Bank Monitoring and Accounting Recognition:
The case of aging-report requirements
Richard Frankel
Olin Business School
Washington University in St. Louis
Campus Box 1133
One Brookings Drive
St. Louis, MO 63130-4899
frankel@wustl.edu
Bong Hwan Kim
American University
Kogod School of Business
4400 Massachusetts Avenue, NW
Washington, DC 20016
bkim@american.edu
Tao Ma
Moore School of Business
University of South Carolina
1705 College Street
Columbia, SC 29208
Tao.Ma@moore.sc.edu
Xiumin Martin
Olin Business School
Washington University in St. Louis
Campus Box 1133
One Brookings Drive
St. Louis, MO 63130-4899
xmartin@wustl.edu
First draft: December 2010
Revised: September 31 2011
We thank seminar participants at the University of Chicago.
Bank Monitoring and Accounting Recognition:
The case of aging-report requirements
Abstract
We study changes in borrower accounting recognition surrounding initiation of loans requiring
the provision of aging schedules to the lender. Our purpose is to understand how scrutiny by
lenders of underlying transactions affects financial reporting incentives. We find that allowance
for doubtful accounts increases significantly after loan initiation controlling for current and
future write-offs, receivable turnover, and the beginning allowance balance. This increase is
more pronounced for loans with increased monitoring frequency. We also find that write-offs
are less persistent following implementation of bank monitoring, consistent with increased
timeliness. Further study of the customer base finds customer concentration declines and credit
quality of largest customers improves after initiation of borrowing base loans. Lastly, we find
borrowers increase the frequency of allowance-for-doubtful-accounts disclosure in their
quarterly financial statements after loan initiation. Our results confirm two notions. Banks add to
the oversight that already exists for public, audited companies and banks influence borrowers to
adopt more conservative accounting policies.
JEL: G14, G21, G24, G28
Key Words: Bank monitoring, borrowing-base loan, aging-report, write-off
I. Introduction
We study whether bank monitoring affects accounting recognition. We identify
loan contracts with covenants requiring the borrower to provide periodic accounts
receivable aging reports to the lender and measure changes in the borrower’s recognition
of allowance for doubtful accounts before and after loan initiation. We also examine
whether these changes are related to monitoring intensity measured via frequency of
aging reports. We find that borrowers report higher allowance balances after borrowing,
and the increase is more pronounced for loans when transmittal of aging reports is more
frequent. Persistence of write-offs declines after borrowing—an indication that writeoffs become more timely. Changes in accounting policy could reflect more lenient
borrower trade-credit policies.
However, after an initial borrowing-base loan, sales
attributable to customers accounting for ten percent or more of sales declines and the debt
ratings of these large customers improves.
Overall, our evidence suggests that
accounting policies become more conservative after initiation of aging report
requirements and this change is not explained by the change in underlying economic
factors.
Our purpose is to understand how the initiation of bank monitoring affects
financial reporting choices. Loans that require aging reports are an excellent setting for
such investigation. This venue enables us to formulate a hypothesis that directs us to
study a particular accounting policy and to use a test calibrated by our understanding of a
specific account. The majority of loans with aging report requirement in our sample are
borrowing base revolvers using accounts receivable as collateral and/or to determine the
maximum loan amount (Flannery and Wang 2011). Traditionally these loans are
1
considered to be risky (Vinter 1998, p. 371). To control the credit risk, lenders request
timely financial reports and aging schedules from the borrower to assess the quality of
borrowing base assets.
Research finds a relation between measures of borrower accounting quality and
the monitoring or screening of higher-reputation banks (Ahn and Choi 2009; Bushman
and Wittenberg-Moerman 2010). Mester et al. (2007) study receivable and inventorybased loans made by one bank to small, management-owned firms. They find banks use
transactions accounts related to these loans to monito borrowers. In this study, we
integrate these two lines of research and test the connection between bank monitoring and
financial reporting
Prior research suggests borrowers’ accounting policies are shaped by lender
preference (e.g., Ahmed et al. 2002; Watts 2003; LaFond and Watts 2008; Khan and
Watts 2009; Frankel and Roychowdhury 2009; Bushman and Wittenberg-Moerman;
2010). However, these papers do not specify and empirically examine the institutional
means used by lenders to independently verify that the borrower is using conservative
accounting. Our study illuminates a communication channel that lenders use to obtain
information necessary to monitor whether borrower accounting choices are consonant
with lender preferences (e.g., Watts 2003). Hence, our paper contributes to this line of
research by providing empirical evidence that banks’ preferences can shape borrower
accounting choices.
By studying the initiation of loans that require an aging report be supplied to
lenders, we isolate borrowers facing bank scrutiny of a specific balance sheet item
(Banrett 1997). The focus on a specific account permits us to construct a tailored model
2
for predicted accruals (McNichols and Wilson 1988; Jackson and Liu 2010). We also
broaden the results of Mester et al. (2007) who study a set of small, private firms by
focusing on a population of audited, public companies that is typically examined by
researchers attempting to determine whether bank loans are associated with enhanced
monitoring or screening (e.g., Mikkelson and Partch 1986; James 1987; Lummer and
McConnell 1989; Best and Zhang 1993; Billet, Flannery et al. 1995).
The majority of loans in our sample are borrowing base revolvers using accounts
receivable as collateral and/or to determine the maximum loan amount (Flannery and
Wang 2011). The total accounts receivable used to compute the borrowing base is
determined by using accounts listed in the aging report which corresponds the reported
value of gross receivables on the balance sheet. Financial reporting decisions are also
likely to be of concern to the lender because loans often contain financial covenants and
because accruals provide ‘hard’ evidence to support discretionary adjustments to the
borrowing based allowed to the lender in the loan agreement. Given the information
available to the lender, a borrower wishing to maintain a lending relationship faces
increased costs when understating reserves.1 Thus, on average, we expect borrowers to
increase allowance account levels after loan initiation.
Changes in allowance account levels can also reflect changes in underlying
default risk of credit customers. If banks prefer borrowers to have a diversified portfolio
of credit customers and borrowers alter their customer portfolios accordingly, then this
1
See Bolton and Scharfstein (1990) for a model supporting the notion that borrowers have an incentive to
repay loans even when cash flows cannot be observed by the lender (or verified by a third party), because
borrower would like to continue the relationship.
3
change can lead to a reduction in allowance account levels after loan initiation. 2
Alternatively, ‘unused fees’ and fixed costs associated with credit lines reduce the
incremental cost associated with additional borrowing once the line is established. This
cost structure could encourage borrowers to provide financing for customers, thereby
relaxing credit policies. The possibility of changing default risk associated with the
initiation of borrowing requires us to control for this effect. 3
Using a key word search of SEC filings between 1994 and 2006, we identify 248
firms with loan contracts requiring the borrower to periodically supply the lender with an
accounts receivable aging report. 4 Our sample size is limited and contains one
observation per firm, because we include only the first aging-report-requirement loan for
each firm in our sample to capture incremental affects associated with a transition to a
regime of increased lender monitoring. We denote the year of loan initiation as year t.
Pooling data between years t-2 and t+1, we regress current allowance for doubtful
accounts on the current balance in the accounts receivable, current and future period
write-offs and other control variables. Our main variable of interest is an indicator equals
one in year t and t+1.
We find that the allowance balance increases by 0.3% of sales after loan initiation.
Because this balance averages 1.3% of sales prior to loan initiation, a 0.3% change
2
Limits are typically imposed on the amount that can be due from any customer in the computation of the
borrowing base. In addition, banks often impose a cross-aging requirement on borrowers, rendering the
entire balance owed by a given customer ineligible for inclusion in the borrowing base if one invoice is past
due.
3
Other factors can affect the observed increased in bad debt expense. If firms engaging in asset-based
borrowing tend to overstate reported allowance for doubtful accounts prior to loan initiation (Jackson and
Liu 2010), then the observed increase will be muted. On the other hand, if bank lending coupled with bank
access to inside information, reduces demand for timeliness of financial reporting (Ball et al. 2000) and
increased allowance balances provide the means for income smoothing (Jackson and Liu 2009) then we
expect increases in bad debt expense recognition.
4
An aging report lists the amounts owed to the borrower by its credit customers and sorts these customer
balances by age outstanding.
4
implies a 23% increase. This increase understates accounting recognition effects because
write-offs also rise significantly in the year of loan initiation. Thus, the income statement
effects are magnified. Bad debt expenses increase by 3.8 million dollars, on average,
after loan initiation, an increase of more than 50%. Moreover, results show a significant
decline in the serial correlation of write-off changes. This suggests that firms adopt more
timely write-off policies after initiation of aging report requirements. Consistent with the
hypothesis that bank monitoring drives this increase, our results show that the increase in
the allowance balance is higher for firms required to provide aging reports on a weekly or
monthly basis than firms with a less frequent aging report requirements. Our results are
robust to inclusion of interactions for size and analyst following and to alternative scaling
variables (assets and accounts receivable) and inclusion of variables to control for
changes in credit policies (future write-offs and accounts receivable turnover). In fact,
credit policies seem to become stricter following monitoring. After loan initiation firms
make fewer sales to their most influential customers (i.e., customers accounting for more
than ten percent of sales) and the credit quality of these customers, measured via credit
ratings, improves. Our last set of analysis finds that borrowers increase the frequency of
allowance for doubtful account disclosure in their quarterly financial statements after
loan initiation. We attribute this finding to the enhancement of the firm’s accounting
system in response to the necessity of supplying aging reports by lenders.
Our study makes several contributions. First, our study advances research that
examines accounting conservatism arising from debt contracting efficiency because our
results suggest active monitoring by lenders of a specific revenue accrual account is
associated with more conservative accounting related to that accrual by borrowers. In this
5
respect, our study complements Tan (2010) who documents increased accounting
conservatism after covenant violation and conjectures that this effect results from stepped
up bank monitoring. In addition, our study suggests that information intermediaries such
as analysts and auditors do not substitute for the unique monitoring effect of banks.
Our study also provides empirical support for the theory that banks have an
advantage in obtaining or producing private information about borrowers (LeLand and
Pyle 1977; Diamond 1984; Fama 1985). Specifically, we document effects arising when
banks obtain detailed information on borrowers’ accounts receivables that is otherwise
not observable by outside investors. Further, our results offer an explanation for the
mixed findings in the literature examining managers’ earnings management incentives to
avoid covenant violations.5 Perhaps, bank access to information can deter managers from
manipulating accounting information to avoid debt covenants. Our results demonstrate
that banks’ active monitoring can reduce such incentives of managers, highlighting the
importance of controlling for banks’ monitoring when examining debt covenant
hypothesis.
II. Background
Relation to Literature
The presence of banks suggests they perform some function intermediating
between borrowers and savers more efficiently than is available via direct exchange in
capital markets. Our research question springs from theory implying the uniqueness of
banks’ monitoring.
Research argues that banks enjoy a comparative advantage in
5
The empirical results on debt covenant hypothesis are mixed. DeFond and Jiambalvo (1994) and
Sweeney (1994) find managers use discretionary accruals or income-increasing accounting changes to
avoid debt covenant constraints. Healy and Palepu (1990) and DeAngelo et al. (1994) do not find support
for the debt covenant hypothesis.
6
producing information that enables them to add value via debt-related monitoring (e.g.,
Diamond 1984). For example, Fama (1985) classifies bank debt as an insider debt
because banks have access to information from an organization’s decision process not
otherwise publicly available. Researchers have sought evidence of bank monitoring in the
reaction of borrower’s stock to loan announcements. They find significant positive
reactions. The results suggest banks have access to non-public information that allows
them to screen or to subsequently monitor borrowers (e.g., Mikkelson and Partch 1986;
James 1987; Lummer and McConnell 1989; Best and Zhang 1993; Billet et al. 1995).
Some studies (i.e., Lummer and McConnell 1989; Best and Zhang 1993) distinguish
between new loans and revisions of existing agreements and find a significant positive
reaction only for agreements that are revised favorably. This result implies that banks
gain an information advantage only after they establish a relationship with borrowers.
These studies do not provide evidence on the methods used by banks to acquire private
information.
Mester et al. (2007) fill this gap, by studying loans made by a Canadian bank to
small, management-owned firms. These borrowers maintain a checking account at the
bank and are required to provide the bank with accounts receivable and inventory
information. Mester et al. find that the transaction information available to the bank
predicts credit down grades, loan write-downs and loan reviews. Thus, the bank acts ‘as
if’ it uses transaction information to assess its loans. Related work analyzing credit lines
suggests credit line usage by borrowers reflects default risk (Jimenez et al. 2009; Sufi
2009) and predicts default (Norden and Weber 2010). The implication is that credit line
usage gives banks private information on their borrowers.
7
We link research on bank monitoring to accounting policy. We test the joint
hypothesis that bank monitoring activities produce effects and that these effects, in turn,
lead to observable alterations in borrowers’ accounting policies. The relation between
bank monitoring and accounting policy emerges from research indicating that lenders
demand conservatism.
Watts (2003) argues that debt financing spurs demand for conservative accounting.
Studies of the relation between accounting conservatism and debt financing infer debt
holders’ demand for conservatism by examining whether conservatism is correlated with
certain loan or firm characteristics. For example, Ahmed et al. (2002) document that
borrowers facing more severe debt holder-shareholder conflict are also more conservative.
Lafond and Watts (2008) and Khan and Watts (2009) find that asymmetric timeliness of
earnings is positively related to leverage. Other papers look for lender benefits associated
with conservatism. Zhang (2008) shows that accounting conservatism is associated with
both timely violations of loan covenants and lower costs of debt, suggesting that
accounting conservatism benefits both lenders and borrowers. Wittenberg-Moerman
(2008) finds that the bid-ask spread in the secondary loan market is lower for more
conservative borrowers. Nikolaev (2010) demonstrates that the intensity of covenant use
in the debt contracts is positively correlated with accounting conservatism and interprets
the positive association as evidence that debt holders demand conservatism to improve
the contracting efficiency of earnings-based covenants.6
The means by which accounting conservatism is enforced has not been explored.
A critical question is how banks assess whether accounting information provided by
6
In related research, Leftwich (1983) and Beatty et al. (2008) infer lender demand for accounting
conservatism via loan covenant computations.
8
borrowing firms is reliable. Absent covenant violations, lenders have no right to decide
accounting policies. That right resides with managers to whom it was granted by
shareholders. Researchers speculate that reputation and legal liability force borrowers to
maintain conservative accounting policies after borrowing (Beatty et al. 2008; Nikolaev
2010). Maintaining a high level of conservatism, however, is costly to borrowers because
such choices can reduce current bonuses or expedite covenant violations, enabling
lenders to exercise decision rights.7 A key component of enforcement is the ability to
verify compliance. 8 In this paper, we identify a set of loan contracts with covenants
requiring borrowers to provide accounts receivable aging reports to lenders. Such
covenants indicate that banks have access to information that allows them to assess the
conservatism of borrower accounting choices with respect to accounts receivable.
Aging Reports and Banks’ Monitoring of Accounts Receivable
In addition to requiring borrowers to maintain certain financial ratios and
providing timely public financial reports, lenders can also require borrowers to grant
access to detailed financial information that is not publicly available. Loan contracts can
contain covenants granting lenders the right to inspect and review all original business
transaction documents and discuss financial matters with managers and independent
auditors. We focus on one such covenant: banks’ requirement that borrowers provide
periodic accounts receivable aging reports.
7
Evidence supporting the debt covenant hypothesis suggests that managers of the borrowing firms have
incentives to reduce accounting conservatism after borrowings to avoid costly covenant violations (Watts
and Zimmerman 1986; DeFond and Jiambalvo 1994; Sweeney 1994; Dichev and Skinner 2002; Kim 2010).
8
The ability to verify a project’s returns reduces expected deadweight liquidation costs relative to a
contract which can only use an unconditional threat of liquidation to give the borrower incentives to repay
the debt (Diamond 1996).
9
The following excerpt from Kontron Mobile Computing Inc.’s 1998 syndicated
loan contract illustrates the aging report requirement:
Borrower agrees it will: (a) Furnish to Lender in the form satisfactory to
Lender: … (iv) Within 10 days after the end of each month, an aging of accounts
receivable together with a reconciliation in a form satisfactory to Lender and an
aging of accounts payable in form acceptable to Lender, both certified as true and
accurate by an officer of the Borrower;
While this covenant requires aging reports to be provided monthly. In our sample, the
frequency of aging report provision ranges from a weekly to annual basis.
The requirement to provide aging reports is usually associated with revolving
credit lines that use accounts receivable to determine the borrowing base of the loan (i.e.,
the maximum loan amount) and/or use accounts receivable as collateral. When accounts
receivable is used as a part of the borrowing base, covenants usually require the borrower
to provide periodic borrowing base certificates to the lender documenting the
computation of the borrowing base. This computation usually begins with total accounts
receivable from which various receivables are excluded to determine “eligible accounts
receivable.” Accounts commonly excluded are
(i)
(ii)
(iii)
(iv)
receivables more than 60 (90) days past the due (invoice) date,
receivables owed by the United States or any government agency,
receivables owed by affiliates or related parties,
receivables owed by a customer with at least 50% of receivables overdue,
and
(v) receivables owned by any one customer in excess of a limit set by the
borrower.
The borrowing base is commonly 85 percent of eligible receivables, but the lender
is allowed discretion to make further adjustments based on business conditions.9 Aging
reports can help banks verify the computation on the borrowing base certificate. In
9
These modifications to GAAP-based receivables are consistent with the findings in Leftwich (1983) that
debt contracts contain clauses that make conservative adjustments to GAAP-based accounting information.
10
addition to the aging reports, banks can require additional information from borrowers
regarding accounts receivable. The following excerpt is also from Kontron Mobile
Computing Inc’s 1998 contract:
Borrower agrees to furnish to Lender, at least weekly, schedules describing
Receivables created or acquired by Borrower (including confirmatory written
assignments thereof), including copies of all invoices to account debtors and
other obligors (all herein referred to as "Customers") … Borrower shall advise
Lender promptly of any goods which are returned by Customers or otherwise
recovered involving an amount in excess of $5,000.00. Borrower shall also advise
Lender promptly of all disputes and claims by Customers involving an amount in
excess of $5,000.00 and settle or adjust them at no expense to Lender.
Covenants also allow the lender to access borrower accounting records and confirm the
existence of receivables. The borrower pays the cost of this investigation. These
examples provide some flavor for the nature of the information available to lenders.
Although banks do not have direct control over firm’s accounting policy, the above
excerpts indicate banks have information necessary to accurately assess the reliability of
the borrowers’ accounting choices with respect to accounts receivable.
We argue that financial reports choices are important to the lender despite the
availability of other information.
First, lending agreements often contain financial
covenants.10 Second, financial statements provide information that permits third parties
(e.g., courts) to verify a state of nature has occurred and therefore can be used to justify
decisions that are potentially damaging to the borrower but that are permitted under
contract to the lender.
The reporting requirements associated with aging reports can have a direct effect
on borrowers’ accounting systems. According to BHF-Bank,
10
In a review of 30 randomly selected loan agreements from our sample, all contained accounting-based
covenants.
11
Our customers have confirmed that the borrowing base reports and our audits are
very useful from a practical point of view as they can significantly enhance the
data in their finance and accounting departments. Many of our customers believe
that our audit and borrowing base reports provide an important external analysis
of their flows of goods and cash, as the reports consistently reveal areas in which
they can optimize their companies’ business operations, both in economic and
legal terms.11 , 12
Heightened legal liability on the part of auditors and borrowers associated with accounts
receivable can also accompany the provision of aging reports. Auditors are aware that an
outside party is independently assessing the quality of accounts receivable and
documenting the age and collectability of accounts. Executives are required to certify
reports to lenders. According to a white paper by managing directors at RSM McGladrey,
Knowledge of misrepresentations of these certifications can result in civil and/or
criminal charges and should be taken very seriously by borrowers.13
In short, legal liability associated with the provision of aging reports is likely to be
another factor that leads to more conservative accounting with respect to accounts
receivable.
III. Research Design
Our goal is to draw conclusions about the effect of bank monitoring on
accounting policy for firms in our sample. We do not make inferences about the effect
that provision of aging reports would have for the general population of borrowers.
Banks likely require aging reports for firms where gathering such data is cost effective,
11
See, ‘FAQ’ on borrowing-base loans.
http://www.bhfbank.com/w3/imperia/md/content/internet/financialmarketscorporates/borrowing_base_faq_en.pdf?teaser=/
w3/financialmarkets_corporates/commodity_finance/borrowing_base.
12
A conversation with a senior-vice president focusing on asset-based lending at a large US bank confirms
this statement and suggests information systems with respect to receivables become more sophisticated at
lower and middle market companies and managers become more aware of problems in receivable
collection when required to provide borrowing base reports.
13
See, “Reading the fine print: What borrowers need to know about loan agreements in the new
recession.” http://mcgladrey.com/pdf/loan_agreements.pdf.
12
so one can reasonably assume that any effects related to provision of aging reports are
likely more pronounced in our sample than in the general population. We concentrate on
the effects within the selected sample precisely because it provides a more powerful
setting to observe the impact of bank monitoring of accounts receivable. Our econometric
concern with regard to factors associated with the decision to provide an aging report
therefore centers around variables jointly associated with this decision and the reported
level of bad debt expense. For example, if a manager decides to borrow from banks to
ease potential capital constraints arising from expected deterioration of customer credit in
the future, bad debt expenses will increase to reflect his expectation of lowered
collectability of accounts receivable rather than bank oversight associated with the loan.
We estimate the following model:
ALLOWit = β0 + β1 × POSTt + β2 × ARit + β3 × ALLOWit-1 + β4 × WOit
+ β5 × WOit+1 + β6 × LEVit + β7 × ARTO_INDjt
+ β8 × SD_SALE_INDjt + β9 × ALT_INDjt
+ β10 × AFjt+ β11 × ASSETjt+ ΣFIRMi +ΣYEARt + εit,
(1)
where POST is a dummy variable equal to 1 if a firm-year falls in the year of or the year
after loan initiation, and 0 otherwise. The coefficient on POST (β1) is expected to be
positive reflecting the effect of bank monitoring on borrowers’ recognition of allowance
for doubtful accounts. To control for time-varying within-firm factors that simultaneously
cause firms to obtain a bank loan with an aging report requirement and drive bad debt
expense levels, we include control variables for factors that can affect firms’ bad debt
expense recognition. Following McNichols and Wilson (1988) and Jackson and Liu
(2010), we include accounts receivable (AR), prior year’s allowance for bad debt
expenses (ALLOW), contemporaneous and future write-off of accounts receivable (WO).
The variables AR, ALLOW, and WO are all scaled by contemporaneous sales. As accounts
13
receivable increases (reducing receivable turnover), we expect the balance of allowance
for doubtful accounts to increase. As the current and future write-off of accounts
receivable increase, the allowance should increase in expectation of increased credit risk.
On the other hand, the bad debt expenses will be lower if previous year’s allowance is
high. Hence, we expect the coefficient β2, β4, and β5 to be positive and the coefficient β3
to be negative.
We also include firm leverage (LEV) to control for other effects of borrowing on
reporting incentives unrelated to monitoring of receivables. For example, managers’ can
inflate earnings (decrease bad debt expenses) to avoid covenant violations (Defond and
Jiambalvo 1994) or managers can have an incentive to increase conservatism given
leverage in the absence of explicit bank monitoring. LEV is defined as total debt to assets.
To further control for factors associated with changes in the expected frequency
of credit-customer defaults, we include controls for industry factors such as industry
receivable turnover (ARTO_IND), industry standard deviation in sales (SD_SALE_IND),
and industry bankruptcy risk (ALT_IND). Analyst following (AF) and total assets (SIZE)
are also included to control for monitoring changes related to firm size or associated with
financing. In addition, we select a set of firms matched with our test firms based on
industry and receivable levels to control for market and industry-wide factors.
We also rely on the cross-section frequency of transmittal of aging report to
investigate whether results are consistent with increased bank monitoring. Specifically,
we examine whether the change in allowance of doubtful accounts after borrowing vary
with the intensity of banks’ monitoring. We partition the test sample into two subsamples
with high and low frequency of aging report, respectively, and then estimate Equation (1)
14
for these two subsamples separately. We define high frequency (HIGHFREQ) equal to 1
if banks require the borrower to provide aging reports at a monthly or weekly basis and
zero otherwise. 14 The coefficient on POST is expected to be greater for the high
frequency subsample than for the low frequency subsample.
IV. Sample Selection, Descriptive Statistics, and Univariate Analysis
Sample
Test sample
We search the material contract sections of filings with the Securities and
Exchange Commission using 10K Wizard to obtain the initial sample of loans containing
covenants requiring aging reports from 1994 to 2006. 15 Our sample period begins in
1994 because 10K Wizard started providing material contracts only after 1994. We end
the sample in 2006 to provide data on post-borrowing variables. We merge this initial
loan sample with the Compustat by firm CIK number. We require each firm to have
financial information on Compustat for the two years before (t-2 and t-1) and two years
after (t and t+1) loan origination where t represents the fiscal year that a loan is originated.
This procedure results in a sample of 1,657 debt contracts with aging report covenants for
803 unique firms. To measure the effect of initiation of aging report requirements, we
only keep the first loan contract with aging report covenants within our sample period for
each firm.16 To do this, we read all 1,657 debt contracts plus 10K and 10Q notes issued
14
We treat monthly or weekly aging reports as high frequency, because most bank contracts require
quarterly financial reports.
15
Specifically, we use the key word ‘aging’ and ‘receivable’ with the condition that the two words are
separated by less than five words. We review the contracts and exclude non debt contracts such as Stock
and Asset Purchase agreements and M&A agreements.
16
Because 10K Wizard started providing loan contract data after 1994, we are less confident that loans
from 1994 and 1995 are the first instance of an aging report requirement for the firm.
15
two years before the origination year to make sure that no similar contracts existed in the
past. Furthermore, we delete contracts that are the renewals of previous contracts with
similar aging report requirements signed before 1994 because we cannot obtain these
original contracts from 10K Wizard. After this procedure, 385 debt contracts from 385
unique firms remain.
Next, we collect data on bad debt expense and write-offs of accounts receivable
from Schedule II of 10K notes. To ensure the accuracy of our data, we reconcile the
beginning balance of the allowance with the ending balance of the allowance for each
firm year. Firms missing bad debt expenses or write-offs for the two years before or the
two years after loan origination are excluded from the sample. This data restriction
eliminates 137 contracts. Our final sample consists of 248 debt contracts with 992 firm
year observations (248 unique firms) spanning from 1992 to 2008.
Our tests use annually reported values of bad debt expenses and various control
variables. Therefore, if a loan is originated between nine months before and three months
after a fiscal year t, we treat the loan originated in year t.17
Control sample
To control for industry-wide factors affecting bad debt expense recognition, we
select a set of firms that match with our borrowing firms (test sample) based on the
following procedure. We begin with the Compustat universe that excludes our test firms
and excludes firms with a borrowing-base or collateralized loan identified from Loan
Pricing Corporation over our sample period. Second, we require the matching firms to be
17
We allow a three-month buffer because firms are required to release their 10Ks within three months after
the fiscal year-end and most firms disclose in financial statement footnotes the loans originated during the
period between the fiscal-year end and the release of 10K. Hence, we assume that loans originated within 3
months after the fiscal year-end affect borrowing firms’ accounting policies for that fiscal year.
16
in the same industry classified by two-digit SIC code as the test firm. Third, for firms that
survive the prior filters, we select the firm that has the smallest difference in accounts
receivable scaled by sales from the test firm (difference<20%). If we cannot find a
matching firm using this criterion, we relax the standard by increasing the difference to
forty percent and to sixty percent etc. If we find multiple matching firms that meet these
criteria, we select the one with the smallest difference in leverage from the test firm. In
the end, we are able to find 248 matching firms that also have bad debt expense and
write-off data available from the 10K.
Descriptive statistics
We first provide a time and industry profile of our sample borrowing firms. Panel
A of table 1 shows that the number of contracts in any given year ranges from 4 in 1994
to 42 in 2001. In particular, a total of 82 contracts (more than 32% of the entire sample)
cluster in 2000 and 2001 when business conditions are weak. This is consistent with the
observation of Rajan and Winton (1995) that collateral requirement varies inversely with
business conditions. To alleviate the concern that our results are driven by economic
conditions in a particular year, we include year fixed effects and other industry-wide
economic indicators in our empirical model. Panel B provides an industry profile of the
borrowing firms. As shown in the table, our sample represents a wide range of industries
and is similar to that shown in Flannery and Wang (2011). For example, manufacturing
industry is heavily represented in our sample (54%) compared to the Compustat Universe
firms (34.5%), but it is comparable to 51.2% reported by Flannery and Wang.
[Insert Table 1 Here]
17
Table 2 provides summary statistics on the loan characteristics for the 248 loan
contracts in our sample. As shown in panel A, the average loan amount is 52 million
dollars with a mean maturity of 2.7 years. Of the 248 loan contracts, 51% are syndicated
loans with more than one lender. The median cutoff is 90 (60) days from the invoice (due)
date of the receivables. 18 Hence, accounts receivable that are outstanding less than 90 (60)
days from the invoice (due) date of the receivables will only be considered as eligible
accounts receivable. Further, 82% of the eligible accounts receivables are used as part of
the borrowing base. Therefore, banks make several conservative adjustments to GAAPbased accounts receivables.
Panel B presents summary statistics on the purpose of aging reports. The most
common purpose (179 contracts) is to verify eligible accounts receivables to derive the
borrowing base. In some cases, accounts receivable serve as both the collateral and the
borrowing base (113). In 66 contracts accounts receivable is used as the borrowing base
but the contract does not provide a schedule of collateral so we cannot verify whether
accounts receivable is also used as the collateral. In 40 contracts, banks require aging
reports and accounts receivables are used as collateral against firm borrowings. 29
contracts require aging reports but provide no indication that accounts receivable are used
as collateral or to set the loan amount. In general, banks appear to require aging reports to
monitor collateral and limit loan amounts to collectible collateral rather than rely on
borrower operating performance.
18
Some contracts calculate the cutoff dates based on both the invoice date and the due date of the
receivables. We also collect data on the cutoff date banks use to calculate eligible accounts receivables
when borrowing firms use accounts receivables as part of the borrowing base. We identify 179 loan
contracts with eligible accounts receivables as borrowing base. Among these 179 loan contracts, 170 (67)
contracts use invoice dates (due dates) of accounts receivables as the base to derive cutoff dates.
18
Panel C of table 2 displays the variation in the periodicity of aging report
requirements. It varies from a weekly basis reports to annual reports. 33 contracts require
borrowing firms to provide aging reports upon lender request. Available information does
not allow us to determine the frequency of such requests. The majority contracts (164 or
66% of the entire contracts) require firms to provide monthly aging reports. This
contrasts with the quarterly financial disclosures to shareholders mandated by SEC.
Hence, lenders require more frequent disclosure of information for firms in our sample
than is available to shareholders.
[Insert Table 2 Here]
Table 3 presents correlations among the dependent variable of allowance for
doubtful accounts (ALLOW), firm characteristics, and loan characteristics for our test
sample. ALLOW and firm characteristics are measured at the fiscal-year end prior to the
loan origination year. Two statistics are noteworthy. First, allowance is positively
associated with return volatility and leverage, but negatively associated with return on
assets, cash flow from operation and the presence of analyst following. These results are
consistent with the intuition that bad debt expense estimate is affected by firm
performance and risk. Second, return volatility increases the frequency of aging reports.
In contrast, higher return on assets and operating cash flow, larger assets, and the
presence of analyst following decrease the frequency of aging reports.
[Insert Table 3 Here]
Univariate analysis
In this section, we present a univariate analysis examining the effect of bank
monitoring on borrowing firms’ bad debt expense recognition. We compare the changes
19
in allowance for doubtful accounts and bad debt expenses for both the borrowing firms
and the matching firms along with changes in other firm characteristics around
origination of loans that require aging reports. We assign the borrowing firm’s
origination date to that of its matching firm. Table 4 presents summary statistics on the
characteristics for the 248 borrowing firms and their 248 matching firms in the two years
both before and in the two years after the loan origination. Allowance for uncollectible
accounts receivables (ALLOW) increases after borrowing for the test sample from 0.013
to 0.015 though this increase is statistically insignificant. For the matching sample,
ALLOW does not change after borrowing. Accounts receivable (AR) decreases in the post
period for both groups. The mean BDX increases from 0.010 (1.0% of total sales) in the
pre-period to 0.012 (1.2% of total sales) in the post-period. The difference (0.002) is
statistically significant at the 5% level. In contrast, BDX decreases from 0.014 to 0.010
for the matching firms and this decrease is statistically significant at the .01 level.
However, the allowance balance increases with bad debt expense and declines with writeoffs. Table 4 indicates that writes-offs increase by 33% (0.004/0.012). These off-setting
effects blunt the increase in the allowance balance. There are no statistically significant
Pre/Post changes in leverage (LEV) between the test sample and the control sample,
which suggests that our matching procedure seems to hold constant accounts receivablebased lending to customers and borrowing across the two samples.
[Insert Table 4 Here]
Figure 1 illustrates the change in the allowance account balance in the four years
around loan origination for both the borrowing sample and the matching sample. The
amount for the borrowing sample starts increasing in t-1, one year before loan origination,
20
suggesting that firms expecting to borrow from banks start to adjust their accounting
policy even before the borrowing. The amount increases sharply in the loan origination
year t, and remains at a level higher than that in the pre-borrowing period. In contrast, the
allowance balance for the control sample drops in the year of loan origination and this
trend remains two years after loan origination, which could reflect overall increases in
credit quality in the customers of the industry.
[Insert Figure 1 Here]
The attenuated increase in ALLOW can be explained, in part, by the increases in
write-offs (WO) in the loan year. The increase in the write-offs can be either due to
reduced prospects for collectability, which could cause firms simultaneously to increase
bad debt expenses and borrow from banks, or changes in accounting policy. The second
explanation assumes the decision to write-off an account involves discretion by the
receivable holder and that active monitoring by banks can prod borrowers to write off
questionable accounts.19 Under this explanation, the write-offs are not a solely a function
of receivable collectability but are also subject to managers’ discretion. On the other
hand, if write-offs indicate future credit risk, a significant increase in write-offs after
borrowings is problematic for our empirical identification, because the increase can cause
a spurious correlation between a loan origination and an increase in bad debt expenses.
Inspection of the data indicates that the increase in write-offs occurs primarily in year t
and that write-off in years t+1 and t+2 resemble pre-loan levels. This evidence suggests
that the increase in write-offs is a temporary phenomenon that coincides with the year of
loan initiation. In any case, these results suggest the necessity of controlling for future
19
This is particularly possible when banks impose a cross-aging requirement on borrowers. Under these
circumstance a borrower would have an incentive to clear past due accounts out of the receivable ledger by
writing them off.
21
write-offs as well as industry performance to distinguish accounting policy changes from
credit quality changes.
A firm’s performance is likely to be negatively affected by deteriorating credit
quality of its customers. We compare borrowing firms’ operating performance to that of
control firms. As shown in the table, cash flows from operations (CFO) increase
significantly during the post-period for sample firms. Figure 2 also illustrates this point.
The mean values of CFO increases monotonically from 0.013 in t-2 to 0.032 in t+1,
suggesting firms’ operating performances improved after loan origination rather than
deteriorated. This increase contrasts with the control sample whose CFO declines; though
Table 4 indicates this decline is moderate.
[Insert Figure 2 Here]
In addition, Table 4 shows that total asset turnovers (SALES) remain the same
after borrowing. Compared to the matching firms, borrowing firms have higher revenue,
are smaller in size and are less likely to be followed by analysts. At the industry level, all
the changes in the economic indicator variables point in the same direction—that the
industries that borrowing firms belong to experience an improvement in the economic
performance. For example, the median accounts receivable turnover ratio at the industry
level (ARTO_IND) increases significantly; both the standard deviation of sales
(SALES_SD_IND) and Altman z-score (ALT_IND) at the industry level decrease
significantly.
V. Multivariate Analyses
Main regression results
22
Table 5 reports the results of testing the effect of bank monitoring on the
allowance account balance for the test sample in column (1) and for the sample
containing both the test firms and the control firms in column (2). The coefficient on
POST is positive and statistically significant in column (1), suggesting that after
borrowing, firms increase their allowance account balance significantly. In terms of
magnitude, borrowing firms experience an increase in allowance for doubtful accounts of
0.003 (0.3% of total sales). The increase is also economically significant. The average
of ALLOW in the period before borrowings is 0.013 (1.3% of total sales), and a change of
0.3% of sales in bad debt expenses represents a more than 23% increase. Given that the
average sales are $542 million, bad debt expenses increase by $1.6 million after
borrowing.
For the control variables, next period’s write-offs of accounts receivable are
positively correlated with ALLOW, suggesting that firm’s allowance balance reflects
expected credit quality of receivables. Current write-offs are negatively related to the
current allowance balance suggesting that the direct affect of write-offs on the current
allowance balance (write-offs reduce the allowance balance) exceeds any explanatory
power that current write-offs have for expected write-offs, conditional on future writeoffs. Alternatively it may suggest that the percentage credit sales method dominates the
aging method for calculating bad debt expenses in our sample, which results in a
mechanical, negative relation between write-offs and the allowance balance. The
coefficient on LEV is positive and only significant in the model that includes control
firms (β6 = 0.004 with a p-value of 0.007), providing evidence that managers tend to
increase the allowance balance as debt increases, consistent with a positive association
23
between leverage and accounting conservatism. The adjusted R-Square is 83%,
suggesting that the model explains a significant portion of the variation in allowance
balance, but this R-square also reflects the explanatory power of firm and year fixed
effects.
In column (2), the coefficient on POST continues to be positive and statistically
significant at the .05 level. In contrast, the coefficient on the interaction between POST
and CONTROL is negative and statistically significant at the .05 level and an F test of the
sum of POST and POST×CONTROL is not statistically significant. These results suggest
that test firms report an increased allowance balance after loan origination whereas this
does not occur to the matching firms. Therefore, the increase in the allowance balance for
the borrowing firms is not significantly related to industry-wide effects. In sum, we
document a significant increase in the allowance balance of firms that borrow with an
aging report requirement. We attribute this increase to bank monitoring of borrowers’
accounts receivable.
[Insert Table 5]
Bank monitoring and the persistence of write-offs
In the previous section we document that borrowers become conservative in that
they recognize more allowance for doubtful account after borrowing. In this section we
investigate whether write-offs become more conservative in the presence of bank
monitoring. Table 4 shows that write-offs increase significantly after borrowing. Thus,
isolating the allowance balance can understate the reporting implications of the
requirement to supply aging reports. We hypothesize that borrowers tend to write off
accounts receivable more fully after borrowing and this leads to a reduction in the
24
persistence of write-offs. The results of this analysis are reported in Table 6 for both the
test and the control firms. The coefficient on the lagged write-offs, WOt-1, is positive and
statistically significant suggesting that write-off is persistent during the pre-borrowing
period for both the test and the control firms. More importantly, the interaction between
POST and WOt-1 is negative and statistically significant at the .10 level for the test firms
only. For the control firms, this coefficient is negative but statistically indistinguishable
from zero. Therefore, consistent with our expectation borrowers are more conservative in
recognizing write-offs.
[Insert Table 6]
Cross-sectional analysis of bank monitoring intensity
In this section, we provide evidence on how the changes in the allowance balance
vary with banks’ monitoring intensity for our test sample. If monitoring is more intense
for loans requiring more frequent aging reports, we expect that the increase in allowance
will be greater for the high frequency subsample. Table 7 reports the results of this
analysis. The coefficient on POST is significant at the 0.05 level only in the high
frequency (HIGHFREQ=1) subsample. The increase in allowance for firms with low
monitoring frequency (quarterly or longer, including upon request) is 0.001 after loan
origination and insignificant. For firms with high frequency of aging reports (weekly or
monthly), the change in allowance is 0.004.
These results are consistent with the
hypothesis that monitoring intensity affects with the observed change in the allowance
balance after the initiation of the requirement to supply aging reports to the lender.
[Insert Table 7 Here]
The real effect of bank monitoring
25
Borrowing-base loans can affect borrower operating decisions, such as the choice
of customers, as well as its financial reporting policy. In discussing the nature of
adjustments made to the accounts receivable balance when it serves as the determinant of
a borrowing base, BHF bank states that “You can influence the amount deducted by
diversifying your accounts receivable and by employing a high-quality risk management
system.”20 Borrowing base loans are often subject to a “concentration cap” that limits the
inclusion of receivables by any one customer to specific percentage of the borrowing
base.21 We therefore test if borrowers reduce sales concentration to specific customers
and improve the credit quality of their customer portfolio.
We obtain all loan contracts using accounts receivable as either borrowing base or
collateral from DealScan for the period 1992–2008.22 We then identify and keep the first
loan contract for a firm over the sample period. We then merge these firms with the
COMPUSTAT Segment File to obtain significant customers.
The COMPUSTAT
Segment File contains information about sales to each customer reported by a supplier in
the footnotes under SFAS 14 and SFAS 131. To focus on significant customers we delete
customers with the percentage of sales less than 10%. We obtain borrowers’ financial
information for the six years centering on loan initiation. 23 After this procedure, we
obtain 5,129 loan-years for 1,148 unique loans and use this sample to examine the change
20
Cite from FAQ document produced by BHF Bank. http://www.bhfbank.com/w3/imperia/md/content/internet/financialmarketscorporates/borrowing_base_faq_en.pdf?teaser=/
w3/financialmarkets_corporates/commodity_finance/borrowing_base
21
For example, the borrowing base certificate contained in the December 16, 2010 Form S-4 of Interline
Brands Inc. excludes receivables of any one customer that exceed 15% of aggregate eligible receivables
from the borrowing base.
22
The merging of our main sample of borrowers with aging report requirement with the COMPUSTAT
Segment File leads to a reduced sample of 154 unique firms. The sample size is further reduced to 60 when
we require all customers to be public firms. Therefore to increase the sample size, thus the test power, we
employ Dealscan dataset for borrowing base loans. Based on the reduced sample, we find qualitatively
similar results as reported in Table 8 though they are not statistically significant.
23
We require each borrower to have at least one observation during both the pre and the post borrowing
period.
26
in borrowers’ sales concentration. To investigate whether the credit quality of customers
changes after borrowing, we identify each customer of a borrower and merge it with the
COMPUSTAT Industry Annual File by customer’s name to obtain monthly S&P
domestic long-term issuer crediting and other financial variables. After this procedure,
we identify 1394 loan-years for 445 loans. If a customer has no S&P domestic long-term
issuer credit rating, we follow the procedure outlined in Barth et al. (2008) and develop
a credit rating that falls within 2 (AAA) – 27 (default).24, 25
Table 8 reports the results of customer concentration and credit quality analyses.
Columns (1) and (2) focus on the change in borrowers’ sales concentration. The
dependent variable of column (1) is the average of sales percentage to each customer for
a borrower’s customer portfolio and the dependent variable of column (2) is the natural
logarithm of total number of significant customers for a borrower. We expect and find
that the coefficient on POST is negative and statistically significant across both columns
suggesting that borrowers diversify account receivable by reducing sales concentration to
significant customers. In column (3), the dependent variable is the average of credit
rating for a borrower’s customer portfolio with a higher value indicating lower credit
quality. As expected, the coefficient on POST is negative and statistically significant at
the .10 level implying that after loan initiation the credit quality of a borrower’s
customers improves. In sum, we find that borrowers diversify accounts receivable and
focus on customers with lower credit risk after initiating a borrowing–base loan.
[Insert Table 8 Here]
24
The credit rating is predicted by a model including the following predictors: the natural logarithm of total
assets, ROA, leverage, a dummy variable measuring whether a firm pays dividend, a dummy variable
measuring whether a firm issues subordinated debt, and a dummy variable measuring whether a firm incurs
loss in the current period.
25
Our results are robust to the deletion of observations where customers have no credit rating.
27
Bank monitoring and disclosure frequency of allowance for doubtful accounts
As noted in section 2.2 the requirement to provide frequent aging reports to the
lender can cause the borrower to alter his internal accounting system. To the extent these
alterations reduce the borrower’s incremental cost of producing reliable quarterly
allowance estimates, we expect that borrowers will be more likely to separately disclose
these estimates in their quarterly filings. To test this prediction, we collect data on
disclosure of allowance for doubtful accounts from EDGAR 10Q for our original sample
of 248 loans. If a loan is initiated on or before 1997, we delete it because some firms do
not file with SEC through EDGAR in earlier years. If a firm discloses its allowance
account in its quarterly financial statements, we code the firm-quarter observation as 1,
and 0 otherwise. We then sum over quarters to calculate the annual frequency of
allowance for doubtful accounts disclosure (ranging between 1 and 4) and compare mean
annual disclosure frequency between the pre-borrowing and the post-borrowing periods.26
Table 9 presents the results of this analysis. Our sample for these tests is 181
borrowers after we exclude loans initiated before 1998 and borrowers that do not have
positive annual allowance balances in each year between t-2 and t+2. The mean
disclosure frequency increases monotonically from two years prior to borrowing up to
one year after borrowing. 27 We see a 0.216 increase in the disclosure frequency of
allowance for doubtful accounts from the pre-period to the post- period. This increase is
about 7% of the mean disclosure frequency of the pre-borrowing period and is
statistically significant at the .03 level. In addition, the increase is 0.61 for borrowers that
26
To be included in our original sample, we require all borrowers disclosure the allowance balance in their
annual report. Thus firms must disclose at the allowance balance at least once per fiscal year.
27
Unreported results show that the median disclosure frequency remains four throughout the four-year
period surrounding a loan initiation.
28
are required to furnish aging reports more frequently to lenders (i.e., weekly or monthly).
In contrast, this increase is only 0.10 for borrowers required to provide less frequent
aging reports (i.e., quarterly or longer). The increase is statistically significant only for
the high frequency group. Therefore, we find evidence consistent with our expectation in
that borrowers increase the disclosure frequency of allowance for doubtful accounts after
loan initiation. We attribute this increase to the enhanced accounting system resulting
from frequent requirement of aging reports by lenders.28
[Insert Table 9 Here]
VI. Robustness Check
Additional control variables
As shown in Table 3 firm size and the presence of analyst following are highly
correlated with multi-lender loans and the frequency of aging reports. Therefore their
interactions with POST can be a correlated omitted variable in our cross-sectional tests.
To address this concern we rerun regressions in Table 6 and include these two interaction
terms. Our results are robust to this procedure suggesting that other governance
mechanisms do not substitute the role of bank monitoring of borrowers’ accounts
receivable.
Scaling variables by accounts receivable or assets
All the results presented are based on scaling bad debt expense and other
independent variables by sales. To investigate whether our results are driven by the
28
An alternative explanation for this increase can be that the amount of allowance for doubtful accounts
increases after borrowing as seen from Table 4 and Table 5 and this increase can push firms beyond the
materiality threshold leading to disclosure. To rule out this explanation, we compare the mean allowance of
firm-years disclosing this account once to firm-years disclosing this account four times. Though we see a
higher mean of allowance account balance for firm-years disclosing four times annually, the difference
between these two groups is not statistically significant. We find similar results when we compare firmyears with decreasing disclosure frequency to firm-years with increasing disclosure frequency.
29
choice of scaling factor, we replace sales with accounts receivable (and, alternatively,
book value of assets) as the scaling variable. The variable AR is removed from the
equation when receivables serves as the scaling variable, because including AR can cause
a mechanical negative association between the dependent variable and AR. Unreported
results show that our findings are not sensitive to the choice of scaling variable.
VII. Conclusion
We study changes in accounting recognition related to accounts receivable
surrounding the initiation of loans requiring the provision of aging schedules to the lender.
We find that the allowance for doubtful accounts balance increases significantly after
loan initiation controlling for write-offs, receivable turnover, and firm and year fixed
effects.
This increase is more pronounced for loans characterized by increased
monitoring intensity. In addition, we find that write-offs are less persistent implying
increased write-off timeliness. Borrowing is also associated with real affects. More
specifically, the initiation of borrowing base loan is associated with reduced sales to
larger customers and improved credit quality of larger customers. Lastly, we demonstrate
that borrowing with an aging reports requirement is associated with an increase in the
frequency of disclosing allowance for doubtful accounts in the quarterly financial
statements by borrowers.
Our results provide direct confirmation of two widely held beliefs in banking and
accounting research. The first is that banks monitor firms. Such monitoring is thought to
be an important reason for the existence of banks. With some notable exceptions, prior
research provides indirect evidence of this monitoring by examining stock-price reactions
to bank loan announcements and by studying accruals before and after loans. The second
30
is that banks demand conservative accounting and that these demands affect firm
accounting policies. Our results suggest that banks’ influence is unique in that it is not
overwhelmed by other monitoring mechanisms in place.
31
REFERENCES
Ahmed, A. B., Billings, R., Morton, and M. Stanford. 2002. The Role of Accounting
Conservatism in Mitigating Bondholder-Shareholder Conflicts over Dividend Policy
and in Reducing Debt Costs. The Accounting Review 77: 867-890.
Ahn, S., and W. Choi. 2009. The Role of Bank Monitoring in Corporate Governance:
Evidence from Borrowers’ Earnings Management Behavior. Journal of Banking and
Finance 33: 425 – 434.
Ball, R., S. P. Kothari, and A. Robin. 2000. The Effect of International Institutional
Factors on Properties of Accounting Earnings. Journal of Accounting and
Economics 29: 1-51.
Barnett, W. 1997. What’s In A Name? A Brief Overview of Asset-Based Lending. The
Secured Lender 53: 80-82.
Barth, M., L., Hodder, and S. Stubben. 2008. Fair Value Accounting for Liabilities and
Own Credit Risk. The Accounting Review 83: 629-664.
Beatty, A., J. Weber, and J. Yu. 2008. Conservatism and Debt. Journal of Accounting
and Economics 45: 154-174.
Best, R., and H. Zhang. 1993. Alternative Information Sources and The Information
Content of Bank Loans. Journal of Finance 4: 1507-1523.
Billet, M. T., Flannery, M. J., and Garfinkel, J. A. 1995. The effect of lender identity on a
borrowing firm’s equity return. Journal of Finance 50: 699–718.
Bolton, P., and D. Scharfstein. 1990. A Theory of Predation Based on Agency Problem in
Financial Contracting. The American Economic Review 80: 93-106.
Bushman, R., and R. Wittenberg-Moerman. 2010. The Role of Bank Reputation in
“Certifying” Future Performance Implications of Borrowers’ Accounting Numbers.
Working paper, University of North Carolina and University of Chicago.
DeAngelo, H., L. DeAngelo, and D. J. Skinner. 1994. Accounting Choice in Troubled
Companies. Journal of Accounting and Economics 17: 113–43.
DeFond, M., and J. Jiambalvo. 1994. Debt Covenant Violation and Manipulation of
Accruals. Journal of Accounting and Economics 17: 145-176.
Diamond, D. W. 1984. 1984. Financial Intermediation and Delegated Monitoring. Review
of Economic Studies 51: 393-414.
32
Diamond, D.W. 1996. Financial intermediation as delegated monitoring: a simple example,
debt maturity structure and liquidity risk. Federal Reserve Bank of Richmond Economic
Quarterly 82/3: 51–66.
Dichev, I., and D. Skinner. 2002. Large-Sample Evidence on The Debt Covenant
Hypothesis. Journal of Accounting Research 40: 1091–1123.
Fama, E. 1985. What’s Different About Banks? Journal of Monetary Economics 15: 2939.
Flannery, M., and X. Wang. 2011. Borrowing Base Revolvers: Liquidity for Risky Firms.
Working Paper, University of Florida.
Frankel, R., and Roychowdhury, S. 2009. Are all special items equally special? The
predictive role of conservatism. Working paper, Washington University in St. Louis
and Boston College.
Healy, P. M., and K. G. Palepu. 1990. Effectiveness of Accounting-Based Dividend
Covenants. Journal of Accounting and Economics 12: 97–133.
Jackson, S. B., and X. Liu. 2010. The Allowance for Uncollectible Accounts,
Conservatism, and Earnings Management. Journal of Accounting Research 48: 565601.
James, C. 1987. Some Evidence of The Uniqueness of Bank Loans. Journal of financial
economics 19: 217-235.
Jimenez, G., J. Lopez, and J. Saurina. 2009. Empirical Analysis of Corporate Credit
Lines. Review of Financial Studies 22: 2059-5098.
Kahn, M., and R. Watts. 2009. Estimation And Empirical Properties of A Firm-Year
Measure of Accounting Conservatism. Journal of Accounting and Economics 48:
132-150.
Kim, B. H. 2010. Ex-Post Change in Conservatism and Debt-Covenant Slack. Working
paper, American University.
LaFond, R., and R. Watts. 2008. The Information Role of Conservatism. The Accounting
Review 83: 447-478.
Leftwich, R. 1983. Accounting Information in Private Markets: Evidence From Private
Lending Agreements. Accounting Review 58: 23–42.
Leland, H., and D. Pyle. 1977. Informational Asymmetries, Financial Structure, and
Financial Intermediation. Journal of Finance, 32: 371-415.
33
Lummer, S., and J. McConnell. 1989. Further Evidence on The Bank Lending Process and
The Capital Market Response to Bank Loan Agreements. Journal of Financial
Economics 25: 99-122.
McNichols, M., and P. Wilson. 1988. Evidence of Earnings Management from the
Provision for Bad Debts. Journal of Accounting Research 26: 1–31.
Mester, L. J., L. L. Nakamura, and M. Renaut. 2007. Transactions Accounts And Loan
Monitoring. Review of Financial Studies, 20, 529-556.
Mikkelson, W., and M. Partch. 1986. Valuation Effects of Securities Offerings and The
Issuance Process. Journal of Financial Economics 15: 31-60.
Nikolaev, V. 2010. Debt Covenants and Accounting Conservatism. Journal of
Accounting Research 48: 137-175.
Norden, L., and M., Weber. 2010. Credit Line Usage, Checking Account Activity, And
Default Risk of Bank Borrowers. Review of Financial Studies, 23: 3665-3699.
Rajan, R. and Winton, A. 1995. Covenants and collateral as incentives to monitor,
Journal of Finance 50: 1113-1146.
Sweeney, A. 1994. Debt-Covenant Violations and Managers’ Accounting Responses.
Journal of Accounting and Economics 17: 281–308.
Sufi, A. 2009. Bank Line of Credit in Corporate Finance: An Empirical Analysis. Review
of Financial Studies 22: 1057-1088.
Tan, L. 2010. Creditor Control, State of Nature Verification, and Financial Reporting
Conservatism. Working paper, Northwestern University.
Vinter, G. D. 1998. Project finance: A legal guide. Sweet & Maxwell, London.
Watts, R. 2003. Conservatism in Accounting, Part I: Explanations and Implications.
Accounting Horizons 17: 207–221.
Watts, R., and J. Zimmerman. 1986. Positive Accounting Theory. Prentice-Hall,
Englewood Cliffs, NJ.
Wittenberg-Moerman, R. 2008. The Role of Information Asymmetry and Financial
Reporting Quality in Debt Contracting: Evidence Form The Secondary Loan Market.
Journal of Accounting and Economics 46: 240-260.
Zhang, J. 2008. The Contracting Benefits of Accounting Conservatism to Lenders and
Borrowers. Journal of Accounting and Economics 45: 27-54.
34
FIGURE 1
This figure shows the time trend of mean and median of allowance for doubtful accounts scaled by total
sales around the year of loan origination for borrowers with loans that require aging reports (test sample) in
Panel A and for firms that do not face aging reports requirements (control sample) in Panel B. Year 0
indicates the year when a loan is originated.
Panel A: Test sample (N = 248)
Test sample
0.016
0.014
0.012
0.01
0.008
0.006
0.004
-2
-1
0
mean allowance
1
2
median allowance
Panel B: Control sample (N = 248)
Control sample
0.018
0.016
0.014
0.012
0.01
0.008
0.006
0.004
-2
-1
mean allowance
0
1
median allowance
35
2
FIGURE 2
This figure shows the time trend of cash from operation scaled by total assets around the year of loan
origination (year = 0) for borrowers with loans that require aging reports (test sample) in Panel A and for
firms that do not have provision of aging report requirements (control sample).
Panel A: Test sample (N = 248)
Test sample
0.06
0.05
0.04
0.03
0.02
0.01
-2
-1
0
mean cash flow/assets
1
2
median cash flow/assets
Panel B: Control sample (N = 248)
Control sample
0.071
0.061
0.051
0.041
0.031
0.021
0.011
0.001
-2
-1
mean cash flow/assets
0
1
median cash flow/assets
36
2
TABLE 1
Time and Industry Profile of Sample
This table describes the yearly distribution of our sample of borrowers with loans that require aging reports
in Panel A and its industry profile in Panel B.
Panel A: Time profile of sample
year
Frequency Percentage
1994
1995
1996
1997
1998
1999
2000
4
12
23
23
31
27
40
Year
1.60%
4.80%
9.20%
9.20%
12.40%
10.80%
16.00%
Frequency
2001
2002
2003
2004
2005
2006
42
19
7
8
8
6
Pecentage
16.80%
7.60%
2.80%
3.20%
3.20%
2.40%
Panel B: Industry profile of sample
Sample Firms
Agriculture, Forestry, &Fishing
0
Frequency
Persentage
0.0%
Mining
6
Construction
2
Compustat Universe Firms
57
Frequency
Percentage
0.3%
2.4%
1,183
6.7%
0.8%
162
0.9%
134
54.0%
6,073
34.5%
Transportation & Public Utilities
5
2.0%
1,610
9.1%
Wholesale Trade
16
6.5%
539
3.1%
Retail Trade
8
3.2%
900
5.1%
Finance, Insurance, &Real Estate
2
0.8%
3,801
21.6%
Services
75
30.2%
3,068
17.4%
Nonclassifiable Establishments
0
0.0%
217
1.2%
248
100.0%
17,608
100.0%
Industry
Manufacturing
Total
Frequency
37
Frequency
TABLE 2
Characteristics of Loan Contracts with Aging Report Requirements
This table presents loan characteristics in Panel A, the purpose of aging reports in Panel B, and the
frequency of aging reports in Panel C. LOAN_AMOUNT is the size of a loan in millions of dollars;
MATURITY is the maturity of a loan in years; MULTILENDERS is a indicator variable equal to one for a
syndicated loan with multiple lenders and zero otherwise; CUTOFF_INVOICE is the maximum number of
days following the invoice date allowable for a customer receivable to be included as eligible accounts
receivable in the computation of the borrowing base; CUTOFF_DUE is the maximum number of days
following the due date of the invoice for a customer accounts receivable to be included in the computation
of the borrowing base; PCT_BASE is the percentage of eligible accounts receivable used as the borrowing
base.
Panel A. Loan characteristics
LOAN AMOUNT
MATURITY
MULTILENDERS
CUTOFF_INVOICE
CUTOFF_DUE
PCT_BASE
N
242
242
248
170
67
179
Mean
52.712
2.790
0.508
104.612
69.701
81.778
Panel B: Purpose of aging report
Purpose
Frequency
Borrowing Base Only
66
Collateral Only
40
Borrowing Base and
113
Collateral
Other
29
Total
248
Panel C. Frequency of aging reports
Frequency
Number Percentage
Weekly
4
1.6%
Monthly
164
66.1%
Quarterly
39
15.7%
Semi-Annually
2
0.8%
Annually
6
2.4%
By Request
33
13.3%
Total
248
100%
38
Lower
Quartile
18.000
2.000
0.000
90.000
60.000
80.000
Median
18.000
3.000
1.000
90.000
60.000
80.000
Upper
Quartile
50.000
3.500
1.000
120.000
90.000
85.000
Std Dev
102.262
1.434
0.501
29.000
18.152
6.555
TABLE 3
Correlation between Variables
This table reports Pearson correlation below the diagonal and Spearman correlation above the diagonal for the test sample. A firm is included in the test sample
when a loan contract when required aging reports can be identified. Firm characteristics are measured at the fiscal-year end immediately prior to the loan
origination year. ALLOW is the allowance for uncollectible accounts receivable; LEV is leverage, defined as total debt (long-term and short-term) divided by
assets. CFO is cash flow from operation scaled by assets; ASSET is natural logarithm of book value of assets; AF is an indicator variable equal to 1 if the firm
has positive analyst following and zero otherwise. MULTILENDER is an indicator variable equal one if a bank loan has multiple lenders and zero otherwise;
HIGHFREQ is an indicator variable equal one if a bank loan requires borrowers to furbish aging reports on a weekly or monthly basis and zero otherwise.
RETVOL is the variance of monthly returns. Correlations with significance 5% (two-tailed) are in bold.
Variable
ALLOW
ALLOW
RETVOL
ROA
CFO
LN(ASSETS)
LEVERAGE
AF
MULTI
HIGHFREQ
0.201
-0.220
-0.095
-0.002
0.034
-0.060
-0.004
-0.048
-0.318
-0.192
-0.174
-0.044
-0.118
-0.084
0.070
0.401
0.063
-0.172
0.187
0.009
-0.064
0.171
-0.060
0.145
0.114
-0.109
0.297
0.357
0.585
-0.188
-0.054
0.252
-0.028
0.141
-0.060
RETVOL
0.107
ROA
-0.229
-0.354
CFO
-0.176
-0.254
0.470
LN(ASSETS)
0.011
-0.185
0.152
0.218
LEV
0.124
0.048
-0.021
-0.043
0.272
AF
-0.130
-0.141
0.173
0.148
0.345
-0.080
HIGHFREQ
-0.034
0.075
-0.070
-0.095
-0.197
-0.027
39
-0.060
-0.108
TABLE 4
Firm Characteristics before and after the Loan Initiation
This table reports mean statistics for firm characteristics in the two years before and two years (after and including) the year of loan initiation for both the test
sample and the control sample. ALLOW is the allowance for uncollectible accounts receivable; AR is the gross accounts receivable; WO is the write-offs of
uncollectible accounts receivable; BDX is bad debt expenses. ALLOW, AR, BDX, and WO are scaled by contemporaneous sales. LEV is leverage, defined as
total debt (long-term and short-term) divided by assets. SALES is total sales scaled by assets; CFO is cash flow from operation scaled by assets; ASSET is
natural logarithm of book value of assets; ‘No. Ana Follow’ is the number of analysts following the borrower measured at the fiscal year end before loan
origination. ARTO_IND is industry median accounts receivable turnover ratio, defined as sales divided by average gross accounts receivable; SALE_SD_IND is
industry median standard deviation of sales using quarterly data for all firms in the same industry with available data in Compustat; ALT_IND is industry median
Altman (1968) z-score computed using all firms in the industry with available data in Compustat. Industry classification is based on two-digit SIC codes. ***, **,
and * indicate the statistical significance for the difference of the mean values at the level of 1%, 5%, and 10%, respectively.
Test sample
Control sample
Mean Diff.
Pre
Post
Pre
Post
(2) - (1)
(4) - (3)
(1) - (3)
(2) - (4)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
ALLOW
AR
0.013
0.015
0.016
0.016
0.002
0.000
-0.003
0.001
0.191
0.179
0.185
0.169
-0.012**
-0.016***
0.006
0.010**
BDX
0.010
0.012
0.014
0.010
0.002**
-0.004***
-0.004
0.002
WO
0.012
0.016
0.013
0.013
0.004***
0.000
-0.001
0.003
LEV
0.252
0.267
0.250
0.244
0.015
-0.006
0.002
0.023
SALES
1.576
1.543
1.270
1.237
-0.033
-0.033
0.306***
0.306***
ROA
-0.027
-0.065
-0.032
-0.035
-0.038***
-0.003
0.005
-0.030*
CFO
0.013
0.032
0.043
0.041
0.019**
-0.002
-0.029**
-0.009
ASSET
4.559
4.842
5.355
5.509
0.283***
0.154***
-0.796***
0.667***
No. Ana Follow
ARTO_IND
2.237
2.332
4.528
4.463
0.095
-0.065
-2.291***
-2.131***
1.974
2.014
1.974
2.014
0.040***
0.040***
SALES_SD_IND
0.033
0.031
0.033
0.031
-0.002***
-0.002***
ALT_IND
3.248
2.821
3.248
2.821
-0.427***
-0.427***
40
TABLE 5
Bank Monitoring and Allowance for Doubtful Account
This table reports the results of regressing allowance for doubtful account on various independent variables.
Each sample firm is included in the regression four times. Two years prior to loan initiation and two years
after. The year of the loan is considered the first year after loan initiation. ALLOW is the allowance for
uncollectible accounts receivable; POST is an indicator variable equal one if the fiscal year is in the
initiation year or after and zero otherwise; AR is gross accounts receivable; WO is the write-offs of
uncollectible accounts receivable; ALLOW, AR, and WO are scaled by contemporaneous sales. LEV is the
leverage, defined as total debt divided by assets. ARTO_IND is the industry median accounts receivable
turnover ratio, defined as sales divided by average gross accounts receivable; SALE_SD_IND is the
industry median standard deviation of sales using quarterly data for all firms in the industry with available
data in Compustat; ALT_IND is the industry median Altman (1968) z-score computed using all firms in the
industry with available data in Compustat. AF is an indicator variable equal to 1 if the firm has positive
analyst following and zero otherwise. ASSET is natural logarithm of book value of assets; Industry
classification is based on the two-digit SIC codes. Standard errors are clustered at the firm level. ***, **,
and * indicate the statistical significance at the level of 1%, 5%, and 10%, respectively.
Dependent Variable = ALLOW t /SALESt
Predicted
Sign
Coeff
p value
Coeff
(1)
p value
(2)
Intercept
?
0.037
0.305
POST
POST×CONTROL
+
0.003
0.004
ARt
+
0.019
WOt
?
WOt+1
+
0.063
0.013
0.003
0.029
-0.004
0.028
0.221
0.022
0.242
-0.115
0.019
-0.125
0.023
0.197
0.000
0.192
0.000
ARt × CONTROL
-0.019
0.508
WOt × CONTROL
0.345
0.000
WOt+1 × CONTROL
-0.050
0.518
LEVt
?
0.006
0.539
0.004
0.007
ARTO_INDt
-
-0.013
0.286
-0.012
0.008
SALE_SD_INDt
+
0.002
0.989
-0.053
0.098
ALT_INDt
-
-0.001
0.367
-0.001
0.001
AFt
?
-0.001
0.128
-0.001
0.000
ASSETt
?
-0.002
0.516
-0.006
0.002
Firm Dummy
Yes
Year Dummy
Yes
Yes
N
992
1984
Adj-R2
0.83
0.77
41
Yes
TABLE 6
The Change in the Persistence of Write-offs and Bank Monitoring
This table reports regression results examining the effect of banks’ monitoring incentive on the persistence
of write-offs. WO is the write-offs of uncollectible accounts receivable; POST is an indicator variable equal
one if the fiscal year is in the initiation year or after and zero otherwise; AR is gross accounts receivable;
ALLOW, AR, and WO are scaled by contemporaneous sales. LEV is the leverage, defined as total debt
divided by assets. ARTO_IND is the industry median accounts receivable turnover ratio, defined as sales
divided by average gross accounts receivable; SALE_SD_IND is the industry median standard deviation of
sales using quarterly data for all firms in the industry with available data in Compustat; ALT_IND is the
industry median Altman (1968) z-score computed using all firms in the industry with available data in
Compustat. AF is an indicator variable equal to 1 if the firm has positive analyst following and zero
otherwise. ASSET is natural logarithm of book value of assets measured at the fiscal year end before loan
origination; ROA is net income before extraordinary item scaled by total assets. Industry classification is
based on the two-digit SIC codes. Standard errors are clustered at the firm level. ***, **, and * indicate the
statistical significance at the level of 1%, 5%, and 10%, respectively.
Dependent Variable = WOt
Predicted
Test Firms
Control Firms
Sign
Coeff
p value
Coeff
p value
Intercept
?
-0.010
0.426
0.001
0.934
WOt-1
+
1.112
< 0.0001
0.823
< 0.0001
POST
+
0.000
0.949
-0.001
0.405
POST * WOt-1
-
-0.206
0.067
-0.025
0.793
ARt
+
0.029
0.338
0.019
0.374
LEVt
?
0.006
0.430
0.006
0.202
ARTO_INDt
-
0.001
0.803
0.004
0.086
SALE_SD_INDt
+
0.022
0.687
-0.116
0.086
ALT_INDt
-
0.000
0.893
0.000
0.942
AFt
?
0.000
0.671
0.000
0.632
ASSETt
?
0.001
0.272
-0.001
0.306
ROA t
-
-0.030
0.013
-0.004
0.948
Year Dummy
Yes
Yes
N
744
744
0.78
0.57
2
Adj-R
42
TABLE 7
Bank Monitoring Intensity and Allowance for Doubtful Accounts
This table reports regression results examining the effect of banks’ monitoring incentive on borrowing
firms’ bad debt expenses. MULTILENDER is an indicator variable equal one if a bank loan has multiple
lenders and zero otherwise; HIGHFREQ is an indicator variable equal one if a bank loan requires
borrowers to furbish aging reports on a weekly or monthly basis and zero otherwise. ALLOW is the
allowance for uncollectible accounts receivable; POST is an indicator variable equal one if the fiscal year is
in the initiation year or after and zero otherwise; AR is gross accounts receivable; WO is the write-offs of
uncollectible accounts receivable; ALLOW, AR, and WO are scaled by contemporaneous sales. LEV is the
leverage, defined as total debt divided by assets. ARTO_IND is the industry median accounts receivable
turnover ratio, defined as sales divided by average gross accounts receivable; SALE_SD_IND is the
industry median standard deviation of sales using quarterly data for all firms in the industry with available
data in Compustat; ALT_IND is the industry median Altman (1968) z-score computed using all firms in the
industry with available data in Compustat. AF is an indicator variable equal to 1 if the firm has positive
analyst following and zero otherwise. ASSET is natural logarithm of book value of assets; Industry
classification is based on the two-digit SIC codes. Standard errors are clustered at the firm level. ***, **,
and * indicate the statistical significance at the level of 1%, 5%, and 10%, respectively.
Dependent Variable = ALLOW/SALESt
Predicted
Sign
HIGHFREQ = 0
Coeff
p value
HIGHFREQ = 1
Coeff
(i)
p value
(ii)
Intercept
?
0.052
0.296
0.041
0.344
POST
+
0.001
0.701
0.004
0.013
ARt
+
0.001
0.984
0.024
0.195
WOt
+
-0.165
0.077
-0.098
0.146
WOt+1
+
0.191
0.000
0.205
0.002
LEVt
?
-0.003
0.773
0.014
0.200
ARTO_INDt
-
-0.016
0.404
-0.016
0.351
SALE_SD_INDt
+
0.362
0.465
-0.059
0.741
ALT_INDt
-
-0.002
0.303
-0.001
0.518
AFt
?
-0.001
0.066
0.000
0.743
ASSETt
?
-0.001
0.772
0.000
0.869
Firm Dummy
Yes
Yes
Year Dummy
Yes
Yes
N
320
672
0.78
0.76
2
Adj-R
43
TABLE 8
The Change in Customer Choice after Borrowing
This table reports regression results of examining the change in sales concentration and customer credit risk after initiation of a borrowing-base loan.
AVGSALEPCT is the average percentage of sales to a customer with the minimum of 10%; LNUMCSTMER is the natural logarithm of the number of
customers with a percentage of sales exceeding10%; CSTMRATING is the weighted average monthly customers' S&P domestic issuer credit rating, weighted by
the percentage of a firm's sales to that customer. If a customer has no credit rating, then the credit rating is predicted by a model using natural logarithm of total
assets, ROA, leverage, a dummy variable measuring whether a firm pays dividend, a dummy variable measuring whether a firm issues subordinated debt, and a
dummy variable measuring whether a firm incurs loss in the current period; POST is an indicator variable equal one if the fiscal year is in the loan initiation year
or the following year and zero otherwise; ASSET is natural logarithm of suppliers’ book value of assets; ROA is suppliers’ net income over suppliers’ total assets;
CFO is suppliers’ cash flow from operation over is total assets; CREDITRATING is suppliers’ monthly average of the S&P domestic long-term issuer credit
rating. If a supplier has no credit rating, then the credit rating is replaced by the predicted credit rating using the same procedure described above; Credit rating
ranges between 2 (S&P rating =AAA) and 27 (S&P rating = D). MKTSHARE is the supplier’s sales over the total sales of all firms in the same three digit SIC
industry. Industry classification of industry dummy is based on the two-digit SIC codes. Standard errors are clustered at the firm level. ***, **, and * indicate the
statistical significance at the level of 1%, 5%, and 10%, respectively.
Dependent Variable = AVGSALEPCT t
Coeff
p value
Dependent Variable = LNUMCSTMERt
Coeff
(1)
Intercept
p value
Dependent Variable = CSTMRATINGt
Coeff
(2)
p value
(3)
0.504
0.000
1.322
0.000
3.042
0.003
POST
-0.012
0.025
-0.020
0.083
-0.162
0.037
ASSETt
-0.007
0.012
0.003
0.664
-0.225
0.000
ROA t
0.024
0.062
0.051
0.041
0.256
0.159
CFOt
-0.056
0.007
-0.029
0.492
-0.375
0.207
CREDITRATINGt
0.001
0.548
0.003
0.172
-0.006
0.522
MKTSHAREt
0.029
0.494
0.241
0.171
0.144
0.779
Industry Dummy
Yes
Yes
Year Dummy
Yes
Yes
Yes
N
5129
5129
1394
Adj-R2
0.08
0.1
0.13
44
Yes
Table 9
The Change in Disclosure Frequency of Allowance after Borrowing
This table reports univariate analysis of the change in the disclosure frequency of allowance for doubtful
accounts in the two years prior to borrowing (-2) through the two years after borrowing (+2). Disclosure is
collected from EDGAR-10Q and the sample contains 181 loans. If a borrower discloses the balance of
allowance, then the disclosure frequency is coded as one, zero otherwise. Quarterly disclosure of
allowance is cumulated to arrive at the annual frequency. The mean annual frequency is presented in each
cell and the difference in mean is also reported.
Frequency
Pre-Period
-2
-1
2.839
2.966
Post-period
0
1
3.116
3.121
45
2
3.044
Difference (Post - Pre)
Mean diff p-value
0.216
0.0353