Underwriting Cycles in Banking: Are Bad Loans...

Underwriting Cycles in Banking:

Are Bad Loans Really Made in Good Times?

John O’Keefe Federal Deposit Insurance Corporation

550 17th Street, NW Washington, DC 20429

[email protected] Phone: (202) 898-3945 FAX: (202) 898-8500

Virginia Olin

(Retired) Federal Deposit Insurance Corporation

550 17th Street, NW Washington, DC 20429

[email protected]

Christopher A. Richardson∗ U.S. Department of Justice and Syracuse University

Washington, DC 20530 [email protected]

Phone: (202) 514-3556

June 2005

JEL classification code: G21, G28, E32, E44 Key words: Banks, Underwriting, Lending, Credit Cycles ∗ We would like to thank Andrew Davenport, FDIC, for helpful comments and suggestions and Kitty Chaney, FDIC, for research assistance. The views expressed here are those of the authors and not necessarily those of the Federal Deposit Insurance Corporation or the Department of Justice.

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1. Introduction

The role of bank lending in the transmission of monetary policy, and the relationship

between bank lending and economic activity, continue to be of interest to academics and policy

makers. This interest is understandable since the existence of such a role suggests a possible

interaction between the government’s monetary policy and its bank supervisory policy. Such

interaction would imply that policies expected to slow the flow of credit, such as increases in the

U.S. Fed Funds rate by the Federal Reserve, may have varying degrees of effectiveness

depending on the state of bank lending policies and the efforts of regulatory agencies to

influence them.1 The interaction between monetary and supervisory policies might lead to a

misunderstanding of the effects of both types of policies—or, worse, might impede policy

coordination. Textbook discussions of the channels by which monetary policy might influence

the economy describe a possible role for bank lending under the “credit view” (Freixas and

Rochet [1997]). According to the credit view, if some portion of bank credit is allocated on a

non-price basis, there is a role for bank lending in the monetary policy transmission mechanism

(see, e.g., Hulsewig 2003). The purpose of our study is to examine one possible cause of non-

price credit allocation—cycles in bank loan-underwriting standards (“underwriting cycles”)—

and the relationship between these cycles and bank lending activity.2

1 Peek and Rosengren (1995) find that direct, explicit regulatory measures taken to influence

bank lending—the use of enforcement actions—are quite effective at curtailing loan supply but

tend mainly to affect loan supply to bank-dependent borrowers.

2 While noncyclical variations in underwriting standards would also be of interest to policy

makers, our empirical analysis is designed to test for cyclical variations.

2

Recent reports in the popular press indicate that bank regulators are concerned about

laxity in underwriting standards, particularly at times when the economy is relatively strong and

price competition has contributed to low interest rates. For example, federal bank regulatory

agencies recently issued a collective warning to banks to tighten their underwriting standards for

home equity loans and lines of credit.3 A similar cautionary note was sounded for underwriting

standards generally in 1994, during the U.S. economy’s emergence from the recession of 1991–

1992.

Measuring the macroeconomic effects of cycles in underwriting standards on loan

volume, however, depends critically on being able to properly identify underwriting standards

and separate changes in standards from changes in lending volume. Although loan-underwriting

standards are documented in a bank’s written lending policies and procedures, they are

characterized more fully by actual lending practices.4 Lending practices can be described in

general terms by the criteria loan officers use to qualify borrowers, pricing and repayment terms,

sources of repayment, and collateral requirements. Lending practices also encompass the

management and administration of the loan portfolio’s growth and distribution among various

loan types and markets, in addition to out-of-area lending and adherence to written underwriting

policies.5 All other things being equal, cyclical variation in these practices could result in

3 Kirstin Downey, “U.S. Warns Lenders to Elevate Standards: Agencies Cite Risks in Home

Equity Loans,” Washington Post (May 17, 2005), A01.

4 This is particularly true when a bank’s lending practices deviate from its written policies.

5 Part 364 of the FDIC Rules and Regulations covers “Credit Underwriting” in a general sense.

Part 365 provides a list of specific factors for underwriting standards. It also covers loan

administration.

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cyclical variation in the supply of bank credit and outstanding loans, as well as variation in

overall loan portfolio credit risk. However, on a macro level, the quantity and quality of

outstanding loans in a market are also influenced by loan demand-side factors and underlying

macroeconomic conditions. Consequently, one cannot unambiguously infer changes in

underwriting standards from changes in the volume and quality of loans. Our study overcomes

this problem of identification by using FDIC bank examiners’ assessments of the riskiness of

banks’ underwriting practices as direct measures of standards. Our goal is to estimate the

probability that banks have changed their loan-underwriting practices and to test whether these

probabilities are related to bank loan growth.

The study proceeds as follows: Section 2 discusses the previous literature on credit cycles

and bank loan-underwriting standards. Section 3 discusses the FDIC underwriting survey and

our sample. Section 4 discusses the methodology we use to investigate underwriting cycles.

Section 5 presents our results. Section 6 concludes.

2. Literature Review

The literature on cycles in bank lending and their relationship with economic activity has

found that bank lending is procyclical: banks lend more during economic expansions and lend

less during economic downturns. Underwriting cycles in lending are aptly described by Ruches

(2004), who points out that at the top of the business cycle, when the economy is strong and

economic activity is high, lenders relax their underwriting standards. As economic growth falls,

fewer profitable investment projects exist and the ratio of good projects to bad projects falls, and

banks tighten their underwriting standards. At the trough of an economic cycle, few profitable

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projects exist. As the economy improves, the ratio of good to bad investment projects increases,

and banks are therefore able to begin to loosen their lending standards.

In his model, Ruches attributes cycles in lending standards to variation in the returns to

screening potential borrowers. Variation in returns to screening, in turn, is driven by variation in

the average credit quality of borrowers over the business cycle. Because screening is costly,

banks do more screening when the expected marginal payoff to screening is high and less when

it is low. The marginal value of screening is lowest at the extremes of the business cycle—the

peak and the trough—because at those times the attractiveness of applicants is known to be

either (largely) universally good or bad without screening.

The implications of Ruches’ theoretical model are supported by several empirical studies

that find that fluctuations in bank lending over the business cycle are due in part to changes in

lending standards (Asea and Blomberg [1998]; Lown and Morgan [2004]; Lown, Morgan, and

Rohatgi [2000]; Rajan [1994]; Weinberg [1995]; Berger and Udell [2002]). Asea and Blomberg

(1998) examine the empirical relationship between changes in lending standards and cyclical

fluctuations in aggregate unemployment. They use the Federal Reserve Survey of Terms of Bank

Lending to construct a panel data set of the terms on individual loan contracts on commercial and

industrial loans for 580 different banks over the period 1977 through 1993. Measures of contract

terms include the loan’s effective interest rate, maturity, collateral, loan commitments, and total

assets of the lender (bank). They find that relatively loose lending standards, which tend to occur

during expansions, exert considerable influence on the dynamics of aggregate fluctuations in

unemployment. Thus, changes in bank lending standards appear to have a more profound effect

on the economy during expansions than during contractions.

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Empirical tests by Lown and Morgan (2004) and Lown, Morgan, and Rohatgi (2000)

show a strong relationship between changes in bank lending standards, commercial loan volume,

and real output. They find that slowdowns in commercial loan growth follow indications of

tighter underwriting standards as evidenced by the Federal Reserve’s Senior Loan Officer

Opinion Survey on Bank Lending Practices. Moreover, they find that slower loan growth has an

adverse effect on aggregate GDP growth. To measure underwriting standards, both studies use a

single question taken from the senior loan officer survey: “Over the past three months, how have

your bank’s credit standards for approving loan applications for C&I loans or credit lines

changed?” Respondents can pick one of five possible answers: “tightened considerably,”

“tightened somewhat,” “remained basically unchanged,” “eased somewhat,” and “eased

considerably.” In both studies the authors use the net percentage of respondents tightening or

loosening standards as their measure of underwriting standards.

The literature offers a wide variety of motivations for why bank managers adjust their

lending standards. Rajan (1994) argues that because the decisions of bank managers, while

rational, are heavily influenced by market perceptions of managers’ effectiveness (as evidenced

by earnings), bank managers tend to have short horizons and are influenced by the credit policies

of other banks. Short-term earnings (and managers’ reputations) suffer if managers fail to

expand credit while other banks are doing so. It follows that all banks will expand lending at the

same time and a portion of that lending will be to less credit worthy customers. This herd

behavior implies that expansions of lending lead to significant increases in subsequent loan

losses. Weinberg (1995), taking another direction, develops a simple theoretical model of bank

lending in which underwriting standards are procyclical. He shows that risk-neutral lenders are

more willing to lend to risky customers, defined in terms of the customers’ probability of failure,

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when the economy is strong and expanding. This result does not rely on market imperfections or

regulatory influences. Rather, Weinberg hypothesizes that standards are relaxed because the

expected payoff on any investment project improves with economic conditions, and therefore the

expected returns on loans to all borrowers rise. Weinberg uses data on the growth rate of total

loans and loan charge-offs in the United States from 1950 to 1992 to show a pattern of increases

in lending preceding increases in loan losses, and argues that these patterns are consistent with

the notion of underwriting cycles.

Berger and Udell (2002) argue instead that procyclical bank lending can arise from a lack

of “institutional memory”: “lending institutions may tend to forget the lessons they learned from

their problem loans as time passes since their last loan ‘bust’” (p. 3). The more time that has

passed since the bank and its loan officers last experienced problems with its loan portfolio, the

harder it may be for banks to recognize potential loan problems. Berger and Udell’s empirical

results indicate general support for their institutional memory hypothesis: loan growth is

generally positively associated with the time since a bank’s last loan bust. However, they do not

find empirical evidence that the lending standards or loan terms of large banks (as measured by

the Federal Reserve’s senior loan officer survey) deteriorate in response to the time since a

bank’s last loan bust. This result, when considered in conjunction with the findings of Weinberg

(1995), Lown, Morgan, and Rohatgi (2000), and Lown and Morgan (2004), suggests that part of

the procyclicality of bank lending is unrelated to changes in lending standards. Market loan

levels increase during economic expansions to some degree without the loosening of standards,

because of increased loan demand. Loans extended during this period may, over the course of

the business cycle, exhibit deteriorating performance and increased riskiness, which may not be

traceable to loosening standards. This collection of studies also shows that, conversely, cycles in

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credit standards are not merely reactions to the macroeconomy but can occur independently of

changes in loan demand.

One particular contractual debt feature that can affect loan demand is the use of bond

covenants. Bond covenants are policies written into corporate debt contracts that restrict or

explicitly alter the behavior of the issuing firms in specific ways that benefit existing

bondholders. Empirical evidence has shown that because bond covenants enhance bondholder

value, they are implicitly priced (Roberts and Bradley [2004]). A relevant result of Roberts and

Bradley (2004) for our study of lending cycles is the finding that the inclusion of bond covenants

varies systematically with macroeconomic factors as well as with supply-side factors. The use of

bond covenants may also be a factor influencing the incentive for banks to monitor their loans to

commercial borrowers. As Rajan and Winton (1995) show, bond covenants make loan

characteristics dependent on monitoring by the lender and thereby increase a lender’s incentive

to monitor. Thus, any cyclical variation in the use of bond covenants may contribute to cyclical

variation in the monitoring effort of banks.

Yet another strand of the literature explores the dynamics of credit risk and its

implications for aggregate economic activity. Fluctuations in credit risk have been found to have

a clear negative effect on output. Koopman and Lucas (2003) investigate the presence of credit

and default cycles using U.S. data on real GDP, credit spreads, and business failure rates. They

find evidence of co-movement between business failure rates and real GDP and between credit

spreads and both failure rates and real GDP. Importantly, and consistent with the findings of the

credit cycle literature, they find that credit risk cycles and business risk cycles do not coincide

precisely. Ladiri and Wang (1996) find that interest rate spreads (which can be thought of as

proxies for aggregate credit risk) successfully predict U.S. business cycle turning points.

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However, these studies do not account for the possibility of variation in aggregate underwriting

standards. In contrast, our empirical methodology allows us to construct estimates of cycles in

banks’ credit standards.

3. Data on Underwriting Standards

Our source of data on underwriting standards is the FDIC supplemental questionnaire on

underwriting practices, or underwriting survey. The underwriting survey, introduced in 1995,

asks examiners to respond to questions only about underwriting practices. FDIC bank examiners

complete the survey at the end of each FDIC-supervised bank examination. They assess material

changes in underwriting practices and evaluate practices in relation to supervisory standards,

rating the risk associated with a bank’s underwriting practices in absolute terms: low, medium, or

high.6 FDIC examiners also classify the frequency of specific risky underwriting practices for

overall lending as “never or infrequently,” “frequently enough to warrant notice,” or “commonly

or as standard procedure.”7 Specifically, the FDIC survey asks examiners about the risk in

6 Low: The level of risk imposed on the institution does not warrant notice by bank supervisors

even when factors that might offset the risk are ignored. Medium: The level of risk should be

brought to the attention of bank supervisors. There may or may not be factors that offset the risk

incurred by the institution; however, the level of risk raises concerns when considered apart from

these offsetting factors. High: The level of risk is high and therefore should be brought to the

immediate attention of bank supervisors. There may or may not be factors that offset the risk

incurred by the institution; however, the level of risk is high when viewed in isolation.

7 Never or infrequently: The institution does not engage in the practice, or does so only to an

extent that does not warrant notice by bank supervisors. Frequently enough to warrant notice:

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current underwriting practices, loan portfolios, purchased loan participations, loan growth and/or

significant changes in lending activities, and loan administration.

In addition, examiners rate the frequency of the following risky practices in overall

lending: lending in amounts that result in concentrations to one industry or borrower, out-of-area

lending, failing to adjust loan pricing for risk, failing to require principal reductions before

renewing loans terms, and deviating from written lending policies. Finally, examiners classify

the frequency of specific risky underwriting practices in seven major loan categories: business,

residential and commercial (nonresidential) real estate construction, commercial real estate,

home equity, agriculture, consumer, and credit cards.8 Lending practices for these seven loan

categories are covered in a separate study (O’Keefe and Olin [2005]). An important aspect of

the FDIC survey is that examiners are asked to evaluate each underwriting practice in isolation

and without regard to possible mitigating factors. We think this makes the FDIC survey data an

ideal measure of the underwriting standards that bank management has chosen. The FDIC

The institution engages in the practice often enough for it to be brought to the attention of bank

supervisors. There may or may not be factors that offset the risks the practice imposes on the

institution. Commonly or as standard procedure: The practice is either common or standard at

the institution and therefore should be brought to the attention of bank supervisors. There may

or may not be factors that offset the risks the practice imposes on the institution.

8 The survey also asks examiners to identify potentially risky loan categories in which the bank is

actively lending: unguaranteed portions of small business administration loans, subprime loans,

dealer paper loans, low-documentation business loans, high loan-to-value home equity loans, and

any other category although not listed. In addition, the survey asks examiners to comment on

which, if any, of these loan categories pose more-than-normal risk to the bank.

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survey data is also a source of information on the riskiness of actual lending practices that has

not been used to study the relationship between underwriting cycles and credit cycles.

The designers of the first survey were staff from the FDIC’s former Division of Research

and Statistics and Division of Supervision (now the Division of Insurance and Research and the

Division of Supervision and Consumer Affairs, respectively). Before selecting the questions, the

designers reviewed the underwriting practices of banks that had failed during the banking crisis

of the 1980s.9 More information on the purpose and design of the FDIC underwriting survey is

presented in the appendix to this study.

Table 1 shows the distribution of responses to the underwriting survey for the nine

questions we used. Before 1998, the survey used different responses to the questions concerning

the level of risk in practices: the risk rankings were “above average,” “average,” and “below

average.” To improve the FDIC’s ability to interpret survey responses, however, absolute risk

rankings were adopted in 1998. For this reason, we use survey data on risk rankings only for the

1998–2004 period. For survey questions that measure the frequency of risky practices, data are

available beginning in 1996.

The number and frequency of bank exams are determined by congressional statute and

supervisory policy. The Federal Deposit Insurance Corporation Improvement Act (FDICIA) of

1991 established minimum examination frequencies of between 12 and 18 months for all banks,

and current requirements call for annual examinations for all banks except those with composite

safety-and-soundness ratings (CAMELS ratings) of 1 or 2 and assets under $250 million. The

FDIC shares examination responsibilities with state bank authorities and typically alternates

examinations with state authorities unless a bank’s condition is poor. As a result, underwriting

9 For a complete history of banking in the 1980s, see FDIC (1997).

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survey responses for any individual FDIC-supervised bank are typically observed every two to

three years.

The FDIC survey has been shown to be highly descriptive of the overall risks of FDIC-

supervised banks. Previous empirical work using the survey (O’Keefe, Olin, and Richardson

[2003]) shows that survey questions characterizing the riskiness of current bank underwriting

practices are statistically significant predictors of future bank safety-and-soundness ratings even

after the current levels of relevant bank financial variables are controlled for. Moreover, survey

responses were found to be related to subsequent changes in bank performance, as measured by

nonperforming assets.

Other U.S. bank regulators also conduct underwriting surveys; however, the FDIC’s

survey is unique in the extent to which it quantifies the level of risk and the frequency of specific

risky underwriting practices. The Federal Reserve Board conducts a Senior Loan Officer

Opinion Survey on Bank Lending Practices, but its primary focus is credit availability. The

Comptroller of the Currency’s annual Survey of Credit Underwriting Practices is closer in spirit

to the FDIC’s survey in that it surveys bank examiners and has questions on credit risk, but it

does not request information about the frequency of specific risky practices.

4. Methodology

We model the selection of bank loan-underwriting practices using a binomial selection

model. Underwriting practices are characterized in our data entirely in terms of their risk to the

bank. As a result, our methodology allows us to estimate the probability that bank managers

chose risky versus safe lending practices at a point in time. Bank managers can, of course,

change lending practices over time. In our model, the probability that the risk in lending

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practices will change from time t to t + 1 is the product of two probabilities: the probability that

the bank had low- (high-) risk practices at time t and the probability that the bank had low- (high-

) risk practices at time t + 1. The credit cycle literature suggests that estimates of transition

probabilities should be related to changes in bank loan levels. We now discuss first our model of

underwriting practice selection and then the way we test whether underwriting cycles are related

to lending cycles in banking.

4.1 Selection of Underwriting Practices

The literature on bank loan-underwriting practices and credit cycles characterizes lending

practices by their possible risks and rewards.10 For this reason, we think the FDIC underwriting

survey data are ideally suited for empirical analysis of the relationship between lending practices

and credit cycles. As shown in table 1, FDIC bank examiners are asked to rate the riskiness of

practices in terms of either absolute risk or the frequency of the practice. Absolute risk ratings

are established using supervisory standards for assessing bank safety and soundness. The

absolute risk levels are low, medium, and high. For practices that are inherently risky, it is more

appropriate to measure the frequency of the practice. Three levels of frequency are used for

risky practices: never or infrequently, frequently enough to warrant notice, and commonly or

standard practice.

Table 1 shows a relatively low percentage of responses for high risk. Consequently we

combine responses of high and medium risk into one category for our empirical analysis.

Similarly, a low percentage of responses are “commonly” for risky practices, so we combine

10 This is because financial and economic theory on firm decision making is largely devoted to

how individuals choose among alternatives whose outcomes are not clear.

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responses of “commonly” and “frequently” into one category. To simplify the discussion we

hereafter treat responses of “commonly” or “frequently” as equivalent to high risk, and responses

of “never” or “infrequently” as equivalent to low risk. These combinations of responses allow us

to measure underwriting-practice risk using a binomial (0,1) variable, where a value of 1

indicates low risk and a value of 0 indicates high risk.

Previous studies suggest several possible determinants of bank loan-underwriting

practices and the likely influence of these determinants on the riskiness of the practices. The

determinants are (1) bank financial condition, (2) local economic conditions, (3) bank

management quality, (4) bank hierarchical complexity, and (5) competition from other lenders.

Equation 1 presents the general form of the model. Our dependent variable,

,practicelending jk,t indicates the occurrence of low-risk lending practice k at bank j at time t.

Our unit of time, t, is one calendar quarter. Equation 1 is estimated by probit regression.11 We

now discuss the motivation for each of the explanatory variables and their expected effect on the

riskiness of lending practices.

)ncompetitiomarket ,complexity alhierarchicbank ,quality management

,conditionseconomic local , condition financial(bank practicelending.)1

111

31

jt-

jt

jt

jttot

jt-

jk,t f

−−

−=

A bank’s financial condition reflects the riskiness of its past lending practices. Studies of

the causes of bank failure (OCC [1988], FDIC [1997]) find that banks that adopted high-risk

lending practices had higher levels of nonperforming loans and loan losses than banks with low-

risk practices. Banks experiencing high loan losses should also have higher allowances for 11 We also considered modeling underwriting practice selection using all three survey response levels and multinomial ordered probit regression. As shown in table 1, however, over 95 percent of survey responses fell into one of two risk categories. This means that empirically, our regressions are best able to differentiate between the two risk categories that comprise over 95 percent of the survey data.

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losses and lower equity capitalization, all other things being equal. Consequently, we measure

financial condition using lagged values for the following variables: nonperforming loans—where

nonperforming loans consist of all loans and leases past due 90 days or more and nonaccrual

loans and leases—as a percentage of gross loans and leases; equity capital as a percentage of

assets; and allowances for loan and lease losses as a percentage of assets. Since we relate the

selection of lending practices to financial condition lagged one quarter, we anticipate that

financial condition reflects ongoing practices. Consequently we anticipate that weak (strong)

prior-period financial condition will be negatively (positively) related to the selection of a low-

risk practice.

The credit cycle literature suggests that local economic conditions influence banks’

lending practices. For example, Rajan (1994), Ruches (2004), and Weinberg(1995) suggest that

strong (weak) economies give banks an incentive to loosen (tighten) lending standards, allowing

banks to mitigate fluctuations in credit risk due to business cycles. We measure local economic

conditions using the growth rate in state unemployment rates.

We anticipate that the quality of bank management is also related to the selection of

lending practices in much the same way that financial condition is. We measure the quality of

bank management using bank examiners’ assessments of overall management quality assigned

during bank examinations (CAMELS management component rating). If bank management has

recently been determined to be of low quality, we expect that ongoing lending practices will be

riskier than if management has been considered high quality.

Studies of bank lending to small businesses have suggested that the extent of

organizational hierarchy present in a bank influences its lending practices (Berger et al. [2002],

Cole et al. [2004]). The argument made in these studies is that large, hierarchical banking

15

organizations prefer to lend to businesses that can provide hard financial data on their condition

and their prospects of repaying bank loans, whereas smaller, less hierarchical banking

organizations prefer to lend to businesses that can provide only limited financial information. In

large, hierarchical banking organizations, hard financial data from loan applicants can be easily

examined by loan officers and reviewed by senior-level bank officers before loan approval. The

“soft” information available on smaller businesses relies on the personal knowledge that loan

officers have of loan applicants’ reputations—something senior-level bank officials find difficult

to assess and use for review of loan applications. These same studies of small business finance

indicate that larger businesses typically are able to provide hard financial data for loan

documentation, while smaller businesses are often only able provide “soft” financial information.

The greater transparency of large firms’ hard financial data and lower failure rates for large

versus small businesses both suggest that hierarchical banks have lower risk lending practices

than non-hierarchical banks. We use three measures of banking organizational hierarchy in our

estimates of equation 1: the natural logarithm of bank assets (bank size), the number of bank

employees, and a dummy variable indicating whether the bank is a member of a multibank

holding company. On the basis of the studies discussed above, we expect that bank size, number

of employees, and multibank holding company membership will all be positively related to

selection of low-risk loan-underwriting practices.

Banking market competition is also expected to influence lenders’ choice of loan-

underwriting practices. Studies of the causes of bank failure (FDIC [1997]) find that competition

from other lenders can encourage a bank to adopt riskier lending practices. Relaxation of

lending standards in the face of market competition is one way a bank can maintain loan market

share or can at least minimize loss of customers to competitors. We measure local banking

16

market competition using a Herfindahl-Hirschman Index (HHI) of county deposit concentrations

obtained from the Summary of Deposit (SOD) data on banks’ branch operations.

We estimate equation 1 separately for each of the nine general practices listed in table 1,

using annual cohorts of banks examined by FDIC bank examiners. Our primary source of bank

financial information is the quarterly financial statements banks file with their primary federal

regulator (Call Reports). From these reports we obtain measures of banks’ nonperforming loans,

capitalization, and the allowance for loan and lease losses. As mentioned previously, local

economic conditions are measured by the growth rate in state unemployment rates for the states

in which banks are chartered. In our estimates of equation 1 we measure local economic

conditions using the growth rates in state unemployment rates for the same quarter in which

underwriting practices are measured and three lagged quarters. Bank management quality is

measured by the rating bank examiners assign to the quality of bank management during periodic

safety-and-soundness examinations (CAMELS management component rating). Management

ratings can very from 1 to 5, with 1 being the best rating and 5 the worst. Management ratings of

1 or 2 indicate good-quality management, ratings of 3 indicate material weaknesses in

management, and ratings of 4 or 5 indicate poor management. In our estimations of equation 1

we incorporate management ratings using dummy variables for rating levels, with rating level 3

as the omitted dummy variable. Since we use lagged management ratings as an explanatory

variable, these ratings come from an examination previous to the FDIC exam from which the

dependent variable (lending practice) is obtained. Moreover, since the FDIC alternates exams

with state authorities, the lagged management ratings are from state banking regulators, not the

FDIC. As discussed above, we measure the extent of bank hierarchy using bank size, number of

employees, and multibank holding company membership, all lagged one quarter. Finally, our

17

measure of market competition is the Herfindahl-Hirschman index (HHI) of deposit

concentration for the county in which the bank operates. This is the same HHI measure that the

Department of Justice and the Federal Trade Commission use when screening proposed bank

mergers for possible anticompetitive effects.

4.2 Changes in Underwriting Practices

We estimate equation 1 using annual cohorts of banks contained in the FDIC

underwriting survey data. If financial, economic, managerial, organizational, or competitive

factors influence the riskiness of underwriting practices, cyclical changes in these explanatory

variables might produce cycles in the risk of lending practices. The credit cycle literature

predicts that changes in bank credit availability coincide with changes in loan-underwriting risk.

The generally accepted notion is that as lenders tighten lending standards, credit supply

decreases. Similarly, relaxation of standards is said to result in increases in credit supply. To

test this hypothesis, we need to measure to extent to which banks are loosening (tightening)

standards or whether they are maintaining standards at either a high- or low-risk level.

Since we model risk in lending practices as a binomial variable, there are four possible

paths for bank lending practices: relaxation of standards from low risk to high risk, tightening of

standards from high risk to low risk, maintenance of low-risk practices, and maintenance of high-

risk practices. The probability that a bank is transitioning along any of these paths is the product

of the probabilities that the bank is using the specified practices in successive periods. If we

represent the probability of selection of low-risk lending practice k at bank j at time t by ,P jk,t

then the probability of the corresponding high-risk practice is .P - 1 jk,t Similarly, the

corresponding probabilities for the next period are jk,t 1P + and ,P - 1 1

jk,t + respectively. To simplify

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notation we use the subscript 1 for a low-risk practice and 2 for a high-risk practice. The general

form of the lending practice transition matrix is given in equation 2.

⎥⎦

⎤⎢⎣

⎡=⎥

⎦

⎤⎢⎣

⎡

++

++

2212

2111

11

11

)P - (1)P - (1)P - (1)(P)(P)P - (1)(P)(P

.)2PPPP

jk,t

jk,t

jk,t

jk,t

jk,t

jk,t

jk,t

jk,t

To estimate this transition matrix, we first estimate the probability of a bank’s adopting a

specific low- (high-) risk lending practice, using equation 1 and annual cohorts of FDIC-

supervised banks that have been surveyed during the year. Next, we forecast the probability that

a bank will be using the low- (high-) risk practice out-of-sample, using the data on the

explanatory variables for all banks in the next quarter. Successive estimations of the selection

model and forecasts yield estimates of the probabilities of practice selection in each period, from

which we compute transition probabilities. The reason for forecasting practices for all banks is

twofold. First, quarterly forecasts provide much more frequent observations on lending

practices, albeit forecasts of practices, than are available from the FDIC survey. As discussed

above, FDIC-supervised banks are surveyed every two to three years. A three-year interval

seems too long for measurement of transition probabilities based on actual surveys, since

practices may change more frequently. Second, while the FDIC underwriting survey includes

only FDIC-supervised banks and thrifts, there is no reason we are aware of to believe that the

determinants of lending practices differ for banks regulated by the other federal bank and thrift

regulators, with one exception. FDIC-supervised banks tend to be smaller, community-based

organizations and are much smaller than most national banks. We therefore exclude from our

forecasts all banks and thrifts with assets over $300 million.

19

In the next section we discuss first the results of estimation of the practice selection

model and then the estimated relationships between practice transition probabilities and loan

growth.

5. Results

As discussed above, the FDIC underwriting survey classifies the riskiness of lending

practices in two ways: absolute risk rankings and the frequency of risky practices. Results of

estimation of the practice selection model indicate similarities in results for practices that use the

same approach to classifying risk. For brevity we summarize results by the type of risk

classification used, and we indicate important differences where they occur.

5.1 Results for Practice Selection Model

Tables 2 through 5 present the results of estimation of the practice selection model for

practices where absolute risk rankings are used. The coefficient estimates measure that change

in the probability of low-risk practice selection for an infinitesimal change in the explanatory

variable, evaluated at the mean values for all regressors. The coefficient estimates therefore

indicate the economic significance of the explanatory variables, as well as the statistical

significance. The results are generally in agreement with our prior expectations. The probability

of selection of a low-risk practice is negatively related to lagged nonperforming assets and the

allowance for loan losses, and occasionally positively related to equity capitalization. The

influence of equity capitalization is, however, generally weak, and in one instance (table 5)

equity capital is negatively related to selection of low-risk practices. Our measure of local

economic conditions—the growth rate in state unemployment rates—is in general not

20

significantly related to practice selection. As anticipated, well-rated bank management is

positively related to low-risk practice selection, whereas poorly rated management is

occasionally negatively related to low-risk practice selection. We believe the weaker results for

the indicator of poorly rated management are due to the relatively low number of problem banks

in our survey sample.12

Tables 2 through 5 also show that the results for our proxies for hierarchical banking

organizations are not, in general, in agreement with our prior expectations. We find that the

number of bank employees is generally not related to practice selection. In addition, the dummy

variable indicating membership in a multibank holding company is positively related to selection

of some low-risk practices but negatively related for others. Specifically, we find the multibank

holding company membership indicator is occasionally positively related to low risk in current

underwriting practices (table 2) and low risk in loan administration (table 5) but is generally not

related to credit risk in the overall portfolio (table 3) and is negatively related to low risk in

purchased loan participations (table 4). The result for purchased loan participations can be

explained by the likelihood that member banks in a holding company are probably purchasing

loans from affiliated banks rather than unaffiliated banks and are therefore participating in the

same types of lending that they themselves originate. Bank asset size is also negatively related

to selection of low-risk practices in tables 2 through 5, contrary to our expectations. One

possible explanation for the bank size result is that asset size may be an incorrect measure of

organizational hierarchy for the smaller community banks that dominate the FDIC underwriting

survey sample. Finally, our measure of local market competition, the HHI index, is in general

not related to the riskiness of lending practices, except to the risk in loan administration (table 5).

12 Between 1998 and 2004 there are only 422 problem banks in a total sample of 15,605 banks.

21

The risk in loan administration is negatively related to our measure of market competition, as

previous studies suggest.

We next summarize results for lending practices where risk is classified by the frequency

of a risky practice, shown in tables 6 through 10. The relationships between selection of low-risk

lending practices and bank financial condition are similar to the relationships seen in tables 2

through 5; however, lagged financial condition appears to be much less significant in

determining the frequency of most risky lending practices than it is for the overall risk in

underwriting practices and the credit portfolio. This weak result for lagged financial condition

may be due to the fact that FDIC bank examiners can rate the frequency of practices without

regard to bank asset quality, whereas assessments of overall underwriting risk or portfolio risk

may incorporate asset quality and financial performance. Tables 6 through 10 also indicate that

the choice of lending practices in not related to our measure of local economic condition but is

strongly related to the quality of bank management. We find a positive relationship between

indicators of good-quality bank management and selection of low-risk lending practices.

Tables 6 through 10 show that the relationships between our measures of bank hierarchy

and low-risk lending practices are not always as expected, and vary by lending practice. For two

practices—concentrations of credit risk (table 6) and failure to adjust loan pricing to reflect risk

(table 8)—we find essentially no relationship between our measures of bank hierarchy and

lending practices. For two additional practices—failure to require principal reduction before

renewing loan terms (table 9) and actual lending practices differing from written policies (table

10)—we find a positive relationship between multibank holding company membership, the

number of bank employees, and low-risk practices, but bank size shows a mixed relationship

with these two lending practices. Table 7 shows a negative relationship between infrequently

22

making loans out-of-area (a low-risk practice) and multibank holding company membership.

The negative relationship is logical, given that a bank’s market area is increased through

membership in a holding company. Finally, there is a negative relationship between banking

market competition (HHI) and selection of low-risk lending practices, but the relationship is

generally not strong.

5.2 Results for Changes in Lending Standards and Loan Growth

Table 11 presents estimates of the correlation between practice transition probabilities

and quarterly growth rates in gross loans and leases for five survey questions: potential risk in

overall underwriting practices, risk in loan portfolio administration, failing to adjust loan pricing

to reflect risk, failing to require principal reduction before renewing loans, and actual lending

practices differing from written polices. We chose these five questions for further study for two

reasons. First, estimates of lending practice selection models indicate good to adequate

explanatory power for these five practices compared with the explanatory power for other

practices studied. Second, these five survey questions cover diverse and important aspects of

banks’ lending practices.

Table 11 shows a positive correlation between the probability that banks maintain low-

risk practices quarter-to-quarter (P11) and quarterly growth rates in gross loans and leases for all

five practices tested. Conversely, we find a negative correlation between the probability that

banks maintain high-risk lending practices (P22) and loan growth for all five practices.

The probability that banks transition from low-risk to high-risk lending practices (P12) is

positively correlated with loan growth for two survey questions (potential risk in overall

practices and risk in loan administration) but negatively correlated for the remaining three

23

questions (failing to adjust pricing to reflect risk, failing to reduce principal before renewing

loans, and actual practices differing from written policies). Previous studies suggest, however,

that the loosening of lending standards leads to increased loan growth.

The probability that banks transition from high-risk to low-risk lending practices (P21) is

positively correlated with loan growth for one survey question (risk in loan administration), not

significantly correlated with the survey question for potential risk in overall underwriting

practices, and negatively correlated for the remaining three questions (failing to adjust pricing to

reflect risk, failing to reduce principal before renewing loans, and actual practices differing from

written policies). Previous studies suggest that the tightening of lending standards leads to lower

loan growth. Taken together, our results do not show that the relaxation (tightening) of lending

standards always leads to higher (lower) loan growth. We next offer several explanations for

these results.

The reasons for our results for practice transition probabilities and loan growth become

clear when we look at the correlations between practice transitions and two measures of financial

performance: nonperforming assets and gross loan charge-offs. Table 12 shows the correlation

between practice transition probabilities and nonperforming assets as a percentage of bank

assets. The table shows that the probability of maintaining low- (high-) risk practices is

negatively (positively) correlated with nonperforming assets. The results for the correlation

between nonperforming assets and the probability of changing risk practices (P21 and P12) are

mixed, however, and have exactly the opposite signs as those for loan growth (table 11). The

results seen in table 12 are also found in table 13 for loan charge-offs. Taken together, tables 11

through 13 show that whenever a risk practice transition probability is positively (negatively)

correlated with adverse financial performance, the transition probability is negatively (positively)

24

correlated with loan growth. In addition, the greater the absolute size of the correlation between

a transition probability and adverse financial performance, the greater the absolute size of the

correlation between the transition probability and loan growth.13 The conclusion we draw from

these correlations is that the main determinant of loan growth is bank financial condition and not

changes in risk practices per se.

6. Conclusions

In this study we examine the determinant of banks’ general lending practices and the

relationships between changes in lending practices and loan growth. We model the selection of

lending practices using a binomial probit model. Our findings for practice selection are

generally in agreement with the previous literature. The probability that banks select a low-risk

lending practice is negatively related to lagged values of nonperforming loans and loan loss

allowances and positively related to lagged values of equity capitalization. The probability of

selection of low-risk lending practices is positively related to lagged measures of good-quality

bank management and negatively related to indicators of poor management quality. Lending

practice selection, however, is not generally related to lagged measures of local economic

conditions (growth rates in state unemployment rates). Although many previous studies suggest

that bank hierarchical complexity is a determinant of lending practices, we do not find a strong

13 We also estimate correlations between practice transition probabilities and growth in net loans

and leases (net of loan loss allowances) as a robustness check. The results are virtually identical

to those for gross loans and leases. In addition, we estimate the correlations between annual loan

growth rates and annual transition probabilities. The results for annual rates are very similar, but

the magnitude of all correlations is somewhat greater.

25

relationship in our estimations. We do, however, find that local market competition is related to

lending practices, and as the literature predicts, increases in local banking market competition

increase the probability that banks select risky lending practices.

We use the estimates of practice selection probabilities to estimate the probability that

banks will change lending practices from quarter to quarter. The lending practice transition

probabilities are then compared with loan growth rates to test for a relationship between cycles in

lending practices and loan growth. The generally accepted hypothesis is that relaxation of bank

lending standards leads to greater loan growth and that tightening of standards leads to reduced

loan growth. We find, however, that our measures of relaxation (tightening) of lending standards

are not consistently related to loan growth and are often related in ways not predicted by the

literature. To help reconcile our findings with the literature, we examine the relationships

between practice transition probabilities and measures of bank performance—nonperforming

loans and loan charge-offs. Our findings indicate that bank financial condition is the overriding

factor in explaining loan growth: good (poor) financial condition is positively (negatively)

correlated with loan growth.

We are not suggesting that large, rapid increases in bank lending are an indicator of good

financial condition or low-risk lending practices. Our empirical analysis and data are measuring

relatively small changes in bank loan growth from quarter to quarter and relatively small changes

in risk practices, as measured by practice transition probabilities. The implication of our findings

is that the quality of bank management and bank financial condition are the major determinants

of bank credit availability. This is consistent with the mandates of the federal bank supervisory

agencies to maintain a safe and sound bank system to help ensure the stability of the U.S.

economy.

26

References

Asea, Patrick K., and Brock Blomberg. 1998. “Lending Cycles.” Journal of Econometrics

83:89–128.

Berger, Allen N., Nathan H. Miller, Mitchell A. Peterson, Raghuram G. Rajan, and Jeremy Stein.

2002. “Does Function Follow Organizational Form? Evidence from the Lending

Practices of Large and Small Banks.” Harvard Institute of Economic Research

Discussion Paper No. 1976.

Berger, Allen N., and Gregory F. Udell. 2002. "The Institutional Memory Hypothesis and the

Procyclicality of Bank Lending Behavior." Financial Intermediation Research Society

Working Paper 03-02.

Cole, Rebel A., Lawrence G. Goldberg, and Lawrence J. White. 2004. “Cookie Cutter vs.

Character: The Micro Structure of Small Business Lending by Large and Small Banks.”

Journal of Financial and Quantitative Analysis 39 (2):227–51.

Curry, Timothy J., John O’Keefe, Jane Coburn, and Lynne Montgomery. 1999. “Financially

Distressed Banks: How Effective Are Enforcement Actions in the Supervisory Process?”

FDIC Banking Review 12 (2):1–18.

Federal Deposit Insurance Corporation (FDIC). 1997. History of the Eighties, Lessons

for the Future. Vol. 1, An Examination of the Banking Crises of the 1980s and Early

1990s. FDIC.

27

———. DOS Manual of Examination Policies. Section 3. FDIC.

Freixas, Xavier, and Jean-Charles Rochet. 1997. Microeconomics of Banking. Cambridge, MA:

MIT Press.

Hulsewig, Oliver. 2003. “Bank Behavior, Interest Rate Targeting and Monetary Policy

Transmission.” Universitat Wurtzburg Economic Papers No. 43.

Koopman, Siem Jan, and Andre Lucas. 2003. “Business and Default Cycles for Credit Risk.”

Tinbergen Institute Discussion Paper TI2003-062/2.

Ladiri, Kajal, and Jiazhuo G. Wang. 1996. “Interest Rate Spreads as Predictors of Business

Cycles.” In G. S. Maddala and C. R. Rao, eds., Handbook of Statistics, vol. 14. Elsevier

Science.

Lown, C. S., and D. P. Morgan. 2004. “The Credit Cycle and the Business Cycle: New Findings

from the Loan Officer Opinion Survey.” Stockholm Institute for Financial Research

Report No. 27.

Lown, C. S., D. P. Morgan, and S. Rohatgi. 2000. “Listening to Loan Officers: The Impact of

Commercial Credit Standards on Lending and Output.” Federal Reserve Bank of New

York Economic Policy Review 6 (2):1–16.

Office of the Comptroller of the Currency (OCC). 1988. “Bank Failure: An Evaluation of the

Factors Contributing to the Failure of National Banks.” OCC.

28

O’Keefe, John, and Virginia Olin. 2005. “The Effects of Underwriting Practices on Loan

Losses: Evidence from the FDIC Survey of Bank Lending Practices.” Working Paper

(accepted for the Western Economic Association Conference, July 2005).

O’Keefe, John, Virginia Olin, and Christopher Richardson. 2003. “Bank Loan Underwriting

Practices: Can Supervisors’ Assessments Contribute to Early Warning Systems?” In

George Kaufman, ed., Research in Financial Services, vol. 15. Also FDIC Working

Paper 2003-06.

Peek, J., and E. Rosengren. 1995. “Bank Regulation and the Credit Crunch.” Journal of

Banking and Finance 19 (3–4):679–92.

Rajan, Raghuram G. 1994. “Why Bank Credit Policies Fluctuate: A Theory and Some

Evidence.” Quarterly Journal of Economics 109 (2):399–441.

Rajan, R. G., and A. Winton. 1995. “Covenants and Collateral as Incentives to Monitor.”

Journal of Finance 50:1113–46.

Roberts, M. B., and Michael Bradley. 2004. “Are Bond Covenants Priced?” Working Paper,

Econometric Society, 2004 North American Summer Meetings.

Ruches, Martin. 2004. “Bank Competition and Credit Standards.” Review of Financial Studies

17 (4):1073–1102.

Weinberg, John A. 1995. “Cycles in Lending Standards?” Federal Reserve Bank of Richmond

Economic Quarterly 81 (3):1–18.

29

Appendix: Purpose and Design of the FDIC Supplementary Questionnaire on Lending Practices

In early 1995, the FDIC began to require that a supplementary examination questionnaire on

current underwriting practices at FDIC-supervised banks be filled out at the end of each FDIC-

supervised bank examination. The questionnaire focuses on three topics: material changes in

underwriting practices for new loans, the overall degree of risk in underwriting practices for new

loans, and the frequency of specific risks in underwriting practices within major categories of loans

(business, consumer, commercial [nonresidential] real estate, agricultural, construction, home

equity, and credit card loans). Examiners are also asked to report whether the institution is active in

additional loan categories (unguaranteed portions of Small Business Administration [SBA] loans,

subprime loans [automobiles, mortgages], dealer paper loans, low- /no-document business loans,

high loan-to-value ratio home equity loans [up to 125%], or any category of loan not mentioned).

The systematic collection and analysis of questionnaire responses provides an early-warning

mechanism for identifying potential lending problems.

Examiners evaluate underwriting practices in terms of FDIC supervisory practices. Until

October 1, 1998, examiners were asked to rate the risk associated with a bank’s underwriting

practices in relative terms: “above average,” “average,” or “below average.” Beginning October

1, 1998, examiners began rating the risk associated with a bank’s underwriting practices in absolute

terms: “low,” “medium,” or “high.” Examiners continue to classify the frequency of specific risky

underwriting practices as “never or infrequently,” “frequently enough to warrant notice,” or, if the

risky practice is used more often, “commonly or as standard procedure.”

30

The questionnaire is completed at the end of each bank examination the FDIC conducts.

Which banks are included during a reporting period, therefore, depends on how the FDIC schedules

bank examinations. Examination schedules are heavily influenced by the financial condition of a

bank, with the examinations generally becoming more frequent the poorer a bank's financial

condition. In addition, the FDIC shares examination authority of state-chartered nonmember banks

(those that are not members of the Federal Reserve System) with state bank regulators. To avoid

excessive regulatory burden, the FDIC generally alternates examinations with state regulators, and

the latter do not fill out questionnaires. Finally, examination schedules are affected by the

availability of examination staff. For these reasons the group of banks included in any given report

is not randomly selected and therefore may not be representative of the population of FDIC-

supervised banks.

31

Question Response Number Percent

Potential Risk in Current Underwriting PracticesHow would you characterize the potential risk Low 8281 60.6%associated with the institution's current underwriting Medium 4748 34.7%practices? High 637 4.7%

Potential Credit Risk in Overall Loan PortfolioHow would you characterize the potential credit risk Low 8160 59.7%of the institution's overall loan portfolio? Medium 4691 34.3% High 814 6.0%

Risk in Purchased Loan ParticipationsHow would you characterize risk in underwriting Low 7831 73.2%practices associated with loan participations purchased Medium 2681 25.1%by the institution? High 182 1.7%

Concentrations of Credit RiskTo what extent has recent lending been made in Never or infrequently 12194 75.9%amounts that resulted in, or contributed to, Frequently 2681 16.7%concentrations of credit to one borrower or industry? Commonly or standard procedure 1194 7.4%

Out-of-Area LendingTo what extent is the institution currently engaged in Never or infrequently 12483 85.5%out-of-area financing? Frequently 1691 11.6% Commonly or standard procedure 430 2.9%

Risk in Loan AdministrationHow would you characterize the risk associated with Low 8182 59.9%loan administration at this institution? Medium 4700 34.4% High 784 5.7%

Fail to Adjust Pricing to Reflect RiskTo what degree does the institution fail to adjust its Never or infrequently 13898 86.5%loan pricing on different quality of loans to reflect Frequently 1803 11.2%differences in risk? Commonly or standard procedure 368 2.3%

Fails to Require Principal Reduction before Loan RenewalTo what extent does the institution fail to require a material Never or infrequently 12012 74.8%principal reduction before renewing loan terms? Frequently 3488 21.7% Commonly or standard procedure 569 3.5%Actual Lending Practices Differ from Written PoliciesTo what extent do the institution's actual lending practices differ Never or infrequently 12364 76.9%from written lending policies? Frequently 3132 19.5% Commonly or standard procedure 573 3.6%

Table 1.Survey Responses for General Underwriting Practices: 1/1998 - 3/2005

32

Explanatory Variable 1999 2000 2001 2002 2003 2004

Nonperforming loans/Gross loans, (t-1) -0.0353 -0.0224 -0.0329 -0.0137 -0.0272 -0.0073(0.0096)*** (0.0095)** (0.0083)*** (0.0076)* (0.0091)*** (0.0041)*

Equity/Assets, (t-1) 0.0004 0.0002 0.0063 0.0045 0.0055 0.0051(0.0030) (0.0026) (0.0029)** (0.0032) (0.0029)* (0.0034)

Allowance for loan losses/Assets, (t-1) -0.1266 -0.0922 -0.0395 -0.1215 -0.1456 -0.0543(0.0266)*** (0.0235)*** (0.0145)*** (0.0268)*** (0.0261)*** (0.0162)***

State unemployment growth rate, (t) 0.0002 -0.0036 0 0.002 -0.0033 -0.0013(0.0027) (0.0024) (0.0022) (0.0037) (0.0036) (0.0025)

State unemployment growth rate, (t-1) -0.0018 -0.0015 -0.0019 0.0007 0.0044 0.0043(0.0029) (0.0024) (0.0026) (0.0028) (0.0048) (0.0028)

State unemployment growth rate, (t-2) 0.0048 0.0007 -0.0028 0.0024 -0.0014 -0.0023(0.0030) (0.0025) (0.0026) (0.0027) (0.0044) (0.0033)

State unemployment growth rate, (t-3) -0.0105 -0.0009 -0.003 0.0011 0.0015 -0.0034(0.0026)*** (0.0023) (0.0025) (0.0025) (0.0041) (0.0043)

Management rating = 1 dummy, (t-1) 0.5015 0.5373 0.5799 0.571 0.529 0.5109(0.0285)*** (0.0231)*** (0.0221)*** (0.0249)*** (0.0271)*** (0.0296)***


Management rating = 4 or 5 dummy, (t-1) -0.1052 -0.0764 -0.0436 -0.0493 0.0007 -0.2703(0.1043) (0.0993) (0.0870) (0.0915) (0.0857) (0.1198)**

Member multibank holding co. dummy, (t-1) 0.0613 0.0288 -0.0168 -0.0224 -0.022 0.06(0.0288)** (0.0262) (0.0283) (0.0290) (0.0302) (0.0312)*

log_e (assets), (t-1) -0.0315 -0.0334 -0.0204 -0.0373 -0.0255 -0.032(0.0119)*** (0.0102)*** (0.0104)** (0.0111)*** (0.0110)** (0.0116)***

Number of employees/1000, (t-1) -0.0256 -0.0086 -0.0135 0.0635 0.0185 -0.0009(0.0423) (0.0262) (0.0316) (0.0398) (0.0406) (0.0424)

Herfindahl-Hirschman Index/1000, (t-1) -0.0006 -0.0093 -0.0061 -0.0119 0.0013 0.0043(0.0082) (0.0070) (0.0071) (0.0075) (0.0073) (0.0077)

Number of Observations 1,783 2,256 2,284 2,201 2,132 1,773

Pseudo R Squared 0.1879 0.1795 0.1984 0.1937 0.1815 0.1388

Chi square (BHC, size, employees) 16.1213 15.4098 6.3815 11.963 6.6285 6.6285Prob > Chi square (0.0011)*** (0.0015)*** (0.0945)* (0.0075)*** (0.0847)* (0.0847)*

* significant at 10%; ** significant at 5%; *** significant at 1%

Table 2. Potential Risk in Current Underwriting PracticesProbit estimates of the choice of a low-risk lending practice

Estimated Marginal Probability (Standard Error)

33


Nonperforming loans/Gross loans, (t-1) -0.0763 -0.0804 -0.0731 -0.0706 -0.0995 -0.016(0.0114)*** (0.0120)*** (0.0100)*** (0.0109)*** (0.0127)*** (0.0045)***

Equity/Assets, (t-1) 0.0072 0.0079 0.0142 0.0133 0.0169 0.0154(0.0032)** (0.0028)*** (0.0033)*** (0.0034)*** (0.0035)*** (0.0037)***

Allowance for loan losses/Assets, (t-1) -0.2297 -0.2575 -0.2551 -0.2832 -0.3197 -0.3687(0.0295)*** (0.0289)*** (0.0287)*** (0.0335)*** (0.0326)*** (0.0349)***

State unemployment growth rate, ( t ) 0.0002 -0.0041 -0.0039 -0.0004 0.0007 -0.0045(0.0028) (0.0025)* (0.0023)* (0.0039) (0.0039) (0.0026)*

State unemployment growth rate, (t-1) -0.0012 -0.0014 0.0032 0.0022 0.004 0.003(0.0030) (0.0025) (0.0027) (0.0029) (0.0051) (0.0029)

State unemployment growth rate, (t-2) 0.0006 0.0003 -0.0045 -0.001 -0.0034 0.0005(0.0030) (0.0026) (0.0028)* (0.0029) (0.0048) (0.0034)

State unemployment growth rate, (t-3) -0.0053 0.0009 -0.0008 0.0017 0.0057 -0.0068(0.0027)** (0.0024) (0.0026) (0.0026) (0.0044) (0.0045)



Management rating = 4 or 5 dummy, (t-1) -0.0033 -0.0025 -0.0944 -0.242 0.0511 -0.0819(0.1035) (0.1060) (0.1130) (0.1362)* (0.1141) (0.1347)

Member multibank holding co. dummy, (t-1) 0.0472 0.0125 -0.0151 -0.0568 -0.0136 0.0341(0.0300) (0.0272) (0.0292) (0.0305)* (0.0321) (0.0333)

log_e (assets), (t-1) -0.0269 -0.0148 -0.0113 -0.0301 -0.0364 -0.0495(0.0125)** (0.0105) (0.0112) (0.0119)** (0.0121)*** (0.0138)***

Number of Employees/1000, (t-1) 0.0198 -0.0042 0.0007 0.0607 -0.0094 0.1266(0.0472) (0.0260) (0.0387) (0.0445) (0.0458) (0.0863)

Herfindahl-Hirschman Index/1000, (t-1) -0.0064 0.0042 -0.0038 -0.0134 -0.0005 0.0071(0.0087) (0.0075) (0.0074) (0.0081)* (0.0078) (0.0079)

Number of Observations 1,783 2,256 2,284 2,201 2,131 1,773

Pseudo R Squared 0.2177 0.2374 0.2436 0.2613 0.2777 0.1970

Chi square (BHC, size, employees) 7.6149 2.8425 1.6142 10.1208 14.0158 14.0158Prob > Chi square (0.0547)* (0.4165) (0.6562) (0.0176)** (0.0029)*** (0.0029)***

Table 3. Potential Credit Risk in Overall Loan Portfolio



Probit estimation of the choice of a low-risk lending practice

34


Nonperforming loans/Gross loans, (t-1) -0.0241 -0.0067 -0.0192 -0.0263 -0.013 -0.0205(0.0081)*** (0.0096) (0.0083)** (0.0088)*** (0.0080) (0.0090)**

Equity/Assets, (t-1) -0.0007 -0.0002 0.0008 -0.0028 0.0015 -0.0015(0.0030) (0.0028) (0.0031) (0.0031) (0.0030) (0.0037)

Allowance for loan losses/Assets, (t-1) 0.0195 -0.0031 -0.0472 -0.0285 -0.1014 -0.0038(0.0267) (0.0185) (0.0255)* (0.0281) (0.0250)*** (0.0176)

State unemployment growth rate, (t) 0.0002 -0.0023 -0.0031 0.0065 -0.003 -0.0022(0.0025) (0.0024) (0.0021) (0.0037)* (0.0035) (0.0025)

State unemployment growth rate, (t-1) 0.0012 -0.0039 0.0036 -0.0033 -0.0001 0.0015(0.0028) (0.0024) (0.0024) (0.0028) (0.0047) (0.0029)

State unemployment growth rate, (t-2) -0.0037 -0.0031 -0.0019 0.0035 0.0043 -0.0005(0.0026) (0.0026) (0.0025) (0.0026) (0.0044) (0.0033)

State unemployment growth rate, (t-3) -0.0041 -0.0007 -0.001 -0.0039 0.0034 0.0012(0.0023)* (0.0023) (0.0024) (0.0024) (0.0040) (0.0043)


Management rating = 2 dummy, (t-1) 0.1592 0.1471 0.1244 0.1027 0.093 0.0628(0.0365)*** (0.0360)*** (0.0341)*** (0.0368)*** (0.0389)** (0.0433)

Management rating = 4 or 5 dummy, (t-1) -0.0148 -0.1584 0.04 -0.0475 0.1299 -0.1187(0.0827) (0.1040) (0.0674) (0.0826) (0.0560)** (0.1105)

Member multibank holding co. dummy, (t-1) -0.0343 -0.035 -0.1203 -0.097 -0.0874 -0.0066(0.0252) (0.0250) (0.0270)*** (0.0275)*** (0.0288)*** (0.0301)

log_e (assets), (t-1) -0.0085 -0.0029 0.0037 0.0092 -0.0003 0.0005(0.0111) (0.0099) (0.0113) (0.0108) (0.0110) (0.0116)

Number of employees/1000, (t-1) 0.0038 -0.0156 -0.105 -0.0175 0.0164 -0.0182(0.0368) (0.0213) (0.0523)** (0.0335) (0.0431) (0.0357)

Herfindahl-Hirschman Index/1000, (t-1) -0.0051 -0.0026 0.0086 0.0018 0.0223 0.0047(0.0069) (0.0071) (0.0069) (0.0076) (0.0076)*** (0.0075)

Observations 1,461 1,596 1,698 1,643 1,620 1,366

Pseudo R Squared 0.0534 0.0555 0.0691 0.0655 0.0507 0.0319

Chi square (BHC, size, employees) 2.685 3.3453 27.8192 14.1325 9.8906 9.8906Prob > Chi square (0.4428) (0.3414) (0.0000)*** (0.0027)*** (0.0195)*** (0.0195)*** * significant at 10%; ** significant at 5%; *** significant at 1%

Table 4. Risk in Purchased Loan ParticipationsProbit estimation of the choice of a low-risk lending practice


35


Nonperforming loans/Gross loans, (t-1) -0.0436 -0.0461 -0.0352 -0.047 -0.02 -0.0056(0.0105)*** (0.0107)*** (0.0089)*** (0.0083)*** (0.0087)** (0.0042)

Equity/Assets, (t-1) -0.005 -0.0029 0.0056 0.0004 0.0028 0.0025(0.0030)* (0.0026) (0.0030)* (0.0031) (0.0030) (0.0034)

Allowance for loan losses/Assets, (t-1) -0.0571 -0.038 -0.0209 -0.0002 -0.0947 -0.0482(0.0250)** (0.0227)* (0.0151) (0.0147) (0.0262)*** (0.0161)***

State unemployment growth rate, (t) -0.0057 -0.0019 0.0001 0.0023 -0.0026 -0.0035(0.0028)** (0.0025) (0.0023) (0.0037) (0.0037) (0.0025)

State unemployment growth rate, (t-1) -0.0035 -0.002 0.0012 -0.0025 -0.0038 0.0008(0.0030) (0.0025) (0.0027) (0.0028) (0.0048) (0.0028)

State unemployment growth rate, (t-2) 0.0046 0.0002 -0.0004 0.0047 -0.0004 -0.0052(0.0031) (0.0026) (0.0027) (0.0027)* (0.0045) (0.0032)

State unemployment growth rate, (t-3) -0.0045 -0.0024 -0.008 -0.0002 -0.0043 -0.0064(0.0027) (0.0024) (0.0025)*** (0.0025) (0.0042) (0.0043)



Management rating = 4 or 5 dummy, (t-1) -0.1654 -0.2491 -0.1896 -0.1256 -0.2489 -0.2371(0.1196) (0.1373)* (0.1153)* (0.1026) (0.1116)** (0.1246)*

Member multibank holding co. dummy, (t-1) 0.0094 0.02 0.0578 0.0013 -0.0218 0.0557(0.0307) (0.0274) (0.0286)** (0.0292) (0.0308) (0.0313)*

log_e (assets), (t-1) 0.0003 -0.0147 -0.0161 -0.018 -0.0073 -0.01(0.0123) (0.0105) (0.0131) (0.0112) (0.0111) (0.0116)

Number of employees/1000, (t-1) -0.0205 0.0073 0.1423 0.0463 0.0149 -0.0215(0.0452) (0.0255) (0.0916) (0.0400) (0.0408) (0.0429)

Herfindahl-Hirschman Index/1000, (t-1) -0.0067 -0.0211 -0.012 -0.0159 -0.0023 -0.0017(0.0086) (0.0074)*** (0.0072)* (0.0076)** (0.0075) (0.0076)

Observations 1,783 2,256 2,284 2,201 2,132 1,773

Pseudo R Squared 0.1958 0.2146 0.221 0.2105 0.2006 0.1556

Chi square (BHC, size, employees) 0.3477 2.5141 6.6603 2.7409 0.9491 0.9491Prob > Chi square (0.9508) (0.4728) (0.0836)* (0.4333) (0.8136) (0.8136) * significant at 10%; ** significant at 5%; *** significant at 1%

Table 5. Risk in Loan AdministrationProbit estimation of the choice of a low-risk lending practice


36


Nonperforming loans/Gross loans, (t-1) 0.0062 0.0035 -0.013 -0.0126 -0.0009 0.0003(0.0062) (0.0074) (0.0060)** (0.0063)** (0.0058) (0.0043)

Equity/Assets, (t-1) 0.0056 0.0078 0.009 0.0173 0.008 0.0091(0.0022)** (0.0023)*** (0.0026)*** (0.0032)*** (0.0027)*** (0.0030)***

Allowance for loan losses/Assets, (t-1) -0.0293 -0.0559 -0.0107 -0.0144 -0.0461 -0.0411(0.0132)** (0.0144)*** (0.0087) (0.0117) (0.0141)*** (0.0139)***

State unemployment growth rate, (t) 0.0013 0.0019 -0.0006 0.0043 0.0067 0.0026(0.0020) (0.0019) (0.0018) (0.0030) (0.0030)** (0.0022)

State unemployment growth rate, (t-1) 0.0051 -0.0021 0.0041 -0.0065 0.0023 0.0048(0.0022)** (0.0020) (0.0021)** (0.0023)*** (0.0039) (0.0024)**

State unemployment growth rate, (t-2) 0.0024 0.0005 0.0059 0 -0.0032 0.0038(0.0020) (0.0020) (0.0022)*** (0.0022) (0.0036) (0.0028)

State unemployment growth rate, (t-3) 0.0001 -0.0011 -0.0026 0.0005 0.0054 0.0053(0.0019) (0.0019) (0.0020) (0.0020) (0.0034) (0.0037)


Management rating = 2 dummy, (t-1) 0.0566 0.0266 0.0871 0.0332 0.0622 0.07(0.0290)* (0.0289) (0.0275)*** (0.0295) (0.0311)** (0.0352)**

Management rating = 4 or 5 dummy, (t-1) -0.1498 -0.0437 -0.0181 -0.1071 -0.1403 -0.01(0.0729)** (0.0667) (0.0547) (0.0658) (0.0685)** (0.0775)

Member multibank holding co. dummy, (t-1) -0.0475 0.0045 -0.0089 -0.0014 -0.0293 0.0134(0.0223)** (0.0213) (0.0227) (0.0236) (0.0252) (0.0270)

log_e (assets), (t-1) 0.0035 0.0004 0.0035 -0.0102 -0.0154 -0.0232(0.0090) (0.0084) (0.0084) (0.0092) (0.0103) (0.0099)**

Number of employees/1000, (t-1) 0.0204 0.0136 -0.0378 0.0218 0.0894 0.0161(0.0395) (0.0248) (0.0279) (0.0341) (0.0604) (0.0367)

Herfindahl-Hirschman Index/1000, (t-1) -0.0115 0.0043 -0.0106 -0.0033 -0.0002 0.0081(0.0056)** (0.0058) (0.0055)* (0.0061) (0.0060) (0.0065)

Observations 2,159 2,256 2,284 2,201 2,132 1,773

Pseudo R Squared 0.0416 0.0258 0.0477 0.056 0.0365 0.0294

Chi square (BHC, size, employees) 5.5172 0.4907 2.107 1.23 4.1153 4.1153Prob > Chi square (0.1376) (0.9209) (0.5505) (0.7458) (0.2493) (0.2493)


Table 6. Concentrations of Credit RiskProbit estimation of the choice of a low-risk lending practice


37


Nonperforming loans/Gross loans, (t-1) -0.0055 -0.0063 0.0065 -0.0031 -0.0019 -0.0017(0.0046) (0.0059) (0.0054) (0.0047) (0.0046) (0.0025)

Equity/Assets, (t-1) -0.0027 -0.0001 0.0037 0.0007 0.0003 -0.0004(0.0014)* (0.0018) (0.0021)* (0.0021) (0.0020) (0.0021)

Allowance for loan losses/Assets, (t-1) -0.0044 -0.0281 -0.0456 -0.0654 -0.0691 -0.0198(0.0101) (0.0108)*** (0.0134)*** (0.0152)*** (0.0141)*** (0.0090)**

State unemployment growth rate, (t) 0.0035 0.0004 0.0008 -0.0009 0.0001 -0.0009(0.0016)** (0.0016) (0.0015) (0.0024) (0.0024) (0.0016)

State unemployment growth rate, (t-1) 0.0014 0 0.0031 0.0016 0.0004 0.0019(0.0018) (0.0016) (0.0017)* (0.0019) (0.0032) (0.0018)

State unemployment growth rate, (t-2) 0.0002 -0.0034 0.0001 -0.0027 0.0011 -0.0002(0.0017) (0.0017)* (0.0018) (0.0017) (0.0029) (0.0021)

State unemployment growth rate, (t-3) -0.0016 -0.0009 -0.003 0.0027 -0.0056 0.0023(0.0016) (0.0016) (0.0016)* (0.0016)* (0.0028)** (0.0028)


Management rating = 2 dummy, (t-1) 0.0807 0.0741 0.1116 0.0815 0.0174 0.062(0.0224)*** (0.0236)*** (0.0224)*** (0.0228)*** (0.0249) (0.0259)**

Management rating = 4 or 5 dummy, (t-1) -0.0657 -0.1289 -0.0297 0.0692 -0.0667 -0.0495(0.0548) (0.0675)* (0.0462) (0.0284)** (0.0552) (0.0640)

Member multibank holding co. dummy, (t-1) -0.0468 -0.0625 -0.0815 -0.0521 -0.0431 -0.0286(0.0188)** (0.0197)*** (0.0209)*** (0.0203)** (0.0215)** (0.0219)

log_e (assets), (t-1) -0.0242 -0.025 -0.025 -0.0113 -0.0327 -0.0204(0.0067)*** (0.0065)*** (0.0079)*** (0.0072) (0.0081)*** (0.0077)***

Number of employees/1000, (t-1) -0.0051 0.0024 0.0654 -0.0042 0.0516 0.0285(0.0221) (0.0149) (0.0482) (0.0234) (0.0407) (0.0368)

Herfindahl-Hirschman Index/1000, (t-1) -0.0065 0.0001 -0.0076 0.0061 -0.0037 -0.0012(0.0045) (0.0047) (0.0045)* (0.0050) (0.0048) (0.0048)

Observations 2,159 2,256 2,284 2,201 2,131 1,773

Pseudo R Squared 0.0496 0.0487 0.0625 0.0472 0.0551 0.0305

Chi square (BHC, size, employees) 27.5173 32.1395 28.6306 11.5861 23.5563 23.5563Prob > Chi square 0.0000 0.0000 0.0000 0.0089 0.0000 0.0000


Table 7. Out-of-Area LendingProbit estimation of the choice of a low-risk lending practice


38


Nonperforming loans/Gross loans, (t-1) -0.0089 0.008 -0.0042 0.0042 -0.0044 -0.0016(0.0045)** (0.0053) (0.0039) (0.0043) (0.0032) (0.0019)

Equity/Assets, (t-1) -0.0025 -0.0001 -0.0007 -0.002 0.0004 -0.0002(0.0015)* (0.0016) (0.0016) (0.0017) (0.0015) (0.0018)

Allowance for loan losses/Assets, (t-1) 0.0046 -0.0081 0.0029 -0.0084 0.0012 0.0017(0.0112) (0.0076) (0.0064) (0.0075) (0.0083) (0.0074)

State unemployment growth rate, (t) -0.0006 -0.0019 -0.0015 -0.0005 -0.005 -0.0015(0.0017) (0.0014) (0.0013) (0.0022) (0.0019)*** (0.0014)

State unemployment growth rate, (t-1) -0.0023 0.0015 0.001 0.0013 0.0008 -0.0005(0.0018) (0.0015) (0.0015) (0.0017) (0.0025) (0.0014)

State unemployment growth rate, (t-2) 0.0008 -0.0011 -0.0016 -0.0016 -0.0015 -0.0016(0.0017) (0.0015) (0.0015) (0.0016) (0.0024) (0.0017)

State unemployment growth rate, (t-3) -0.0006 0.0014 -0.0002 -0.0002 0.0022 -0.0064(0.0016) (0.0014) (0.0014) (0.0015) (0.0023) (0.0023)***



Management rating = 4 or 5 dummy, (t-1) -0.0861 -0.1287 -0.1461 -0.1304 -0.155 -0.1913(0.0569) (0.0612)** (0.0545)*** (0.0543)** (0.0555)*** (0.0769)**

Member multibank holding co. dummy, (t-1) -0.0138 0.0195 -0.0145 0.0195 -0.0146 -0.0011(0.0184) (0.0159) (0.0171) (0.0164) (0.0173) (0.0173)

log_e (assets), (t-1) 0.0118 -0.0025 -0.0015 0.0013 -0.0103 -0.0049(0.0071)* (0.0061) (0.0075) (0.0065) (0.0073) (0.0069)

Number of employees/1000, (t-1) -0.04 -0.0168 0.0712 -0.0064 0.0693 0.0285(0.0234)* (0.0127) (0.0658) (0.0205) (0.0568) (0.0467)

Herfindahl-Hirschman Index/1000, (t-1) 0.002 -0.0049 -0.0045 -0.0075 0.003 -0.0018(0.0047) (0.0042) (0.0039) (0.0042)* (0.0040) (0.0039)

Observations 2,159 2,256 2,284 2,201 2,132 1,773

Pseudo R Squared 0.0799 0.1065 0.1283 0.1188 0.1312 0.1009

Chi square (BHC, size, employees) 4.4456 3.9615 2.2927 1.3694 2.8428 2.8428Prob > Chi square (0.2172) (0.2656) (0.5139) (0.7127) (0.4165) (0.4165)


Table 8. Fail to Adjust Pricing to Reflect RiskProbit estimation of the choice of a low-risk lending practice


39


Nonperforming loans/Gross loans, (t-1) -0.0331 -0.032 -0.0302 -0.0264 -0.0333 -0.0081

(0.0065)*** (0.0075)*** (0.0062)*** (0.0058)*** (0.0059)*** (0.0030)***

Equity/Assets, (t-1) -0.0003 0.0037 -0.0028 -0.0018 0.0047 0.0065(0.0021) (0.0023) (0.0023) (0.0026) (0.0025)* (0.0029)**

Allowance for loan losses/Assets, (t-1) 0.004 0.0011 -0.003 -0.0083 -0.0114 -0.0142(0.0149) (0.0110) (0.0091) (0.0115) (0.0136) (0.0109)

State unemployment growth rate, (t) -0.0009 -0.002 -0.0009 -0.0002 -0.0074 -0.0013(0.0021) (0.0020) (0.0019) (0.0031) (0.0029)*** (0.0021)

State unemployment growth rate, (t-1) 0.0004 0.0016 0.0044 0.0004 -0.0021 0.003(0.0023) (0.0020) (0.0022)** (0.0023) (0.0038) (0.0022)

State unemployment growth rate, (t-2) -0.0016 -0.001 0.001 0.0002 -0.0015 0.0017(0.0022) (0.0021) (0.0022) (0.0022) (0.0036) (0.0026)

State unemployment growth rate, (t-3) -0.0015 0.0004 -0.005 0.0041 0.0044 -0.0074(0.0020) (0.0019) (0.0020)** (0.0021)* (0.0033) (0.0035)**



Management rating = 4 or 5 dummy, (t-1) -0.0208 -0.2499 -0.125 -0.271 -0.1129 -0.2424(0.0644) (0.0944)*** (0.0692)* (0.0814)*** (0.0689) (0.1006)**

Member multibank holding co. dummy, (t-1) 0.0521 0.0378 0.0154 0.0131 0.0017 0.0154(0.0219)** (0.0212)* (0.0232) (0.0239) (0.0246) (0.0256)

log_e (assets), (t-1) 0.0174 0.0143 -0.0038 0.0007 0.024 0.0169(0.0096)* (0.0086)* (0.0109) (0.0092) (0.0089)*** (0.0095)*

Number of employees/1000, (t-1) 0.0031 -0.0124 0.1501 -0.0081 -0.0353 -0.054(0.0433) (0.0215) (0.0963) (0.0316) (0.0331) (0.0344)

Herfindahl-Hirschman Index/1000, (t-1) -0.0294 -0.0312 -0.0325 -0.0203 -0.0184 -0.0005(0.0059)*** (0.0055)*** (0.0055)*** (0.0061)*** (0.0057)*** (0.0061)

Observations 2,159 2,256 2,284 2,201 2,132 1,773

Pseudo R Squared 0.1174 0.1608 0.1572 0.1458 0.1752 0.1139

Chi square (BHC, size, employees) 10.3256 5.9552 3.7294 0.3569 7.3785 7.3785Prob > Chi square (0.0160)** (0.1138) (0.2922) (0.9490) (0.0608) (0.0608)*


Table 9. Fail to Require Principle Reduction before Renewing LoanProbit estimation of the choice of a low-risk lending practice


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Nonperforming loans/Gross loans, (t-1) 0.0093 -0.0117 -0.0015 -0.0044 -0.0067 0.0016(0.0060) (0.0073) (0.0064) (0.0052) (0.0051) (0.0040)

Equity/Assets, (t-1) -0.0028 -0.0024 -0.001 0.0005 -0.0004 0.0015(0.0019) (0.0021) (0.0023) (0.0023) (0.0022) (0.0025)

Allowance for loan losses/Assets, (t-1) -0.0647 0.007 -0.0103 0.0088 -0.0051 -0.0091(0.0169)*** (0.0113) (0.0114) (0.0136) (0.0144) (0.0109)

State unemployment growth rate, (t) -0.0038 0.0008 0.0001 -0.0021 -0.0025 -0.0009(0.0020)* (0.0020) (0.0019) (0.0027) (0.0027) (0.0018)

State unemployment growth rate, (t-1) -0.0023 0.0006 0.0016 0.0006 -0.006 0.001(0.0022) (0.0020) (0.0022) (0.0021) (0.0036)* (0.0020)

State unemployment growth rate, (t-2) 0.0056 0.0009 0.0004 0.0005 -0.002 -0.0032(0.0021)*** (0.0021) (0.0022) (0.0020) (0.0033) (0.0024)

State unemployment growth rate, (t-3) -0.0036 -0.0013 -0.0021 0.0017 -0.0003 0.0012(0.0019)* (0.0020) (0.0020) (0.0019) (0.0031) (0.0031)



Management rating = 4 or 5 dummy, (t-1) -0.0149 -0.1618 -0.2367 -0.3006 -0.234 -0.1701(0.0568) (0.0825)** (0.0799)*** (0.0809)*** (0.0772)*** (0.0873)*

Member multibank holding co. dummy, (t-1) 0.0411 0.0394 0.0378 0.0217 0.047 0.0434(0.0206)** (0.0215)* (0.0228)* (0.0210) (0.0218)** (0.0222)*

log_e (assets), (t-1) -0.0048 -0.0103 -0.0235 -0.0178 -0.0122 0.009(0.0090) (0.0089) (0.0113)** (0.0105)* (0.0104) (0.0085)

Number of employees/1000, (t-1) 0.006 0.0246 0.2355 0.2157 0.1271 -0.0591(0.0339) (0.0295) (0.1024)** (0.0950)** (0.0816) (0.0313)*

Herfindahl-Hirschman Index/1000, (t-1) -0.019 -0.0241 -0.0121 -0.011 0.0007 0.0037(0.0056)*** (0.0057)*** (0.0057)** (0.0054)** (0.0055) (0.0056)

Observations 2,159 2,256 2,284 2,201 2,132 1,773

Pseudo R Squared 0.1809 0.1635 0.1816 0.1856 0.2038 0.1596

Chi square (BHC, size, employees) 3.9864 4.577 8.1698 5.9246 6.4756 6.4756Prob > Chi square (0.2629) (0.2055) (0.0426) (0.1153) (0.0906)* (0.0906)* * significant at 10%; ** significant at 5%; *** significant at 1%

Table 10. Actual Lending Practices Differ from Written PoliciesProbit estimation of the choice of a low-risk lending practice


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Potential Risk in Overall Risk in Loan Fail to Adjust Pricing Fail to Require Principal Actual Practices DifferTransition Probability Underwriting Practices Administration to Reflect Risk Reduction before Loan Renewal from Written Policies

(2000 - 2004) (2000 - 2004) (1997 - 2004) (1997 - 2004) (1997 - 2004)

P11 0.02525 0.03607 0.02435 0.05395 0.03674(0.0001)*** (0.0001)*** (0.0001)*** (0.0001)*** (0.0001)***

P12 0.01005 0.00687 -0.01774 -0.01725 -0.01452 (0.0002)*** (0.0118)** (0.0001)*** (0.0001)*** (0.0001)***

P21 0.00032 0.00563 -0.00883 -0.00365 -0.01063(0.9074) (0.0390)** (0.0001)*** (0.0746)* (0.0001)***

P22 -0.03098 -0.04243 -0.01753 -0.05166 -0.0313(0.0001)*** (0.0001)*** (0.0001)*** (0.0001)*** (0.0001)***

Observations 134,562 134,562 239,211 239,165 239,211


Table 11. Transition Probabilities for Lending Practices and Loan Growth

Pearson's Correlation Coefficient (p-value) Lending Practice

(All banks and thrifts with assets of $300 million or less)The correlation between estimates of quarterly transition probabilities for lending practices and quarterly growth rates for gross loans and leases

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(2000 - 2004) (2000 - 2004) (1997 - 2004) (1997 - 2004) (1997 - 2004)

P11 -0.25247 -0.25609 -0.13209 -0.24207 -0.20143(0.0001)*** (0.0001)*** (0.0001)*** (0.0001)*** (0.0001)***

P12 0.00818 -0.00703 0.07323 0.05765 0.06852 (0.0027)*** (0.0099)*** (0.0001)*** (0.0001)*** (0.0001)***

P21 -0.02045 -0.0392 0.0467 0.00921 0.05239(0.0001)*** (0.0001)*** (0.0001)*** (0.0001)*** (0.0001)***

P22 0.27445 0.28555 0.1048 0.24254 0.17839(0.0001)*** (0.0001)*** (0.0001)*** (0.0001)*** (0.0001)***

Observations 134,562 134,562 239,211 239,165 239,211



Table 12. Transition Probabilities for Lending Practices and Nonperforming LoansThe correlation between estimates of quarterly transition probabilities for lending practices and nonperforming loans as a percentage of bank assets.

(All banks and thrifts with assets of $300 million or less)

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(2000 - 2004) (2000 - 2004) (1997 - 2004) (1997 - 2004) (1997 - 2004)

P11 -0.17817 -0.16599 -0.06377 -0.10013 -0.10596(0.0001)*** (0.0001)*** (0.0001)*** (0.0001)*** (0.0001)***

P12 -0.0205 -0.0302 0.03401 0.01104 0.02781 (0.0001)*** (0.0001)*** (0.0001)*** (0.0001)*** (0.0001)***

P21 -0.02045 -0.03723 0.02706 0.00469 0.02147(0.0001)*** (0.0001)*** (0.0001)*** (0.0219)** (0.0001)***

P22 0.20732 0.1989 0.04914 0.10498 0.09957(0.0001)*** (0.0001)*** (0.0001)*** (0.0001)*** (0.0001)***

Observations 134,562 134,562 239,211 239,165 239,211



Table 13. Transition Probabilities for Lending Practices and Loan Charge-offsThe correlation between estimates of quarterly transition probabilities for lending practices and annual loan charge-offs as a percentage of bank assets

(All banks and thrifts with assets of $300 million or less)

Underwriting Cycles in Banking: Are Bad Loans...

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