks compared to small Non-Community banks (Table 6 in the Appendix). The positive effect of a larger asset base is much more pronounced for small Non-Community banks than it is for Community, whereas more equity increases stability more for Community banks than it does for small Non-Community. In contrast to our results from Table 4, Non- interest income significantly decreases stability for small Non-Community banks.

21

Industrial lenders were reporting lower Return on Assets. These are loans of longer maturity

that increases the riskiness of bank’s portfolio. Construction and Development and

Commercial Real Estate Loans are found to be significant factors in determining only Non-

Community banks’ Z-score. Non- Community banks focused on secured real estate loans

only after 2000. These are loans that are secured by nonfarm, non-residential properties.

Similarly, the results from Wheelock and Wilson (2000) report that Real Estate Loans are

insignificant in determining US bank failures and acquisitions. Agricultural Loans however

have a significant negative coefficient in all regressions. This may reflect the impact of the

farm crisis during 1980-1984. The adverse effect of concentration in agricultural loans has a

less pronounced effect for Community banks. Loans to Individuals appear with a negative

coefficient for Community banks and a positive for Non-Community. The result for

Community banks is similar to the findings of Cole and Gunther (1995) who found a

significant negative coefficient in this type of loan as determinant of the survival and survival

time of US banks. The negative impact could offer a justification for the fact that following

2000 community banks shifted away from their retail lending focus.

[Table 7 here]

4.4 Financial stability and banking crises

Additional analysis of the Z-scores is undertaken to identify the effect of the banking crises

on Community banks’ stability. Following Berger and Bouwman (2013) two banking crises

are considered that are relevant to our sample. These are the credit crunch of the early 1990s

(from 1990:Q1 to 1992:Q4) and the subprime lending crisis (from 2007:Q3 to 2009:Q4). The

subprime lending crisis began in 2007 and interrupted an upward trend in the house prices,

the supply of mortgages and consumer loans and an expanding residential construction

activity. These trends had fuelled the balance sheet expansion of both bank types and

stimulated economic growth in metro and non-metro areas. During periods of distress,

Community banks were exposed to the same market conditions as their counterparts. Given

the differences in their business model, it is important to investigate how they weathered the

crises in comparison with Non-Community banks. Our model is capturing the sensitivities of

Community and Non-Community banks' Z-score to bank-specific characteristics during

periods of financial distress. In order to do so, we add to the regression the triple interaction

term of the Community bank dummy, a Crisis dummy and the control variables that have

been previously used in the model. The Crisis dummy variable takes the value of 1 if it

signifies a banking crisis period and 0 otherwise.

22

Thus, the model is specified as follows:

, , , , , , ,

, , ,

Where the dependent variable is Z-score , ’ for bank i at period t; , is the Community

banks dummy variable. is the crisis dummy variable; , is a vector of bank-specific

independent variables.; , is the interaction between the Community dummy and

the crisis dummy; , , is the interaction between the Community dummy and the

bank-specific variables; , is the interaction between the crisis dummy and the

bank-specific control variables; , , is the triple interaction of the bank and

crisis dummy and the vector of bank-specific variables; νi is the unobserved random effect

that varies across banks but not over time; εi,t is an idiosyncratic error term.

Estimated coefficients and standard errors are reported in Table 8. Column 1 reports the

coefficients for the bank-specific variables, column 2 reports the coefficients for the

interactions of the Community bank dummy with the bank-specific variables and column 3

reports the coefficients of the triple interaction term between the Community bank dummy,

the crisis dummy and the bank specific variables The parameter of the triple interaction term

is masking the change in the effect that the bank-specific variable has on the z-score for

Community banks during crises. The interactions with the crisis dummy are included in the

model but not reported. The overall message is that during crises Community banks are less

stable during crisis as it becomes evident from the negative coefficient of the interaction term

between Community bank and Crisis dummies. Someone could argue that Community banks

are not as well equipped as Non-Community banks to weather the banking crises. The crisis

dummy variable enters the regression with a positive coefficient. In order to trace the reason

behind that we decompose the z-score to its components and report their mean values of them

during the two banking crises and for the time intervals pre and post crisis (Table 9). The

Return on Assets falls during the banking crisis for Non-Community banks whereas for

Community banks it increases after 1990 and remains stable until 2013. The volatility of

ROA seems to be higher during the crisis of 2007-2009. The ratio of Equity to Assets is

higher during periods of crisis, possibly as a result of tighter regulatory requirements.

With respect to the bank specific variables, the positive coefficient of the triple interaction

with assets shows statistically significant increases in the effect of asset size on bank stability

23

for Community banks. Community banks that are larger in size prove to be more resilient to

the banking crises that smaller Community banks. When it comes to the ratio of Equity to

Assets, the negative coefficient suggests that the beneficiary effect of higher equity is less

pronounced during crises for Community banks. The negative coefficient on Loan Loss

Reserves reduces the negative effect of this variable on the Community bank’s stability,

which suggests that during non-normal times holding more reserves can offer additional

protection for the bank. Concentration on Net loans in general reduces stability for

Community banks but the effect is less pronounced during periods of crisis. The effect of

ROA on Community banks’ stability decreases during non-normal times as evidenced by the

negative and statistically significant coefficient of the triple interaction term.

[Table 8 here]

[Table 9 here]

24

5. Conclusion

Evidence suggests that Community banks compete effectively with Non-Community banks.

They conduct business in ways that are different from those of Non-Community banks. They

complement the role of those banks by specializing in relationship banking and providing

credit to small and medium size businesses. In addition, they serve customers in rural and

small metro areas that are not served by large banks.

Our primary aim is to compare the financial stability of Community and Non-Community

banks and to test whether their Z-score shows similar sensitivity to key bank-specific and

macroeconomic indicators. Using Z-score regressions and controlling for both bank-specific

and macroeconomic indicators, it is found that Community banks tend to be more financially

stable than Non-Community banks and they face lower probability of failure. More

specifically, Community banks do not benefit as much from a larger asset base as Non-

Community. More loan loss reserves offer more protection for Community banks than it does

for Non-Community. The effect of ROA on Z-score is less pronounced for Community banks

probably because the objectives of Community banks are not so heavily oriented towards

profitability. When it come to the macroeconomic environment, Inflation growth significantly

affects stability of Community banks since they are more heavily influenced by movements

of the interest rates. Concentration of the loan portfolio on Commercial and Industrial loans is

highly important in determining the Z-score of Community banks but not Non-Community.

At the same time, concentration on Agricultural loans negatively affects stability for both

bank types, however, the effect on Community banks is less pronounced. Moreover, the

stability of Community banks significantly lowers in periods of crisis. Nonetheless, larger

Community banks are proved to be more resilient in crises than smaller ones.

The analysis of banking industry in this study gives a basis for future research on Community

banks issues. It has been shown that the determinants of bank stability vary across

Community and Non-Community banks. In light of the upcoming Basel III, future research

could focus on whether the current “one-size-fits-all” regulatory framework could apply. In

order to enhance bank stability, the new regulatory framework introduces measures

concerning capital adequacy, risk exposure, leverage limits and liquidity creation. However,

the sensitivity on failure risk shows variations among the two bank types. In the light of Basel

I, Basel II and the upcoming Basel III, need for transparency has increased and regulatory

costs have risen for banks. Non-Community banks have always had an advantage against

25

Community banks because they have better access to more sophisticated financial

instruments. This creates additional challenges for Community banks who need to compete in

the same environment with Non-Community banks. Therefore, the implication of any bank

policy should take into consideration the distinct risk profile of the two bank types.

Measuring the effect of bank regulation remains a critical issue that poses substantial

challenges for the supervisors. The competitive effects of regulation between the two bank

types could provide ground for further study.

26

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32

Tables and Graphs

Table 1. Variables used in the analysis Variable Definition Type

Z-score (dependent)

It is computed as z≡ (k+μ)/σ, where k is equity capital and reserves as percentage of assets, μ is average return as percentage of assets, and σ is standard deviation of return on assets.

Financial ratio

Community Bank Binary variable, equals 1 for community banks; 0 otherwise

Qualitative

Assets Logarithm of total assets Balance sheet

Equity/Assets Ratio of equity capital to total assets Financial Ratio

Loan Loss Allowance/Total Loans Ratio of loan loss allowance to total loans & leases

Financial Ratio

Net loans/Total Assets Ratio of Gross Loans minus Loan loss allowance minus unearned interest to total loans & leases

Financial Ratio

ROA Ratio of net income to total assets Financial Ratio

Cost/Income Ratio of operating expenses, the mail component of which is salaries to operating income plus interest income

Financial Ratio

Non- Interest Income/Total income Ratio of noninterest income to total operating income

Financial Ratio

Agricultural Loans Ratio of agricultural loans to total assets Financial Ratio

33

Table 1 (continued) Commercial & Industrial Loans(C&I)

Ratio of commercial & industrial loans to total assets

Financial Ratio

Commercial Real Estate Loans(CRE)

Ratio of real estate loans to total assets Financial Ratio

Construction & Development Loans(C&D)

Ratio of loans for construction & land development to total assets

Financial Ratio

Loans to Individuals Ratio of loans to individuals to total assets Financial Ratio

Residential Mortgages Ratio of mortgages secured by 1-4 family resid. mortgages to total assets

Financial Ratio

GDP Quarterly growth of real gross domestic product Macroeconomic

Inflation Implicit price deflator, change period over period Macroeconomic

Oil Price Index Quarterly growth of crude oil index Macroeconomic

House Price Index House price index, change period over period Macroeconomic

34

Table 2. Descriptive Statistics for the variables used in the model Variable Observations Mean Std. Dev. Min Max

Community Bank 1,683,845 0.3937 0.4885 0 1 Assets 1,683,845 202187.5 611738.4 2806 4940161 Equity/Assets 1,683,845 0.0965 0.0373 0.0389 0.2830 Loan Loss Allowance/Total Loans 1,683,845 0.0139 0.0090 0 0.0554Net loans/Total Assets 1,683,845 0.5585 0.1484 0.1598 0.8704ROA 1,481,765 0.0055 0.0072 -0.0295 0.0224Cost/Income 1,204,973 0.4313 0.0745 0.2858 0.8097Non-Interest Income/Total Income 1,204,973 0.0955 0.0720 0.0045 0.4549Agricultural Loans 1,678,408 0.0587 0.0925 0 0.4222Commercial & Industrial Loans 1,280,303 0.1095 0.0820 0 0.3965 Commercial Real Estate Loans 1,683,844 0.2914 0.1770 0.0042 0.7628 Construction & Development Loans 1,683,842 0.0253 0.0422 0 0.2328

Loans to Individuals 1,678,405 0.1002 0.0815 0.0005 0.4068 Residential Mortgages 1,683,845 0.1482 0.1193 0 0.5946 Z-score 1,481,651 20.1872 10.0295 1.4385 52.8183 Note: Assets is expressed in millions USD.

35

Table 3. Mean difference t-tests for bank-specific variables

Variable Community Bank Non-Community Bank

Assets 128770.4 249868.1***

Equity/Assets 0.1034 0.0920***

Loan Loss Allowance/Total Loans 0.0142 0.0136*** Net Loans/Total Assets 0.5600 0.5575***

ROA 0.0062 0.0050***

Cost/Income 0.4181 0.4421***

Non-Interest Income/Total Income 0.0920 0.0983*** Agricultural loans 0.0738 0.0489***

Commercial & Industrial Loans 0.0878 0.1193***

Commercial Real Estate Loans 0.3185 0.2738***

Construction & Development Loans 0.0237 0.0264***

Loans to Individuals 0.0805 0.1129***

Residential Mortgages 0.1605 0.1402 ***

Z-score 22.6432 18.4645*** .Note: *** denotes statistical significance at the 1% level; Assets is expressed in millions USD.

36

Figure 1. Mean Comparison for the two bank types across time

Panel A: Total Assets (in millions) Panel B: Equity/Assets

Panel C: Loan Loss Allowance/Total Loans Panel D: Return on Assets

Panel E: Cost/Income Panel F: Non Interest Income/Total Income

37

Figure 1. (continued)

Panel G: Net Loans/Total Assets Panel H: Z-score

Note: Graphs demonstrate the time evolution of key financial characteristics in Community and Non-Community banks.

38

Table 4. Mean difference t-tests for bank-specific variables (small and large Non-Community Banks)

Variable Community Bank Non-Community Bank

Small Non-Community Bank

Large Non-Community Bank

Assets 128770.4 249868.1 *** 6739.926*** 275602.5***

Equity/Assets 0.1034 0.0920 *** 0.1185*** 0.0891***

Loan Loss Allowance/Total Loans 0.0142 0.0136 *** 0.0110*** 0.0139***

Net Loans/Total Assets 0.5600 0.5575 *** 0.5004*** 0.5635***

ROA 0.0062 0.0050 *** 0.0033*** 0.0051***

Cost/Income 0.4181 0.4421*** 0.5005*** 0.4394***

Non- Interest Income/Total Income 0.0920 0.0983*** 0.0847*** 0.0989***

Agricultural loans 0.0738 0.0489*** 0.1190*** 0.0415***

Commercial & Industrial Loans 0.0878 0.1193*** 0.0940*** 0.1224***

Commercial Real Estate Loans 0.3185 0.2738*** 0.1547*** 0.2864***

Construction & Development Loans 0.0237 0.0264*** 0.0102*** 0.0281***

Loans to Individuals 0.0805 0.1129*** 0.1289*** 0.1112***

Residential Mortgages 0.1605 0.1402 *** 0.0898*** 0.1455***

Z-score 22.6432 18.4645*** 18.7358*** 18.4427***

Notes: ***denotes statistical significance at the 1% level respectively; small Non-Community banks are defined as those that have assets fewer than $1 billion; Assets is expressed in millions USD.

39

Table 5. Regression Results (dependent variable Z-score) All banks Community Banks Non Community Banks micro macro micro macro micro macro micro macro (1) (2) (3) (4) (5) (6) (7) (8) Community Bank 0.236***

(0.001) 0.238*** (0.001)

0.243*** (0.001)

0.275*** (0.001)

Assets 0.016*** (0.001)

0.018*** (0.001)

0.017*** (0.001)

0.020*** (0.001)

0.013*** (0.001)

0.014*** (0.001)

0.017*** (0.001)

0.020*** (0.001)

Equity/Assets 7.308*** (0.001)

7.316*** (0.001)

7.342*** (0.001)

7.346*** (0.001)

7.257*** (0.001)

7.263*** (0.001)

7.341*** (0.001)

7.345*** (0.001)

Loan Loss Allowance/Total Loans

-2.112*** (0.001)

-2.068*** (0.001)

-2.452*** (0.001)

-2.345*** (0.001)

-1.430*** (0.001)

-1.424*** (0.001)

-2.473*** (0.001)

-2.361*** (0.001)

Net Loans/Total Assets -0.1495*** (0.001)

-0.145*** (0.001)

-0.165*** (0.001)

-0.160*** (0.001)

-0.120*** (0.001)

-0.120*** (0.001)

-0.167*** (0.001)

-0.162*** (0.001)

ROA 6.346*** (0.001)

6.397*** (0.001)

7.394*** (0.001)

7.462*** (0.001)

4.494*** (0.001)

4.540*** (0.001)

7.414*** (0.001)

7.478*** (0.001)

Cost/ Income -0.685*** (0.001)

-0.670*** (0.001)

-0.706*** (0.001)

-0.687*** (0.001)

-0.655*** (0.001)

-0.648*** (0.001)

-0.707*** (0.001)

-0.688*** (0.001)

Non Interest Income/Total Income

0.203*** (0.001)

0.199*** (0.001)

0.217*** (0.001)

0.207*** (0.001)

0.193*** (0.001)

0.190*** (0.001)

0.214*** (0.001)

0.205*** (0.001)

GDP 0.001*** (0.000)

0.002*** (0.001)

0.000*** (0.003)

0.002*** (0.001)

Inflation 0.000*** (0.001)

0.000 (0.798)

0.001*** (0.001)

0.000 (0.778)

Oil Price Index -0.002** (0.037)

-0.012*** (0.001)

0.012*** (0.001)

-0.012*** (0.001)

House Price Index 0.001*** (0.000)

0.003*** (0.001)

0.000** (0.044)

0.003*** (0.001)

Com

mu

nit

y B

ank

Inte

ract

ion

s

Assets×CB -0.004** (0.032)

-0.005*** (0.006)

Equity/Assets×CB -0.086 (0.104)

-0.082 (0.116)

Loan Loss Allowance/Loans×CB

1.015*** (0.001)

0.918*** (0.001)

Net Loans/Assets×CB 0.043*** (0.001)

0.040*** (0.001)

ROA×CB -2.899*** (0.001)

-2.921*** (0.001)

Cost/ Income×CB 0.049** (0.039)

0.038 (0.120)

Non Interest Income/Income×CB

-0.024 (0.239)

-0.016 (0.421)

GDP×CB -0.002*** (0.001)

Inflation×CB 0.001*** (0.008)

Oil Price Index×CB 0.024*** (0.001)

House Price Index×CB -0.003*** (0.001)

Constant 2.152*** (0.001)

2.105*** (0.001)

2.154*** (0.001)

2.097*** (0.001)

2.397*** (0.001)

2.372*** (0.001)

2.153*** (0.001)

2.096*** (0.001)

Wald chi 84981.69*** 88196.8*** 86278.28*** 90739.09*** 37823.25*** 40149.87*** 47167.06*** 48945.05*** R2 0.2653 0.2656 0.2686 0.2689 0.2404 0.2409 0.223 0.2233 Observations 1191563 1191563 1191563 1191563 537893 537893 653670 653670Notes: Table reports estimated coefficients and robust standard errors in brackets. ***, **, * denote statistical significance at the 1, 5 and 10% level respectively; Assets is expressed in millions USD. CB denotes a community bank.

40

Table 6. Robust Regression Results for small Non-Community Banks

Community banks Non- Community Small banks

micro macro micro macro

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

Assets 0.013*** 0.014*** 0.2673*** 0.2685***

(0.001) (0.001) (0.001) (0.001)

Equity/Assets 7.257*** 7.263*** 4.7141*** 4.7131***

(0.001) (0.001) (0.001) (0.001)

Loan Loss Allowance/Total Loans

-1.430*** -1.424*** -0.4927 -0.3583

(0.001) (0.001) (0.270) (0.425)

Net Loans/Total Assets

-0.120*** -0.120*** -0.1538*** -0.1527***

(0.001) (0.001) (0.001) (0.001)

ROA 4.494*** 4.540*** 8.7718*** 8.7714***

(0.001) (0.001) (0.001) (0.001)

Cost/ Income -0.655*** -0.687*** -0.2250*** -0.2245***

(0.001) (0.001) (0.002) (0.002)

Non-Interest Income/Total Income

0.193*** 0.190*** -0.3870*** -0.3878***

(0.001) (0.001) (0.001) (0.001)

GDP 0.000*** 0.0066***

(0.003) (0.001)

Inflation 0.001*** 0.0012

(0.001) (0.668)

Oil Price Index 0.012*** -0.0047

(0.001) (0.669)

House Price Index 0.000** -0.0177***

(0.044) (0.001)

Constant 2.152***

2.372*** 0.0287 0.0064

(0.001) (0.001) (0.816) (0.959)

Wald chi 40149.87*** 1395.51 1459.55***

R2 0.2409 0.3954 0.3948

Observations 537893 28,886 28,886

Notes:***, **, * denote statistical significance at the 1, 5 and 10% level respectively; small Non- Community banks are defined as those that have assets fewer than $1 billion; Assets is expressed in millions USD.

41

Table 7. Regression Results on Loan Specialty

All banks Community banks Non- Community Banks

micro macro micro macro micro macro micro macro

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

Agricultural -0.060**

(0.022)

-0.070***

(0.008)

-0.084***

(0.008)

-0.094***

(0.003)

-0.117**

(0.022)

-0.121**

(0.018)

-0.061*

(0.063)

-0.075**

(0.022)

Commercial & Industrial -0.019

(0.407)

-0.021

(0.366)

-0.011

(0.639)

-0.013

(0.597)

-0.135***

(0.01)

-0.135***

(0.01)

0.011

(0.683)

0.008

(0.743)

Commercial Real Estate 0.098***

(0.001)

0.100***

(0.001)

0.105***

(0.001)

0.108***

(0.001)

0.012

(0.795)

0.014

(0.765)

0.124***

(0.001)

0.125***

(0.001)

Construction & Development -0.090***

(0.001)

-0.087***

(0.001)

-0.100***

(0.002)

-0.096***

(0.003)

-0.049

(0.302)

-0.051

(0.279)

-0.112***

(0.001)

-0.106***

(0.001)

Loans to Individuals 0.028

(0.212)

0.031

(0.173)

0.052**

(0.032)

0.055**

(0.024)

-0.120**

(0.011)

-0.119**

(0.012)

0.075***

(0.004)

0.078***

(0.003)

Residential Mortgages 0.014

(0.277)

0.016

(0.214)

0.014

(0.384)

0.017

(0.311)

0.008

(0.679)

0.007

(0.716)

0.017

(0.293)

0.022

(0.181)

Com

mun

ity B

ank

Inte

ract

ions

Agricultural×CB 0.053*

(0.087)

0.054*

(0.082)

Commercial & Industrial×CB -0.027

(0.298)

-0.028

(0.272)

Commercial Real Estate×CB -0.020

(0.297)

-0.021

(0.273)

Construction & Development×CB 0.035

(0.546)

0.026

(0.647)

Loans to Individuals×CB -0.087***

(0.001)

-0.088***

(0.001)

Residential Mortgages×CB -0.001

(0.952)

-0.002

(0.931)

42

Table 7 (continued)

Constant 2.289***

(0.001)

2.264***

(0.001)

2.284***

(0.001)

2.257***

(0.001)

2.670***

(0.001)

2.638***

(0.001)

2.231***

(0.001)

2.212***

(0.001)

Wald chi 57069.88*** 58639.55*** 58084.55*** 59604.81*** 19035.81*** 20176.1*** 39457.07*** 40533.4***

R2 0.2888 0.289 0.2886 0.2887 0.298 0.2989 0.2528 0.2525

Observations 799633 799633 799633 799633 280189 280189 519444 519444

Notes: Table reports estimated coefficients and robust standard errors in brackets. Bank-specific and macroeconomic variables are also included but not reported for brevity ***, **, * denote statistical significance at the 1, 5 and 10% level respectively; Assets is expressed in millions USD. CB denotes a community bank.

43

Table 8. Regression results for the performance of Community Banks during banking crises.

Bank specific variables Community Bank Interactions Community Bank and Crisis Interactions Community Bank (CB) 0.274***

(0.029)

Crisis 0.137*** Crisis×CB -0.049*

(0.019) (0.026)

Assets 0.020*** Assets×CB -0.006*** Assets×CB×Crisis 0.004***

(0.001) (0.001) (0.001)

Equity/Assets 7.256*** Equity/Assets ×CB -0.002 Equity/Assets×CB×Crisis -0.267***

(0.035) (0.054) (0.056)

Loan Loss Allowance/Total Loans -2.002*** Loan Loss Allowance/Total Loans×CB 0.661*** Loan Loss Allowance/Total Loans×CB×Crisis 0.799*** (0.114) (0.165) (0.222) Net Loans/Total Assets -0.143*** Net Loans/Total Assets×CB 0.032*** Net Loans/Total Assets×CB×Crisis 0.042***

(0.007) (0.009) (0.008)

ROA 6.767*** ROA×CB -2.397*** ROA×CB×Crisis -2.322***

(0.142) (0.207) (0.367)

Cost/Income -0.654*** Cost/Income×CB 0.033 Cost/Income×CB×Crisis 0.029** (0.019) (0.025) (0.038)

Non-Interest Income/Total Income 0.196*** Non-Interest Income/Total Income×CB -0.012 Non-Interest Income/Total Income×CB×Crisis 0.004**

(0.015) (0.021) (0.027) Constant 2.100***

(0.001)

Wald chi-square 95776.77*** Adjusted R2 0.269 Observations 1,191,563

Notes: Table reports estimated coefficients and robust standard errors in brackets. Interactions with the crisis dummy variables are also included but not reported for brevity. ***, **, * denote statistical significance at the 1, 5 and 10% level respectively; Assets is expressed in millions USD. CB denotes a community bank. .

44

Table 9. Descriptive Statistics for the components of the Z-score pre and post crisis

1976-1989 1990-1992

1993-2006 2007-2009 2010-2013

All

bank

s

Com

mun

ity b

anks

Non

-Com

mun

ity

bank

s

All

bank

s

Com

mun

ity b

anks

Non

-Com

mun

ity

bank

s

All

bank

s

Com

mun

ity b

anks

Non

-Com

mun

ity

bank

s

All

bank

s

Com

mun

ity b

anks

Non

-Com

mun

ity

bank

s

All

bank

s

Com

mun

ity b

anks

Non

-Com

mun

ity

bank

s

Z-score 18.875 20.718 18.866 18.935 21.950 18.620 21.521 21.672 19.643 20.88 22.390 18.742 22.253 22.497 18.894

ROA 0.5618 0.5771 0.5364 0.4923 0.6010 0.5093 0.6441 0.6208 0.5449 0.3103 0.6051 0.4970 0.3844 0.611 0.4976

SD(ROA) 0.6790 0.6486 0.7113 0.6704 0.5963 0.7383 0.6686 0.6283 0.7130 0.7465 0.5925 0.7414 0.6984 0.5871 0.7394

8.8867 9.5748 9.2882 9.0437 10.1371 9.2224 0.4747 10.27974 9.5308 11.21207 10.37979 9.31833 10.95529 10.3655 9.3415

Note: Values for ROA, SD(ROA) and Equity/Assets are multiplied with 100

45