Auditor Quality

7/28/2019 Auditor Quality

1/39Electronic copy available at: http://ssrn.com/abstract=873598

Auditor Quality, Tenure, and Bank Loan Pricing

By

J eong-Bon Kim, Byron Y. Song and J udy S. L. Tsui

Current DraftMarch 2007

____________

The first author is at Concordia University and The Hong Kong Polytechnic University. The second andthird authors are at The Hong Kong Polytechnic University. We thank Jong-Hag Choi, Annie Qiu, HaninaShi, Cheong H. Yi, Suk Heun Yoon, Yoonseok Zang, and participants of the 2006 Annual Meeting ofAAA, and Ph.D./DBA research seminars at The Hong Kong Polytechnic University, and Seoul National

University for their useful comments. The first and last authors acknowledge partial financial support forthis research obtained from the Competitive Earmarked Research Grant of The Hong Kong SARGovernment and the Area of Strategic Development (ASD) Research Grant, the Faculty of Business, TheHong Kong Polytechnic University. All errors are our own.

Correspondence: Judy Tsui, Chair Professor and Dean, the Faculty of Business, The Hong KongPolytechnic University, Hung Hom, Kowloon, Hong Kong ([email protected]).


2/39Electronic copy available at: http://ssrn.com/abstract=873598

Auditor Quality, Tenure, and Bank Loan Pricing

SUMMARY: Using a large sample of US bank loan data over the 9-year period from 1996 to 2004, weinvestigate the effect of two auditor characteristics, namely auditor quality and tenure, on the price termof bank loan contracts. Our results show the following: First, we find that banks charge a significantlylower rate for borrowers with Big 4 auditors than for borrowers with non-Big 4 auditors. Further analysisshows that banks charge a higher loan rate for borrowers who change their auditors in general, and theycharge a substantially higher loan spread for borrowers who downgrade their auditors from Big 4 to non-Big 4 auditors in particular. Second, we find that the loan spread is inversely related to auditor tenure,suggesting that banks view auditor tenure as a credit risk-reducing factor. Third, we find that the relationbetween loan spread and audit quality is conditioned upon the level of credit risk perceived by creditrating agencies. Our study provides direct evidence that banks take into account audit quality whenassessing borrowers credit quality and determining the price term of loan contracts.

Keywords: Auditor quality; Auditor tenure; Loan pricing; Loan spread; Private debt.


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Auditor Quality, Tenure, and Loan Pricing

INTRODUCTION

Audited financial statements play a crucial role in facilitating financial contracts in

general and loan contracts between lenders and borrowers in particular. However, previous

research has paid little attention to the role of audit quality in loan contracting, although audit

quality is an important factor determining the credibility and quality of audited financial

statements. In particular, no previous research has examined the issue of whether audit quality

differentiation between Big 4 (previously 5, 6, or 8) and non-Big 4 auditors does matter in the

market for private debts such as bank loans, though Big 4 audits have been documented to be of

greater value to participants in the equity and bond markets, compared with non-Big 4 audits

(e.g., Mitton 2002; Mansi et al. 2004; Pittman and Fortin 2004).1

Given the lack of empirical evidence on the role of audit quality in private debt

contracting, this study aims to provide systematic evidence on whether two auditor

characteristics, i.e., auditor quality and tenure, influence the price term of loan contracts. To do

so, we first investigate whether the loan rate that lenders charge to borrowers are lower for

borrowers with Big 4 auditors than for those with non-Big 4 auditors after controlling for

borrowers credit quality and loan-specific characteristics. Second, we investigate whether and

how auditor tenure, measured by the length of the auditor-client relationship, affects loan pricing.

1 To our knowledge, there are three studies that examine the role of audit per se in loan pricing. Johnson et al.(1983) provide experimental evidence suggesting that auditor association is not a significant factor affecting thebank loan rate. Blackwell et al. (1998) investigate economic values of varying levels of audit assurance (i.e., audits,reviews, compilations), using a small sample of 212 private (closely held) firms that have revolving creditarrangements with six banks located within a single state in the US. Kim et al. (2005) examine the effect ofvoluntary, non-statutory audits on the interest expenses (relative to short-term and long-term debts) using a sampleof privately held Korean firms. Both Blackwell et al. (1998) and Kim et al. (2005) report evidence that audit per seleads to a lower loan rate or a lower interest rate. However, none of the above studies examine the issue of auditquality differentiation in loan pricing forpublicly heldborrowers.

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A series of recent incidents of audit failures that started with the 2001 Enron debacle and the

subsequent Andersen collapse have triggered a world-wide debate over whether the long-term

auditor-client relationship potentially leads to the impairment of auditor independence and thus

audit quality. Since the enactment of the Sarbanes-Oxley Act of 2002 which called for a study

and review of the potential effects of requiring mandatory rotation of audit firms, several

researchers have examined the effect of auditor tenure on audit and earnings quality (e,g., Davis

et al. 2002; Myers et al. 2003). To our knowledge, however, no previous research has examined

whether and how lenders take into account auditor tenure when assessing borrowers credit

worthiness and setting the price term of loan contracting. In this paper, we aim to provide direct

evidence on the effect of auditor tenure on loan pricing.

Finally, as a supplemental analysis, we also examine whether the relation, if any, between

the loan rate and two auditor characteristics, i.e., auditor quality and tenure, is conditioned upon

information intermediation and monitoring activities by credit rating agencies. Previous research

suggests that the information intermediation by credit rating agencies helps outside investors

reduce the information asymmetry, which in turn contributes to increasing firm valuation (Lang

et al. 2004) and lowering a cost of capital from the bond markets (e.g., Mansi et al. 2004, 2005).

It is therefore interesting to examine how the audit quality variables interact with the information

intermediation variable in the context of loan pricing. For this purpose, our analysis focuses on

whether the loan rate-reducing effect, if any, of auditor quality and tenure differs systematically

between borrowers with good credit ratings and those with poor credit ratings.

Our regression results reveal the following. First, we find that lenders charge a

significantly lower loan rate for borrowers with Big 4 auditors than for borrowers with non-Big 4

auditors. Further analysis shows that lenders charge a higher loan rate for borrowers who change

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their auditors in general, and they charge a substantially higher loan rate for borrowers who

downgrade their auditors from Big 4 to non-Big 4 auditors in particular. Second, we find that the

loan rate is inversely related to auditor tenure, suggesting that lenders view auditor tenure as a

credit risk-reducing factor. Finally, we find that the loan rate-reducing effect of audit quality is

greater for borrowers with low credit ratings than for borrowers with high credit ratings. This

suggests that high-quality audits are of greater value to lenders when borrowers are faced with

high credit risks. Overall, our empirical evidence is consistent with the view that audit quality

plays a more important role in loan pricing, in particular, when borrowers have poor credit

ratings.

Our study adds to the existing auditing literature in the following ways. To our

knowledge, this is the first study that documents direct evidence that banks take into account

auditor quality and tenure when assessing borrowers credit quality and setting the loan rate. Our

study also contributes to the existing loan contracting literature as well. We provide evidence

that the quality of external audits is an additional factor that favorably impacts the price term of

loan contracting, and that this positive effect is not subsumed by information intermediation and

monitoring activities by credit rating agencies. Our evidence is consistent with the view that

lenders view higher-quality audits and longer tenure as credit risk-reducing factors. Given the

scarcity of empirical evidence on the issue, our findings provide useful insights into the role of

auditor quality and tenure in the market for private debts such as bank loans.

The remainder of the paper is structured as follows: In section 2, we develop our research

hypotheses. In section 3, we specify an empirical model linking the loan spread with our test

variables, namely auditor quality and tenure, and borrower-specific and loan-specific control

variables. In section 4, we describe our sample and data sources and present descriptive statistics

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on our variables. Section 5 reports the results of our univariate tests. Section 6 reports the results

of multivariate tests. In section 7, we conduct a variety of robustness check for our main

regression results. In section 8, we perform further analysis to investigate the impact of auditor

changes on loan pricing. We also examine whether the loan rate-audit quality relation is

conditioned on credit risk perceived by credit rating agencies. The final section provides

summary and concluding remarks.

HYPOTHESIS DEVELOPMENT

The Effect of Auditor Quality on Loan Pricing

Banks are the largest providers of private debts and bank loans are the most important

source of external finance for most firms around the world. Bank loan officers typically rely on

audited financial statements to assess borrowers credit quality. On one hand, the use of high-

quality auditors enhances the credibility of audited financial statements, and thus alleviates

information asymmetries between lenders and borrowers, which in turn reduces lenders

monitoring costs. Therefore banks are likely to charge a lower loan rate for borrowers with Big 4

auditors than for borrowers with non-Big 4 auditors. On the other hand, banks themselves are

more sophisticated information processors, compared with a representative investor in the stock

and/or public debt (bond) markets. Banks have the ability, skill and resources to collect, produce,

and analyze relevant information and to assess the credibility of financial reports and the credit

worthiness of a borrower. Moreover, banks often have access to private information about the

borrower, for example, through direct communications with management. One may thus argue

that the value of high-quality auditors may not be as high to banks as it is to investors in the

equity and bond markets. In other words, the information-enhancing value of high-quality audits,

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if any, may be subsumed by the superior ability of banks to acquire, verify and process borrower-

specific information and to assess the credibility and quality of borrowers financial statements.

In such a case, there would be no significant difference in loan rates charged to borrowers with

Big 4 auditors vis--vis those with non-Big 4 auditors.

Given the two conflicting views on the value of auditor quality in the bank loan market, it

is an empirical issue whether or not the use of Big 4 auditors by borrowers has an incremental

value to banks, when banks assess borrowers credit quality (before loan decisions are made),

and monitor credit quality and/or renegotiate loan contract terms subsequent to changes in credit

quality (after loans are granted). To provide empirical evidence on the issue, we test the

following hypothesis with no prediction on the directional effect:

H1: The loan rate charged by banks differs systematically between borrowerswith Big 4 auditors and borrowers with non-Big 4 auditors, other thingsbeing equal.

The Effect of Auditor Tenure on Loan Pricing

There are two conflicting views on the relation between auditor tenure and audit quality,

which is at the center of current debates over the pros and cons of mandatory auditor rotation.

One strand of research argues that as auditor tenure increases, auditor independence erodes, and

thus client firms are given more flexibility in financial reporting. In this scenario, mandatory

auditor rotation may contribute to improving audit quality by truncating the existing auditor-

client relationship. Davis et al. (2002) provide evidence suggesting that discretionary accruals

increase with auditor tenure. Choi and Doogar (2005) show that auditors with long tenure are

less likely to issue going concern opinions, suggesting that audit quality decreases with the

length of the auditor-client relationships.

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The other strand of research argues that audit quality increases with auditor tenure, and

provides evidence supporting a positive association between audit quality and auditor tenure.

Using several accrual measures as proxies for earnings quality (and thus audit quality), Myers et

al. (2003) document a positive relation between audit quality and auditor tenure. Ghosh and

Moon (2005) find that the magnitude of earnings response coefficients increases with auditor

tenure, suggesting a positive relation between auditor tenure and audit quality. Mansi et al. (2004)

document an inverse relation between auditor tenure and the cost of debt financing in the public

bond market (measured by bond yield spreads over the benchmark yield).2

To our knowledge, however, no previous research has examined the effect of auditor

tenure on audit quality in the context of bank loan pricing. To provide empirical evidence on

whether and how banks take into account auditor tenure when assessing borrowers credit quality

and setting the loan rate, we test the following hypothesis with no prediction on the directional

effect:

H2: The loan rate charged by banks differs systematically between borrowerswith long- tenure auditors and borrowers with short-tenure auditors, otherthings being equal.

EMPIRICAL MODEL

To investigate the effect of auditor quality and tenure on bank loan pricing, we specify

the following regression model:

ErrorTermsYearDummiemmiesIndustryDu

esDummiesLoanPurposSyndicateeLogLoanSizyLogMaturitLossBetayTangibilityofitabilitioCurrentRat

eRatioLogCoveragMBLeverageSizeTenureBigAIS

+++

+++++++++

++++++=

)()(

)(Pr

321

98765

4321210

(1)

2 Johnson et al. (2002) and Geiger and Raghunandan (2002) also provide evidence suggesting a positive associationbetween auditor tenure and audit quality in the context of reporting quality and the likelihood of a bankrupt firmreceiving a going concern audit opinion, respectively.

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In Equation (1), the dependent variable,AIS, is the cost of the bank borrowing which is measured

by the drawn all-in spread in basis points. This all-in spread represents the interest rate charged

by banks (plus annual fee and the upfront or maturity fee) over the benchmark rate, i.e., LIBOR,

and is paid by the borrower on all drawn lines of credit. We measure the cost of loan using a

spread over LIBOR because most bank loans are priced in terms of the floating rate. Commercial

banks typically assess the risk of a loan based upon the information on the business nature and

performance of borrowing firms, and then set a markup over a prevailing benchmark rate such as

LIBOR to compensate for the credit risk. The AIS variable thus reflects the banks perceived

level of risk on a loan facility provided to a specific borrower.

Our test variables,Bigand Tenure, represent auditor quality and tenure, respectively.Big

is a dummy variable which is equal to 1 if the incumbent auditor of a borrowing firm is one of

Big 4 (or previously 5, 6 or 8) auditors which include Arthur Andersen, Arthur Young, Coopers

& Lybrand, Ernst & Young, Deloitte & Touche, KPMG Peat Marwick, PricewaterhouseCoopers,

Touche & Ross, and merged entities among them, and 0 otherwise. To the extent that Big 4

auditors are better able to help banks overcome the information problem, we expect the

coefficient onBigto be negative (i.e., 1 < 0 ), and its magnitude captures the difference in the

loan spreads charged to borrowers with Big 4 auditors vis--vis those with non-Big 4 auditors.

Tenure is measured by the number of years of the auditor-client relationship. For example, if

banks view longer (shorter) tenure as being associated with higher (lower) audit quality, one

would observe a negative coefficient on Tenure i.e., 2 < 0 (2 > 0).

To isolate the loan pricing effect of audit quality from the effect of other borrowers

characteristics, we include a set of borrower-specific variables that are deemed to affect

borrowers credit quality and thus loan pricing, i.e., Size, Leverage, MB, Current Ratio, Log

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Beta for each year, we estimate the market model for each year using daily returns on an

individual stock and the equally-weighted market returns. Loss is a dummy variable which is

equal to 1 for loss firms and 0 otherwise. We expect a positive coefficient on bothBeta andLoss.

Previous research on bank loan contracts shows that several loan-specific characteristics

are related to the interest rate charged by banks (e.g. Strahan 1999; Dennis et al. 2000; Bharath et

al. 2006). We include in Equation (1) a set of loan-level variables to isolate the potential effect of

loan characteristics on the loan spread from the effect of our test variables, namely Big and

Tenure. TheLog Maturity variable is the natural log of the loan maturity period (in months). The

Log Loan Size variable is measured by the natural log of the amount of loan facility given to a

borrower. Previous research provides evidence that banks charge a higher interest rate for the

longer-term loan and for the smaller loan facility, respectively (e.g., Bae and Goyal 2003;

Bharath et al. 2006). We therefore expect a positive sign onLog Maturity and a negative sign on

Log Loan Size (1 > 0 and 2 < 0, respectively). The Syndicate variable is a dummy variable that

equals 1 for the syndicate loans and 0 otherwise. We include this variable to capture any

difference, if any, in the interest rate charged between the syndicate and non-syndicate loans. In

addition, we include Loan Purpose Dummies to control for any difference in loan pricing

associated with the different purposes of loan facilities.3 Finally, we includeIndustry Dummies

and Year Dummies to control for potential differences in the loan spreads across industries and

over years.

3 Our sample includes loan facilities with 22 different purposes specified by the LPC Dealscan database. We useonly seven Loan Purpose dummies to capture the seven most common purposes, that is, corporate purposes, debtrepayment, working capital, CP backup, takeover, acquisition line, LBO/MBO. The number of loan facilities witheach of these seven purposes exceeds one percent of our sample, while the number of loan facilities with each of theother purposes is less than one percent of our sample.

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SAMPLE, DATA SOURCES, AND DESCRIPTIVE STATISTICS

The initial list of our sample consists of all publicly traded firms with bank loan data that

are included in the LPC Dealscan database during the sample period, 1996-2004. The LPC

Dealscan database is an online database which contains a variety of historical bank loan data and

other financial arrangements that are compiled from the Securities and Exchange Commission

(SEC) filings by public firms and self-reporting by banks.4 The database includes the loan data

starting from 1986, and expands its coverage over time, in particular, after 1995. Thus we select

1996 as the starting year of our sample period. The loan data in the LPCDealscan database are

compiled for each deal and facility. Each deal, i.e., a loan contract between a borrower and

bank(s) at a specific date, may have only one facility or have a package of several facilities with

different price and non-price terms.5 We consider each facility as a separate observation in our

sample since many loan characteristics and the loan spreads vary across facilities. 6 Our sample

includes term loans, revolvers and 364-day facilities, but excludes bridge loans and non-fund

based facilities such as lease and standby letters of credit. We also require that all loan facilities

in our sample are senior debts.7 We then match the loans with borrowers financial statement

data in Compustat, using the ticker symbol and name of each borrower.8

We require that all the

relevant annual accounting data of borrowers are available in the fiscal year immediately before

4 Other papers which use the LPC Dealscan database include Strahan (1999), Dichev and Skinner (2002), Beatty

and Weber (2003), Asquith et al. (2005), Bharath et al. (2006), etc.5 For instance, a deal may comprise a line of credit facility and a term loan with longer maturity.6 As will be further discussed in Section 7, we also estimate our main regressions using only one facility withineach deal and each firm year, and find that the results remain qualitatively identical with those using each facility asa separate observation.7 Our sample selection criteria are similar to those used by Bharath et al. (2006).8 This procedure leads to a substantial reduction in the number of available loan facilities because many borrowersincluded in theDealscan database are subsidiaries of public firms, private firms and government entities rather thanpublicly traded companies, and some public companies are not covered by Compustat(Strahan 1999; Dichev andSkinner 2002).

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the loan year. After applying the above procedures, we obtained a sample of 7,656 loaned

facilities borrowed by 1,911 firms over the 1996-2004 period. Table 1 presents the distribution of

loan facilities in our sample by year and loan type. As shown in the table, nearly 57 percent of

7,656 loan facilities in our sample are for revolvers, while about 23 percent and 20 percent are

for term loans and 364-day facilities, respectively. The number of loan facilities increases with

years, reflecting an increase in theDealscan coverage.

(Insert Table 1 here)

Panel A of Table 2 provides descriptive statistics on various characteristics of loan

facilities in our sample. As shown in Panel A, the mean and median of the drawn all-in spread

over LIBOR (i.e.,AIS) are 172 and 150 basis points, respectively, with its standard deviation of

about 130 basis points. The large standard deviation ofAISrelative to its mean indicates a wide

variation inAISacross loan facilities and deals. The mean (median) maturity period is about 41

(36) months with its standard deviation of about 24 months. The mean and median of loan

facilities size are $313 and $146 millions (in US dollars) with a large standard deviation of $652

million, suggesting that its distribution is skewed with a wide variation across loan facilities and

deals. About 93 percent of the loan facilities are syndicate loans with an average of more than

nine different lenders (commercial banks and other financial institutions such as investment

banks and insurance companies) in a syndicate group underwriting the loan facilities. The loan

characteristics in our sample are, by and large, comparable with those of the Bharath et al. (2006)

sample except for the size of the loan facility. The mean and median of loan facility size in our

sample is much bigger than those in their sample ($177.5 and $50 millions), reflecting the fact

that our sample period includes more recent years and the facility size has increased over time.


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Panel B of Table 2 presents descriptive statistics on borrowers characteristics in our

sample. As shown in Panel B, 95.1 percent of all firm-years are audited by Big 4 auditors. The

mean and median ofTenure (i.e., the length of the auditor-client relationship in years) are 8.446

years and 8.000 years, respectively, with its standard deviation of 5.157 years, suggesting that

the Tenure variable is reasonably distributed in our sample. The mean and median of the Size

variable are 6.775 and 6.753, respectively, with its standard deviation of 1.893. On average, our

sample firms have the long-term debt-to-total asset ratio of 26.2 percent, the market-to-book

ratio of 1.753, and the current ratio of 1.818. The Log Coverage Ratio variable, measured by the

natural log of one plus the interest coverage ratio, has the mean (median) of 2.177 (1.969) with a

standard deviation of 1.150. The descriptive statistics on Profitability and Tangibility show that

for our sample, 14.5 percent and 34.9 percent of total assets are, respectively, EBITDA and

tangible assets (i.e., PP&E). Our sample firms have, on average, security beta close to one, and

18.4 percent of our sample firms have experienced a loss during the sample period.

RESULTS OF UNIVARIATE TESTS

To assess the effect of auditor quality (Big 4 vs. non-Big 4 auditors) on loan pricing, we

partition the full sample into two sub-samples: (1) the Big 4 sample of borrowers with Big 4

auditors; and (2) the non-Big 4 sample of borrowers with non-Big 4 auditors. Panel A of Table 3

reports descriptive statistics on our major research variables separately for the Big 4 sample and

for the non-Big 4 sample, along with the results of tests for the mean and median differences

between the two samples (t-test and Wilcoxon z-test, respectively). As shown in Panel A, the

mean and median of the drawn all-in spread (AIS) are 168 and 150 basis points, respectively, for

the Big 4 sample, while they are 252 and 250 basis points, respectively, for the non-Big 4 sample.

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Both the mean and median differences of 84 and 100 basis points are significant at less than the

onepercent level, suggesting that banks charge a significantly lower loan rate for borrowers with

Big 4 auditors than for borrowers with non-Big 4 auditors. These differences are economically

significant as well, considering the mean and median ofAISfor the full sample are 172 and 150

basis points, respectively (as reported in Table 2). The mean and median ofTenure are 8.581 and

8.000 years, respectively, for the Big 4 sample, while they are 5.833 and 5.000 years,

respectively, for the non-Big 4 sample. These mean and median differences are significant at less

than the one percent level, suggesting that, on average, Big 4 auditors have a longer tenure than

non-Big 4 auditors.

With respect to a set of nine variables representing borrowers characteristics (Size to

Loss), we observe that the mean and median ofSize, Leverage, MB, Current Ratio, Tangibility

andBeta are significantly different between the Big 4 and non-Big 4 samples at less than the one

percent level. On average, borrowers in the Big 4 sample are larger, more leveraged, have a

higher market-to-book ratio and a lower current ratio, more tangible assets and a higher beta,

compared with borrowers in the non-Big 4 sample. However, we observe no significant

difference in the mean and median ofLog Coverage Ratio, Profitability, and Loss between the

two sub-samples. With respect to a set of four variables representing loan characteristics,

borrowers in the Big 4 sample, on average, have a larger loan facility, and are more likely to

have a syndicate loan, and attract more participant lenders, compared with those in the non-Big 4

sample.

To assess the effect of auditor tenure on loan pricing, we partition the full sample into

two sub-samples on the basis of the median tenure of 8 years: (1) the long-tenure sample of

borrowers with their auditor tenure longer than or equal to eight years; and (2) the short-tenure

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sample of borrowers with their auditor tenure less than eight years. Panel B of Table 3 reports

descriptive statistics on our major research variables separately for the long-tenure sample and

for the short-tenure sample, along with the results of tests for the mean and median differences

between the two sub-samples. As shown in Panel B, the mean and median ofAISare about 145

and 111 basis points, respectively, for the long-tenure sample, while they are about 202 and 200

basis points, respectively, for the short-tenure sample. Both the mean and median differences of

57 and 89 basis points, respectively, are significant at less than the one percent level. These

differences are economically significant as well, considering the mean and median ofAISfor the

full sample are 172 and 150 basis points, respectively (as reported in Table 2). In short, our data

reveal that the loan spread decreases significantly with auditor tenure, suggesting that banks take

into account auditor tenure when assessing the credibility of financial statements and setting the

loan rate. 96.9 percent of borrowers in the long-tenure sample have Big 4 auditors, while 93.1

percent in the short-tenure sample. This difference is significant at less than the one percent level.

With respect to a set of nine variables representing borrowers characteristics, there are

significant differences in their mean and median values of most variables between the long-

tenure and short-tenure samples. Compared with borrowers in the short-tenure sample, on

average, those in the long tenure sample are larger in size, and have a lower current ratio, more

tangible assets, a smaller beta, and a lower likelihood of incurring a loss. With respect to a set of

four variables representing loan characteristics, borrowers in the long-tenure sample, on average,

have a shorter maturity period and a larger loan facility, are more likely to have a syndicate loan,

and attract more participant lenders, compared with those in the short-tenure sample.


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Table 4 reports Pearson correlation coefficients among all the variables included in

Equation (1). Consistent with the results of our univariate tests in Table 3, AIS is negatively

correlated with Bigand Tenure at less than the one percent level with their magnitude of -0.14

and -0.23, respectively, suggesting that the use of Big 4 auditors and long tenure auditors is

inversely associated with a lower loan spread. Consistent with our expectation,AISis positively

correlated withLog Maturity and negatively correlated with Syndicate. This suggests that banks

charge a lower (higher) loan rate for short-term (long-term) loans and syndicate (non-syndicate)

loans. The negative correlations ofAISwith Size, MB, Log Coverage Ratio, Profitability, and

Tangibility suggest that banks charge a lower loan rate for borrowers with low credit risks. The

positive correlations ofAISwithLeverage, Current Ratio, Beta, andLoss support the view that

banks charge a higher loan rate for borrowers with high credit risks. With respect to the

correlations among explanatory variables in Equation (1), Size is highly correlated with Log

Loan Size with the magnitude of 0.83. This is as expected because banks are highly likely to

offer large loan facilities to large firms. The correlations between other explanatory variables in

Equation (1) are reasonable with the highest correlation of -0.56 between Log Coverage Ratio

andLeverage.


In summary, the results of univariate tests suggest that banks charge a lower loan rate for

borrowers with Big 4 auditors or long tenure than those with non-Big 4 auditors or short tenure,

respectively. However, the significant differences in the borrower-specific and loan-specific

variables between the Big 4 and non-Big 4 samples and between the long-tenure and short-tenure

samples suggest that the effect of these variables on loan pricing should be controlled for when

assessing the impact of auditor quality and tenure on the loan spread. In the next section, we

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therefore conduct multivariate tests to isolate the loan pricing effect of auditor quality and tenure

from the effect of borrower-specific and loan-specific characteristics.

RESULTS OF MULTIVARIATE TESTS USING THE FULL SAMPLE

Table 5 presents the results of the OLS regressions in Equation (1) using the full sample

of 7,656 facility-years over the 1996-2004 period. As shown in Columns (1) and (2) of the table,

the coefficient onBig(Tenure) is significantly negative when AISis regressed on Big(Tenure)

and other control variables, suggesting that banks charge a lower loan rate for borrowers with

Big 4 (long-tenure) auditors after controlling for all other borrower-specific and loan-specific

variables. As shown in Column (3), when bothBigand Tenure are included in the regression, the

coefficients on Bigand Tenure are both significant with negative signs. The above results are

consistent with the view that banks take into account auditor quality and tenure when assessing

borrowers credit quality and setting the loan spread. Our results support the view that high-

quality audits alleviate the information asymmetry between lenders and borrowers and the

associated monitoring costs, which in turn contributes to lowering the loan spread charged by

banks. Overall, our results are consistent with Mansi et al. (2004) who document that external

audits by Big 4 auditors and long-tenure auditors lead to a reduced cost of debt in the public

bond market and Pittman and Fortin (2004) who document that Big 4 audits are associated with a

lower interest cost of debt in early public years of IPO firms.

In Column (3), the coefficient onBigis -13.557 (t = -2.26), indicating that the difference

in loan spread between borrowers with Big 4 and non-Big 4 auditors is nearly 14 basis points. As

reported in Table 2, the average amount of loan facility is about $313 millions for our sample

and the mean maturity is about 41 months or 3.5 years. This means that, on average, borrowers

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with Big 4 auditors can save the interest cost of about $438,200 per year over the maturity period

of 3.5 years, which is economically significant as well. In Column (3), the coefficient on Tenure

is -1.267 (t = -5.52), suggesting that, on average, borrowers can save an interest rate of around

1.3 basis points by retaining their relationship with the incumbent (Big 4 or non-Big 4) auditor

for one additional year. The associated amount of interest cost saving is about $40,690.


With respect to the coefficients on control variables, we find all coefficients except for

MB and Syndicate are significant at less than the one percent level with their signs consistent

with our expectations and the findings of previous research such as Bharath et al. (2006). More

specifically,AISis significantly and positively associated withLeverage, Beta, andLoss, while it

is negatively associated with Size, Current Ratio, Log Coverage Ratio, Profitability, and

Tangibility. In addition,AISis positively associated withLog Maturity and negatively associated

withLog Loan Size.

ROBUSTNESS CHECKS

We perform several sensitivity tests to check the robustness of our main results reported

in Table 5. Our analyses in Table 5 consider each loan facility as an independent observation

although a borrower can obtain several facilities in the same year. As a sensitivity check, we use

the following ways to reconstruct our sample and then re-estimate Equation (1): (i) including

only one facility of each deal (the largest facility in terms of facility size); (ii) including only one

facility for each firm year (the largest facility in the first deal in each year); (iii) conducting

Fama-MacBeth regressions on the reduced sample constructed in (ii). Columns (1) to (3) of

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Table 6 report the corresponding results. The magnitude, sign, and significance of the

coefficients onBigand Tenure in Table 6 are similar to those in Table 5.

To further check whether our inferences on the test variables, namely Bigand Tenure, are

distorted by the existence of potential endogeneity problems, we re-estimate Equation (1) using

one-year lagged values ofBigand Tenure. As shown in Column (4) of the table, the use of one-

year lagged values for our test variables does not alter our results reported in Table 5, suggesting

that our earlier results are robust to potential endogeneity problems associated with our test

variables.

In our regression specification in Equation (1), the loan spread is linked to borrowers

auditor choice (i.e., Big 4 vs. non-Big 4) and many other variables. Suppose that borrowers with

high credit quality (and thus having lowerAIS) are more likely to choose Big 4 auditors. In such

a case, the error term in Equation (1) is likely to be correlated with whether borrowers choose

Big 4 auditors or not, and our estimate of the coefficient onBigis likely to be biased. To address

a concern over this potential self-selection bias, we estimate the two-stage treatment-effect

model (Greene 2000). In the first stage, we estimate a probit auditor-choice model, and then

obtain the Inverse Mills ratios. 9 In the second stage, we then estimate Equation (1) after

9 The probit auditor-choice model is specified as follows:

++

+++++

+++++++=

)(

)(arg111098

76543210

esYearsDummi

DummiesIndustriesShrinctHighAnalysInvtratinginM

TurnoverDPInvrevyTangibilitMBLiabilitySaleBig(2)

WhereBigis an indicator variable which is equal to one for borrowers with Big 4 auditors and zero otherwise;

Sale is the natural log of net sales; Liability is total liabilities divided by total assets; MB is the market-to-bookratio; Tangibility is net PP&E divided by total assets; Invrev is the sum of inventory and receivables over totalassets;DPis depreciation and amortization over total assets; Turnoveris net sales divided by total assets;Marginis income before extraordinary items divided by net sales; Invtratingis a dummy variable which is equal to onefor borrowers with S&P investment grade rating (BBB- or above) and zero for firms with non-investment graderating or without rating value; HighAnalystis a dummy variable which is equal to 1 for firms followed by morethan sevem (the median) analysts and zero for firms followed by less than seven analysts or not covered by IBES;Shrinc is a dummy variable which is equal to one if the number of shares outstanding increases by more than 10percent during the current fiscal year and zero otherwise. The sample size used for estimating Equation (2) as wellas Equation (1) with the Inverse Mills ratio included reduces to 7,559 from 7,656 facility-year observations

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including the Inverse Mills ratio (obtained in the first stage) as an additional independent

variable. Column (5) of Table 6 reports the result of the second-stage regression. We find that the

coefficient on Inverse Mills Ratio is significantly positive at less than five percent level,

suggesting that self-selection bias may not exist. The coefficients on Big and Tenure are

significantly negative at less than one percent level. Overall, the inclusion of the Inverse Mills

ratio strengthens our result in the sense that the coefficients on Big and Tenure reported in

Column (5) of Table 6 are more significantly negative and larger in magnitude, compared with

those reported in Column (3) of Table 5. This suggests that our main regression results reported

in Table 5 (without including the Inverse Mills ratio) are robust with respect to potential self-

selection biases.

Though not tabulated, we also conduct several additional sensitivity checks. First, we

estimate Equation (1) after including the Loan Type dummies to distinguish among different

types of loan facilities in our sample, i.e. term loans, revolvers greater than one year, revolvers

less than one year and 364-day facilities. Not reported is that the inclusion of the loan type

dummies does not alter our main results reported in Table 5. Second, we estimate Equation (1)

after including an additional dummy variable, Secured, which takes the value of one for secured

loans and zero otherwise. Though not reported, we find that the inclusion of this Secureddummy

does not alter our main results presented in Table 5. We find that the coefficient on Secured is

significantly positive with its magnitude of 70.950 and its t-value of 23.70. The result indicates

that banks charge a higher loan spread for secured loans by the amount of about 71 basis points

because we lose some observations due to missing values required for estimating Equation (2). For brevity, theestimation results of auditor-choice model are not reported here.

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than for unsecured loans.10 Third, we also estimate Equation (1) after including thePerformance

Pricing dummy which equals one for loans with performance pricing provisions and zero

otherwise. Under a typical performance pricing provision, the loan rate is allowed to decrease

directly with the improvement in credit quality. Not reported is that the inclusion of the

Performance Pricingdummy in Equation (1) does not alter our main results presented in Table 5.

We also find that the coefficient on Performance Pricing is significantly negative with its

magnitude of -30.719 and its t-value of -13.09. This suggests that banks charge a lower rate for

loans with the performance pricing provision by the amount of 31 basis points than loans without

it, a finding consistent with Asquith et al. (2005).

Finally, in our analyses so far, we measure auditor tenure by the number of years of the

auditor-client relationship. We also use a dummy variable which equals one if the tenure for a

firm year is longer than the median tenure in our sample (eight years) and zero otherwise, and

then re-estimate Equation (1) using this new measure of auditor tenure. Though not reported, we

find that the coefficient on this dummy variable is significantly negative. In addition, following

the procedure suggested by prior research on auditor tenure (e.g., Myers et al. 2003; Ghosh and

Moon 2005; Mansi et al. 2004), we construct a reduced sample of borrowers with at least five

years of auditor tenure and re-estimate Equation (1) using this reduced sample. The results using

this reduced sample remain qualitatively similar to those reported in Table 5.

In short, our main results reported in Table 5 are robust to a variety of sensitivity checks

such as alternative treatments of multiple loan facilities of each deal and for each firm year,

potential residual cross correlation, potential endogeneity problems associated with auditor

10 This finding is consistent with Dennis et al. (2000) and Berger and Udell (1990) who find that banks aremore likely to require collaterals for borrowers with high credit risk and to charge higher rates for secured loansthan for unsecured loans.

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quality and tenure, and the inclusion of various indicator variables representing Loan Type,

Secured, andPerformance Pricing.


FURTHER ANALYSES

The Impact of Auditor Changes on Bank Loan Pricing

To alleviate a concern that our levels results so far are possibly driven by correlated

omitted variables and to examine the effect of auditor switches on the change in the loan spread,

we examine the relation between changes in auditors and changes in loan spreads. In so doing,

we measure the change in the drawn all-in spread, i.e., AIS, by the change in the facility-size-

weighted average of AIS on all loan facilities for a firm from year t - 1 to year t. Similarly, we

measure the change in loan maturity, i.e., Log Maturity, by the change in the natural log of

facility-size-weighted average of maturity periods (in months) for all loan facilities for a firm

from year t - 1 to year t. We measure the change in loan facility size, i.e., Log Loan Size, by the

change in the natural log of average dollar amount of all loan facilities for a firm from year t - 1

to year t. We do not include the change in Syndicate for our changes regressions because it is

difficult to identify the Syndicate status for the yearly facility-size-weighted average loan. We

use five different auditor change dummies, i.e., Change, Upgrade, Downgrade, Big and

NonBig to capture any type of auditor change, a change from a non-Big 4 auditor to a Big 4

auditor, a change from a Big 4 auditor to a non-Big 4 auditor, a change within Big 4 auditors,

and a change within non-Big 4 auditors, respectively.

After applying the above definitions of the change variables, we obtain a total of 2,974

observations available to this change analysis. Out of 2,974, there are 388 observations of all

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types of auditor changes which include seven observations of upgrade changes, 14 observations

of downgrade changes, 353 changes within Big 4 auditors, and 14 changes within non-Big 4

auditors. Table 7 presents the results of change regressions where all variables are measured in

terms of their changes from year t - 1 to year t. In Columns (1) and (2), we include Change to

capture the effect of (any type of) auditor changes on the loan spread change. In Columns (3) and

(4), we include the dummy variables indicating four types of auditor changes, i.e., Upgrade,

Downgrade,Big and NonBig, instead of Change. As a sensitivity check, two loan-specific

control variables (i.e., Log Maturity and Log Loan Size) are excluded in Columns (1) and (3),

but they are included in Columns (2) and (4).

As reported in Columns (1) and (2) of the table, the coefficient on Change is 12.671 and

11.087, respectively, which is significant at less than the one percent and five percent levels,

respectively. This suggests that, on average, banks perceive auditor changes as an event that

deteriorates the quality and/or credibility of accounting information, and thus they charge a

higher loan spread for borrowers with auditor changes. As shown in Columns (3) and (4), the

coefficients on Upgrade and Big are insignificantly positive. However, the coefficients on

Downgrade are 79.897 (t= 2.33) and 79.917 (t = 2.32) as reported in Columns (3) and (4),

respectively. In other words, banks charge a higher loan spread for borrowers who switch their

auditors from Big 4 to non-Big 4 auditors by the amount of nearly 80 basis points, which is

economically significant as well. Also the coefficients on NonBig are 60.230 (t = 2.27) and

59.440 (t = 2.32) in Columns (3) and (4), respectively. This suggests that similar to auditor

downgrading, banks perceive auditor switches within non-Big 4 auditors to be a credit quality-

deteriorating event. Consistent with our expectation, banks charge a significantly higher rate for

clients with auditor downgrading than for those with auditors switches within non-Big 4 auditors

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by the amount of about 20 basis points. The results here are in sharp contrast with those reported

by Mansi et al. (2004) that only the auditor upgrade leads to a significant decrease in bond yield

spreads. However, our results are consistent with the finding of Fried and Schiff (1981) that there

is a negative stock price reaction to auditor switches including the switch from a small to a large

auditor.


Effect of Credit Rating

Previous research provides evidence that the information uncertainty is greater for high-

risk firms than for low-risk firms (e.g., Beneish 1997; Christensen et al. 1999). Moreover, Mansi

et al. (2004) find that the favorable effect of auditor quality on the bond yield spread is

significant for the non-investment-grade sample, but is insignificant for the investment-grade

sample. They also find that the favorable effect of auditor tenure on the bond yield spread is

more significant for the non-investment-grade sample than for the investment-grade sample.

Their study suggests that the value of high-quality audit in the public bond market is more

pronounced for high-risk firms than for low-risk firms.

We investigate whether the effect of audit quality on lowering the loan spread is greater

for high-risk firms than for low-risk firms. To address this question, we partition the full sample

using S&P Issuer Bond Rating data (Compustat item 280). 11 We then partition the full sample

into two sub-samples, namely: (1) the investment-grade sample of borrowers with their S&P

Issuer Bond Rating of BBB- or above (N =2,275); and (2) the non-investment-grade sample of

11 The Issuer Credit Rating (ICR) is a current opinion of an issuers overall creditworthiness apart from its abilityto repay individual obligation and focuses on the obligors capacity and willingness to meet its long-term financialcommitments. Prior to September 1, 1998, this item is named as S&P Senior Debt Rating.

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borrowers with their S&P Issuer Bond Rating of BB+ or below (N = 1,422).12 We then estimate

Equation (1) after including theRatingvariable as an additional independent variable, separately,

for the combined sample of both investment-grade and non-investment-grade firms, for the

investment-grade sub-sample, and for the non-investment-grade sub-sample respectively. In so

doing, we recode S&P Issuer Bond Ratings from AAA to D or SD by assigning a value of one if

a firm is rated AAA and increasing the numerical rating value by one as the rating decreases by

one notch (e.g., AA+ and AA are assigned a value of two and three, respectively, and so on).13

In Table 8, for brevity, we report the estimated coefficients for the test variables (i.e., Bigand

Tenure) and the partitioning variable (i.e.,Rating).

As shown in Table 8, the coefficients on Bigand Tenure are highly significant for the

combined sample (N = 3,697). The same coefficients are highly significant for non-investment

grade firms (N = 1,422), but they are insignificant for the investment-grade sample (N = 2,275).

In addition, the coefficient on Big is significantly larger in magnitude for the non-investment-

grade sample than for the investment-grade sample. Note also that the coefficient on Rating is

significantly positive across all three samples, suggesting that the loan spread increases as the

credit rating becomes downgraded.

The above results, taken as a whole, indicate that the value of high-quality audits in the

context of bank loan pricing is more pronounced for borrowers with high credit risk than for

borrowers with low credit risk. Moreover, our results suggest that while the value of credit rating

12 When S&P Issuer Credit Ratings are involved in our analysis, the sample reduces to 3,697 observations sincemany firms in our sample have no values of Issuer Credit Ratings. The decrease of sample size may weaken thepower of our tests.13 Though not reported in Table 9, the mean and median ofAISare, respectively, 135 and 87 basis points for thecombined sample, 71 and 50 basis points for the investment-grade sub-sample, and 248 and 225 basis points for thenon-investment-grade sub-sample. The mean and median differences between the two investment-grade and non-investment-grade sub-samples are highly significant, indicating that banks charge a significantly higher loan spreadfor firms with non-investment grades than for firms with investment grades.

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information offered by credit rating agencies is useful for banks to assess borrowers credit

quality (as reflected in the highly significant coefficient on Rating), the value of audit quality in

bank loan pricing is not subsumed by the value of credit rating information, in particular, when

banks assess the credit quality of borrowers with poor credit quality.


SUMMARY AND CONCLUDING REMARKS

While previous auditing research has examined the role of audit quality in the equity

and/or bond market, it has paid little attention to the role of audit quality in the market for private

debts such as bank loans. To fill this gap, we investigate the effect of two auditor characteristics,

namely auditor quality and tenure, on the price term of bank loan contracts. We perform our

analysis using a large sample of US bank loan data over the 9-year period from 1996 to 2004.

Our results can be summarized as follows: First, we find that the loan spread charged by

banks is significantly lower for borrowers with prestigious Big 4 auditors than for borrowers

with non-Big 4 auditors. The results of our multivariate tests indicate that the loan spread

difference between borrowers with Big 4 and non-Big 4 auditors are about 32 and 49 basis points

for the full sample and the non-investment-grade sub-sample, respectively. These differences are

economically significant as well. Further analysis suggests that banks view the auditor switch as

a credit risk-increasing event. Our results show that banks charge a higher loan spread for

borrowers who change their auditors in general, and they charge a substantially higher loan

spread for borrowers who downgrade their auditors from Big 4 to non-Big 4 auditors in

particular. Second, we find that banks charge a lower loan spread for borrowers with long-tenure

auditors than for those with short-tenure auditors, suggesting that banks view auditor tenure as a

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credit risk reducing factor. Third, we find that the relations between the loan spread and auditor

quality and between the loan spread and auditor tenure are conditioned upon the level of credit

risk perceived by credit rating agencies. In particular, we find that the loan spread-reducing

effects of auditor quality and tenure are greater for non-investment-grade firms than for

investment-grade firms. This suggests that high-quality audits are of greater value to banks when

borrowers have lower credit quality. Finally, the results of our main regressions are robust to a

variety of sensitivity checks.

In conclusion, our study provides direct evidence that banks take into account audit

quality when assessing borrowers credit quality and determining the loan spread. Our results

provide new insights into the role of audit quality in the private debt market. Our study focuses

only on the effect of audit quality on the price term of loan contracting. However, the price term

is likely to be determined jointly with the non-price terms such as loan covenants, loan

securitization, loan size, loan maturity and other loan-specific characteristics. Warranted is

further research on the effect of audit quality on the non-price terms of loan contracts. We leave

this issue to future research.

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REFERENCES

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Bae, K-H., and V. Goyal. 2003. Property rights protection and bank loan pricing. Working Paper,Social Science Research Network.

Betty, A., and J. Weber. 2003. The effects of debt contracting on voluntary accounting methodchanges. The Accounting Review 78: 119-142.

Beneish, M. D. 1997. Detecting GAAP violation: implications for assessing earningsmanagement among firms with extreme financial performance. Journal of Accounting andPublic Policy 16: 271-309.

Berger, A. N., and G. F. Udell. 1990. Collateral, loan quality and bank risk. Journal of Monetary

Economics 25: 21-42.

Bharath, S. T., J. Sunder, and S. V. Sunder. 2006. Accounting quality and debt contracting.Working Paper, Social Science Research Network.

Blackwell, D. W., T. R. Noland, and D. B. Winters. 1998. The value of auditor assurance:Evidence from Loan Pricing.Journal of Accounting Research 36: 57-70.

Choi, J-H., and R. Doogar. 2005. Auditor tenure and audit quality: Evidence from going concernqualifications issued during 1996-2001. Working Paper, Social Science Research Network.

Christensen, T. E., R. E. Hoyt, and J. S. Paterson. 1999. Ex ante incentives for earningsmanagement and the informativeness of earnings. Journal of Business Finance andAccounting26: 807-832.

Davis, L. R., B. Soo, and G. M. Trompeter. 2002. Auditor tenure, auditor independence andearnings management. Working Paper, Social Science Research Network.

Dennis, S., D. Nandy, and I. G. Sharpe. 2000. The determinants of contract terms in bankrevolving credit agreements.Journal of Financial and Quantitative Analysis 35: 87-110.

Dichev, I. D., and D. J. Skinner. 2002. Large-sample evidence on the debt covenant hypothesis.Journal of Accounting Research 40: 1091-1123.

Fried, D., and A. Schiff. 1981. CPA switches and associated market reactions. The AccountingReview 56: 326-341.

Geiger, M. A., and K. Raghunandan. 2002. Auditor tenure and audit reporting failures. Auditing:A Journal of Practice & Theory 21: 67-78.

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Ghosh, A., and D. Moon. 2005. Auditor tenure and perceptions of audit quality. The AccountingReview 80: 585-612.

Greene, W. H. 2000.Econometric Analysis. 4th ed. Prentice Hall, Upper Saddle River, N.J.Johnson, D. A., K. Pany, and R. White. 1983. Audit reports and the loan decision: Actions and

perceptions.Auditing:A Journal of Practice & Theory 2: 38-51.

Johnson, V. E., I. K. Khurna, and J. K. Reynolds. 2002. Audit-firm tenure and the quality offinancial reports. Contemporary Accounting Research 19: 637-660.

Kim, J-B., D. Simunic, M. T. Stein, and C. H. Yi. 2005. Voluntary audit and the cost of debtcapital of privately held firms: Korean evidence. Working Paper, Social Science ResearchNetwork.

Lang, M. H., K. L. Lins, and D. P. Miller. 2004. Concentrated control, analyst following, andvaluation: Do analysts matter most when investors are protected least. Journal of Accounting

Research 42: 589-623.

Mansi, S. A., W. F. Maxwell, and D. P. Miller. 2004. Does auditor quality and tenure matter toinvestors? Evidence from the bond market.Journal of Accounting Research 42: 755-793.

__________. 2006. Information risk and the cost of debt capital. Working Paper, University ofArizona.

Mitton, T. 2002. A cross-firm analysis of the impact of corporate governance on the East Asianfinancial crisis.Journal of Financial Economics 64: 215-241.

Myers, J. N., L. A. Myers, and T. C. Omer. 2003. Exploring the term of the auditor-clientrelationship and the quality of earnings: A case for mandatory auditor Rotation. TheAccounting Review 78: 779-799.

Pittman, J. A., and S. Fortin. 2004. Auditor choice and the cost of debt capital for newly pubicfirms.Journal of Accounting and Economics 37: 113-136.

Strahan, P. E. 1999. Borrower risk and the price and nonprice terms of bank loans. WorkingPaper, Social Science Research Network.

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TABLE 1Sample Distribution by Year and Loan Type

Year Term Loans Revolvers364-Day-Facilities

All Facilities

1996 133 450 45 628

1997 137 484 65 686

1998 183 402 120 705

1999 212 419 147 778

2000 176 429 246 851

2001 188 496 288 972

2002 214 477 274 965

2003 237 530 233 1,000

2004 295 666 110 1,047

Total 1,775 4,353 1,528 7,656

Percent (%) 23.18 56.86 19.96 100.00

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TABLE 2Descriptive Statistics

Panel A: Loan Facility Characteristics (N =7,656)

Variables Mean 1st Quartile Median 3rd Quartile Std.Deviation

AIS (Basis Points) 172.379 62.500 150.000 250.000 130.219

Maturity (Months) 40.705 12.000 36.000 60.000 24.037

Loan Size(Millions of US$)

313.385 45.000 146.080 350.000 652.210

Syndicate 0.929 1.000 1.000 1.000 0.257

Number of Lenders 9.045 2.000 6.000 12.000 9.514

Panel B: Borrowing Firm Characteristics (N =7,656)

Variables Mean 1st Quartile Median 3rd QuartileStd.

Deviation

Big 0.951 1.000 1.000 1.000 0.216

Tenure 8.446 4.000 8.000 13.000 5.157

Size 6.775 5.414 6.753 8.086 1.893

Leverage 0.262 0.112 0.240 0.368 0.201

MB 1.753 1.119 1.418 1.980 1.213

Current Ratio 1.818 1.051 1.509 2.215 1.590

Log CoverageRatio

2.177 1.483 1.969 2.664 1.150

Profitability 0.145 0.096 0.136 0.182 0.078

Tangibility 0.349 0.157 0.286 0.512 0.237

Beta 1.007 0.540 0.922 1.359 0.671

Loss 0.184 0.000 0.000 0.000 0.388

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TABLE 3Comparisons of Loan and Firm Characteristics

Panel A: Big Auditor vs. Non-Big Auditor

Big Auditor Non-Big Auditor

Test for Difference

(Non-Big - Big)VariablesN Mean Median N Mean Median t Z

AIS (BasisPoints)

7,279 168.231 150.000 377 252.474 250.000 11.43*** 12.54***

Tenure 7,279 8.581 8.000 377 5.833 5.000 -12.20***-

10.20***

Size 7,279 6.891 6.836 377 4.541 4.323 -28.68***-

21.86***

Leverage 7,279 0.266 0.246 377 0.174 0.134 -10.42*** -9.66***

MB 7,279 1.766 1.425 377 1.511 1.283 -5.71*** -5.20***

Current Ratio 7,279 1.793 1.501 377 2.301 1.654 3.95*** 4.84***

Log CoverageRaito

7,279 2.177 1.967 377 2.175 2.035 -0.04 -0.47

Profitability 7,279 0.145 0.136 377 0.141 0.139 -1.09 -0.92

Tangibility 7,279 0.351 0.290 377 0.304 0.235 -4.11*** -3.75***

Beta 7,279 1.012 0.924 377 0.918 0.868 -2.65*** -2.26**

Loss 7,279 0.182 0.000 377 0.215 0.000 1.58 1.58

Maturity(Months)

7,279 40.771 36.000 377 39.430 36.000 -1.06 -1.34

Loan Size(Millions of

US$)7,279 326.093 150.000 377 68.012 18.000 -24.33***

-19.40***

Syndicate 7,279 0.938 1.000 377 0.748 1.000 -8.43***-

14.00***

Number ofLenders

7,279 9.318 7.000 377 3.769 1.000 -18.51***-

16.31***

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TABLE 3 (continued)

Panel B: Long Tenure vs. Short Tenure

Long Tenure (>=8 yrs) Short Tenure (


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TABLE 4Pearson Correlation Coefficients

Variables AIS Big TenureLog

Maturity

Log

Loan

Size

Syndicate Size Leverage MBCurrentRatio

LogCoverag

AIS 1.00

Big -0.14*** 1.00

Tenure -0.23*** 0.12*** 1.00

Log Maturity 0.17*** 0.01 -0.12*** 1.00

Log Loan Size -0.46*** 0.25*** 0.24*** -0.01 1.00

Syndicate -0.15*** 0.16*** 0.07*** 0.11*** 0.46*** 1.00

Size -0.45*** 0.27*** 0.31*** -0.18*** 0.83*** 0.36*** 1.00

Leverage 0.20*** 0.10*** -0.02** 0.16*** 0.17*** 0.16*** 0.17*** 1.00

MB -0.20*** 0.05*** -0.01 -0.06*** 0.07*** -0.02* 0.02* -0.16*** 1.00

Current Ratio 0.03** -0.07*** -0.06*** 0.07*** -0.19*** -0.10*** -0.24*** -0.12*** 0.06*** 1.00

Log CoverageRatio

-0.32*** 0.00 0.01 -0.05*** -0.02 -0.05*** -0.09*** -0.56*** 0.42*** 0.22*** 1.000

Profitability -0.27*** 0.01 0.00 0.02** 0.09*** 0.05*** -0.03*** -0.10*** 0.54*** 0.01 0.57**

Tangibility -0.06*** 0.04*** 0.02* -0.01 0.13*** 0.07*** 0.18*** 0.24*** -0.15*** -0.26*** -0.14**

Beta 0.10*** 0.03*** -0.07*** 0.07*** -0.01 -0.01 0.02* -0.03** 0.13*** 0.11*** 0.06***

Loss 0.39*** -0.02 -0.06*** -0.00 -0.17*** -0.07*** -0.11*** 0.18*** -0.15*** -0.05*** -0.39**

One, two and three asterisks respectively denote the significance at the 10%, 5% and 1% level in a two-tailed test.

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TABLE 5Full Sample Results of Regressions of Drawn All-in Spread on

Auditor Quality, Tenure, and Other Control Variables

ModelVariable

(1) (2) (3)Test Variables

Big-15.259**

(-2.54)-13.577**

(-2.26)

Tenure-1.300***

(-5.68)-1.267***

(-5.52)

Borrower-specific Characteristics

Size-15.553***

(-12.47)-14.853***

(-11.67)-14.506***

(-11.38)

Leverage93.847***

(10.57)90.815***

(10.30)92.433***

(10.44)

MB-1.612(-1.00)

-1.917(-1.16)

-1.820(-1.11)

Current Ratio-2.525***

(-3.29)-2.471***

(-3.29)-2.508***

(-3.32)

Log Coverage Ratio-16.051***

(-9.14)-16.268***

(-9.28)-16.060***

(-9.15)

Profitability-100.534***

(-4.21)-98.804***

(-4.13)-100.653***

(-4.20)

Tangibility-34.543***

(-4.68)-33.306***

(-4.53)-33.686***

(-4.59)

Beta18.698***

(8.38)18.343***

(8.25)18.285***

(8.23)

Loss58.173***

(14.78)58.006***

(14.73)58.162***

(14.78)

Loan-specific Characteristics

Log Maturity8.516***

(4.70)8.186***

(4.52)8.267***

(4.58)

Log Loan Size-19.607***

(-14.38)-19.721***

(-14.47)-19.641***

(-14.42)

Syndicate-3.693(-0.69)

-5.381(-1.01)

-4.667(-0.87)

Intercept and Dummies

Intercept587.510***

(22.96)580.181***

(22.73)587.635***

(22.84)

Loan Purpose Dummies Included Included Included

Industry Dummies Included Included Included

Year Dummies Included Included IncludedN 7,656 7,656 7,656

Adj. R-sq (%) 51.82 51.98 52.02

Ndenotes the number of observations used in each model. The t-statistics in the parentheses are based on White(1980)s heteroscedasticity-corrected standard errors. One, two and three asterisks respectively denote thesignificance at the 10%, 5% and 1% level in a two-tailed test.

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TABLE 6Results of Various Robustness Tests

Model

Variable (1)One facility

per deal

(2)One facility

per firm-year

(3)Fama-MacBeth

regressions

(4)One-year lags

of testvariables

(5)Inverse Millsratio included

Test Variables

Big-14.549**

(-2.08)-16.079**

(-2.20)-18.430*(-2.12)

-11.476**(-1.96)

-44.880***(-2.74)

Tenure-1.306***

(-5.16)-1.478***

(-5.80)-1.563***

(-6.75)-1.264***

(-5.21)-1.262***

(-5.43)Borrower-specific Characteristics

Size-17.801***

(-12.01)-19.249***

(-12.47)-18.764***

(-13.12)-14.589***

(-11.45)-13.902***

(-10.45)

Leverage92.773***

(9.00)101.441***

(9.56)88.080***

(8.49)91.770***

(10.35)95.993***

(10.54)

MB-0.442

(-0.29)

-0.879

(-0.51)

-1.966

(-0.99)

-1.825

(-1.11)

-1.546

(-0.95)

Current Ratio-2.220**(-2.38)

-2.640**(-2.53)

-3.194*(-2.21)

-2.557***(-3.38)

-2.648***(-3.42)


(-7.38)-13.273***

(-6.70)-13.121***

(-4.78)-16.189***

(-9.23)-15.756***

(-8.81)


(-4.96)-124.181***

(-4.79)-140.166***

(-5.57)-99.689***

(-4.16)-103.141***

(-6.09)

Tangibility-40.047***

(-4.77)-36.885***

(-4.18)-35.623**

(-3.19)-33.285***

(-4.53)-34.836***

(-4.62)

Beta17.715***

(7.13)18.124***

(7.29)15.937***

(5.03)18.366***

(8.26)19.084***

(8.46)

Loss56.143***

(13.07)52.966***

(11.68)53.111***

(8.95)58.048***

(14.74)58.936***

(15.77)

Inverse Mills Ratio 18.309**(2.12)

Loan-specific Characteristics

Log Maturity4.416**(2.03)

2.399(1.07)

0.600(0.13)

8.321***(4.60)

8.491***(4.65)


(-9.03)-14.579***

(-8.27)-14.478***

(-6.50)-19.639***

(-14.43)-19.222***

(-13.82)

Syndicate-7.426(-1.30)

-7.757(-1.30)

5.685(0.56)

-4.914(-0.92)

-4.093(-0.75)


Intercept527.942***

(17.63)540.251***

(17.44)602.475***

(15.13)586.956***

(23.15)602.777***

(22.13)

Loan Purpose

Dummies

Included Included Included Included Included

Industry Dummies Included Included Included Included Included

Year Dummies Included Included Excluded Included Included

N 5,507 4,885 9 7,655 7,559

Adj. R-sq (%) 52.03 54.60 51.53 51.99 52.80

Ndenotes the number of observations used in each model. The t-statistics in the parentheses are based on White(1980)s heteroscedasticity-corrected standard errors. One, two and three asterisks respectively denote thesignificance at the 10%, 5% and 1% level in a two-tailed test.

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TABLE 7Results of Regressions of Changes in Drawn All-in Spreads on

Auditor Changes and Changes in Control Variables

ModelVariable

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

Changes in Test Variables

Change12.671***

(2.64)11.087**

(2.33)

Upgrade39.708(1.37)

40.390(1.36)

Downgrade79.897**

(2.33)79.917**

(2.32)

Big7.411(1.54)

5.675(1.20)

NonBig60.230**

(2.27)59.440**

(2.32)

Changes in Borrower-specific Characteristics

Size-15.635***

(-2.95)-9.014*(-1.72)

-16.638***(-3.15)

-10.007*(-1.93)

Leverage37.405**

(2.14)33.803**

(1.97)37.159**

(2.22)33.559**

(2.05)

MB0.142(0.04)

0.131(0.04)

0.157(0.05)

0.147(0.05)

Current Ratio-2.294*(-1.80)

-2.330*(-1.85)

-2.383*(-1.83)

-2.422*(-1.89)


(-3.48)-8.992***

(-3.24)-9.819***

(-3.46)-8.921***

(-3.22)


(-4.26)-146.684***

(-3.93)-166.459***

(-4.35)-148.609***

(-4.02)

Tangibility21.086

(0.84)

27.392

(1.09)

19.354

(0.76)

25.586

(1.01)

Beta4.300(1.22)

4.153(1.22)

4.515(1.28)

4.377(1.29)

Loss23.881***

(5.17)23.400***

(5.19)24.254***

(5.28)23.788***

(5.31)

Changes in Loan-specific Characteristics

Log Maturity2.382(1.05)

2.429(1.08)


(-7.56)-18.681***

(-7.62)


Intercept-32.302***

(-3.96)-24.834**

(-2.22)-30.428**

(-2.23)-22.886*(-1.82)

Industry Dummies Included Included Included IncludedYear Dummies Included Included Included Included

N 2,974 2,974 2,974 2,974Adj. R-sq (%) 12.51 14.84 12.93 15.28

denotes a change from year t - 1 to year t. N denotes the number of observations used in each model. The t-statistics in the parentheses are based on White (1980)s heteroscedasticity-corrected standard errors. One, two andthree asterisks respectively denote the significance at the 10%, 5% and 1% level in a two-tailed test.

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TABLE 8Results of Regressions for Sub-samples Partitioned by S&P Issuer Bond Rating

Variable(1)

Full, CombinedSample

(2)Investment

Grade

(3)Non-investment

Grade

Differencebetween (3)-(2)

Big-31.595***

(-2.98)8.375(1.12)

-49.373***(-3.37)

-57.747*(-1.81)

Tenure-0.683***

(-2.68)-0.368(-1.62)

-1.290**(-2.01)

-0.922(-0.05)

Rating16.642***

(21.40)9.735***(12.74)

17.624***(6.64)

7.889***(3.71)

N 3,697 2,275 1,422 ---

Adj. R-sq (%) 62.91 40.33 44.92 ---

N denotes the number of observations used in each model. The t-statistics in the parentheses are based onWhite (1980)s heteroscedasticity-corrected standard errors. One, two and three asterisks respectivelydenote the significance at the 10%, 5% and 1% level in a two-tailed test.

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