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
[email protected]
Bong Hwan Kim
American University
Kogod School of Business
4400 Massachusetts Avenue, NW
Washington, DC 20016
[email protected]
Tao Ma
Olin Business School
Washington University in St. Louis
Campus Box 1133
One Brookings Drive
St. Louis, MO 63130-4899
[email protected]
Xiumin Martin
Olin Business School
Washington University in St. Louis
Campus Box 1133
One Brookings Drive
St. Louis, MO 63130-4899
[email protected]
First draft: December 2010
Revised: March 2011
We thank…
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 documents affects financial reporting incentives. We find that bad debt
expense recognition 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 characterized by increased monitoring intensity. Our results provide direct
confirmation of two notions. Banks add to the oversight that already exists for public, audited
companies and banks influence borrowers to adopt more conservative accounting policies.
1
1. 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 bad debt expense before and after loan initiation. We also examine whether these
changes are related to proxies for banks’ monitoring incentives and monitoring intensity.
We find that borrowers recognize higher bad debt expense after borrowing, and the
increase in the bad debt expense is more pronounced for loans with a single lender, for
loans where transmittal of aging reports is more frequent, and during periods when
lenders indicate covenants on new loans are more binding.
Our purpose is to understand how the initiation of bank monitoring affects
financial reporting incentives. Research finds borrowers’ signed discretionary accruals
(Ahn and Choi [2009]) and the future cash flow implications of current accruals
(Bushman and Wittenberg-Moerman [2010]) are related to the size of the lender and
concludes that monitoring or screening by higher reputation banks improves accounting
quality. Mester, Nakamura, and Renault [2007] study receivable and inventory-based
loans made by one bank to small, management-owned firms. They find transactions
accounts related to these loans provide lenders with information useful for monitoring
borrowers. We integrate these approaches to test the connection between bank monitoring
and financial reporting.
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] and Jackson and Liu [2009]). We
also broaden the results of Mester et al., [2007] who study a set of small, private firms to
the 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, and Garfinkel [1995]).
Our research also sheds light on an institutional mechanism that can give lenders
access to information necessary to monitor whether borrower accounting choices are
consonant with lender preferences (e.g., Watts [2003]). Research assumes borrowers’
accounting policies are shaped by lender wishes without indentifying methods used by
lenders to monitor conformance (e.g., Ahmed, Billings, Morton, Stanford-Harris [2002];
Watts [2003]; LaFond and Watts [2008]; Khan and Watts [2009]; Frankel and
Roychowdhury [2009]). Such monitoring presumably requires an information source
beyond the current financial statements.
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
3
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
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.
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.
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 [2009]), 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, Kothari, and
Robin [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
Pooling data between years t-2 and t+1, we regress current bad debt expense on the prior
balance in the allowance for doubtful accounts, 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 amount of bad debt expenses increases by 3.8 million dollars
after loan initiation, an increase of more than 50%. Consistent with the hypothesis that
bank monitoring drives this increase, our results show that the increase in bad debt
expenses for firms with single-lender loans is more than twice that of firms with
syndicated loans. Furthermore, firms required to provide aging reports on a weekly or
monthly basis increase bad debt expenses more than firms with a less frequent aging
report requirements. We also find that the significant increase in bad debt expense is
concentrated in periods when lenders tend to set tighter loan covenants as indicated by
the Federal Reserve survey of lending officers. Our results are robust to inclusion of
interactions for size and analyst following and to alternative scaling variables (assets and
accounts receivable).
Our study makes several contributions. First, prior studies examining accounting
conservatism arising from debt contracting efficiency are mute on the institutional
features used to enforce accounting conservatism. The results of our paper indicate that
active monitoring by banks of a key revenue accrual account is associated with more
conservative accounting by borrowing firms. In this 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
5
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.
2. Background
2.1 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
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
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 Paleppu [1990] and DeAngelo, DeAngelo and Skinner [1994]
do not find support for the debt covenant hypothesis. 6
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, Flannery,
and Garfinkel [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, Nakamura, and Renault [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, Lopez, and
Saurina [2009] and Sufi [2009]) and predicts default (Norden and Weber [2010]). The
implication is that credit line usage gives banks private information on their borrowers.
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,
7
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, Billings, Morton, and Harris
[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
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
6
In related research, Leftwich [1983] and Beatty, Weber, and Yu [2008] infer lender demand for
accounting conservatism via loan covenant computations.
8
shareholders. Researchers speculate that reputation and legal liability force borrowers to
maintain conservative accounting policies after borrowing (Beatty, Weber, and Yu [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.
2.2. 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.
The following excerpt from Kontron Mobile Computing Inc.’s 1998 syndicated
loan contract illustrates the aging report requirement:
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
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
addition to the aging reports, banks can require additional information from borrowers
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
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,
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
10
In a review of 30 randomly selected loan agreements from our sample, all contained accounting-based
covenants.
11
important external analysis of their flows of goods and cash, as the reports consistently reveal
areas in which they can optimise their companies’ business operations, both in economic and legal
, 12
terms.11
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.
3. 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,
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
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
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:
BDXit = 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)
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 receivable
increases (reducing receivable turnover), we expect the recognition of bad debt expenses
to increase. As the current and future write-off of accounts receivable increase, bad debt
expenses 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.
13
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 characteristics of loan contracts to investigate
whether results are consistent with increased bank monitoring. Specifically, we examine
whether bad debt expenses vary with the intensity of banks’ monitoring. We expand
Equation (1) by interacting the variable POST with variables that proxy for bank
monitoring incentive and therefore monitoring intensity, MONCHAR.
BDXit = β0 + β1 × POSTt + β2 × POSTt * MONCHARi + β3 ×ARit
+ β4 × ALLOWit-1+ β5 × WOit + β6 × WOit+1 + β7 × LEVit
+ β8 × ARTO_INDjt + β9 × SD_SALE_INDjt+ β10 × ALT_INDjt
+ β11 × AFit + β12 × ASSETit+ ΣFIRMi +ΣYEARt + εit
(3)
We use two different proxies to measure bank monitoring intensity. The first one is
whether a loan is syndicated with multiple lenders or a sole-lender loan.
MULTILENDERS is an indicator variable equal one if a loan is syndicated and zero
14
otherwise. Monitoring incentives can be weaker for multiple leader loans if the lead
arranger does not capture the full benefits of monitoring (Sufi [2009]). We also use the
frequency of aging reports to proxy for banks’ monitoring intensity. HIGHFREQ is an
indicator variable equal one if banks require the borrower to provide aging reports at a
monthly or weekly basis and zero otherwise.14
4. Sample Selection, Descriptive Statistics, and Univariate Analysis
4.1 Sample
4.1.1 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 manually 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
4.1.2 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 match this sample with Loan Pricing Corporation to exclude firms with a borrowingbase or collateralized loan over our sample period. Second, we require the matching firms
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
to be 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 40%, and to 60% 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.
4.2. 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 bad debt expense
(BDX), firm characteristics, and loan characteristics for our test sample. BDX and firm
characteristics are measured at the fiscal-year end prior to the loan origination year. Two
statistics are noteworthy. First, bad debt expense 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 reduces the likelihood of a multiple-lender loan and increases the
frequency of aging reports. In contrast, higher operating cash flow, larger assets, and the
presence of analyst following increase the likelihood of a firm borrowing via a multiplelender loan but decrease the frequency of aging reports.
[Insert Table 3 Here]
4.3. Univariate analysis
19
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
in 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. 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. Allowance for uncollectible accounts receivables (ALLOW)
also increases after borrowing but it is not significant. In contrast, BDX decreases from
0.014 to 0.010 for the matching firms and this decrease is statistically significant at
the .01 level. Similarly to the test sample, allowance for doubtful accounts does not
exhibit a difference across the two periods. There are no statistically significant Pre/Post
changes in accounts receivable (AR) and leverage (LEV) between the test sample and the
control sample, which suggests that our matching procedure seems to hold constant
accounts receivable-based lending to customers and borrowing across the two samples.
[Insert Table 4 Here]
Figure 1 illustrates the change in the recognition of bad debt expense 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, 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
20
origination year t, and remains at a level higher than that in the pre-borrowing period. In
contrast, the bad debt expense 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. The univariate analysis and
the graph provide initial evidence supporting our hypothesis that firms report higher bad
debt expense given the requirement to provide an aging report to a bank.
[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 the
deteriorating collectability of receivables, 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 on
the part of the holder of the receivable and that active monitoring by banks can pressure
borrowers to write off questionable accounts.19 Under this explanation, the write-offs of
accounts receivables are not purely driven by the performance or the riskiness of
receivables and instead are subject to managers’ discretion. On the other hand, if writeoffs 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 year t and that writeoff 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
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
initiation. In any event, these results suggest the necessity of controlling for future writeoffs 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.
5. Multivariate Analyses
5.1. Main regression results
22
Table 5 reports the results of testing the effect of bank monitoring on the
recognition of bad debt expense for the test sample in column (i) and for the sample
containing both the test firms and the control firms in column (ii). The coefficient on
POST is positive and statistically significant in column (i), suggesting that after
borrowing, firms increase their bad debt expense significantly. In terms of magnitude,
borrowing firms experience an increase in bad debt expenses of 0.007 (0.7% of total
sales). The increase is also economically significant. The average of BDX in the period
before borrowings is 0.010 (1.0% of total sales), and a change of 0.7% of sales in bad
debt expenses represents a more than 50% increase. Given that the average sales are 542
million dollars, bad debt expenses increase by 3.8 million dollars after borrowing.
For the control variables, both current and next period’s write-offs of accounts
receivable are positively correlated with bad debt expenses, suggesting that firm’s bad
debt expense reflects expected credit quality of receivables. The coefficient on LEV is
negative but not significant (β6 = -0.007 with a p-value of 0.299), providing weak
evidence that managers tend to reduce bad debt expenses as debt increases. Further, the
coefficients on the three industry-wide economic factors all have predicted signs but are
not statistically significant. The adjusted R-Square is 79%, suggesting that the model
explains a significant portion of the variation in bad debt expenses, but this R-square also
reflects the explanatory power of firm and year fixed effects.
In column (ii), 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 .01 level and an F test of the
sum of POST and POST×CONTROL is not statistically significant. These results suggest
23
that test firms recognize more bad debt expense after loan origination whereas this does
not occur to the matching firms. Therefore, the increase in bad debt expense recognition
for the borrowing firms is not significantly related to industry-wide effects. In sum, we
document a significant increase in bad debt expense recognition for firms that borrow
with an aging report requirement. We attribute this increase to bank monitoring of
borrowers’ accounts receivable.
[Insert Table 5]
5.2 Cross-sectional analysis of bank monitoring incentive and intensity
In this section, we provide evidence on how the changes in bad debt expense vary
with banks’ monitoring intensity for our test sample. We expect that a bank’s monitoring
incentive is weaker when a loan has multiple lenders and a bank’s monitoring intensity is
stronger when a loan requires more frequent aging reports. Results presented in table 6
demonstrate that the increase in bad debt expense is lower for syndicated loans with
multiple lenders. As shown in column (i), the coefficient on POST × MULTILENDERS is
negative and statistically significant at the .10 level. Whereas firms with single-lender
loans increase their bad debt expenses by 0.009, the increase for firms with syndicated
loans is only 0.005 (0.009 – 0.004) and the F-statistic indicates that this increase remains
significant at the .05 level. Hence, the increase in bad debt expenses for borrowers with
single-lender loans is approximately twice that of borrowers with multiple-lender loans.
In column (ii) we examine whether the frequency of bank monitoring affects the
changes in bad debt expenses. The coefficient on POST * HIGHFREQ is positive and
marginally significant at the .10 level. The increase in bad debt expenses for firms with
low monitoring frequency (quarterly or longer, including upon request) is 0.004 after loan
24
origination. For firms with high frequency of aging reports (weekly or monthly), the
change in bad debt expense is 0.007 (0.004 + 0.003).
[Insert Table 6 Here]
5.3 Business conditions and bank monitoring
Bank monitoring incentives and therefore intensity can both change overtime. For
example, during weak economic conditions firms tend to perform poorly, resulting in
increased borrower credit risk and divergence between borrower and lender interests.
Bank vigilance can increase during such periods as lenders strive to protect their
investments prior to deterioration in the value of expected payments and collateral. If the
observed increase in bad debt expense results from bank monitoring, we expect a greater
increase when banks show heightened attention to covenants and credit standards.
We test this prediction using Senior Loan Officer Survey data from the Federal
Reserve. 20 The Federal Reserve generally conducts quarterly surveys of senior loan
officers at approximately sixty large domestic banks and twenty-four U.S. branches and
agencies of foreign banks. Survey questions cover changes in the standards and terms of
the banks' lending and the state of business and household demand for loans. We focus
on loan officers’ response to the ‘loan terms’ question. The response has five levels
(tightened considerably, tightened somewhat, unchanged, eased somewhat, and eased
considerably).21 We compute an annual score, equal to the sum of quarterly scores. For
detailed description of the survey and the computation of the annual score, please refer to
20
We obtain Loan Officer Survey data from the Federal Reserve Board website at
http://www.federalreserve.gov/boarddocs/SnLoanSurvey/.
21
The question reads as follows, “For applications for C&I (ed: commercial and industrial) loans or credit
lines—other than those to be used to finance mergers and acquisitions—from large and middle-market
firms and from small firms that your bank currently is willing to approve, how have the terms of those
loans changed over the past three months?” We use responses related to larger borrowers (above $50M in
sales) as these firms correspond to those in our tests.
25
Appendix A. We then partition our test sample into two subgroups based on the median
value of the annual score for loan covenant and run equation (1). We expect the
coefficient on POST to be greater in the tight period where the annual score is less than
11.845. Results are reported in Table 7. Indeed, we observe the coefficient of interest is
0.007 for the tight credit period and in contrast it is 0.002 in the loose credit period,
which is consistent with our expectation that increased recognition of bad debt expense
for borrowers after loan origination becomes more pronounced when banks are more alert
to financial distress.
[Insert Table 7 Here]
6. Robustness Check
6.1 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.
6.2 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
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
26
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.
7. 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 bad debt expense recognition increases significantly after loan initiation
controlling for write-offs, receivable turnover, the beginning allowance balance, and firm
and year fixed effects. This increase is more pronounced for loans characterized by
increased monitoring incentive and intensity, and during the periods of higher credit
standards. In addition, monitoring by security analysts does not substitute for this effect.
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
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.
27
APPENDIX
Computation of Loan Officer Evaluation Score
This appendix provides detailed information on Senior Loan Officer Opinion Survey data
obtained from the Federal Reserve Board website at and describes the procedure for
computing the evaluation score based on this data and used in our paper.22
A. 1 Loan Officer Survey data
The Federal Reserve generally conducts the survey on approximately sixty large
domestic banks and twenty-four U.S. branches and agencies of foreign banks quarterly.
The agency times it so that results are available for the January/February, April/May,
August, and October/November meetings of the Federal Open Market Committee. The
Federal Reserve occasionally conducts one or two additional surveys during the year.
Questions cover changes in the standards and terms of the banks' lending to the business
and to the household. The survey often includes questions on one or two other topics of
current interest. We focus on responses to the second question about commercial and
industrial loans (C&L), which are deemed to be more relevant to our study. The response
is organized by partitioning participating banks into big and small banks at the cutoff of
$15 billion of domestic assets.
Question 2:
For applications for C&I loans or credit lines-other than those to be used to
finance mergers and acquisitions- from large and middle-market firms that your
bank currently is willing to approve, how have the terms of those loans changed
over the past three months (please assign each term a number between 1 and 5
using the following scale: 1=tightened considerably; 2=tightened somewhat;
3=remained basically unchanged; 4=eased somewhat; 5=eased considerably.)
The response is summarized as follows for loan covenants for illustration purpose.
The ones for maximum maturity, cost of credit lines, spreads of loan rates, premiums
charged on riskier loans, loan covenants, and collateralization requirements are also
provided at the website.
Tightened
considerably
Tightened
somewhat
22
All Respondents
Banks
Percent
3
5.5
Large Banks
Banks
Percent
2
6.3
Other Banks
Banks
Percent
1
4.3
38
25
13
69.1
78.1
http://www.federalreserve.gov/boarddocs/SnLoanSurvey/ 28
56.5
Remained
basically
unchanged
14
25.5
5
15.6
9
39.1
Eased
somewhat
0
0
0
0
0
0
Eased
considerably
0
0
0
0
0
0
Total
55
100
32
100
23
100
A.2 Computation of loan officer evaluation score
We compute the loan officer evaluation score for each surveyed quarter by multiplying
the level of response (1=tightened considerably; 2=tightened somewhat; 3=remained basically
unchanged; 4=eased somewhat; 5=eased considerably) by the percentage of banks having this
level of response first. Second, we sum it up across all levels of responses for both large banks
and small banks for small borrowing firms only to arrive at a weighted -average response for each
dimension of credit terms. We choose responses for small firms because our sample firms are
relatively small. Third, we take the average of the weighted-average response score across big
and small banks. Fourth, the quarterly score is summed to derive our annual measure of
evaluation score for each dimension of credit terms.
In the regression, we use the annual score for loan covenant as the measure of credit
market condition.
To gauge bank monitoring intensity that is orthogonal to borrowers’ credit risk, we use
regression technique to regress loan covenant tightness on spreads of loan rates over base rates
and obtain the residual of the regression as the measure of bank monitoring intensity. A higher
level of the residual indicates a more favorable credit market condition for borrowers, thus lower
bank monitoring intensity. The summary statistic for the seven dimensions of credit terms is as
follows for the period 1997-2007.
COLLATERAL
LOAN COVENANT
PREMIUM RISK
SPREADS
COST OF CL
MAX SIZE
MAX MATURITY
COMPOSITE SCORE
RESIDUAL LOAN COVENANT
N
11
11
10
11
11
11
3
11
11
Mean
11.700
11.770
10.339
12.277
11.998
11.957
11.237
11.665
-0.072
29
Lower
Quartile
11.250
11.215
10.045
11.365
11.390
11.705
9.430
10.933
-0.324
Median
11.920
11.845
11.170
12.205
12.000
11.985
11.937
11.780
-0.036
Upper
Quartile
12.030
12.275
12.030
13.200
12.650
12.190
12.345
12.361
0.053
Std Dev
0.477
0.614
2.796
0.978
0.656
0.384
1.578
0.701
0.215
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FIGURE 1
This figure shows the time trend of mean and median of bad debt expense scaled by 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
0.018
0.016
0.014
0.012
0.01
0.008
0.006
0.004
0.002
0
‐2
‐1
0
1
Panel B: Control sample (N = 248)
Control sample
0.016
0.014
0.012
0.01
0.008
0.006
0.004
0.002
0
‐2
‐1
mean bad debt expense
0
1
median bad debt expense
33
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
34
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
1994
1995
1996
1997
1998
1999
2000
Frequency Percentage
4
12
23
23
31
27
40
1.60%
4.80%
9.20%
9.20%
12.40%
10.80%
16.00%
Year
2001
2002
2003
2004
2005
2006
Frequency
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
Industry
Agriculture, Forestry, &Fishing
Mining
Construction
Manufacturing
Transportation & Public Utilities
Wholesale Trade
Retail Trade
Finance, Insurance, &Real Estate
Services
Nonclassifiable Establishments
Total
Sample Firms
Frequency
Frequency
Persentage
0
0.0%
6
2.4%
2
0.8%
134
54.0%
5
2.0%
16
6.5%
8
3.2%
2
0.8%
75
30.2%
0
0.0%
248
100.0%
35
Compustat Universe Firms
Frequency
Frequency
Percentage
57
0.3%
1,183
6.7%
162
0.9%
6,073
34.5%
1,610
9.1%
539
3.1%
900
5.1%
3,801
21.6%
3,068
17.4%
217
1.2%
17,608
100.0%
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
Lower
Upper
N
Mean
Quartile
Median
Quartile
Std Dev
LOAN AMOUNT
242
52.712
18.000
18.000
50.000
102.262
MATURITY
242
2.790
2.000
3.000
3.500
1.434
MULTILENDERS
248
0.508
0.000
1.000
1.000
0.501
CUTOFF_INVOICE
170
104.612
90.000
90.000
120.000
29.000
CUTOFF_DUE
PCT_BASE
67
179
69.701
81.778
60.000
80.000
60.000
80.000
90.000
85.000
18.152
6.555
Panel B: Purpose of aging report
Purpose
Frequency
Borrowing Base Only
66
Collateral Only
Borrowing Base and
Collateral
Other
40
113
Total
248
29
Panel C. Frequency of aging reports
Number
Percentage
Weekly
Frequency
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%
36
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. BDX is bad debt expenses scaled by contemporaneous sales; 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
BDX
RETVOL
ROA
CFO
ASSET
LEV
AF
MULTILENDER
HIGHFREQ
BDX
0.168
-0.228
-0.158
-0.017
0.129
-0.137
0.003
0.045
RETVOL
ROA
CFO
ASSETS
LEVERAGE
AF
0.168
-0.196
-0.318
-0.058
-0.192
0.401
-0.022
-0.174
0.063
0.171
0.061
-0.044
-0.172
-0.060
0.297
-0.064
-0.118
0.187
0.145
0.357
-0.054
-0.354
-0.254
-0.185
0.048
-0.141
-0.082
0.075
0.470
0.152
-0.021
0.173
0.051
-0.070
0.218
-0.043
0.148
0.130
-0.095
0.272
0.345
0.552
-0.197
37
-0.080
0.241
-0.027
0.141
-0.060
MULTILEN
HIGHFREQ
DER
0.019
-0.014
-0.084
0.070
0.009
-0.064
0.114
-0.109
0.585
-0.188
-0.028
0.252
0.141
-0.060
-0.108
-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. BDX is bad debt expenses; AR is the gross accounts receivable; ALLOW is the allowance for uncollectible accounts receivable;
WO is the write-offs of uncollectible accounts receivable. BDX, AR, ALLOW, 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 measured at the fiscal year end before loan origination; ‘No. Ana Follow’ is the number of analysts following the borrower.
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.
Panel A: Descriptive statistics
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)
BDX
AR
0.010
0.012
0.014
0.010
0.002**
-0.004***
-0.004
0.002
0.191
0.179
0.185
0.169
-0.012**
-0.016***
0.006
0.010**
ALLOW
0.013
0.015
0.016
0.016
0.002
0.000
-0.003
0.001
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***
38
TABLE 5
Bank Monitoring and Bad Debt Expenses
This table reports the results of regressing bad debt expense 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. BDX is the bad debt expenses scaled by
sales; 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 is the allowance for uncollectible accounts receivable;
WO is the write-offs of uncollectible accounts receivable. AR, ALLOW, 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) zscore 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; 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 = BDXt
Predicted
Sign
Coeff
p value
Coeff
(i)
Intercept
POST
POST×CONTROL
ARt
ALLOWt-1
WOt
WOt+1
LEVt
ARTO_INDt
SALE_SD_INDt
ALT_INDt
AFt
?
+
+
+
+
?
+
?
0.051
0.007
0.123
0.002
0.006
-0.168
0.467
0.086
-0.007
-0.004
0.027
0.000
0.001
ASSETt
?
-0.002
F stat: POST + POST×CONTROL
p value
(ii)
0.752
0.233
0.000
0.024
0.299
0.646
0.843
0.941
0.803
0.063
0.004
-0.005
-0.003
-0.095
0.516
0.124
-0.009
-0.012
-0.075
0.000
0.000
0.018
0.049
0.003
0.827
0.334
0.000
0.001
0.084
0.147
0.475
0.628
0.818
0.381
-0.001
0.503
0.210
Firm Dummy
Year Dummy
N
Yes
Yes
992
39
Yes
Yes
1984
TABLE 6
Bank Monitoring Strength and Bad Debt Expenses
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. BDX is the bad debt
expenses scaled by contemporaneous sales; POST is an indicator variable equal one if the fiscal year is in
the contract year or the following year and zero otherwise; AR is the gross accounts receivable; ALLOW is
the allowance for uncollectible accounts receivable; WO is write-offs of uncollectible accounts receivable.
AR, ALLOW, 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; 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 = BDXt
Intercept
POST
POST×MULTILENDER
POST×HIGHFREQ
ARt
ALLOWt-1
WOt
WOt+1
LEVt
ARTO_INDt
SALE_SD_INDt
ALT_INDt
AFt
ASSETt
F-stat: POST + POST×MULTILENDERS
Firm Dummy
Year Dummy
N
Predicted
Sign
Coeff
?
+
+
+
+
+
?
+
?
?
0.053
0.009
-0.004
0.111
0.002
0.086
0.005
-0.172
0.471
0.084
-0.006
-0.004
0.029
0.000
0.001
-0.002
6.13**
Yes
Yes
992
0.793
0.223
0.000
0.026
0.317
0.601
0.835
0.847
0.840
0.351
Adj-R2
p value
0.79
40
Coeff
(i)
p value
(ii)
0.136
0.004
-0.015
0.034
0.003
0.007
-0.172
0.467
0.085
-0.006
-0.003
0.034
0.000
0.001
-0.001
0.103
0.711
0.220
0.000
0.027
0.310
0.679
0.805
0.936
0.755
0.430
Yes
Yes
992
0.79
TABLE 7
Business Condition and Bad Debt Expenses
This table reports regression results of examining the effect of business condition on bank monitoring of
borrowing firms’ bad debt expenses. Tight credit market is defined as loan officer evaluation score for loan
covenant above the median (11.845) during the period 1997 -2007. Loan officer evaluation score is
discussed in Appendix B based on quarterly loan officer survey on seven dimensions obtained from Federal
Reserve website; POST is a indicator variable equal one if the fiscal year is in the contract year or the
following year after and zero otherwise; ALLOW is the allowance for uncollectible accounts receivable;
WO is the write-offs of uncollectible accounts receivable. AR, ALLOW, and WO are scaled by
contemporaneous sales. LEV is the leverage, defined as total debt divided by asset. 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) zscore computed using all firms in the industry with available data in Compustat. ASSET is natural
logarithm of book value of assets measured at the fiscal year end before loan origination; 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 = BDXt
Intercept
POST
ARt
ALLOWt-1
WOt
WOt+1
LEVt
ARTO_INDt
SALE_SD_INDt
ALT_INDt
AFt
ASSETt
Firm Dummy
Year Dummy
N
Adj-R2
Predicted Tight Credit Market (<=11.845) Loose Credit Market (>11.845)
Sign
p value
Coeff
p value
Coeff
?
+
+
+
+
?
+
?
?
-0.004
0.008
0.018
-0.114
0.551
0.188
-0.013
-0.001
0.134
0.000
0.002
0.001
0.907
0.024
0.470
0.540
0.000
0.376
0.186
0.950
0.392
0.867
0.546
0.743
0.049
0.005
0.003
-0.857
0.407
0.096
0.006
-0.012
-0.224
0.001
0.004
-0.003
0.432
0.152
0.868
0.000
0.084
0.483
0.449
0.581
0.628
0.625
0.511
0.311
Yes
Yes
500
Yes
Yes
321
0.79
0.90
41