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Last updated: 29/03/14 CRICOS Code: 00098G
School of Accounting Seminar Series Semester 1, 2014
The role of audit verification in
debt contracting: Evidence from
covenant violations
Hui Zhou
Melbourne Business School
Date: Friday 4th April, 2014
Time: 3.00pm – 4.30pm
Venue: ASB 216
Australian School of Business
Accounting
The Role of Audit Verification in Debt
Contracting: Evidence from Covenant Violations
Liangliang Jiang Department of Economics
Lingnan University 8 Castle Peak Road
Tuen Mun, New Territories, Hong Kong Tel: +852 26167040
Email: [email protected]
Hui Zhou* Melbourne Business School
University of Melbourne 200 Leicester Street
Carlton, VIC 3053 Australia Tel: +61 3 93498710
Email: [email protected]
* Corresponding author. We thank Paul Coram , Jim Frederickson, Ganapathi Narayanamoorthy, Padmakumar Sivadasan, participants at the 2012 New Accounting Faculty Conference in Melbourne (especially the discussant Bin Ke), and participants at the 2012 International Symposium on Audit Research for helpful comments. Arbitor Ma provided excellent research assistance. We gratefully acknowledge the financial support from Lingnan University and Melbourne Business School. An earlier version of this paper was circulated under the title “Do Auditors Play a Positive Role in the Resolution of Debt Covenant Violations?”.
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The Role of Audit Verification in Debt Contracting: Evidence from Covenant Violations
Abstract
We examine whether debt covenant violations result in a demand for differentially higher levels
of audit verification. Using audit fees as the proxy for the level of audit verification, we find that
violating firms pay for higher-priced audit services following the violation event than otherwise
similar non-violating firms. Additional tests show that auditor litigation risk is unlikely to be the
primary driver of the audit fee response to debt covenant violations. Consist with the notion that
the higher audit fees arise from the demand for differentially higher levels of audit following
covenant violations, the audit fee response to covenant violations is more pronounced for firms
with more intensive lender and shareholder monitoring. Moreover, audit committees respond to
covenant violations by including more independent directors and meeting more frequently.
Collectively, our findings help shed light on the role of audit verification in the resolution
process following debt covenant violations.
Keywords: audit verification; debt covenant violations; audit committee
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1. Introduction
Financial reporting and audit verification of financial statements play an important role in
contracting and monitoring (Armstrong et al., 2010; Bushman and Smith, 2001; Lambert, 2001;
Shivakumar, 2013; Watts and Zimmerman, 1983, 1986). In the context of debt contracts, theory
posits that accounting-based debt covenants help mitigate the agency cost arising from the
conflict of interest between managers and creditors. While previous literature documents
substantial evidence that accounting-based debt covenants provide ex-ante contracting efficiency
incentives, little is known about the role of accounting and auditing following debt covenant
violations. This study contributes to the literature by investigating whether covenant violations
result in a demand for differentially higher levels of audit verification in the resolution process.
Previous research has established that covenant violations occur frequently, affect a large
number of firms, are usually observed well outside of financial distress, and rarely lead to
acceleration of the loan (Dichev and Skinner, 2002; Gopalakrishnan and Parkash, 1995; Nini et
al., 2012). Moreover, recent empirical evidence suggests an improvement in both the operating
and stock market performance following covenant violations due to increased lender monitoring
that serves to reduce managerial slack (Chava and Roberts, 2008; Nini et al., 2009, 2012). These
findings highlight the importance of the resolution of covenant violations in debt contracting and
raise questions about the role of accounting and auditing in the resolution process.
Dichev and Skinner (2002) conclude that lenders use debt covenant violations as early
warning signals that allow them to review and renegotiate debt agreements. As accounting
information plays an important role in informing the negotiation process that determines the key
parameters of debt contracts (Shivakumar, 2013), an enhanced credibility of accounting
information can alleviate the information uncertainty faced by the lender and thus help reduce
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the renegotiation costs following covenant violations. Previous research provides evidence that
the verification of financial statements performed by independent auditors serves as a mechanism
for improving the credibility of accounting information and mitigating borrowing costs. For
example, Kim et al. (2011) and Minnis (2011) document that voluntary external audits are
associated with lower costs of debt using samples of private firms not subject to mandatory audit.
We propose that debt covenant violations will result in a heightened demand for higher levels of
audit verification as a mechanism to mitigate the renegotiation costs in the resolution process.1
We investigate the demand for audit verification following covenant violations by examining
both the change in the pricing of audit services and the response of audit committees. Following
prior literature that uses higher audit fees to capture the differential demand for higher levels of
audit services (e.g. Ball et al., 2012), our first set of analysis examines the audit fee response to
covenant violations. Using a comprehensive data set that covers U.S. public firms with
outstanding loans, we examine the incremental impact of covenant violations after controlling
for other determinants of audit fees in the literature. We document that firms that have recently
violated covenant violations pay for higher priced audit services than their non-violating peers
during the violation year, but not in the year immediately before the violation event.
While the above results are consistent with the notion that covenant violations lead to
increased demand for higher levels of audit verification, an alternative explanation attributes the
higher audit fees to potential auditor litigation risk associated with covenant violations. It is
worth to note that a covenant violation is not a violation of financial reporting or auditing
requirements. Thus, the link between a covenant violation and the probability of the auditor
1 Here we implicitly assume that the level of audit verification reflects the choice of the client in an environment
where external audit is mandatory. This view is consistent with the existing literature that treats audit as a differentiated product with a private demand rather than a standardized commodity mandated by regulation (e.g. Ball et al., 2012; Watts and Zimmerman 1983, 1986).
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being sued for negligence is not clear unless the violation is associated with other events that can
result in increased auditor litigation risk. For example, a stylized fact is that lawsuits against
auditors and other types of class action lawsuits tend to follow sharp declines in stock prices
(Shu, 2000). If covenant violations tend to trigger large drops in stock prices, auditors will face
higher litigation risk in the post violation years.
To directly investigate the effect of auditor litigation risk, we compare the audit fee pattern
for violating firms of different levels of auditor litigation risk. Specifically, we use firm stock
returns, net profit, and operating cash flows to gauge auditor litigation risk. We do not find
statistical difference in the audit fee responses to covenant violations for violating firms that
experience positive vs. negative stock returns during the violation year, that report net profit vs.
loss during the violation year, or that report positive vs. negative operating cash flows during the
violation year. These results show that the higher audit fees following covenant violations are
unlikely due to the violation event being a signal of deteriorating financial prospect that
significantly increases auditor litigation risk.
To address the concern that audit fee differences between the violating and non-violating
firms are driven by other omitted variables, we use the propensity score matching approach to
construct an optimal comparison group consisting of firms that share similar characteristics but
have not reported a covenant violation. We estimate the average audit fee differences between
the violating firms and firms in this comparison group. Unlike multivariate regression models
that require the control variables to be linearly related to audit fees, the propensity score
matching approach has the advantage of not restricting the specific relationship between audit
fees and the firm characteristics affecting audit pricing. The results from the propensity score
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matching analysis confirm that violating firms pay for higher priced audit services than
otherwise similar firms that have not reported a debt covenant violation.
After documenting the immediate impact of covenant violations, we examine whether the
audit fees for violating firms stay at higher levels following the violation year. As we suspect
that the demand for higher levels of audit services may persist for several years after the initial
violation, we use dynamic models to investigate how the audit fees evolve following the events
of violation. The results show that violating firms pay higher audit fees than non-violating firms
for at least three years after the initial violation. The documented differences in audit fees in post
violation years are both statistically and economically significant, ranging from 5% to 10%.
Having found evidence on the impact of covenant violations on audit fees, we further
investigate whether the audit fee response to covenant violations varies with the level of lender
and shareholder monitoring. If the observed increase in audit fees arises from heightened demand
for higher-level audit verification following covenant violations, the audit fee response should be
more pronounced for firms with more intensive lender and shareholder monitoring. We find
supporting evidence by using leverage as the proxy for lender monitoring (Klein, 2002) and
institutional ownership as the proxy for shareholder monitoring (Bhojraj and Sengupta, 2003).
Our second set of analysis examines the corporate governance mechanisms underlying the
observed audit fee response to debt covenant violations. To this end, we focus on the response of
the audit committee to covenant violations as the audit committee is the most important oversight
element in the financial reporting process. A primary channel for the audit committee to exercise
this oversight role is by selecting and monitoring the external auditor to ensure the quality of the
financial reporting verification process performed by the external auditor. In the context of
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covenant violations, the heightened demand for differentially higher-quality audit services is
likely to manifest through the response of audit committee to the violation event.
We examine two audit committee characteristics in our analysis of audit committee response
to covenant violations: the number of independent directors on the audit committee and the
frequency of audit committee meetings. Anderson et al. (2004) provide evidence that lenders
price more effective monitoring of the financial reporting process in debt agreements by
documenting that more independent and active audit committees are associated with lower cost
of debt. Focusing on the same audit committee characteristics, we find evidence that audit
committees respond to covenant violations by including more independent directors and meeting
more frequently in the post violation years. Our results extend the findings in Anderson et al.
(2004) by shedding light on the role of audit committee characteristics in the resolution process
following covenant violations.
This study provides new evidence that covenant violations result in a heightened demand for
higher-level audit verification. The findings extend the evidence that the financial statement
verification process performed by external auditors helps mitigate contracting costs in debt
financing (Kim et al., 2011; Minnis, 2011). We show that this role does not stop at a debt
covenant violation but extends to the resolution process, thus providing novel evidence
highlighting the importance of audit verification in debt contracting.
Our paper complements previous research documenting that creditors start to play a more
active corporate governance role following debt covenant violations (Chava and Roberts, 2008;
Nini et al., 2009, 2012). We find evidence that other stakeholders in the corporate governance
system (i.e. the audit committee and the external auditor) also respond to the event of covenant
violation. The findings add to evidence in the literature showing that the heightened demand for
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monitoring from the corporate governance system leads to the acquiring of a differentially higher
level of audit services (Carcello et al., 2002; Fan and Wong, 2005).
This study is also related to the line of research that investigates the consequences of debt
covenant violations (e.g. Beneish and Press, 1993; Beneish and Press, 1995; DeFond and
Jiambalvo, 1994; Sweeney, 1994). While we document that covenant violations result in
significantly higher audit fees, we caution against interpreting the increase in audit fees as a ‘cost’
of covenant violations that invariantly yields a negative outcome. Notably, recent empirical
evidence suggests that ‘penalties’ imposed by creditors in response to covenant violations (e.g. in
the form of restrictions on capital expenditures) yield positive externalities to equity holders by
constraining value-reducing managerial behavior (Nini et al., 2009, 2012). In similar vein,
increased auditor monitoring in response to covenant violations, while resulting in higher out-of-
pocket audit expense, represents a governance mechanism for mitigating managerial agency
problems and thus may positively impact firm value.
The remainder of this paper proceeds as follows. The next section describes the sample
construction process and our data. Section 3 presents the empirical tests and our findings. We
summarize and conclude in Section 4.
2. Data
2.1. Sample construction process
We start our sample collection from the merged COMPUSTAT-Audit Analytics dataset. For
each observation in the merged sample, we collect accounting information from COMPUSTAT
and auditor/audit fee information from Audit Analytics.2 We then match the merged dataset with
2 It is important to note that fees for due diligence performed by external auditors related to loan initiation are part of non-audit fees and therefore not included in audit fees.
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the Reuters Loan Pricing Corporation’s Dealscan database using the DealScan-COMPUSTAT
link file constructed by Chava and Roberts (2008). This ensures that our sample excludes firms
that have never borrowed loans since year 1987 as Dealscan started recording bank loans in 1987.
We then merge this sample with the comprehensive dataset of covenant violations used in
Roberts and Sufi (2009) and Nini et al. (2012).3 Finally, we exclude all financial firms and utility
firms (SIC codes between 6,000 and 6,999, and between 4,900 and 4,999, respectively) in the
sample.
Our sample period starts from 2000 because audit fee data became available starting year
2000. Nonetheless, we still keep the covenant violations recorded between year 1996 and 1999 in
order to minimize the truncation problem. The sample construction process yields 2,103 new
covenant violations by 1,682 firms out of the total of 4,561 sample firms between year 2000 to
2007. We follow Roberts and Sufi (2009) and consider a violation ‘new’ if there is no financial
covenant violation by the same firm in the previous four quarters.4 The events of covenant
violation in our sample are not clustered in any particular years.
2.2. Violation variables
As our sample includes both the violating and non-violating firms, we use an array of dummy
variables to identify 1) whether the firm-year observation represents a violating firm, and 2) the
distance to violation event if the observation represents a violating firm. We use an approach
similar to that employed by Bogart and Chaudhary (2012) in order to track both the immediate
and long term impact of covenant violations.
3 The dataset is compiled by Professor Amir Sufi and can be downloaded from http://faculty.chicagobooth.edu/amir.sufi/data.html 4 Our main results do not change if we change the definition of “new” violation from four quarters to eight quarters.
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Specifically, we create a sequence of dummy variables (Vio_Pre, Vio_Event, Vio_PostY1,
Vio_PostY2, and Vio_PostY3) to capture the distance to the event of violation for violating
firms. For firm-year observations that represent a violating firm, Vio_Event indicates the
violation year (the year that the firm reported a covenant violation). Vio_Pre indicates the year
immediately before the violation year. Vio_PostY1, Vio_PostY2, and Vio_PostY3 indicates the
year immediately following the violation year, two years after the violation year, and three years
after the violation year respectively. Accordingly, a firm-year observation that represents a non-
violating firm will have zero values for all the dummy variables. The mean of the dummy
variable Vio_Event is 0.09, indicating that about 9% of the firm-year observations included our
sample represent a newly reported covenant violation.
2.3. Control variables
We include factors that have been shown to affect audit fess and audit committee in the
literature as control variables in our analysis (See Appendix for variable definitions). Consistent
with previous literature, the control variables cover auditee size, complexity, operating
performance, growth potential, leverage level, liquidity status, accounting fraud risk, auditor
characteristics, and audit outcomes (Bell et al., 2001; Doogar et al., 2010; Francis et al., 2005;
Hay, et al., 2006; O’Keefe et al., 1994; Simunic, 1980). As our sample period spans from 2000 to
2008, we include an additional indicator variable to control for the effect of Section 404(b) of the
Sarbanes-Oxley Act. It is set to one if the firm is an accelerated filer (filer status reported in
Audit Analytics) and the year is 2004 or later and set to zero otherwise.5 The inclusion of this
variable is important as Section 404(b) of the Sarbanes Oxley Act expands the scope of auditing
5 Filing both the management report on internal controls (required under Section 404(a) )and the auditor attestation
of this management report (required under Section 404(b)) went into effect starting fiscal year 2004 for accelerated filers while non-accelerated filers were never subject to the requirement of Section 404 (b).
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by requiring auditors to evaluate the effectiveness of the internal controls and attest to the
assessment made by management, which can significantly impact the overall level of audit fees.
Table 1 reports the descriptive statistics for audit fees and control variables used in our
analysis. All the dollar value variables in Table 1 have been converted to constant 2006 U.S.
dollars using the U.S. Bureau of Labor Statistics (BLS) CPI series as deflator. The mean and
median of audit fees is $1.015 million and $0.337 million respectively. The mean firm size is
about $2.14 billion and the median firm in our sample has total assets of $259 million. The
median and mean of FOREIGN is 0 and 0.1 respectively, suggesting that about 10% of our
sample firms have significant foreign operations.
[Insert Table 1 here]
As reported in Table 1, about 73% of our sample firms hire a Big 4 auditor (the mean of
BIG4 is 0.73) and about 23% of our sample firms hire an auditor with industry specialization
(the mean of SPECIALIST is 0.23). The mean of FRSK is 0.31, indicating that about 31% of the
sample observations are associated with higher audit risk based on the measure adopted by
Doogar et al. (2010). The percentage is lower but still comparable to the 42 percent reported by
Doogar et al. (2010). Finally, about 31% of the sample observations represent accelerated filers
that are subject to both Section 404 (a) and 404(b) of the Sarbanes-Oxley Act since 2004 (the
mean of SOX is 0.31).
3. Empirical Analysis and Results
3.1. Audit fees before and after covenant violations: Graphical analysis
Our empirical tests start with the univariate analysis that explores the trend of audit fees
around covenant violations for the violating firms. Figure 1 presents the unconditional mean of
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audit fees for the violating firms in our sample along the covenant violation timeline. The
vertical axis represents the mean of audit fees per dollar of firm assets (audit fees scaled by the
firm’s total assets). The horizontal line represents the timeline around the events of covenant
violations (three years before the violation year to three years after the violation year), with year
0 being the year of violation.
[Insert Figure 1 here]
Figure 1 indicates that the violating firms experience a significant increase in audit fees
following the event of covenant violation while there is no discernible trend in audit fees during
the pre-violation period. Specifically, the unconditional mean of audit fees per dollar of firm
assets for violating firms rises from around 0.018 cents in the year immediately before the
violation year to more than 0.26 cents in the violation year. This increase in audit fees also shows
persistence as Figure 1 suggests that audit fees continue to stay at a high level in the post-
violation period.
Overall, the trend of the unconditional average of audit fees around covenant violations for
the violating firms presented in Figure 1 is consistent with our expectation that auditors exercise
closer monitoring and thus charge higher fees following debt covenant violations. The magnitude
of the increase in the unconditional mean of audit fees following covenant violations is both
statistically and economically significant (an increase of about 40 percent from year -1 to year 0).
An alternative explanation for the increase in audit fees shown in Figure 1 is that the trend is
driven by economy-wide changes during the sample period that affect the overall level of audit
fees regardless of whether a covenant violation has occurred or not. We address this issue by
examining the audit fee trend for a subsample of the non-violating firms in our sample that are
similar to the violating firms in terms of industry and size. Specifically, for each firm-year
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observation for each violating firm in our sample, we select matching firm-year observations
from our sample that meet the following criteria: 1) from the same calendar year, 2) the matching
firm has the same 2-digit SIC code as the violating firm and, 3) the total asset of the matching
firm is within +/-25% of the violating firm’s total asset,6 and 4) the matching firm has no record
of covenant violations.
[Insert Figure 2 here]
Figure 2 presents the mean audit fee (scaled by firm size) for the constructed subsample of
non-violating firms along the covenant violation timeline. The graph shows no significant
difference in audit fees between the pre- and post-violation period for these matching firms
during the same period that we observe a significant increase in audit fees for the violating firms
in our sample. The results thus help rule out the alternative explanation that the audit fee
response to covenant violations following covenant violations is driven by changes in the
auditing environment unrelated to the violation event. However, we recognize that it is difficult
to accurately estimate the effect of covenant violations using univariate analysis given the
existing confounding factors. In the next section, we perform multivariate analysis to investigate
the effect of covenant violations after taking into consideration other determinants of audit fees.
3.2. Covenant violations and audit fees: Immediate impact
This section describes regression analysis that examines the immediate impact of covenant
violations on audit fees. We first run a regression with the natural logarithm of audit fees as the
dependent variable, Vio_Pre as our main variable of interest, and all the control variables
(Column 1 Table 2). The purpose of this regression is to account for the potential differences in
audit fees between violation and non-violating firms in absence of the violation event. In the next
6 This is consistent with the cutoff value for size matching in Farber (2005).
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step, we estimate the differences in audit fees between violating firms and non-violating firms
during the violation year by running a regression with the natural logarithm of audit fees as the
dependent variable, Vio_Event as our main variable of interest, and the control variables
(Column 2 Table 2).
[Insert Table 2 here]
Table 2 reports the regression results on the immediate impact of covenant violations.
Notably, the coefficient on Vio_Pre in column (1) is -0.0201, indicating that violating firms
actually pay lower audit fees than non-violating firms before the violation event occurs. In
contrast, the coefficients on the violation event dummy variables is positive (0.0410) and
statistically significant. The results show that violating firms pay higher audit fees during the
violation year but not before the violation event occurs, suggesting that events of covenant
violations lead to higher audit fees.
As reported in Table 2, the overall goodness of fit for each of the regression models is
comparable to previous research (adjusted R2 is around 0.6 in each case). The results for the
control variables are largely consistent with those reported in the extant literature. Most notably,
audit fees are positively associated with control variables that proxy for size (LNTA), complexity
(LNSEG), and delay in the financial reporting process (DELAY). Consistent with our
expectation, the coefficient on SOX is around 0.4 and statistically significant (p<0.01) in each
model, suggesting that accelerated filers in the post-SOX period face significantly higher audit
fees. The results are consistent with the findings complied by Audit Analytics that accelerated
filers experienced a spike in audit fees in 2004 due to the implementation of Section 404 (b) of
the Sarbanes-Oxley Act (Audit Analytics, 2011).
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3.3 Litigation risk and audit fee response to covenant violations
An alternative explanation attributes the observed association between audit fees and
covenant violations to heightened auditor litigation risk as covenant violation may represent a
signal of the deteriorating financial prospect that significantly increases litigation risk for the
engaged auditor. While previous research using large samples has established that covenant
violations rarely lead to loan acceleration or financial distress (Dichev and Skinner, 2002;
Gopalakrishnan and Parkash, 1995; Nini et al., 2012), we further address this concern by
examining the cross sectional differences in audit fees for violating firms with varying level of
auditor litigation risk.
As both anecdotal evidence and findings from empirical research show that lawsuits against
auditors tend to follow sharp declines in stock prices (Shu, 2000), auditors may face higher
litigation risk in the post violation years if covenant violations tend to trigger large drops in stock
prices. To address this concern, we partition the violating firms into two groups: violating firms
with positive annual stock return during the violation year vs. violating firms with negative
annual stock return during the violation year. The statistics (untabulated) show that about 43% of
the new covenant violations in our sample represent violating firms that experienced positive
annual stock returns during the violation year while the remaining 57% represent those that
experienced negative annual stock returns during the violation year. If the audit fee response to
covenant violations is driven by litigation risk, we should observe higher audit fees for the
violating firms with negative stock returns during the violation year than for violating firms with
positive stock returns during the violation year, using non-violating firms in the same period as
the base group.
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Similar to the analysis based on stock price performance, we examine cross sectional
differences in audit fees by partitioning the violating firms into loss vs. profit and negative
operating cash flows vs. positive operating cash flows groups, respectively. The statistics show
that about 55% (63%) of the new covenant violations in our sample represent violating firms that
reported a profit (positive operating cash flows) during the violation year while the remaining 45%
(37%) represent those that reported a loss (negative operating cash flows) during the violation
year. If higher audit fees following covenant violations arise from heightened auditor litigation
risk as covenant violations signal deteriorating financial prospect, we should observe higher
audit fees for violating firms with poor operating performance and cash flow generation.
Therefore, we compare the audit fees for the violating firms that report a loss (negative operating
cash flows) with those for the violating firms that report a profit (positive operating cash flows),
using the non-violating firms in the same period as the baseline group.
[Insert Table 3 here]
Table 3 presents the results for the cross sectional analysis of audit fees across different levels
of auditor litigation risk. The dependent variable for all the regression models in Table 3 is the
natural logarithm of audit fees and all the control variables are the same as defined earlier.
Column 1 compares the audit fees for violating firms with positive vs. negative stock returns
during the violation year, using the non-violating firms as the baseline group. As reported in
Table 3, the coefficients on Positive_Return and Negative_Return are 0.0508 and 0.0338
respectively, both being statistically significant. The results show that audit fees for both groups
of violating firms are significantly higher than the baseline group of non-violating firms.
However, the audit fees for violating firms with poor stock market performance during the
violation year are no higher (actually lower) than those for violating firms with positive stock
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returns during the violation year (0.0338<0.0508), although the difference between audit fees
across the two groups of violating firms is not statistically different. As lawsuits against auditors
typically follow sharp declines in stock prices, the results show that covenant violations result in
higher priced audit services even when the violation event is unlikely to be associated with
increased auditor litigation risk.
Column 2 of Table 3 compares the audit fees for violating firms that report a profit vs. loss
during the violation year. The results show that the difference in audit fees across the two groups
of violating firms is not statistically significant, although both groups face significantly higher
audit fees than the baseline group of non-violating firms. Column 3 reports similar results from
the comparison of audit fees for violating firms that report positive vs negative operating cash
flows. Again, we find that the audit fees for both positive- and negative-cash-flow-violating-
firms are higher compared to those of non-violating firms, but the difference across the two
groups of violating firms is not statistically significant. Overall, the findings suggest that the
higher audit fees following covenant violations are unlikely due to the violation event being a
signal of deteriorating financial prospect that significantly increases auditor litigation risk.
3.4 Propensity score matching
To further address the concern that audit fee differences between the violating and non-
violating firms are driven by other omitted variables, we use the propensity score matching
approach to construct an optimal control group of non-violating firms and estimate the average
audit fee differences between the violating firms and this control group. Unlike multivariate
regression models that require the control variables to be linearly related to audit fees, the
propensity score matching approach has the advantage of not restricting the specific relationship
17
between audit fees and the firm characteristics affecting audit pricing. Therefore, this approach
helps alleviate the concern that the results from our multivariate regression analysis are biased as
some non-linear terms of the firm characteristics may affect both the likelihood of a covenant
violation occurrence and the level of audit pricing. The propensity score matching approach has
been increasingly used in finance and accounting literature (see, for example, Drucker and Puri,
2005; , Minnis, 2011; Houston, et al., 2014) as econometric studies have shown that propensity
score matching can allow for a more accurate analysis (e.g. Conniffe et al., 2000).
Using the propensity score matching approach, we match violating firms with non-violating
firms based on propensity scores and then calculate the average audit fee differences between the
two groups of the firms.7 The propensity scores are estimated via a probit model. The dependent
variable for the probit model is the dummy variable indicating whether the firm reported a new
covenant violation during the current year. The independent variables are the same as the control
variables in the audit fee regression model described earlier, including firm size, profitability,
leverage, growth, segments, foreign assets ratio, auditee loss indicator for net income, inventory
and receivable over total assets, current and quick ratio, credit rating, fraud risk, big-four auditor
dummy variable, specialist, busy, and delay indicator, going concern opinion, SOX indicator,
firm and year fixed effects.
[Insert Table 4 here]
Table 4 presents the results from the propensity score matching analysis using different
specifications (nearest neighbor matching, radius matching, Epanechnikov kernel-based
functions, and stratification matching). The results show that violating firms consistently pay
higher audit fees than similar non-violating firms during the violation year. The differential
estimates range from 3.9% to 8.5% of the sample mean, which are not only statistically
7 The Appendix of Drucker and Puri (2005) has provided detailed explanation of this estimation method.
18
significant but also economically comparable to the results from our multiple regression analysis.
This result alleviates the concern that endogeneity biases our main findings.
3.5. Covenant violations and audit fees: Long-term impact
In this section, we examine whether the audit fees for violating firms stay at higher levels
following the violation year after controlling for other determinants of audit fees. We first run
regressions similar to those reported in Table 2 but with Vio_PostY1, Vio_PostY2, and
Vio_PostY3 as the main variable of interest respectively (Columns 1-3 Table 5). Finally, we use
a dynamic model to examine the long term impact of covenant violations on audit fees.
Specifically, we run a regression with all the violation event dummies Vio_PostY1, Vio_PostY2,
and Vio_PostY3 jointly included in the model (Column 4 Table 5). The purpose of this
regression is to examine how audit fees for violating firms change in the post-violation years,
after controlling for the trend of audit fees for non-violating firms during the same period.
[Insert Table 5 here]
Table 5 reports the regression results on the long-term audit fee response to covenant
violations. Notably, the coefficients on the violation event dummy variables are consistently
positive and statistically significant across Column 1 to Column 3, indicating that violating firms
pay higher audit fees than non-violating firms until three years after the initial violation. As the
dependent variable is the natural logarithm of audit fees, the results reported in Column 4
provide an estimate of the percentage magnitude of the long term impact of covenant violations
on audit fees. For the average violating firm, after controlling the trend of audit fees for non-
violating firms during the same period, audit fees are 8.34%, 10.86%, 8.91%, and 5.2% higher
during the violation year, the year immediately following the violation, two years after the
19
violation, and three years after the violation respectively compared with the non-event years.
Overall, the results suggest that the increases in audit fees in response to debt covenant violations
persist for at least three years after the initial violation.
3.6. Effect of lender and shareholder monitoring
In this section, we investigate whether the increase in audit fees following debt covenant
violations varies with the level of lender and shareholder monitoring. If the observed increase in
audit fees arises from heightened demand for higher-level audit verification following covenant
violations, we expect the audit fee response to be more pronounced for firms with greater level
of lender monitoring. Similar to Klein (2002), we use leverage to capture the variation in the
level of lender monitoring as we expect covenant violations to be more consequential events for
high leverage firms.
In addition to lender monitoring, previous research suggests that shareholder monitoring also
plays an important role in debt contracting. In particular, Bhojraj and Sengupta (2003)
demonstrate that greater institutional ownership is associated with lower borrowing costs as more
effective monitoring by the shareholders helps reduce potential conflicts of interests between the
management and providers of capital, including debt holders. Consistent with this notion, we
expect the increased demand for higher-level of audit verification following covenant violations
to be more pronounced for firms that have greater institutional ownership. We test this prediction
by examining whether the increase in audit fees following covenant violations varies with the
level of institutional ownership.
As we are interested in how lender and shareholder monitoring moderates both the immediate
and long term audit fee response to violation events, we use the dynamic model described in the
20
last section. Specifically, we create interaction terms for each of the post-violation dummy
variables in the dynamic model (Vio_Event, Vio_PostY1, Vio_PostY2, and Vio_PostY3) with
leverage and institutional ownership respectively and add these interaction terms to the dynamic
model from the last section to examine how the audit fee response to covenant violations varies
with the level of leverage and institutional ownership.
[Insert Table 6 here]
Table 6 reports the results on the effect of lender and shareholder monitoring. Consistent
with our expectation, regression coefficients for the interaction terms of leverage and the
violation dummy variables are consistently positive, with the coefficients for Leverage*Event,
Leverage*PostY2, and Leverage*PostY3 being statistically significant. The results indicate that
higher leverage level is associated with steeper increase in audit fees following covenant
violations. Similarly, the regression coefficients for the interaction terms of institutional
ownership and the violation dummy variables (are positive and statistically significant. The
results suggest that the increase in audit fees in response to covenant violations is more
pronounced for firms with greater level of shareholder monitoring. Overall, the results reported
in Table 6 support the notion that the increase in audit fees following covenant violations is
driven by heightened demand for higher-level audit verification.
3.7. Audit committee response to covenant violations
To better understand the mechanisms underlying audit fee response to covenant violations,
we examine the change in audit committee characteristics following debt covenant violations. As
one of the key committees of the board of directors, the audit committee selects the external
auditor and serves as “the ultimate monitor” of the financial accounting reporting process (NYSE
21
and NASD 1999, page 7). Consistent with the Blue Ribbon Committee Report and findings from
previous research, we focus on two audit committee characteristics: audit committee
independence and the frequency of audit committee meetings.
Advocacy for placing more outside directors on the audit committee has a long tradition
based on the notion that only outside directors who are independent of the management can
perform effective monitoring. Klein (2002) provides evidence that firms choose the level of audit
committee independence to suit their specific economic environments. However, in contrast to
her expectation, she finds no association between audit committee independence and the proxy
for the level of monitoring demanded by creditors (asset-to-debt ratio). We revisit this question
in the setting of covenant violations by examining whether creditors’ demand for increased
monitoring following covenant violations results in higher level of audit committee
independence. In addition to audit committee composition, how frequently the audit committee
meets is also important as it signals the diligence level of the audit committee (Anderson et al.,
2004; Farber, 2005; Sommer, 1991). Thus, we examine whether the audit committee meets more
frequently as the demand for monitoring increases following a debt covenant violation.
Klein (2002) measures audit committee independence as the percentage of outside directors
on the audit committee (the number of outside directors on the audit committee/audit committee
size). This measure is not applicable to studies using data after 2000 due to the regulatory change.
In response to SEC’s call for better oversight of the financial reporting process, NYSE and
NASDAQ modified their listing requirements in December 1999. The new standards require
firms to maintain audit committees composed solely of outside directors. As a result, the
majority of firms in our sample (with the sample period of 2000 to 2008) have audit committees
composed of 100% outside directors. However, this does not mean that there is no variation in
22
the level of audit committee independence after 2000. In particular, the absolute number of
outside directors on the audit committee is also an important determinant of audit committee
independence. This notion is behind the modified NYSE & NASDAQ listing standards requiring
that all firms must maintain audit committees with at least three independent directors. Therefore,
we use the absolute number of outside directors on the audit committee to capture the level of
audit committee independence.
The audit committee’s membership information is obtained from RiskMetrics. When such
information is not directly available from RiskMetrics, we manually searched the firm’s proxy
statement. We have also hand collected information on the number of audit committee annual
meetings from the firm’s proxy statement. We lose some observations due to lack of audit
committee membership/meeting data. As a result, the sample size is reduced to 19,204 firm-year
observations for the audit committee independence regression and to 17,878 observations for the
audit committee meeting regression.
Similar to the dynamic model we use for audit fee analysis, we use five dummy variables
(VIO_pre, Vio_Event, Vio_PostY1, Vio-PostY2, and Yio-PostY3) to indicate the distance to the
event of violation. When constructing the baseline model for audit committee independence and
the frequency of audit committee meetings, we retain the control variables in the audit fee model
discussed earlier.
[Insert Table 7 here]
Table 7 reports estimation results on the response of audit committee to debt covenant
violations. Klein (2002) hypothesizes a positive association between audit committee
independence and creditors’ demand for monitoring, but finds no relation between audit
committee independence and the proxy for the level of monitoring demanded by creditors (debt-
23
to-assets ratio). Consistent with this finding, our regression results on audit committee
independence (Column 1 Table 5) also indicate that leverage does not have explanatory power
over audit committee independence. However, we find some evidence that audit committee
independence improves in response to creditors’ increased demand for monitoring following
debt covenant violations (the coefficient on Vio_PostY1 being positive and statistically
significant). The results on audit committee meetings (Column 2 Table 5) indicate that audit
committees meet more frequently in response to debt covenant violations, with the coefficient on
Vio_Event, Vio_PostY1, and Vio_PostY2 all being statistically significant.
Overall, the results reported in Table 7 indicate that debt covenant violations lead to
improved audit committee independence and more frequent audit committee meetings in the
post-violation period. As the audit committee has the responsibility to review with the external
auditor the scope of audit work and audit fee, the findings suggest a link between the demand for
audit verification and the audit fee response following debt covenant violations. The findings are
consistent with previous research showing that firms with more independent/diligent boards are
more likely to purchase higher-quality audit services and thus pay higher audit fees (Carcello et
al., 2002).
4. Conclusion
Accounting theory posits that accounting-based debt covenants help mitigate the agency cost
in debt contracting by providing a basis for benchmarking and monitoring managerial behavior.
We show that this role does not stop at a debt covenant violation and extends to the resolution
process by documenting that the external auditor and audit committee exercise closer monitoring
in response to debt covenant violations. Using audit fee as the proxy to capture increased auditor
24
monitoring, we find that the audit fees for firms that reported a new debt covenant violation
experience a significant increase in the violation year and continue to stay at a high level until at
least three years after the initial violation. Additional tests show that audit fees are not
statistically different for violating firms of different levels of auditor litigation risk, suggesting
that auditor litigation risk is unlikely to be the primary driver of the audit fee response to debt
covenant violations.
Further analysis shows that the audit fee response to covenant violations is more pronounced
for firms with more intensive lender and shareholder monitoring. Moreover, we find evidence
that audit committees respond to covenant violations by including more independent directors
and meeting more frequently in the post violation years. These results support the notion that the
observed audit increase following covenant violations is driven by the heightened demand for
differentially higher-quality audit services in response to the violation event.
Our findings provide new evidence that covenant violations result in a heightened demand for
higher-level audit verification. The findings complement the evidence that financial statement
verification process performed by external auditors helps mitigate contracting costs in debt
financing (Kim et al., 2011; Minnis, 2011). We show that this role does not stop at a debt
covenant violation but extends to the resolution process, thus providing novel evidence
highlighting the importance of audit verification in debt contracting.
Additionally, the implications of our results are consistent with the broader view of corporate
governance in the literature that models the corporate governance system as a dynamic web of
various stakeholders (Triantis and Daniels, 1995). External auditors represent an important yet
understudied stakeholder in the corporate governance system. Although auditors do not have
direct authority for corporate governance, they actively participate in the governance process by
25
assuming the responsibility to reveal internal control weaknesses as well as to check on the
quality of financial reporting outputs. Through its interaction with the board (particularly the
audit committee), the auditor both responds to the level of monitoring demanded by the
governance body and provides feedback to the governance body regarding the level of
monitoring required. Despite this important role that external auditors play, there is relatively
little (albeit growing) research that investigates auditing phenomena from the governance
perspective. More research is warranted to provide insight in this area.
26
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28
Appendix: Variable Definitions
Variable Definition
Vio_Pre Dummy variable that takes the value of 1 if the firm reports a new covenant violation in the immediately following year and the value of 0 otherwise.
Vio_Event Dummy variable that takes the value of 1 if the firm reports a new covenant violation in the current year and the value of 0 otherwise.
Vio_PostY1 Dummy variable that takes the value of 1 if the firm reports a new covenant violation in the immediately preceding year and the value of 0 otherwise.
Vio_PostY2 Dummy variable that takes the value of 1 if the firm reports a new covenant violation two years before the current year and the value of 0 otherwise.
Vio_PostY3 Dummy variable that takes the value of 1 if the firm reports a new covenant violation three years before the current year and the value of 0 otherwise.
Audit Fee Audit fees in thousands of US dollars.
LN(Audit Fee) The natural logarithm of audit fees (in thousand $).
Assets Total assets in millions of dollars.
LN(Assets) The natural logarithm of total assets (in millions $).
SOX Dummy variable which takes the value of 1 if the firm is an accelerated filer (filer status reported in Audit Analytics) and the year is 2004 or later, and 0 otherwise.
BIG4 Dummy variable that takes the value of 1 if the firm's auditor is big 4 and 0 otherwise.
Fraud Risk Accounting fraud indictor variable calculated according to Doogar et al. (2010). FRSK equal to 1 if auditee fraud risk score is higher than 1 and 0 otherwise.
LN(Segment) The natural logarithm of the number of segments (see Doogar et al., 2010).
Foreign Dummy variable that takes the value of 1 if the auditor reports foreign currency transaction and 0 otherwise.
ROA Net income/total assets.
Loss Dummy variable that takes the value of 1 if net income is negative and 0 otherwise.
INVREC Sum of Inventories and accounts receivables divided by total assets.
Leverage Total liabilities/total assets.
Busy Dummy variable that takes the value of 1 if fiscal year end in January or December and the value of 0 otherwise.
Delay Dummy variable that takes the value of 1 if number of days delayed is greater than statutory filing period and the value of 0 otherwise.
Going Concern Dummy variable that takes the value of 1 if auditor issues 'going concern' opinion and the value of 0 otherwise.
29
Market to Book Market value of equity/book value of equity.
Current Ratio Current assets/total assets.
Quick Ratio (Current assets - inventory)/current liabilities.
Specialist Dummy variable which takes the value of 1 if the audit firm receives the highest audit fee revenue from the corresponding two-digit SIC code during the year and 0 otherwise.
SP Rating SP rating long term dummy, ranging from 1 through to 21 corresponding to the lowest quality S&P rating to the highest quality S&P rating.
Institutional Ownership Percentage of common stock owned by institutional investors.
Positive_Return Dummy variable that takes the value of 1 if the firm is a violating firm and the firm's stock return during the violation year is positive and the value of 0 otherwise.
Negative_Return Dummy variable that takes the value of 1 if the firm is a violating firm and the firm’s stock return during the violation year is negative and the value of 0 otherwise.
Positive_ROA Dummy variable that takes the value of 1 if the firm is a violating firm and reports a positive ROA during the violation year and the value of 0 otherwise.
Negative_ROA Dummy variable that takes the value of 1 if the firm is a violating firm and reports a negative ROA during the violation year and the value of 0 otherwise.
Positive_CFO Dummy variable that takes the value of 1 if the firm is a violating firm and reports positive operating cash flows during the violation year and the value of 0 otherwise.
Negative_CFO Dummy variable that takes the value of 1 if the firm is a violating firm and reports negative operating cash flows during the violation year and the value of 0 otherwise.
30
Table 1. Summary Statistics Table 1 reports the summary statistics for variables used in the empirical analysis. See Appendix for variable definitions.
Variable N Mean SD P25 Median P75 Min Max
Audit Fee (in thousands) 24390 1015.16 2559.03 137.42 336.94 914.56 1.72 67732.4
LN(Audit Fee) 24390 5.91 1.35 4.92 5.82 6.82 0.54 11.12
Assets (in millions) 24390 2141.07 7704.9 55.36 259.07 1075.82 0.09 92803.76
LN(Segment) 24390 1.28 0.68 0.69 1.39 1.79 0 3.33
Foreign 24390 0.1 0.31 0 0 0 0 1
ROA 24390 -0.01 0.28 -0.02 0.06 0.12 -1.75 0.37
Loss 24390 0.37 0.48 0 0 1 0 1
INVREC 24390 0.3 0.2 0.14 0.27 0.42 0 1
Market to Book 23473 2.68 4.94 1.06 1.95 3.41 -17.46 32.55
Leverage 24390 0.57 0.42 0.33 0.51 0.69 0.07 3.1
Current Ratio 23898 0.51 0.24 0.32 0.5 0.69 0 1
Quick Ratio 23916 1.82 3.88 0.7 1.14 1.94 0 264
SP Rating 22597 3.21 5.35 0 0 8 0 21
Fraud Risk 24390 0.31 0.46 0 0 1 0 1
BIG4 24390 0.73 0.44 0 1 1 0 1
Specialist 24390 0.23 0.42 0 0 0 0 1
Busy 24390 0.72 0.45 0 1 1 0 1
Delay 24390 0.22 0.42 0 0 0 0 1
Going Concern 24390 0.07 0.25 0 0 0 0 1
SOX 24390 0.31 0.46 0 0 1 0 1
31
Figure 1: Audit Fees and Covenant Violations
Notes: Figure 1 presents the mean audit fees around the event of covenant violation for the violating
firms in our sample. The vertical axis represents the mean of audit fees (scaled by firm size), and the
horizontal line represents the timing of the covenant violation between year -3 to 3 with negative numbers
representing pre-violation period and positive numbers representing post-violation period. Year 0
represents the violation year (the year in which a new covenant violation is reported).
0.0
01.0
02.0
03au
dit f
ee o
ver
firm
siz
e
-3 -2 -1 0 1 2 3
Audit Fees Before and After Covenant Violation
32
Figure 2: Audit Fee Trend for Matching Firms without Covenant Violations
Notes: Figure 2 graphs mean audit fees (scaled by firm size) for the hand-constructed subsample
of non-violating firms. For each firm-year observation representing a violating firm in our sample, we
select matching firm-year observations representing non-violating firms in our sample that meet the
following criteria: 1) from the same calendar year, 2) the matching firm has the same SIC code as the
violating firm and, and 3) the total assets of the matching firm are within +/-25 of the violating firm’s
total assets.
0.0
005
.001
.001
5au
dit f
ee o
ver
firm
siz
e
-3 -2 -1 0 1 2 3
Audit Fee Trend for Matching Firms w/o Covenant Violation
33
Table 2. Covenant Violations and Audit Fees: Immediate Impact This table presents OLS regression results on the effects of covenant violation on audit fees. Specifically, we examine the difference in audit fees between violating and non-violating firms one year before the violation event (column 1) and during the violation year (column 2). We use the natural logarithm of audit fees as the dependant variable. Variable definitions are illustrated in Appendix. Reported in the bracket are robust standard errors clustered at the firm level. *, **, *** indicate significance at the two-tailed 10%, 5% and 1% levels, respectively.
Independent Variables
(1)
(2)
Vio_Pre
-0.0211*
(0.0123)
Vio_Event
0.0410***
(0.0109)
LN(Assets)
0.3363***
0.3340***
(0.0154)
(0.0154)
LN(Segment)
0.1076***
0.1072***
(0.0174)
(0.0174)
Foreign
0.0212
0.0218*
(0.0130)
(0.0130)
ROA
-0.1784***
-0.1765***
(0.0330)
(0.0330)
Loss
0.0511***
0.0484***
(0.0105)
(0.0105)
INVREC
0.2918***
0.2840***
(0.0736)
(0.0736)
Market to Book
-0.0010
-0.0010 (0.0009) (0.0009) Leverage
0.1234***
0.1225***
(0.0262)
(0.0261)
Current Ratio
-0.1941***
-0.1889***
(0.0565)
(0.0565)
Quick Ratio
-0.0016
-0.0016
(0.0034)
(0.0034)
SP Rating
0.0001
0.0001
(0.0029)
(0.0029)
Fraud Risk
-0.0098
-0.0095
(0.0087)
(0.0087)
BIG4
0.1898***
0.1891***
(0.0179)
(0.0179)
Specialist
0.0178
0.0175
(0.0131)
(0.0131)
Busy
0.1460**
0.1445**
(0.0729)
(0.0726)
Delay
0.1202***
0.1186***
(0.0126)
(0.0126)
Going Concern
0.0720***
0.0736***
(0.0269)
(0.0269)
SOX
0.4111***
0.4105***
(0.0211)
(0.0211)
Firm Fixed YES YES Year Fixed YES YES N 21700 21700 R-sq 0.5873 0.5876
34
Table 3. Litigation Risk and Audit Fee Response This table presents OLS regression results with the natural logarithm of audit fees as the dependant variable. Specifically, we include positive vs. negative stock return dummies (column 1), positive vs. negative ROA dummies (column 2) and positive vs. negative cash flow dummies (column 3) to partition the violating firms into two groups. Coefficients are relative to the audit fees of non-violating firms. Variable definitions are illustrated in Appendix. Reported in the bracket are robust standard errors clustered at the firm level. *, **, *** indicate significance at the two-tailed 10%, 5% and 1% levels, respectively.
Independent Variables
(1)
(2)
(3)
Positive_Return
0.0508***
(0.0174)
Negative_Return
0.0338**
(0.0145)
Positive_ROA
0.0380***
(0.0145)
Negative_ROA
0.0449***
(0.0171)
Positive_CFO
0.0508***
(0.0134)
Negative_CFO
0.0217
(0.0194)
LN(Assets)
0.3342***
0.3340***
0.3342***
(0.0154)
(0.0154)
(0.0154)
LN(Segment)
0.1072***
0.1072***
0.1073***
(0.0174)
(0.0174)
(0.0174)
Foreign
0.0219*
0.0219*
0.0216*
(0.0131)
(0.0130)
(0.0130)
ROA
-0.1768***
-0.1761***
-0.1776***
(0.0330)
(0.0331)
(0.0330)
Loss
0.0487***
0.0482***
0.0487***
(0.0106)
(0.0106)
(0.0105)
INVREC
0.2843***
0.2838***
0.2863***
(0.0736)
(0.0736)
(0.0737)
Market to Book
-0.0010
-0.0010
-0.0010
(0.0009)
(0.0009)
(0.0009)
Leverage
0.1225***
0.1226***
0.1224***
(0.0261)
(0.0261)
(0.0261)
Current Ratio
-0.1891***
-0.1886***
-0.1891***
(0.0565)
(0.0565)
(0.0565)
Quick Ratio
-0.0016
-0.0016
-0.0016
(0.0034)
(0.0034)
(0.0034)
SP Rating
0.0001
0.0001
0.0001
(0.0029)
(0.0029)
(0.0029)
Fraud Risk
-0.0096
-0.0095
-0.0093
(0.0087)
(0.0087)
(0.0087)
35
Table 3 Continued
Independent Variables
(1)
(2)
(3)
BIG4
0.1891***
0.1890***
0.1890***
(0.0179)
(0.0179)
(0.0179)
Specialist
0.0175
0.0176
0.0175
(0.0131)
(0.0131)
(0.0131)
Busy
0.1447**
0.1443**
0.1450**
(0.0727)
(0.0726)
(0.0727)
Delay
0.1186***
0.1185***
0.1186***
(0.0126)
(0.0126)
(0.0126)
Going Concern
0.0740***
0.0736***
0.0738***
(0.0270)
(0.0269)
(0.0270)
SOX
0.4102***
0.4104***
0.4101***
(0.0211)
(0.0211)
(0.0211)
T-test (p-value)
Positive_Return =Negative_Return
Positive_ROA
=Negative_ROA
Positive_CFO =Negative_CFO
0.4567
0.7588
0.2198
Firm Fixed Effect
YES
YES
YES
Year Fixed Effect
YES
YES
YES
N
21700
21700
21700
R-sq 0.5884 0.5884 0.5884
36
Table 4. Propensity Score Analysis This table reports the results from the propensity score analysis comparing the audit fees for violating firms and non-violating firms. The propensity scores are estimated by a probit model. The dependent variable for the probit model is a dummy variable that equals 1 if the firm reported a new covenant violation in the current year and 0 otherwise. The independent variables include firm size, profitability, leverage, growth, segments, foreign assets ratio, auditee loss indicator for net income, inventory and receivable over total assets, current and quick ratio, credit rating, fraud risk, big-four auditor dummy variable, specialist, busy, and delay indicator, going concern opinion, SOX indicator, firm and year fixed effects. See Appendix for the definition of the independent variables included in the probit model. Column 1 presents the matching methods. Matching estimators are nearest neighbour matching using n loans without covenant violations (where n=10 in our analysis), radius matching, Kernel-based matching techniques (Epanechnikov kernel functions), and bootstrap matching (with 100 replications), respectively. Column 2 provides the sample averages of the audit fee differences between covenant violating firms and non-violating firms. Column 3 reports p-values. ***, **, * indicate significantly different than zero at the 1%, 5%, and 10% levels, respectively.
(1) (2) (3)
Matching method Difference p-value
Nearest neighbor 0.07 0.029**
Radius matching 0.069 0.025**
Kernel-based (Epanechnikov) 0.039 0.21
Bootstrap matching 0.085 0.051*
37
Table 5. Audit Fee Response to Covenant Violations: Long-term Impact This table presents OLS regression results using a dynamic model with the natural logarithm of audit fees as dependant variable. Variable definitions are illustrated in Appendix. Reported in the bracket are robust standard errors clustered at the firm level. *, **, *** indicate significance at the two-tailed 10%, 5% and 1% levels, respectively.
Independent Variables
(1)
(2)
(3)
(4)
Vio_Event
0.0834***
(0.0135)
Vio_PostY1
0.0583***
0.1086***
(0.0110)
(0.0152)
Vio_PostY2
0.0408***
0.0891***
(0.0106)
(0.0145)
Vio_PostY3
0.0133
0.0520***
(0.0109)
(0.0131)
LN(Assets)
0.3345***
0.3361***
0.3360***
0.3307***
(0.0154)
(0.0154)
(0.0154)
(0.0154)
LN(Segment)
0.1074***
0.1068***
0.1072***
0.1060***
(0.0174)
(0.0174)
(0.0174)
(0.0173)
Foreign
0.0209
0.0214
0.0215*
0.0200
(0.0130)
(0.0131)
(0.0130)
(0.0129)
ROA
-0.1765***
-0.1798***
-0.1785***
-0.1776***
(0.0330)
(0.0330)
(0.0330)
(0.0328)
Loss
0.0498***
0.0524***
0.0525***
0.0407***
(0.0104)
(0.0104)
(0.0104)
(0.0104)
INVREC
0.2895***
0.2896***
0.2894***
0.2782***
(0.0736)
(0.0736)
(0.0736)
(0.0732)
Market to Book
-0.0010
-0.0010
-0.0010
-0.0011 (0.0009) (0.0009) (0.0009) (0.0009) Leverage
0.1217***
0.1220***
0.1235***
0.1160***
(0.0261)
(0.0261)
(0.0262)
(0.0258)
Current Ratio
-0.1912***
-0.1927***
-0.1925***
-0.1825***
(0.0565)
(0.0565)
(0.0565)
(0.0565)
Quick Ratio
-0.0016
-0.0016
-0.0016
-0.0017
(0.0034)
(0.0034)
(0.0034)
(0.0033)
SP Rating
0.0002
0.0002
0.0001
0.0010
(0.0029)
(0.0029)
(0.0029)
(0.0029)
Fraud Risk
-0.0087
-0.0101
-0.0098
-0.0076
(0.0087)
(0.0087)
(0.0087)
(0.0087)
BIG4
0.1886***
0.1896***
0.1896***
0.1890***
(0.0179)
(0.0179)
(0.0179)
(0.0178)
Specialist
0.0171
0.0169
0.0176
0.0142
(0.0131)
(0.0131)
(0.0131)
(0.0130)
Busy
0.1434**
0.1463**
0.1459**
0.1411*
(0.0730)
(0.0728)
(0.0728)
(0.0728)
Delay
0.1199***
0.1208***
0.1206***
0.1149***
(0.0126)
(0.0126)
(0.0126)
(0.0125)
Going Concern
0.0706***
0.0719***
0.0728***
0.0678**
(0.0269)
(0.0268)
(0.0269)
(0.0267)
SOX
0.4110***
0.4107***
0.4110***
0.4085***
(0.0211)
(0.0211)
(0.0211)
(0.0210)
Firm Fixed Effect
YES
YES
YES
YES
Year Fixed Effect
YES
YES
YES
YES N
21700
21700
21700
21700
R-sq 0.5880 0.5876 0.5872 0.5905
38
Table 6. Effect of Lender and Shareholder Monitoring This table presents OLS regression results with sets of interaction terms (the violation dummy variables with Leverage or Institutional Ownership). The dependent variable is the natural logarithm of audit fees. All variable definitions are illustrated in Appendix. Reported in the bracket are robust standard errors clustered at the firm level. *, **, *** indicate significance at the two-tailed 10%, 5% and 1% levels, respectively.
Independent Variables
(1)
(2)
Vio_Event
0.0422*
0.0448*
(0.0235)
(0.0229)
Vio_PostY1
0.0556**
0.0727***
(0.0245)
(0.0259)
Vio_PostY2
0.0512**
0.0814***
(0.0244)
(0.0253)
Vio_PostY3
0.0510**
0.0069
(0.0226)
(0.0225)
Leverage*Vio_Event
0.0685**
(0.0319)
Leverage*Vio_PostY1
0.0859***
(0.0319)
Leverage*Vio_PostY2
0.0616*
(0.0335)
Leverage*Vio_PostY3
0.0022
(0.0317)
Institutional Ownership*Vio_Event
0.0790*
(0.0431)
Institutional Ownership*Vio_PostY1
0.0925*
(0.0502)
Institutional Ownership*Vio_PostY2
0.0108
(0.0492)
Institutional Ownership*Vio_PostY3
0.0907**
(0.0428)
Institutional Ownership
0.1066**
(0.0422)
LN(Assets)
0.3286***
0.3225***
(0.0154)
(0.0180)
LN(Segment)
0.1054***
0.0914***
(0.0173)
(0.0183)
Foreign
0.0203
0.0164
(0.0129)
(0.0133)
ROA
-0.1782***
-0.1476***
(0.0326)
(0.0387)
Loss
0.0403***
0.0424***
(0.0104)
(0.0113)
INVREC
0.2789***
0.3066***
(0.0730)
(0.0781)
Market to Book
-0.0012
-0.0017* (0.0008) (0.0009)
39
Table 6 Continued
Independent Variables
(1)
(2)
Leverage
0.0946***
0.1329***
(0.0270)
(0.0323)
Current Ratio
-0.1873***
-0.2890***
(0.0563)
(0.0590)
Quick Ratio
-0.0017
0.0006
(0.0033)
(0.0031)
SP Rating
0.0012
-0.0010
(0.0029)
(0.0030)
Fraud Risk
-0.0075
-0.0055
(0.0087)
(0.0094)
BIG4
0.1897***
0.1794***
(0.0178)
(0.0197)
Specialist
0.0148
0.0177
(0.0130)
(0.0130)
Busy 0.1418*
0.1887**
(0.0725)
(0.0740)
Delay 0.1141***
0.1268***
(0.0125)
(0.0138)
Going Concern 0.0630**
0.0778***
(0.0266)
(0.0297)
SOX 0.4083***
0.3652***
(0.0210)
(0.0237)
Firm Fixed Effect
YES
YES
Year Fixed Effect YES
YES
N 21700
18635
R-sq 0.5910 0.6102
40
Table 7. Audit Committee Independence and Meeting Frequency This table presents OLS regression results with audit committee independence (column 1) and audit committee meeting frequency (column 2) as dependant variables, respectively. The audit committee independence is measured as the number of independent directors on the audit committee. The audit committee meeting frequency is measured as the number of audit committee meetings during the year. Other variable definitions are illustrated in Appendix. Reported in the bracket are robust standard errors clustered at the firm level. *, **, *** indicate significance at the two-tailed 10%, 5% and 1% levels, respectively.
Independent Variables
(1)
(2)
Vio_Pre
0.0144
-0.0421
(0.0248)
(0.1032)
Vio_Event
0.0231
0.2173*
(0.0260)
(0.1174)
Vio_PostY1
0.0675**
0.4631***
(0.0263)
(0.1200)
Vio_PostY2
0.0312
0.3116***
(0.0239)
(0.1167)
Vio_PostY3
0.0033
0.1471
(0.0211)
(0.0999)
LN(Assets)
0.0631***
0.2972***
(0.0221)
(0.0834)
LN(Segment)
-0.0077
0.1097
(0.0253)
(0.1020)
Foreign
-0.0065
0.0143
(0.0224)
(0.0939)
ROA
-0.0337
-0.1007
(0.0461)
(0.2024)
Loss
-0.0117
0.1223
(0.0166)
(0.0771)
INVREC
0.0839
0.2421
(0.0961)
(0.4363)
Market to Book
0.0016
-0.0116**
(0.0013)
(0.0057)
Leverage
-0.0513
0.0396
(0.0411)
(0.1707)
Current Ratio
-0.0055
-0.1363
(0.0718)
(0.3358)
Quick Ratio
-0.0013
-0.0227**
(0.0016)
(0.0111)
SP Rating
-0.0038
-0.0334
(0.0050)
(0.0238)
Fraud Risk
-0.0079
-0.1186**
(0.0150)
(0.0599)
BIG4
0.0095
0.0172
(0.0256)
(0.1133)
Specialist
-0.0110
0.0467
(0.0224)
(0.0930)
41
Table 7 Continued
Independent Variables
(1)
(2)
Busy
0.0245
0.0809 (0.1009)
(0.3902)
Delay
-0.0014
0.0983
(0.0163)
(0.0868)
Going Concern
-0.0282
0.0973
(0.0361)
(0.1599)
SOX
0.0492*
0.9370***
(0.0297)
(0.1294)
Firm Fixed Effect
YES
YES
Year Fixed Effect
YES
YES
N
19204
17878
R-sq 0.0557 0.3404