AN EMPIRICAL ANALYSIS OF AUDITOR INDEPENDENCE IN THE BANKING INDUSTRY
AN EMPIRICAL ANALYSIS OF AUDITOR INDEPENDENCE IN THE BANKING INDUSTRY
Kiridaran Kanagaretnam
DeGroote School of Business
McMaster University
1280 Main Street West
Hamilton, Ontario, Canada L8S 4M4
Phone: (905) 525-9140 ext 27857
Fax: (905) 521-8995
E-mail: giri@mcmaster.ca
Gopal V. Krishnan
Department of Accounting
College of Business and Economics
Lehigh University
Bethlehem, PA 18015
Phone : (610)-758-3451
E-mail: gok208@lehigh.edu
Gerald J. Lobo*
C. T. Bauer College of Business
University of Houston
Houston, TX 77204-6021
Tel: (713) 743-4838
Fax: (713) 743-4828
E-mail: gjlobo@uh.edu
October 2008
*Corresponding author
We thank James Bierstaker, Jeff Chen, Chris Jones,
Sok-Hyon Kang, Krishna Kumar, Ying Li, Lihong Liang, Jim Largay III, Erin
Moore, Nandu Nayar, Dan Neeley, Mike Peters, H. Sami, Mary Sullivan, Bill Zhang
and seminar participants at George Washington University, Hong Kong Polytechnic
University, Lehigh University, McMaster
University, University of Queensland, for their helpful suggestions.
Kanagaretnam and Lobo thank the Social Sciences and Humanities Research Council
of Canada (SSHRC) for its financial support.
AN EMPIRICAL ANALYSIS OF AUDITOR INDEPENDENCE IN THE
BANKING INDUSTRY
Abstract
We examine auditor independence in the banking industry by analyzing the relation between fees paid to the auditors of banks and the extent of earnings management through loan loss provisions. We also examine whether this relation differs across large banks whose managements are required under the Federal Deposit Insurance Corporation Improvement Act of 1991 to evaluate the internal control over financial reporting and whose auditors must attest to the report on the effectiveness of internal controls over financial reporting, and small banks that are not subject to such controls. Our results indicate a positive association between fees paid to the auditor and income-increasing earnings management through loan loss provisions. They suggest that, although banks face high levels of regulatory scrutiny, economic bonding between the auditor and the bank potentially impairs auditor independence. Our findings also indicate that this bonding is stronger for smaller banks that are subject to less regulatory oversight than are larger banks. Our results also suggest that the economic bond between the auditor and the bank is reflected in delayed recognition of loan write-offs and in a higher incidence of earnings benchmark beating behavior.
AN EMPIRICAL ANALYSIS OF AUDITOR INDEPENDENCE IN THE
BANKING INDUSTRY
I. INTRODUCTION
Auditor independence is vital to maintaining public confidence in the capital markets and to the integrity of corporate financial statements. The objective of this study is to examine auditor independence in the banking industry. Banks represent more than 20% of the total public equity market and are vital to the functioning of the economy as a whole. Fields et al. (2004, p. 54) state, “Despite the economic importance of the banking industry, however, accounting researchers have done little to investigate the various relationships that exist between banks and their auditors.” Specifically, we provide empirical evidence on the relation between fees paid to auditors of banks and the extent of earnings management via loan loss provisions (LLP).
The banking industry offers a unique context to study auditor independence for a number of reasons. First, banks are subject to the scrutiny of the FDIC, the Federal Reserve Board, and other governmental agencies. Whether this intense regulatory oversight enhances auditor independence is clearly an important and relevant question. Second, external audits are required for all national banks with $500 million or more in total assets. Additionally, the Federal Deposit Insurance Corporation Improvement Act of 1991 (FDICIA), which was passed in response to the savings and loan debacle of the 1980’s and became effective in 1992, imposed new auditing, corporate reporting, and governance reforms on each depository institution with assets exceeding $500 million and on its auditors (Murphy 2004). Section 112 of the Act requires the management of these institutions to evaluate the internal control over financial reporting and the auditor must attest to the report on the effectiveness of internal controls over financial reporting. Whether auditor independence is greater for larger banks that are subject to greater regulatory oversight relative to smaller banks is also an interesting and important question. Currently, there is very little empirical evidence on whether the reforms initiated by FDICIA contributed to enhanced auditor independence.
Third, bank LLP is well-suited to studying earnings management for the
following reasons. LLP is by far the largest and most important accrual for banks. The mean (median) ratio of LLP to earnings before LLP is 19.8% (15.1%) for our sample firms. Further, prior research indicates that banks use LLP to manage earnings (Wahlen 1994; Kanagaretnam et al. 2003; 2004). To the extent that banks can leverage “fee dependence” to influence their auditors to accept abnormal LLP, examining the relation between abnormal LLP and auditor fees is likely to reveal such a linkage. We believe that abnormal LLP is a better proxy for earnings management than the abnormal accrual measures used in prior research. This study mitigates error in measuring managerial discretion by focusing on a single accrual and a single industry. Focusing on a single accrual facilitates a sharper separation into its normal (nondiscretionary) and abnormal (discretionary) components. We use a number of industry-specific variables to better isolate the abnormal LLP from the normal LLP. Also, focusing on a single, relatively homogeneous industry provides control over other determinants of cross-sectional differences in accruals, thus increasing the reliability of the inferences from our empirical analysis.
Our sample consists of 1,810 bank-year observations representing years 20002006. We estimate abnormal LLP as residuals from a regression of LLP on beginning loan loss allowance, total loans outstanding, changes in total loans outstanding, net loan charge-offs, beginning balance of non-performing loans, change in non-performing loans, loan mix, and control for years (see Kanagaretnam et al. 2004 and Wahlen 1994). Next, we estimate a regression of abnormal LLP on fee measures, an indicator variable for small banks (with total assets of $500 million or less) that are subject to less regulation, the interaction of the fee and the indicator variable for small banks, auditor type (Big 5 vs. non-Big 5 auditor), and several control variables. We use various measures of fees to capture an auditor’s economic dependence on the client, including audit fees, nonaudit fees, total fees, ratio of nonaudit fees to total fees (fee ratio), abnormal (unexpected) audit fees, abnormal nonaudit fees, and abnormal total fees.
We report several key findings. First, we find that the level of total fee, nonaudit fee, and audit fee are negatively and significantly associated with negative (incomeincreasing) abnormal LLP for all banks. In other words, bank-year observations with higher fees exhibit higher income-increasing LLP. Similarly, total and audit fees are positively associated with positive (income-decreasing) abnormal LLP for all banks. However, when we use abnormal (unexpected) fee measures, we find a significant relation between abnormal total fee and abnormal nonaudit fee only with negative (income-increasing) abnormal LLP. Taken together, these results suggest that the economic bond (fee dependence) between the auditor and the bank appears to exacerbate earnings management by banks.
Second, in a regression of negative (income-increasing) abnormal LLP, the
coefficient on the interaction of fee and the indicator variable for small banks is negative and significant for all four fee measures (i.e., total fee, nonaudit fee, audit fee, and fee ratio). We do not observe such a relation for positive (income-decreasing) abnormal LLP. These results indicate that small banks engage in more income-increasing earnings management and there is stronger economic bonding between the small banks and their auditors. The results also indicate that, while the reforms of the FDICIA are effective at reducing auditor bonding for large banks, they do not fully eliminate it.
Third, we examine whether current-period auditor fees are associated with nextperiod bank loan charge-offs. We find that after controlling for size, current-period LLP, and current-period charge-offs, the level of total fee, nonaudit fee, and audit fee are all positively and significantly associated with next-period charge-offs. We obtain similar results when we use unexpected fee measures. Given that bank managers have incentives to postpone writing off bad loans to future periods so that the current period’s financial position is presented in a favorable manner, our results suggest that the economic bond between the auditor and the bank is associated with delayed recognition of loan chargeoffs. This finding also holds when we replace actual fees with unexpected fees. Finally, for the (income-increasing) abnormal LLP sub-sample, we examine the association between auditor fees and beating earnings benchmarks as do Frankel et al. (2002) and Ashbaugh et al. (2003). The results indicate a significant, positive relation between our proxy for benchmark beating behavior and three fee measures – total fee, nonaudit fee, and audit fee. We obtain similar results when we use unexpected fee measures.
Our findings indicate that economic bonding between the auditor and the bank is associated with income-increasing earnings management through loan loss provisions. They also indicate that this bonding is stronger for small banks. Collectively, our results suggest that auditor fee dependence on the audit client is a threat to auditor independence, particularly among banks with less than $500 million in assets that are not subject to the same level of regulatory scrutiny as larger banks.
The rest of this paper is organized as follows. The next section develops the empirical models used to estimate abnormal LLP and test the relation between abnormal LLP and fees paid to auditors. Section three describes the sample selection, section four discusses the results, and section five provides the conclusions of the study.
II. RESEARCH DESIGN
Our conceptual model, described in Figure 1, is based on the predictive validity model of Kinney and Libby (2002) that was developed by Runkel and McGrath (1972) and Libby (1981). As in prior research, the economic bond to the audit client and earnings management by the client (link 1) are the two theoretical constructs we examine. We identify proxies to measure the economic bond and earnings management because these two concepts are unobservable. We use multiple fee measures to capture the economic bond (link 2). Kinney and Libby (2002) state that “more insidious effects on the economic bond may result from unexpected nonaudit and audit fees that may more accurately be likened to attempted bribes.” We use unexpected audit, nonaudit, and total fees as measures of the economic bond and the abnormal component of a bank’s LLP as the measure of earnings management (link 3). We examine banking firms because they provide a setting where a stronger link 3 than used in prior research is possible. By using an industry-specific measure of earnings management, we are better able to separate the discretionary accruals from the nondiscretionary accruals than has prior research. We control for several bank characteristics that may affect LLP (link 4). We then examine the relation between the various fee measures and abnormal LLP (link 5) to draw inferences about link 1. We also conduct two supplementary tests of link 5. First, we examine the relation between the various fee measures and future loan charge-offs. Second, we study the extent to which banks use abnormal LLP to meet earnings benchmarks. We describe these tests in a later section.
[Insert Figure 1 About Here]
We use a two-stage approach to examine link 5. First, we describe the model used to estimate abnormal LLP (link 4). We first estimate the normal or nondiscretionary component of LLP by regressing LLP on beginning loan loss allowance, beginning balance of non-performing loans, change in non-performing loans, net loan charge-offs, changes in total loans outstanding, total loans outstanding, loan mix, and controls for period effects using the following model:
LLPit = γ0 + γ1 BEGLLA + γ2 BEGNPL + γ3 CHNPL + γ4 LCO + γ5 CHLOANS + γ6 LOANS + <LOAN CATEGORIES> + <YEAR CONTROLS> + eit (1)
We define the variables as follows (all variables are deflated by beginning total assets):
LLP = Provision for loan losses;
LLP = Provision for loan losses;
BEGLLA = Beginning loan loss allowance;
BEGNPL = Beginning nonperforming loans;
BEGNPL = Beginning nonperforming loans;
CHNPL = Change in nonperforming loans;
LCO = Net loan charge-offs;
CHLOANS = Change in total loans outstanding;
LOANS = Total loans outstanding; and
LOAN CATEGORIES = amount of commercial loans (COMM), consumer loans (CON), real estate loans (RESTATE), agriculture loans (AGRI), loans to foreign banks and governments
(FBG), and loans to other depository institutions (DEPINS).
The residuals from model (1) are the abnormal component of LLP, referred to as ALLP. We expect a negative coefficient on BEGLLA (i.e., the accumulated LLP less write-offs at the beginning of the year) as a higher initial loan loss allowance will require a lower LLP in the current period. Consistent with prior research, we expect γ2, γ3, γ4 and γ6 to be positive. Higher levels of nonperforming loans indicate problems with the loan portfolio will require higher loss provisions. Therefore, the beginning balance of nonperforming loans (BEGNPL) will be positively related to LLP. Change in nonperforming loans (CHNPL) in the current period will also have a positive effect on LLP because an increase in nonperforming loans will require a higher loss provision in the current period. The amount of net loan charge-offs (LCO) is positively related to LLP. As noted in Beaver and Engel (1996), “current loan charge-offs can provide information about future loan charge-offs which, in turn, may influence expectations of the collectability of current loans” and, hence, current LLP. A higher level of loans (LOANS) will also require higher provisions. We do not offer a prediction for γ5 because the effect of change in total loan portfolio on LLP is unpredictable due to the uncertainty in the quality of incremental loans.
Although nonperforming loans and loan charge-offs serve as measures of risk, we include six additional variables to control for differences in loan composition which also likely contribute to differences in risk. For example, banks with higher proportions of commercial and real estate loans are likely to have higher loan loss provisions than banks with higher proportions of consumer loans. Failure to account for these differences makes the residuals from the loan loss provision model a function of the bank type. This, in turn, may affect the inferences of our auditor independence tests because both audit fees and ALLP may systematically vary across banks based on their loan portfolio mix.
The six loan portfolio composition variables included in the model are commercial loans (COMM), consumer loans (CON), real estate loans (RESTATE), agriculture loans (AGRI), loans to foreign banks and governments (FBG), and loans to other depository institutions (DEPINS). We also include six year-dummy variables representing years 2000 through 2005 in model (1) to control for period-specific effects.
Next, we test the association between the signed values of abnormal LLP (ALLP) and auditor fees separately for negative (income-increasing) ALLP and positive (incomedecreasing) ALLP. Negative ALLP are of particular interest because of their positive impact on reported earnings. We control for the following factors that prior research has documented are associated with abnormal accruals (Ashbaugh et al. 2003): firm size, auditor type, market-to-book ratio, level of accruals, and performance. We use log of market value of equity to measure size. We represent performance by two variables, existence of loss and earnings before LLP, and growth by market-to-book ratio. We use past LLP to capture the reversal of accruals over time. To control for any capital management incentives, we include the beginning of year tier 1 capital ratio and total capital ratio. Our model is as follows:
ALLP = β0 + β1 FEE + β2 BIG5 + β3 MB + β4 LMVE+ β5 LOSS + β6 PASTLLP + β7 EBP + β8 TIER1t-1+ β9 TCAPt-1 + <YEAR CONTROLS> + ε (2)
We define the variables as follows:
ALLP = Abnormal loan loss provision;
FEE = Natural log of audit fees (LAFEE) or total fees (LTOTFEE = ln (audit fees + nonaudit fees)) or nonaudit fees (LNAFEE), or fee ratio (FEERATIO = nonaudit fees/ total fees);
BIG5 = Indicator variable set equal to 1 if audited by a Big 5 firm, and 0 otherwise;
MB = Market-to-book ratio at the end of the year;
LMVE = Natural log of market value of common equity;
LOSS = Indicator variable set equal to 1 if net income < 0, and 0 otherwise;
PASTLLP = prior year’s LLP divided by total assets at the beginning of the year;
EBP = Net income before extraordinary items and loan loss provisions divided by total assets at the beginning of the year;
TIER1 = Tier 1 risk-adjusted capital at the beginning of the year; and
TCAP = Total risk adjusted capital at the beginning of the year.
The variable of interest in model (2) is FEE. For negative (income-increasing) ALLPs, a negative coefficient on β1 is consistent with auditor fee dependence on the client, i.e., higher fees are associated with greater (more negative) income-increasing ALLP. Ashbaugh et al. (2003) argue that total fee, rather than the fee ratio is the more appropriate measure of economic bonding. In addition to total fee we also use audit fee, nonaudit fee, and the fee ratio (nonaudit fees/ total fees).
Next, we examine the impact of the FDICIA on auditor independence, particularly on auditors of smaller banks, i.e., banks with total assets less than $500 million. Recall that FDICIA imposed new auditing, corporate reporting, and governance reforms on each depository institution with assets exceeding $500 million and on its auditors. We modify model (2) to include an indicator variable SMALL which equals one, when beginning of the year total assets is lower than $500 million and also interact the
FEE variable with SMALL. We discuss sample selection in the next section.
III. SAMPLE SELECTION
We identify our sample banks from banks listed in the 2007 Bank Compustat annual data files and obtain fees paid to the auditors for the years 2000-2006 from Audit Analytics. The intersection of the Audit Analytics with Bank Compustat data results in an initial sample of 2,044 bank-year observations. We hand collect non-performing loans data for the period 1999-2006 from annual reports and obtain data on loan portfolio composition from the Federal Reserve Bank Holding Company Database (call reports).
Our final sample with available data for all variables comprises 1,810 bank-year observations for 304 banks.
Panel A of Table 1 reports descriptive statistics for the scaled variables used in the regressions. More than 52% of the sample observations are audited by Big 5 auditors.
Note that the mean abnormal LLP (ALLP) is zero by construction. Turning to the bank loan variables, the ratios of average LLP, loan charge-offs and beginning nonperforming loans to beginning total assets are 0.003, 0.002 and 0.004, respectively.
[Insert Table 1 About Here]
Panel B of Table 1 reports correlations for the scaled dependent and independent variables. As expected, LLP is positively correlated with non-performing loans
(BEGNPL), change in non-performing loans (CHNPL) and loan charge-offs (LCO). Also, LLP is positively correlated with total loans (LOANS) and change in loans outstanding (CHLOANS). The correlations between LLP and nonaudit fees and fee ratio are positive and statistically significant at the 0.01 level while the correlations between LLP and total fee and audit fee are not significant.
IV. RESULTS
Estimation of Abnormal LLP
We report the estimation results of model (1) in Table 2. The t-statistics reported in Table 2 and in other tables are based on standard errors adjusted for firm level clustering. The results in Table 2 show that the coefficients on the determinants of LLP have the expected signs. The coefficients on BEGLLA, CHNPL, LCO and LOANS are significant at the 0.01 level and the coefficient on BEGNPL is significant at the 0.10 level. Among the variables that reflect loan type, only commercial and consumer loans are statistically significant. The explanatory power of the model is high (adjusted R2 = 63.15%) indicating that our model describes the variation in LLP quite well. ,
[Insert Table 2 About Here]
Association Between Income-increasing (negative) Abnormal LLP and Fee Measures
The results of model (2) relating abnormal LLP to the fee measures (total fee, nonaudit fee, audit fee, and fee ratio) are reported in Tables 3 and 4. Table 3 reports the results for negative (income-increasing) ALLP and Table 4 reports the results for positive (income-decreasing) ALLP. We first discuss the results in Table 3. For each of the four fee measure, we present the results for two specifications. The first specification does not distinguish between small and large banks, while the second specification distinguishes between small and large banks by addding the variable SMALL, which equals 1 for small banks (with total assets less than $500 million) and 0 for large banks, and its interaction with the fee variable to model (2). This specification examines whether the association between ALLP and fees is greater for small banks than for large banks. Recall that the FDICIA imposed new auditing, corporate reporting, and governance reforms on each depository institution with assets exceeding $500 million and on its auditors. Therefore, small banks are subject to less scrutiny than large banks, which may result in more pronounced fee dependence for small banks relative to large banks.
The results indicate that three of the four fee measures (LTOTFEE, LNAFEE, and LAFEE) are negatively and significantly (p < 0.01) associated with ALLP, indicating that our results are not sensitive to the definition of the fee measure. Overall, the results indicate that abnormal LLP, our measure of earnings management, is more negative (i.e., more income-increasing) for banks that pay higher fees to their auditors. In terms of the control variables, LMVE, PASTLLP, and MB are significant. Interestingly there is no difference in abnormal LLP between clients of BIG5 and non-BIG5 auditors. As expected, the capital ratios are not significantly associated with ALLP, confirming the reduced capital management incentives through ALLP in the post-1990 period.
Turning to the second specification, the results for the main effect (fee variable) continue to hold for total fee (p < 0.10, one-tailed test), nonaudit fee, and audit fee. More importantly, the coefficient on the interaction of SMALL and the fee measure is negative and significant at the 0.05 level for total fee, nonaudit fee, and fee ratio. Furthermore, the sum of the coefficients on FEE and FEE×SMALL for total fee and nonaudit fee is significant at the 0.01 level. Overall, these results support the notion that small banks engage in greater earnings management via income-increasing LLPs relative to large banks, and suggest that the potential impairment of auditor independence is more serious for small banks than for large banks.
[Insert Table 3 About Here]
Association Between Income-decreasing (positive) Abnormal LLP and Fee Measures
The results reported in Table 4 indicate that two of the four fee measures (log of total fees (LTOTFEE) and log of audit fees (LAFEE) are positively and significantly associated with ALLP at the 0.01 level. Nonaudit fee is marginally significant at the 0.10 level for a one-tailed test. The interesting finding in Table 4 is that the coefficient on the interaction of SMALL and the fee measure is not significant for any of the four fee measures. Recall that the interaction of SMALL and the fee measure is significant for total fee, nonaudit fee, and fee ratio for income-increasing ALLP. These findings suggest that auditor fee dependence is more problematic for small banks relative to large banks. Further, note that after controlling for the effect of SMALL, LTOTFEE and LAFEE have a stronger positive association with ALLP, indicating that the associations between LTOTFEE and LAFEE and income-increasing ALLP are more pronounced for large
banks.
[Insert Table 4 About Here]
We conduct several sensitivity checks of model (1). First, Ahmed et al. (1999) suggest that the earnings management through LLP reported in prior research is conditional on the inclusion of beginning non-performing loans (NPLt-1) in the discretionary LLP model. When we re-estimate model (2) for income-increasing ALLP after excluding NPLt-1 in model (1), LTOTFEE, LNAFEE and FEERATIO are significant at the 0.01 level and LAFEE is significant at the 0.10 level. Second, we assess the sensitivity of our results to extreme values. We re-estimate model (1) after deleting observations in the top 1% and bottom 1% for each variable (LLP, CHLOAN, LCO, BEGNPL and CHNPL). This reduces the number of observations from 1,810 to 1709 and the adjusted R2 is 68.94%. The test results for this reduced sample are consistent with those reported in Table 3. Third, we scale the variables in model (1) by beginning total loans instead of beginning total assets. Once again, our results for income-increasing
ALLP are robust to the choice of scaling variable. LTOTFEE and LNAFEE are significant at the 0.01 level, and LAFEE and FEERATIO are significant at the 0.05 level. Fourth, we estimate model (1) separately for small and large banks. The results based on this specification indicate that negative ALLP are significantly associated with all four fee measures for small banks. The results are weaker for large banks.
Unexpected Fee Measures
Auditor fees are also influenced by various economic determinants including size and complexity of audit task. Therefore, controlling for the economic determinants of auditor fees and using unexpected (abnormal) auditor fees may be a better measure of auditor fee dependence than using actual fees. For example, Kinney and Libby (2002) argue that unexpected fees are a better measure of the auditor-client economic bond because they reflect the “excess” profit derived from an audit client. Unexpected fees are estimated in two steps. First, we estimate the expected (normal) fees as the predicted values from a regression of audit fee or nonaudit fee or total fee on a set of firm characteristics. We then compute the residuals from this regression which represent the unexpected fees.
Prior research models auditor fees as a function of a firm’s auditor choice, audit complexity, and audit risk, in addition to other variables (Firth 1997 and Ashbaugh et al. 2003). Fields et al. (2004) examine the determinants of normal audit fees in the banking industry. Using the variables identified in Fields et al. (2004) as determinants of audit fees, we estimate unexpected fees for three of our fee measures (LAFEE, LTOTFEE and LNAFEE). Audit fees and other fees are likely to be higher when the auditor is a Big 5 auditor. Auditor size also proxies for client size. We measure firm size as the natural log of total assets. The normal audit fee is directly related to a bank’s credit risk, operating risk, liquidity risk and capital risk. We include NPL, LCO, COMM, CON and RESTATE as proxies for a bank’s credit risk and the efficiency ratio (EFFICIENCY) as a proxy for operating risk. We measure the efficiency ratio as the ratio of total operating expenses to total revenues. As in Fields et al. (2004), we use SECURITIES as a proxy for liquidity risk, and intangible assets (INTANG) and total capital ratio (TCAP) to account for capital risk. We estimate the following model:
FEE = α0 + α1 BIG5 + α2 LASSETS + α3 SECURITIES + α4 NPL + α5 LOSS + α6 INTANG + α7 EFFICIENCY + α8 LCO+ α9 COMM + α10 CON + α11 RESTATE + α12 TCAP + <YEAR CONTROLS> + ε (3)
We define the variables as follows:
FEE = Natural log of audit fees (LAFEE) or total fees (LTOTFEE = ln (audit fees + nonaudit fees) or nonaudit fees (LNAFEE);
BIG5 = Indicator variable set equal to 1 if audited by a Big 5 firm, and 0
otherwise;
LASSETS = Natural log of total assets;
SECURITIES = [1-(total securities/total assets)];
NPL = Nonperforming loans over lagged total loans;
LOSS = Indicator variable set equal to 1 if ROA < 0, and 0 otherwise;
INTANG = Intangible assets over total assets;
EFFICIENCY = Total operating expenses over total revenues;
LCO = Net loan charge-offs over loan loss allowance;
COMM = Total commercial and agriculture loans over total loans;
CON = Total consumer loans over total loans;
RESTATE = Total real estate loans over total loans; and
TCAP = Total risk-adjusted capital ratio.
We also use alternate specifications of model (3) where we include audit fees (nonaudit fees) as a control variable when we estimate unexpected nonaudit fees (audit fees).
Those results are discussed in a later section.
Panel A of Table 5 reports the results of estimating model (3). As expected, we find a positive relation between our three fee measures and BIG5, LASSETS, SECURITIES, NPL, EFFICIENCY, TCAP and COMM. The signs of the coefficients are generally consistent with Fields et al. (2004). For the audit and total fee models, the adjusted R2 are, respectively, 82.52% and 74.99%, indicating a very good fit. For the nonaudit fee model the adjusted R2 is 51.45%. These R2 values are higher than the adjusted R2 values reported in Ashbaugh et al. (2003) for industrial firms and consistent with the R2 values reported in Fields et al. (2004).
[Insert Table 5 About Here]
We use the residuals from model (3) as the unexpected (abnormal) fees for our three fee measures. We then use the following model to examine whether banks’ abnormal accrual choices are associated with abnormal fees paid to the auditors:
ALLP = χ0 + χ1UFEE + χ2 BIG5 + χ3 MB + χ4 LMVE+ χ5 LOSS + χ6 PASTLLP + χ7 EBP + χ8 TIER1t-1+ χ9 TCAPt-1 + <YEAR CONTROLS> + ε (4)
We define the variables as follows:
ALLP = Abnormal loan loss provision;
UFEE = Unexpected audit fees or total fees or non audit fees from model (3);
BIG5 = Indicator variable set equal to 1 if audited by a Big 5 firm, and 0 otherwise;
MB = Market-to-book ratio at the end of the year;
LMVE = Natural log of market value of common equity;
LOSS = Indicator variable set equal to 1 if net income < 0, and 0 otherwise;
PASTLLP = prior year’s LLP divided by total assets at the beginning of the year;
EBP = Net income before extraordinary items and loan loss provisions divided by total assets at the beginning of the year;
TIER1 = Tier 1 risk adjusted capital at the beginning of the year; and
TCAP = Total risk adjusted capital at the beginning of the year.
Model (4) is identical to model (2) except that we substitute UFEE for FEE where UFEE is unexpected fees, i.e., the residuals from model (3). Again, we use three measures of unexpected fees, total fees, audit fees, and nonaudit fees.
Panel B of Table 5 reports the results of model (4) for income-increasing (negative) ALLP. The relation between income-increasing ALLP and unexpected total fee and unexpected nonaudit fee are both positive and significant at the 0.05 level. There is no significant relation between income-increasing ALLP and unexpected audit fee. Overall, the results in panel B of Table 5 along with the results in Table 3 provide strong evidence that income-increasing ALLP is higher for those audit clients that pay higher fees to their auditors.
To examine whether the strength of the association between unexpected fees and income-increasing ALLP is greater for small banks, we add the variables SMALL and SMALL× UFEE to model (4). We find that the coefficient on the interactive term SMALL×UFEE is more negative for both unexpected total fee and unexpected nonaudit fee. Also, the sum of the coefficients on UFEE and SMALL×UFEE is negative and significant at the 0.01 level for unexpected total fee and unexpected nonaudit fee, a result which confirms the finding in Table 3 that income-increasing earnings management is higher for those audit clients of small banks that pay higher fees to their auditors.
We also examine the relation between ALLP and UFEE for income-decreasing (positive) ALLP. Untabulated results indicate that none of the three abnormal fee measures is related to income-decreasing ALLP. This is also true when we introduce controls for small banks.
Analysis of Next Period Loan Charge-Offs
As an additional test we examine the relation between fees paid to auditors in the current period and the next period’s loan charge-offs. Bank managers do have some discretion in recognizing loan charge-offs (Wahlen 1994) and might postpone charging off bad loans to future periods so that the current period’s financial position is presented in a favorable manner.
We model next-period loan charge-offs as a function of fees paid to auditors, current period LLP, current-period loan charge-offs (LCO) and the natural log of total assets, a proxy for bank size. In addition, we include the indicator variable SMALL to control for differences between small and large banks. Following Altamuro and Beatty (2006), we use LLP, LCO, SMALL and the natural log of total assets as control variables in the model explaining future charge-offs which is formulated as follows
LCOt+1 = χ0 + χ1 FEEt + χ2 SMALLt + χ3 LLPt + χ4 LCOt + χ5 SIZEt-1 + <YEAR CONTROLS> + ε (5)
We define the variables as follows (all variables are deflated by beginning total assets):
LCO = Net loan charge-offs;
FEE = Natural log of audit fees (LAFEE) or total fees (LTOTFEE = ln (audit fees + nonaudit fees)) or nonaudit fees (LNAFEE);
SMALL = 1 if beginning total assets are less than $500 million;
LLP = Provision for loan losses; and
SIZE = Natural log of beginning total assets.
Panel A of Table 6 reports the results of model (4) for the full sample. As expected, all three of our fee variables (LTOTFEE, LAFEE and LNAFEE) have a significant (p<0.01), positive relation with next-period loan charge-offs (FLCO). The two control variables, current-period loan loss provisions (LLP) and loan charge-offs (LCO), have a strong, positive association with FLCO.
[Insert Table 6 About Here]
In Panel B of Table 6, we repeat our analysis using unexpected fees estimated from model (3). Again, all three of the unexpected fee measures (UTOTFEE, UAFEE and
UNAFEE) have strong, positive relations with next-period loan charge-offs (FLCO). Overall, the significant association between current-period fees and future loan chargeoffs suggests that bank managers delay recognition of loan charge-offs when the auditorclient economic bond is stronger. These results are consistent with our earlier results on the association between ALLP and fees in that auditors appear more tolerant of earnings management via income-increasing LLP or discretionary loan charge-offs when their fees are higher.
Earnings Benchmark Tests
Frankel et al. (2002) and Ashbaugh et al. (2003) examine the association between several fee measures and earnings benchmark beating behavior of firms. Following prior research, we investigate the association between auditor fees and the likelihood of banks reporting a small earnings increase (INCREASE) for the income-increasing ALLP subsample. We estimate the following logit regression for the earnings benchmark tests:
INCREASEt = χ0 + χ1 FEEt + χ2 SMALLt + χ3 ALLPt + χ4 MBt + χ5 LMVEt + χ6 BIG5t + χ7 TIER1t-1+ χ8TCAPt-1 + <YEAR CONTROLS> + ε (6)
We define the variables as follows:
INCREASE = 1 when the change in net income scaled by beginning of year assets falls in the interval [0.000, 0.002] and 0 otherwise.
FEE = Natural log of audit fees (LAFEE) or total fees (LTOTFEE = ln (audit fees + nonaudit fees)) or nonaudit fees (LNAFEE);
SMALL = 1 if beginning total assets are less than $500 million;
ALLP = Abnormal loan loss provision;
MB = Market-to-book ratio at the end of the year;
LM E = Natural log of market value of common equity;
BIG5 = Indicator variable set equal to 1 if audited by a Big 5 firm, and 0 otherwise;
TIER1 = Tier 1 risk adjusted capital at the beginning of the year; and
TCAP = Total risk adjusted capital at the beginning of the year.
The variable of interest in model (6) is FEE. A positive association between FEE
and INCREASE will indicate that auditor fee dependence increases the bank’s earnings management behavior to meet or narrowly beat the previous year’s earnings. Since small banks have less regulatory monitoring, we expect a positive association between SMALL and INCREASE. Following Ashbaugh et al. (2003), we include current period abnormal accruals (ALLP) in the model. More negative ALLP for the income increasing sub-sample will result in a higher probability of beating the earnings benchmark; therefore, we expect a negative association between ALLP and INCREASE. Consistent with Frankel et al.
(2002) and Asbaugh et al. (2003), we include controls for growth (MB), size (LMVE) and Big 5 auditors (BIG5). We also include beginning capital ratios as bank specific controls. Panel A of Table 7 reports the results of model (6) for the income increasing ALLP sub-sample. As expected, all three of the fee variables (LTOTFEE, LAFEE and LNAFEE) are significantly, positively related with bank’s earnings management behavior to meet or narrowly beat the previous year’s earnings. The indicator variable for small banks (SMALL) has a positive and significant (p<0.05) relation with INCREASE. As predicted ALLP is negatively associated with INCREASE and this relation is significant at the 0.01 level.
[Insert Table 7 About Here]
In Panel B of Table 7, we repeat our analysis using the unexpected fees from model (3). Again, all three of the unexpected fee measures (UTOTFEE, UAFEE and
UNAFEE) have a strong, positive relation with earnings benchmark beating behavior. Overall, the significant association between both fees and abnormal fees and earnings benchmark beating behavior is consistent with the argument that auditors who are paid higher fees are more tolerant of earnings management via income-increasing LLP.
V. SUMMARY AND CONCLUSIONS
We examine auditor independence in the banking industry. Banks are subject to the scrutiny of the FDIC, the Federal Reserve Board, and other governmental agencies. In addition, the Federal Deposit Insurance Corporation Improvement Act (FDICIA) which became effective in 1992, requires the management of depository institutions with assets exceeding $500 million to evaluate the internal control over financial reporting and the auditor to attest to the report on the effectiveness of internal controls over financial reporting. We provide empirical evidence on the relation between fees paid to the auditor and the extent of earnings management via loan loss provisions in the banking industry. We also study whether this relation differs across small and large banks.
Our findings indicate a positive association between fees paid to the auditor and income-increasing earnings management through loan loss provisions. They suggest that economic bonding between the auditor and the bank potentially impairs auditor independence. This result is especially interesting given the high level of regulatory scrutiny faced by banks. Our findings also indicate that this bonding is stronger for small banks that are subject to less regulatory oversight than large banks. Collectively, our results suggest that auditor fee dependence on the audit client is a threat to auditor independence, particularly among banks with less than $500 million in assets that are not subject to the same level of regulatory scrutiny as larger banks. They suggest that although the high level of regulatory oversight of banks does not eliminate economic bonding, it does so to a greater extent for large banks subject to more stringent controls. Our results have several implications. First, they suggest that the FDIC and other banking regulators and inspectors should closely review the loan loss provisions of banks where the fee dependence is high. Second, that banks can engage in earnings management despite the high level of scrutiny by the FDIC and other regulatory agencies, suggests that the extent of earnings management in industries that are less closely regulated could be even greater. Third, the FDICIA can be viewed as a precursor to the Sarbanes-Oxley Act of 2002 for large banks. There has been considerable discussion about relaxing the requirements of the Sarbanes-Oxley Act for small firms. If generalizable to other industries, our results of increased earnings management by small banks that are less closely regulated suggest that reducing the requirements of the Sarbanes-Oxley Act for small firms should be approached with caution.
The results of two additional tests corroborate our findings of an economic bond between the auditor and the bank. First, we find a significant, positive association between the level of total fee and the next-period charge-offs indicating that the fee level is positively related to delaying the recognition of loan charge-offs. Second, we observe a significant, positive relation between various fee measures and benchmark beating
behavior.
We note that one limitation of our study is that the results could be driven by an alternative hypothesis that the audit fees reflect audit risk that is captured by the discretionary loan loss provision. Auditors are likely to charge higher fees to firms that are more difficult to audit and, if firms that are more difficult to audit have higher incentives to engage in earnings management, this will be manifested in a positive relationship between audit fees and discretionary loan loss provisions. We attempt to address this concern by including variables that reflect differences in audit risk across banks as well examining the relationship using abnormal fees after explicitly taking account of possible factors driving the normal fees. Nevertheless, we cannot completely rule out this alternative explanation and recognize it as a limitation of our study.
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