BalanceSheetConservatismandDebtContracting
Jayanthi Sunder
a
Shyam V. Sunder
b
Jingjing Zhang
c
Kellogg School of Management
Northwestern University
April 2009
___________________________________________________________________________
a
Northwestern University, 6245 Jacobs Center, 2001 Sheridan Road, Evanston, IL 60208. E-mail: j-
sunder@kellogg.northwestern.edu; Phone: (847)491-2671.
b
Northwestern University, 6226 Jacobs Center, 2001 Sheridan Road, Evanston, IL 60208. E-mail:shyam-
sunder@kellogg.northwestern.edu; Phone: (847)467-3343.
c
Northwestern University, 6218 Jacobs Center, 2001 Sheridan Road, Evanston, IL 60208. E-mail:jingjing-
[email protected]thwestern.edu; Phone: (847)467-4630.
We thank Anne Beatty, Thomas Lys, Darren Roulstone, Sugata Roychowdhury, workshop participants at
Northwestern University, Ohio State University, Florida State University, FARS 2009 conference and especially
Sudipta Basu (discussant). We also thank the Accounting Research Center at the Kellogg School of Management for
financial support.
Balance Sheet Conservatism and Debt Contracting
Abstract
We study the role of cumulative conservatism in asset values (balance sheet conservatism) on
private debt contracting. We focus on balance sheet conservatism to isolate its effect from
conditional conservatism which has been studied in the prior literature. We hypothesize that
balance sheet conservatism provides lenders greater confidence in the collateral value of the
firm’s assets and reduces the risk in the loan (Asset Value Hypothesis). Second, we hypothesize
that balance sheet conservatism constrains future conditional conservatism such that debt
contracting efficiency is high only when the balance sheet conservatism is not high (Constraint
Hypothesis). Using a sample of bank loans we study interest spreads, deal size, covenant
intensity and covenant slack and find results consistent with our hypotheses. Our study sheds
light on the screening and monitoring role of balance sheet conservatism in debt contracting.
BalanceSheetConservatismandDebtContracting
1. Introduction
Lenders rely upon financial statements for screening and monitoring of borrowers. Prior
research has provided evidence of the linkages between borrower financial reporting choices and
debt contracting (see surveys by Holthausen and Watts, 2001; Fields, Lys, and Vincent, 2001;
and a discussion by Sloan, 2001). This study focuses on how conservative accounting choices in
borrowers’ financial statements impacts contract terms in private debt contracts. The evidence
builds upon insights from recent literature which has examined a similar question (for example,
Beatty, Weber and Yu, 2008; Zhang, 2008; Frankel and Litov, 2007; Nikolaev, 2007). The
primarily focus of these studies to examine how ongoing conditional conservatism facilitates
monitoring of the borrower. In contrast, in this study we focus on how conservative asset values
on the borrowers’ balance sheet impact the setting of both the initial contract terms and the post-
loan monitoring terms by lenders.
1
We define “balance sheet conservatism” as the cumulative conservatism in asset values and it
includes the effects of both conditional and unconditional ongoing conservatism in periods prior
to the loan contracting year. Therefore, balance sheet conservatism results in downward-biased
estimates of asset values. We conjecture that the role of balance sheet conservatism in debt
contracting could be twofold.
First, balance sheet conservatism could provide important information for screening the
borrowers. The downward-biased asset value estimates could provide valuable information to
lenders about the collateral value of the assets of the firm and the risk of non-realization of
1
In general lenders are interested in the assessment of liquidation values of asset-based collateral as reflected on the
balance sheet and the ability of the borrower to make periodic interest payments as reflected in the income statement
and cash flow statements. Our focus is primarily on the debt contracting effects of the borrower’s balance sheet
values of assets.
2
loaned amounts. For example, Watts (2003) highlights the role of conservative asset values in
alleviating the concern of lenders with respect to preservation of asset values in the event of
potential repayment problems of the borrower.
2
However, based on Ball and Shivakumar (2006)
it is not clear whether balance sheet conservatism would affect debt contracting above and
beyond past conditional conservatism.
3
Based on their argument, to the extent that the balance
sheet conservatism is driven by past unconditional conservatism, any known bias in asset values
can be undone. This suggests that any economic role of balance sheet conservatism in debt
contracting would be largely subsumed by conditional conservatism of the borrower. However,
while the effects of unconditional conservatism could be inverted, information asymmetries
between the borrower and lender could make it hard for the lender to completely achieve this
inversion. Thus the ultimate effect of balance sheet conservatism, through downward biased
asset valuation, on debt contracting remains an open empirical question.
To explore these effects we relate the borrowers’ level of balance sheet conservatism at the
time of loan initiation on the cost of debt, access to debt and level of monitoring terms set by the
lender. We expect that if downward biased asset values are valuable to the lender, borrowers
with higher balance sheet conservatism would have a lower cost of debt, larger loan size, lower
ex ante monitoring provisions (measured as number of covenants and slack in covenants).
Alternatively, if understated asset values merely add noise then we expect that it would be
contracting neutral or may even increase borrowing costs. We label these conjectures as the
“Asset Value Hypothesis” of balance sheet conservatism.
2
According to Watts (2003), understated asset values (driven by asymmetric treatment of gains and losses) could
“prevent actions by managers and others that reduce the size of the pie available to all claimants on the firm” (p.
215).
3
While unconditional conservatism that is invariant to news always introduces a downward bias in asset values, the
downward-bias in asset values arising from conditional conservatism arises from the combination of timely loss
recognition and delayed gain recognition based on realization. Watts (2003) does not explicitly distinguish between
conditional and unconditional conservatism, Basu (2001) and Ryan (2006) suggest that Watts’ argument may
involve both types of conservatism.
3
The second role of balance sheet conservatism in debt contracting relates to monitoring of
borrowers. Balance sheet conservatism includes timely recognition of adverse economic events
(i.e. conditional conservatism) in the past that could signal the borrower’s willingness to make
conservative accounting choices.
4
Such conditional conservatism is valuable for lenders who
could then monitor the firm using accounting based covenants and they reward borrowers with
lower spreads (Zhang, 2008). However, firms who have been very conservative in the past are
constrained in their ability to use write-downs to signal negative economic shocks in future if
their asset values are already reported at their lower bound estimates, even when they
consistently apply the same conservative accounting policies. We conjecture that balance sheet
conservatism provides an estimate of the degree of the constraint on future conservatism at the
time of loan contracting. High balance sheet conservatism would reduce the monitoring benefits
to the lenders who would then be unwilling to offer lower spreads. We label this conjectured role
of balance sheet conservatism on design of monitoring terms as the “Constraint Hypothesis”.
5
We measure balance sheet conservatism by building on Roychowdhury and Watts (2007)
who suggest that cumulative conservatism can be measured as the extent to which reported asset
values understate the fair value of separable assets. As they point out, the market-to-book ratio
would be a noisy measure of balance sheet conservatism because the market value contains the
value of monopoly rents in addition to the value of separable assets. Further, several papers view
market-to-book as the proxy for unconditional conservatism. In fact existing studies have
documented mixed results on the effect of market-to-book ratio on debt contracting (Wittenberg-
4
Prior literature has argued that litigation risk and reputation concerns will prevent firms from changing their
conservative accounting policies.
5
The constraining effect of the asset values in balance sheet on income statement conservatism has been discussed
in prior research by Basu (2001), Givoly et al. (2006), and Ryan (2006), modeled by Beaver and Ryan (2005), and
empirically tested by Pae et al. (2005), Ball and Shivakumar (2005), Gassen et al. (2006) and Roychowdhury and
Watts (2007). However prior studies examining effects of conditional conservatism on debt contracting have tended
to assume that the level of past conditional conservatism is a good proxy for the level of future conditional
conservatism in earnings (Zhang, 2008).
4
Moerman, 2008; Beatty et al., 2008; Zhang, 2008; Ahmed et al., 2002).
6
Therefore, to avoid
issues related to noise in measurement of cumulative conservatism using market-to-book ratio,
we adopt a different approach. We implement a model to tease out the effects of economic rents,
growth options, distress, and market sentiment inherent in the market-to-book ratio. The idea
behind the approach is to arrive at an estimate of the fair value of the borrower’s separable assets
to the book value at the point of the loan grant. We compute our measure of balance sheet
conservatism as the residual from a regression of the book-to-market ratio on proxies for rents,
misvalutions in the market value, and default risk.
7
We perform a battery of robustness tests to check the validity of this measure. First, we
regress the two components of book-to-market from our model, the fitted value (representing
growth opportunities and rents) and the residual (representing balance sheet conservatism) on
measures of timely loss recognition, conservative accruals and unconditional conservatism. We
find that while our measure of balance sheet conservatism is related to proxies for past
conservatism in the expected way, the fitted value does not demonstrate such relations. Second,
when we estimate the Basu (1997) regression on groups of balance sheet conservatism and fitted
value, only the balance sheet conservatism groups demonstrate patterns in timely loss
recognition consistent with balance sheet conservatism resulting from past conservatism and
constraining future conservatism. We describe these in greater detail in Section 4.3.
With regards to conditional conservatism, we use two alternative measures to address
concerns inherent with individual firm-level measures.
8
The measures are the sensitivity of
6
While Beatty et al. (2008) and Ahmed et al. (2002) document that market-to-book ratio or its adjusted version
(following Beaver and Ryan, 2000) is related to debt contracting, Zhang (2008) and Wittenberg-Moerman (2008)
find no evidence that market-to-book ratio affects either interest spread or trading spread.
7
Such a method is similar in spirit to what Beaver and Ryan (2000) do to decompose the book-to-market ratio into
two components, persistent bias and temporary lags in book value.
8
See Ryan (2006), Dietrich et al. (2007), and Givoly et al. (2007) for detailed discussions of measurement issues of
conditional conservatism.
5
earnings to bad news from Basu (1997), and the amount of negative non-operating accruals from
Givoly and Hayn (2000). We then use principal components analysis to obtain the first principal
component of these measures as a parsimonious measure of conditional conservatism of each
firm.
9
To test our hypotheses, we examine loan contracts during the period 1996 through 2006.
With respect to our conjecture about the screening role of balance sheet conservatism we find
that firms with higher balance sheet conservatism, on average face lower interest spreads. The
change in interest spread is economically significant. Going from the 25
th
to the 75
th
percentile,
balance sheet conservatism decreases the spreads for borrowers by 11 basis points. Next, we find
that controlling for firm and deal characteristics, the size of the deal is increasing in balance
sheet conservatism suggesting that borrowers’ access to capital is increasing in balance sheet
conservatism.
Finally, we examine whether the bank’s monitoring effort is designed to be lower if balance
sheet conservatism helps in better ex ante screening. We find that firms with higher balance sheet
conservatism have debt agreements containing fewer covenants, both accounting based financial
covenants and general covenants that restrict actions of the management. Further, the net worth
covenant slack is also looser for these borrowers. Taken together, the results suggest that lenders
do not ex ante expect to intensively monitor borrowers with higher balance sheet conservatism.
We then examine the constraining effect of balance sheet conservatism. Conditional
conservatism is expected to improve debt contracting efficiency through the monitoring role only
when the balance sheet conservatism is not high. Ignoring the constraint effect, we find some
evidence that on a stand-alone basis, conditional conservatism results in lower spreads,
consistent with Zhang (2008), and higher reliance on financial covenants (defined as the ratio of
9
Our results are robust to using a composite rank measure as well.
6
the number of financial covenants to the number of total covenants). However, past conditional
conservatism and balance sheet conservatism are related constructs and therefore the effects of
past conservatism alone cannot be interpreted without accounting for the balance sheet
conservatism. We therefore interact past conditional and balance sheet conservatism at the firm
level to examine the constraint hypothesis. We create nine mutually exclusive groups out of the
interaction of independent sorts of conditional conservatism and balance sheet conservatism into
three groups each (low, medium, and high). Holding constant the level of balance sheet
conservatism, we find that spreads are decreasing in conditional conservatism only in the low
balance sheet conservatism group, consistent with the constraint hypothesis. Further, we find that
this result is driven by firms that have a high usage of financial covenants relative to general
covenants.
Finally, we find that conditional conservatism is positively associated with reliance on
accounting based covenants to monitor borrowers. After we interact past conditional and balance
sheet conservatism using the nine groups based on a two-way sorting, we find that the positive
association only exists for the groups that have low balance sheet conservatism, again supporting
the constraint hypothesis.
This study highlights the difference in contractibility between conditional conservatism and
balance sheet conservatism when designing debt contracts. While lenders value ongoing future
timely recognition of losses, borrowers must be both willing and able to follow conservative
accounting after the loan origination. In contrast, balance sheet conservatism represents pre-
commitment by borrowers and provides the lenders ex ante benefits in terms of lower bound
asset valuation. Our results show that lenders recognize this and consequently charge lower
spreads and grant bigger loans for firms with high balance sheet conservatism and impose fewer
7
covenants and provide more slack in their net worth covenants. Balance sheet conservatism also
affects the ability of firms to be conditionally conservative in the future and thus has an
additional indirect impact on debt contracting. Lenders value the role of ongoing conditional
conservatism only when balance sheet conservatism is not binding.
The rest of the paper is organized as follows: Section 2 introduces various concepts of
conservatism. Section 3 outlines the research hypotheses. Section 4 describes the sample, the
variable measurements, and the research design. Section 5 presents the summary statistics and
the empirical results. Section 6 concludes the study.
2. Conditional, unconditional and balance sheet conservatism
Two types of conservatism result in understatement of the book values of net assets relative
to the economic values. One is defined by Basu (1997) as representing “accountants’ tendency to
require a higher degree of verification for recognizing good news than bad news in financial
statements” (p. 4). The asymmetric verification leads to timely recognition of economic losses
but not economic gains. Examples of this type of conservatism include lower of cost or market
accounting for inventories and asset write-downs. Under timely loss recognition, reported
earnings are more sensitive to contemporaneous losses, which make the income statement more
informative to users who care about firms’ downward risks but not the upside potential. The
impact on the income statement also flows through to the balance sheet due to the relation
between the two financial statements. Writing down assets under bad news but not writing up for
good news can result in persistent understatement of net assets on the balance sheet.
The other aspect of conservatism that causes understatement of assets is the selection of
‘conservative’ accounting methods (Basu, 1997; Givoly et al., 2007). Examples of such
unconditional conservative accounting are immediate expensing for R&D costs, the use of
8
accelerated depreciation method relative to economic depreciation, and LIFO inventory
valuation. This type of conservatism lowers asset values, and such a balance sheet effect persists
over time while it generally result in understating earnings in the early years of an asset’s life to
eventually overstating earnings in the later years.
Both types of conservatism lead to understatement of asset values, but they differ in their
potential to convey new information in the financial statements (Ball and Shivakumar, 2005;
Beaver and Ryan, 2005; Ryan, 2006). Timely loss but not timely gain recognition introduces
understatement conditional on the type of the news and is therefore called conditional
conservatism. In contrast, applying conservative accounting methods brings in understatement by
systematically allocating the cost over the life of an asset, without reflecting new information
about changes in asset values (Basu, 2001, p. 1334), and is therefore referred to as unconditional
conservatism. Ball and Shivakumar (2005) argue that the known biases (in earnings and asset
values) are likely to reduce contracting efficiency as the biases do not bring any new information
but noise to contracting parties.
In this study, we focus on balance sheet conservatism, which is the cumulative effect of past
application of conditional and unconditional conservatism. The cumulative effect is reflected as
persistent understatement of net asset values on the balance sheet. Balance sheet conservatism
relates to conditional conservatism in two respects. On one hand, conditional conservatism, by
writing down, but not up, the book asset values, contributes to balance sheet conservatism at the
end of the period. On the other hand, balance sheet conservatism at the beginning of the period
creates accounting slack that constrains future application of conditional conservatism, affecting
9
both the likelihood and the magnitude of future write-downs.
10
For a detailed discussion also
refer to Beaver and Ryan (2005), for a model of the interactions between conditional
conservatism and unconditional conservatism at a conceptual level.
While the first effect can be easily understood from how balance sheet conservatism is
defined, the second one is less obvious and is illustrated in the following example. Suppose a
firm has a very low book value of an asset compared to its economic value, either caused by past
asset write-downs or by adopting very conservative accounting methods or both. When there is a
negative shock, unless the shock is sufficiently big so that the economic value drops below the
book value, the firm will not recognize the bad news in the financial statement. Therefore, over a
wide range of economic shocks conditional conservatism would not be observed for the firm.
Moreover, even if the negative shock was big enough to trigger a write-down, the amount of the
write-down for such a firm would be smaller than for firms with less accounting slack.
3. Hypotheses Development
3.1 Asset Value Hypotheses
One strand of literature on conservatism emphasizes that downward bias in net asset values
help to address the agency problem in debt contracting.
11
Early literature on the study of
accounting choices argues that income-decreasing accounting methods are preferred in debt
contracting because they result in lower distributions to shareholders and management and thus
leave a bigger pie to lenders. By examining samples of debt contracts, Leftwich (1983) finds
evidence consistent with the argument that the adjustments to measurement rules make lending
agreements systematically more conservative.
10
Accounting slack is usually defined as the difference between economic value and book value. However,
according to Roychowdhury and Watts (2007), accounting slack is only the difference between market value of net
separable assets and book value of net assets.
11
The other strand points out that only timely loss recognition (conditional conservatism) increases contracting
efficiency. Such an argument will be discussed in developing the Constraint Hypothesis.
10
Based on Basu (1997), Watts (2003) incorporates the aspect of “asymmetric verification
requirements for gains and losses” (p. 208) into his argument on the role of accounting
conservatism in contracting. Watts argues that understatement of net assets serves to constrain
management opportunism and wealth transfer when contracting parties have “asymmetric
information, asymmetric payoffs, limited horizons, and limited liability” (p. 209). Specifically,
reporting net assets at the lower bound, derived from either prior timely loss recognition or
unconditional conservative accounting methods, increases verifiability of net asset values, given
managers’ incentives to introduce bias and noise in financial reporting. Understatement of net
asset values not only helps to prevent improper distribution of firm wealth to managers and
shareholders at the expense of debtholders and as a result increases the loan value, but also
lowers the risk of uncertainty in asset valuations for lenders when borrowers are in the worst case
scenario. Consequently, lenders would be willing to lend larger amounts to borrowers with
higher balance sheet conservatism at lower interest spreads.
Further, balance sheet conservatism increases the collateral value of net assets when
assessing liquidation value of the firm. Since lenders in private debt mostly have senior claims
against net assets of the firm, more confidence on net asset values may reduce the need to
monitor the loan. Therefore, for borrowers with higher balance sheet conservatism, lenders
would rely less on the use of covenants and if using net worth covenant, would set looser net
worth covenant to avoid frequent covenant violations, which could be costly in debt contracting
process. Formally, our first set of the hypotheses based on asset values are stated in the
alternative form as:
H1a: Interest spread is decreasing in balance sheet conservatism.
11
H1b: Loan size is increasing in balance sheet conservatism.
H1c: Covenant intensity is decreasing in balance sheet conservatism.
H1d: Net worth slack is increasing in balance sheet conservatism.
3.2 Constraint Hypotheses
Basu (1997) and Ball and Shivakumar (2005) highlight the importance of conditional
conservatism in contracting. By timely reflecting contemporaneous loss information in financial
statement, conditional conservatism increases contracting efficiency. Specifically in debt
contracting, timely loss recognition affects the effectiveness of the use of covenants. Once a
borrower’s financial condition deteriorates, timely loss recognition triggers covenant violation
more quickly. Therefore, lenders are able to obtain the control rights in a timely manner and take
necessary actions to protect their interests.
What is essential in the above argument is that it is ongoing conditional conservatism with its
potential to provide new information to contracting parties that really matters in the contracting
process. Since lenders cannot observe future conditional conservatism at loan origination, prior
research studying how conservatism affects debt contracting terms assumes that lenders use past
level of conditional conservatism as a proxy for the borrower’s willingness to be conditionally
conservative in the future. Zhang (2008) and Nikolaev (2007) explicitly address the validity of
this assumption in their studies examining the effect of past conditional conservatism on loan
pricing and covenant intensity, respectively. They point out that borrowers’ reputation effects
and other constraints, such as the threat of auditor litigation or using fixed GAAP in computing
covenants, would keep borrowers from changing accounting practice. But, even if borrowers
could precommit to apply the same accounting practice after entering into the debt contracts, it is
12
still uncertain whether borrowers could keep the same level of conditional conservatism given
the interactions between conditional and balance sheet conservatism.
12
Beaver and Ryan (2005) conceptually use a model and simulation to capture how past
applications of unconditional conservatism and conditional conservatism create accounting slack
that preempts future conditional conservatism. The model is rich in terms of analyzing different
forms of unconditional conservatism and frictions in the application of conditional conservatism
and emphasizes that the application of conditional conservatism is probabilistic and history-
dependent (p. 272). Consistent with Beaver and Ryan’s (2005) conjectures on the constraining
effect, empirical studies document that a negative association between the market-to-book ratio
as a proxy for accounting slack caused by past conservatism and subsequent conditional
conservatism (Pae et al., 2005; Ball and Shivakumar, 2006; Gassen et al., 2006; Roychowdhury
and Watts, 2007). The constraining effect of balance sheet on income statement has also been
examined by Barton and Simko (2002) in a different context. They find that overstated net assets
on the balance sheet constrain managers’ ability to bias earnings upwards in the future.
Due to the constraining effect of balance sheet conservatism on future ongoing conditional
conservatism, we hypothesize that lenders would consider such a constraining effect and
structure contract terms accordingly. Specifically, the relation between past conditional
conservatism and debt contracting terms documented in prior studies would be driven by the
firms with low levels of balance sheet conservatism (i.e. where the balance sheet conservatism
does not constrain future conditional conservatism). We focus on two contracting terms, loan
pricing and covenant intensity. As Zhang (2008) finds that lenders reward more conditionally
12
Borrowers’ willingness to commit to the same accounting practices has been examined in the studies testing debt
covenant hypothesis (DeAngelo et al., 1994; DeFond and Jiambalvo, 1994; Sweeney, 1994; Dichev and Skinner,
2002). The results are mixed. In this paper, we assume that borrowers are willing to apply the same accounting
practices and focus on borrowers’ capability to maintain the level of conditional conservatism.
13
conservative borrowers with lower interest rates, we expect that such a negative relation would
be driven by firms with lower accounting slack that are not constrained in reflecting future
timely loss recognition. Ongoing conditional conservatism accelerates covenant violation and
thus makes the use of covenants more effective. Nikolaev (2007) documents a positive relation
between conditional conservatism and covenant intensity, confirming that conditional
conservatism increases the effectiveness of covenants. Hence we expect that this positive relation
would be driven by firms with low balance sheet conservatism. Formally, our second set of the
hypotheses based on constraining effect are stated in the alternative form as:
H2a: Past conditional conservatism is associated with lower spreads only when balance sheet
conservatism is not high.
H2b: The benefit of lower spreads is further consistent with it being a reward when a lender
expects to monitor using accounting based covenants.
H2c: Past conditional conservatism is associated with greater reliance on financial covenants
for monitoring the firm.
4. Data and research design
4.1 Sample selection
We collect private debt information from the Dealscan database for the time period from
1996 through 2006. The basic unit in Dealscan is a loan, which is also referred to as a “facility”.
A borrower usually enters into multiple loans at the same time with either a single bank or a
group of banks. These loans are grouped into a package, which is also called as a “deal”. The
analyses in this study are conducted at the facility level. To avoid over-weighing those loans that
are issued in the same year, which would have the same conservatism measures and control
14
variables, we only keep the loan with the largest borrowing amount for each borrower in each
year.
Consistent with prior studies, we focus on dollar denominated loans borrowed by US firms.
Borrowers in financial and regulated utility industries are excluded as the debt contract terms for
these industries differ substantially from other industries. We retain revolvers with a maturity
greater than one year and term loans. Further, we drop any loan without spread, maturity, and
loan amount information.
We manually match borrowers in the loan data to firms in the COMPUSTAT universe by
matching on company name. We require that each firm in the sample have necessary accounting
information and stock return data to obtain borrower specific control variables and to estimate
accounting conservatism. The final sample contains 4,835 loans.
4.2 Measuring debt contracting terms
The debt contracting terms studied in this paper are spread, deal size, covenant intensity,
(tangible) net worth covenant slack, and usage of financial covenants. Spread is measured by the
all-in-drawn spread (AIS). Dealscan computes this figure as the sum of the borrowing spread
over the 6-month LIBOR and the related fees for each facility, assuming that the facility is fully
used. Such a computation enables comparison of borrowing costs across facilities with different
fee structures.
Access to capital is measured as the ratio of the deal size to total assets. Deal size is
computed as the sum of all facilities included in a package.
Covenant intensity is measured as the number of financial covenants or the number of
general covenants contained in a debt contract. According to Drucker and Puri (2007), Dealscan
contains coding errors whereby some loans with covenants are misclassified as loans without any
15
covenants. But they also note that as long as Dealscan reports the existence of at least one
covenant for the loan, the information for all other covenants appears to be correct. Therefore to
minimize measurement errors in computing covenant intensity, we exclude loans for which
Dealscan does not report any covenants when examining covenants related contracting terms.
(Tangible) net worth covenant slack is computed as the (tangible) net worth slack scaled by
assets. (Tangible) net worth slack is the difference between (tangible) net worth at the end of the
quarter before loan origination and the (tangible) net worth threshold specified in the debt
contract. We examine tangible net worth and net worth separately because Frankel et al. (2007)
and Beatty et al. (2008) document that the usage of these two types of covenants are very
different. Tighter slack means higher restrictions imposed on the borrower, as the borrower is
more likely to violate the covenant and transfer the control rights to the lenders.
4.3Measuring balance sheet conservatism
The measure of balance sheet conservatism is based on an adjusted version of the book-to-
market ratio. The market-to-book ratio reflects the understatement of net asset values to
economic values and is a natural way to proxy for balance sheet conservatism. However,
according to Roychowdhury and Watts (2007), accounting slack that arises from past
conservatism is only the difference between market value of net separable assets and book value
of net assets. The market-to-book ratio measures conservatism with errors as it also includes
rents enjoyed by the firm in its current and future projects. To address the concern that the results
might be caused by the things other than balance sheet conservatism, we regress book-to-market
ratio on a set of variables that proxy for rents, growth, distress, and market sentiment, with
industry and year fixed effects. The residual from the regression is our measure of Balance Sheet
Conservatism. Specifically, the model is:
16
1
&
(I)
where Book-to-Market is computed as the book value of assets divided by the market value
of equity plus the book value of debt.
13
We multiply Book-to-Market by -1 so that the resulting
measure is increasing in balance sheet conservatism.
We employ two forward looking growth measures to proxy for rents possessed by the firm
and reflected in the stock price. We expect that the higher growth opportunities in the future, the
higher Book-to-Market. The first growth measure is Long-Term Growth Forecasts, which is the
median of all long term growth estimates made by analysts in the fiscal year prior to loan
origination obtained from the IBES database. The second growth measure, Sales Growth, is
based on Compustat information, defined as sales in the year of loan origination divided by sales
in prior fiscal year.
We further use the interaction of Industry Concentration and Indicator of Top Four
Companies to proxy for rents generated from market power. We expect that Book-to-Market is
positively associated with the interaction term. Industry Concentration is the Herfindahl index
calculated by summing the squares of the individual firm market shares based on sales for the
four largest companies in an industry (four-digit sic code). We divide the measure by 10,000 to
avoid very small coefficients. Indicator of Top Four Companies equals to 1 if the company is
among the top four companies based on sales in an industry and 0 otherwise.
13
We use book-to-market instead of market-to-book since the former has better distributional properties than the
latter.
17
To proxy for market sentiment that may lead to market overvalues or undervalues certain
firms because their growth prospect, we use two market indexes. One is Consumer Sentiment
Index. It is the index of the consumer sentiment from University of Michigan. According to Qiu
and Welch (2006), this index is a good proxy for investor sentiment. The other index is S&P
Index, which is the level of the S&P’s Composite Index (NYSE/AMEX only) from CRSP. We
expect a positive association between these two market indexes and Book-to-Market.
Last, we control for firm specific variables that proxy for distress. Profitability is measured as
EBITDA scaled by the lag of assets. Credit Rating is S&P LT Domestic Issuer Credit Rating
from Compustat. For those firms without credit rating information, we following Barth et al.
(2008) and Beatty et al. (2008) to estimate ratings.
14
Higher value of Credit Rating means lower
credit quality. Standard Deviation of Returns is the measure of volatility of returns, defined as
the standard deviation the daily return less the corresponding decile returns times 100 over 365
days right before the loan origination date. Higher volatility is suggestive of higher default risk
(Frankel and Litov, 2007). We expect that the dependent variable (Book-to-Market*-1) is
positive associated with Profitability and negatively associated with Credit Rating and Standard
Deviation of Returns.
In order to validate our measure of Balance Sheet Conservatism, we perform two types of
analyses to compare the properties of the residual value and fitted value from the first-stage
regression.
Validation 1
: In the first analysis, we regress the residual and fitted values respectively on
several alternative measures of conservatism, similar to the validation method used in Beaver
14
We first regress the rating on Log(Assets), ROA, Debt-to-Assets, Dividend Indicator, Subordinated Debt Indicator
and Loss Indicator, with industry and year fixed effects for rated firms. We then use the estimated coefficients from
the first regression and the firm’s financial information to compute a credit rating for each firm in each year. The
computed rating values are winsorized at 2 and 27 to be consistent with the range of ratings reported in Compustat.
18
and Ryan (2000). The idea is that if the residual value captures Balance Sheet Conservatism,
which is the cumulative effect of past conservatism, we should expect that it is positively
associated with other measures proxy for past conservatism. Such a positive association,
however, does not necessarily exist for the fitted value unless alternative conservatism measures
are positively related to growth, market sentiment and distress. Specifically, we run the following
regressions:
&
(II)
Where LIFO Reserve Indicator is 1 if LIFO Reserve is positive and 0 otherwise. Accelerated
Depreciation Indicator takes value of 1 if the firm only uses accelerated depreciation and 0
otherwise. Advertising Reserve is amortized advertising expenses using a sum-of-the-years-
digits method over two years. R&D Reserve is amortized R&D expenditures using a sum-of-the-
years-digits method over five years. Asymmetric Timeliness and Timely Loss Recognition are
the estimated coefficients from Basu’s (1997) market-based model at industry level (three-digit
sic codes) for each year of the sample period using prior ten years of data. The details on
estimating Asymmetric Timeliness and Timely Loss Recognition are included Section 4.4. Non-
Operating Accruals is measured following Beatty et al. (2008), which is the average of non-
operating accruals scaled by assets over a period with a maximum of 5 years and a minimum of 2
years.
19
Validation 2:
The second analysis follows Roychowdhury and Watts (2007) to focus on the
relation between Asymmetric Timeliness / Timely Loss Recognition and Balance Sheet
Conservatism. We start by assigning observations to three groups ranked by either the residual
value or the fitted value. In each group, we then run pooled Basu (1997) regression over a pre-
period and a post-period separately. The pre-period consists of a period covering three years
before Book-to-Market is measured. The post-period is defined as a period covering three years
after Book-to-Market is measure. By such a design, we study how Asymmetric Timeliness or
Timely Loss Recognition is related to end-of-period and beginning-of-period balance sheet
conservatism. Since the paper by Roychowdhury and Watts (2007) and other related research
show that asymmetric timeliness is positively associated with end-of-period Market-to-Book and
is negatively associated with beginning-of-period Market-to-Book, we expect to find such a
pattern when the groups are ranked by the residual value but not when the groups are ranked by
the fitted value.
Table 3 Panel A displays the results of measuring balance sheet conservatism. All the
variables except Industry Concentration for which we do not have a predicated sign behave in
the expected direction and are significant. The results indicate that Book-to-Market is positively
associated with firm growth and market sentiment and negatively associated with the distress
factor.
Panel B provides the results for the first validation analysis. When the dependent variable is
the residual value, all the signs of the coefficients are consistent with our expectations. In other
words, the balance sheet conservatism proxied by the residual value is increasing in all other
measures of past conservatism. In contrast, when the dependent variable is the fitted value,
almost all the signs of the coefficients are in the opposite direction. The only except is for R&D
20
Reserve. The positive relation between R&D Reserve and the fitted value is likely to be driven
by the fact that R&D Reserve is also a good proxy for growth opportunity besides being a
measure of conservatism.
Panel C shows the results for the second validation analysis. First, in the pre-period, which is
a three-year period before Book-to-Market is measured, we find that Asymmetric Timeliness or
Timely Loss Recognition increases in the groups ranked by the residual value. The differences of
coefficients between high and low groups are highly significant. However, when we move to the
post-period, the pattern dramatically changes. Asymmetric Timeliness or Timely Loss
Recognition decreases in the groups ranked by the residual value, with a significant difference
between high and low groups. Such a finding supports that past conditional conservatism
contributes to balance sheet conservatism and balance sheet conservatism constrains future
conditional conservatism. When the groups are ranked by the fitted value, we do not observe
such a change of pattern moving from the pre-period to the post-period. Asymmetric Timeliness
and Timely Loss Recognition always decrease from low to high groups. The contrast between
the results on the residual value and on the fitted value again validate our measure of balance
sheet conservatism capturing cumulative effect of past conservatism and being a better measure
than the raw Book-to-Market.
4.4 Measuring conditional conservatism
Following Beatty et al. (2008) and Zhang (2008), we base our measure on alternative metrics
of conditional conservatism to address problems associated with each individual measure
identified by Ryan (2006) and Givoly et al. (2007). We use a composite measure of conditional
conservatism computed computed as the principal component of the individual measures. We
hope this composite measure, labeled as Conditional Conservatism, captures conditional
21
conservatism while minimizing the noise in any individual measure. This composite measure is
our primary measure of conditional conservatism.
The first measure, Timely Loss Recognition, is the sensitivity of earnings to bad news
derived from Basu’s (1997) market-based model (referred to as the “Basu model” in the rest of
the paper). In the model, stock return is used as a proxy for contemporaneous economic gains
and losses. Because of accountants’ higher verification requirement to recognize good news vs.
bad news, earnings are expected to be more sensitive to negative returns than to positive returns.
Specifically, the model is:
(I)
where
is annual income before extraordinary items for firm in the fiscal year deflated by
the market value of equity at the beginning of the year and adjusted by the average for sample
firms in year ,
is the 12-month return on firm i ending three months after the end of the
fiscal year less the corresponding CRSP equal-weighted market return, and
is an indicator
variable equal to one if the firm’s market-adjusted return
is negative and zero otherwise.
Observations with the deflated earnings or the returns falling to the top and bottom 1 percent are
excluded. In the above regression, is the measure of Timely Loss Recognition.
We estimate the Basu model at industry level since firm-specific time-series regressions have
very few observations for each firm and are likely to result in noisy estimates with a downward
bias (see Givoly el al. 2007 for detailed discussion). Specifically, we run the regressions by
three-digit SIC codes for each year of the sample period of 1996 through 2006 using prior ten
years of data. Industries with less than ten firms are excluded to ensure a reliable estimate of
conditional conservatism. The corresponding industry-year measure of conditional conservatism
is assigned to each sample firm.
22
The second measure, Non-Operating Accruals, are based on Givoly and Hayn (2000). We
follow Beatty et al. (2008) to estimate this measure. Non-Operating Accruals is the average of
non-operating accruals deflated by assets over the period with a maximum of 5 years and a
minimum of 2 years before the loan origination year. The non-operating accruals are calculated
using the annual data as (item 172 + item 14 – item 308 + item 302 + item 303 + item 304 + item
305). In order to make the direction of this measure consistent with other measures, we multiply
the non-operating accruals by negative one.
15
4.5 Research design
We use two models to analyze the relation between contract terms and conservatism. The
first model examines balance sheet conservatism in isolation to see how it relates to contract
terms. The second model incorporates interactions of conditional and balance sheet
conservatism. We use the first model to test the asset value hypothesis and the second model to
test the constraint hypothesis. Specifically, we estimate the following OLS regression including
deal purpose fixed effects and industry fixed effects in Model (1):
(1)
Where the loan terms is either Spread, Deal-to-Assets, Number of Financial Covenants, Number
of General Covenants, or (Tangible) Net Worth Covenant Slack. Besides in the case of covenants
and slack, we exclude collateral on the RHS since it is included as a general covenant and use the
15
We considered using the relative skewness of accruals versus cash flows as a third measure but the data
requirements for estimating the firm-specific skewness measure reduced the data size considerably.
23
deal level versions of the other loan variables. For the Spread specification, we also include the
Default Spread and Term Spread measured for the month of loan initiation. Balance sheet
conservatism is the residual value from the first stage regression. The Asset Value Hypothesis,
predicts that the coefficient on balance sheet conservatism will be negative for Spread (H1a),
positive for Deal Size (H1b), negative for Covenant Intensity (H1c) and positive for Net Worth
Slack (H1d), since balance sheet conservatism by reporting net asset values at lower bonds
reduces the risk of the loan since asset valuations are more conservative.
We include a set of control variables to proxy for firm-specific and loan-specific risks that
are likely to affect loan spreads. Firm-specific controls are computed using the financial and
return data prior to loan origination. Besides the control variables already described in the
previous section, the control variables include Log Assets measured as the log of the total assets
for each firm, which is a proxy for reputation and information asymmetry. Leverage is measured
as debt to capital as in Rajan and Zingales (1995). Following Berger et al. (1996), Asset
Tangibility is computed as: Asset Tangibility = (Cash and Short-Term Investments + 0.715 ×
Receivables + 0.547 × Inventories + 0.535 × PPE Net) / Assets.
The loan-specific controls include Facility-to-Assets, representing the ratio of the loan
amounts to assets. Log Maturity is the log of the maturity (in months) of the loan, a proxy for the
length of the loan. These loan characteristics can either convey borrowers’ credit risks or
represents trade-offs in contracting terms. Therefore, the signs of these control variables can go
either way depending on whether debt terms complement or substitute with each other.
Collateral Indicator indicates whether the loan is secured with collateral. Finally, we include
dummies for the deal purpose, revolver and industry. All the standard errors are clustered at the
firm and year levels.
24
In the second model to test interactions of conditional and balance sheet conservatism, we
divide the observations into mutually exclusive nine groups, based on independent sorts of
balance sheet conservatism and conditional conservatism into three groups each (high, medium,
and low). We create nine indicator variables to represent the different combinations of
conditional and balance sheet conservatism, ranging from Low CC & Low BC (captured in the
intercept) to High CC & High BC. These groupings allow us to isolate the effect of one
dimension of conservatism while keeping the other fixed. Specifically, the model is:
∑
(2)
Controls refers to the set of control variables that are used in Model (1) and are described above.
The intercept captures the effects of the Low CC and Low BC group.
The Asset Value Hypothesis predicts that in comparison to groups with low balance sheet
conservatism (Low BC), groups with high balance sheet conservatism (High BC) are associated
with higher deal amount, lower loan spreads, less covenant intensity, and looser net worth
covenant slack. The Constraint Hypothesis predicts that the relation between the spread and
conditional conservatism should depend upon the specific balance sheet conservatism group that
a firm is in. This is because past conditional conservatism is rewarded with lower spreads and
results in the effective use of financial covenants only if such conditional conservatism is
expected to persist in the future. Further the benefit is most likely when the lender uses
accounting based covenants to monitor the borrower and so we examine the spread results for
sub-samples based on the extent of financial covenants use.
25
5. Empirical results
This section is organized as follows. Section 5.1 discusses summary statistics and correlation
matrix for the variables used in the later tests. Section 5.2 reports the multivariate analyses
examining the effect of the two dimensions of accounting conservatism on loan pricing, deal
size, covenant intensity, and net worth covenant slack.
5.1 Summary statistics
Table 1, Panel A provides the distribution of loans over the sample period from 1996 through
2006. Panel B displays the industry distribution of loans based on the industry classification in
Barth et al. (1998). We exclude finance and utilities industries. Firms from the durable
manufacturing industry comprise more than one fourth of the sample. Retail, services, and
computers are the next three major industries in the sample.
Table 2 provides summary statistics of firm, loan, and deal characteristics as well as various
measures of accounting conservatism. There is significant variation in firm size with the mean
value of total assets being over $3 billion while the median is $694 million. The average firm is
profitable and the median rating is almost 14 which corresponds to BB-. The median spread is
125 basis points and the median maturity is almost five years. The distributions of firm size and
loan maturity are skewed and therefore we transform these variables to their log forms.
5.2 Multivariate Analysis
In this section, we investigate the relation between accounting conservatism and loan pricing,
deal size, and covenants.
5.2.1 Tests of the Asset Value Hypothesis
26
Table 4 presents the results of the regression of spreads on balance sheet conservatism and
control variables. The coefficient on balance sheet conservatism is negative and significant
suggesting that lenders reward firms that provide lower-bound asset values with lower spreads.
This is consistent with H1a of the Asset Value Hypothesis. The coefficients on most of the
control variables representing firm characteristics have the expected signs. Larger firms with
higher profitability, lower leverage, better credit ratings, less volatile returns, and larger portion
of fixed assets tend to incur lower borrowing costs. The loan characteristics, such as size of loan
and maturity, are negative and significant, consistent with the prior literature and suggesting that
the loan terms may be proxying for a dimension of risk. The coefficient on collateral is
significantly positive contrary to the expectation of a trade-off between the use of collateral and
loan pricing. However this is consistent with Bharath et al. (2008) in a similar regression of
spreads. We then estimate the regression after excluding high-tech firms (computers and
pharmaceuticals) and young firms and continue to find strong results. Overall, Table 4 provides
strong support for H1a of the Asset Value Hypothesis in the full sample as well as the sub-
samples.
Table 5 reports the results for the relation between balance sheet conservatism and deal size.
We find that the coefficient on balance sheet conservatism is positive and significant consistent
with H1b of the Asset Value Hypothesis. The deal size is increasing in profitability and
decreasing in volatility of returns and growth opportunities. We also find that it is associated
with higher levels of leverage overall and longer maturities
Next, we examine the use of covenants in loan contracts (Covenant Intensity). The results
are reported in Table 6. The dependent variable are the number of financial covenants and the
number of general covenants. We find that covenant intensity, both financial and general, is
27
reducing in balance sheet conservatism, consistent with a lower need for monitoring. Control
variables do not behave exactly the same when the dependent variable changes from the number
of financial covenants to the number of general covenants, suggesting that the process to select
financial vs. general covenants is different. Overall, the results in Table 6 are consistent with H1c
of the Asset Value Hypothesis and suggests that firms with higher balance sheet conservatism
have lower covenant intensity. In unreported tests, we re-estimate the model using a Poisson
regression since our dependent variable is a count variable of the covenants and the results are
very similar.
Finally, Table 7 reports the results from a regression of net worth slack on balance sheet
conservatism. Here we find that the coefficient of interest is positive and significant, consistent
with H1d of the Asset Value Hypothesis. Firms with higher levels of balance sheet conservatism
tend to have higher net worth slack. We also find that large firms with better credit quality tend
to have looser covenant slack.
Overall, the results in Table 4 through Table 7 highlight the important role for balance sheet
conservatism in the debt contracting process as laid out under the Asset Value Hypothesis.
5.2.2 Tests of the Constraint Hypothesis
We next examine the constraint hypothesis by forming nine groups based on the interaction
of conditional conservatism and balance sheet conservatism. We study the joint effect of these
two dimensions of conservatism on spreads and covenant usage. Table 8 examines the relation
between spreads and conservatism allowing for the interaction between past conditional
conservatism and balance sheet conservatism. We first examine the effect of conditional
conservatism on spreads, ignoring the interaction with balance sheet conservatism. In
specification 1, we find that spreads are decreasing in conditional conservatism, consistent with
28
Zhang (2008). In specification 2, we regress spreads on the nine groups and conduct F-tests for
the differences in coefficients across groups. Based on the Constraint Hypothesis H2a, we expect
that the negative relation between past conditional conservatism (CC) and loan pricing should be
driven by the firms with low levels of balance sheet conservatism (BC). We find that the
difference between High CC and Low CC within the group of Low BC firms is negative and
significant.
This specification also allows us to revisit the issue of whether the balance sheet
conservatism results are concentrated in firms with high past conditional conservatism. We find
that irrespective of the level of conditional conservatism, spreads reduce ranging from 17 to 25
basis points when you go from the low BC group to the high BC group . This suggests that the
understatement of assets is valuable to the lenders regardless of the source of the conservatism.
In Table 8 panel C, we divide the sample into two groups based on financial covenant
intensity relative to total covenant intensity and find that the reduction in spreads is driven by
high use of financial covenants.
Finally, in Table 9 we report the results examining the reliance on financial covenants as a
monitoring mechanism and find that while conditional conservatism by itself increases the
reliance on financial covenants relative to general covenants., Once we interact conditional and
balance sheet conservatism, the reliance on financial covenants relative to general covenants
increases in conditional conservatism only for Low BC group.
Overall, taken together our results provide strong evidence that lenders care about borrowers’
balance sheet conservatism in setting contract terms. Further, balance sheet conservatism
imposes a constraint on the ongoing ability of the firm to be conservative and therefore past
29
conditional conservatism reduces the borrowing cost only when balance sheet conservatism is
not high.
6. Conclusions
We shed light on the debt contracting implications of different dimensions of accounting
conservatism. We study the property of conservative financial reporting wherein assets are
reported in financial statements at their lower bound values. To measure the effect of
conservatism on asset values we develop the construct of balance sheet conservatism. Balance
sheet conservatism is the total accumulated conditional conservatism and unconditional
conservative resulting from application of conservative accounting methods.
We hypothesize that the magnitude of balance sheet conservatism improves the confidence of
the lender in the asset values that serve as collateral for the borrower and reduces the risk in the
loan (Asset Value Hypothesis). Consequently, higher the level of balance sheet conservatism in
the borrower financial reports, lower would be interest spreads, higher the deal size, lower the
reliance on covenants, and higher the slack for the net worth covenant.
Another effect of balance sheet conservatism on debt contracting is through its impact on the
future ability of firms to be conditionally conservative. Prior research assumes that the level of
past conservatism is a good proxy of future conditional conservatism in earnings. However firms
with high balance sheet conservatism are constrained in their ability to use write-downs to signal
negative economic shocks in future since their asset values are already reported at their lower
bound estimates. Thus balance sheet conservatism interacts with conditional conservatism in
impacting the firm’s ability to be conditionally conservative in the future. Therefore we
hypothesize that conditional conservatism will improve debt contracting efficiency only when
30
the balance sheet conservatism is not too high (Constraint Hypothesis). Accordingly, we expect
lower spreads and greater reliance on covenants for firms that are conditionally conservative
only if current balance sheet conservatism is low and not a constraint. We find results consistent
with our hypothesis.
Overall, our study adds to the understanding of the effect of accounting conservatism on debt
contracting efficiency. We show that conservatism in asset values reported on the financial
reports of the borrower at the time of the lending decision has a significant effect on debt
contracting through screening and monitoring. Further, while prior literature has focused on the
efficiency gains from conditional conservatism, we show that the benefits from conditional
conservatism are constrained by balance sheet conservatism.
31
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34
Appendix: Description of Variables
Firm Characteristics
Log of Assets The logarithm of assets.
Profitability EBITDA scaled by the lag of assets.
Loss Years The percentage of losses over the past 5 years. The loss is defined as negative net
income before extraordinary income.
Leverage The sum of long-term debt and debt in current liabilities divided by capital
(defined as total debt plus equity)
Credit Rating Credit Rating is S&P LT Domestic Issuer Credit Rating. Otherwise, Credit Rating
is estimated using a method similar to Barth et al. (2008) and Beatty et al. (2008).
First, we regress ratings on Log(Assets), ROA, Debt-to-Assets, Dividend
Indicator, Subordinated Debt Indicator, and Loss Indicator, with industry and year
fixed effects for rated firms. We then use the estimated coefficients from the first
regression and the firm's financial information to compute a rating for each firm in
each year. The computed rating values are winsorized at 2 and 27.
Standard Deviation of
Returns
The standard deviation of the daily return less the corresponding decile returns
times 100 over 365 days right before the loan origination date.
Long-Term Growth Forecasts The median of all long-term growth estimates by analysts obtained from IBES.
Asset Tangibility Following Berger et al. (1996), Asset Tangibility is computed as: Asset
Tangibility = (Cash and Short-Term Investments + 0.715 × Receivables + 0.547 ×
Inventories + 0.535 × PPE Net) / Assets.
Loan Characteristics
Spread The interest rate spread over LIBOR on all drawn lines of credit.
Facility-to-Assets The amount of facility divided by assets.
Deal-to-Assets The amount of deal divided by assets.
Log of Maturity The logarithm of maturity in months.
Collateral Indicator An indicator variable taking value 1 if the loan is secured with collateral, and 0
otherwise. Missing values are treated as 0.
Performance Pricing
Indicator
An indicator variable taking value 1 if the loan has a performance pricing option
tying the promised yield to one or more accounting measures of performance, and
0 otherwise. Missing values are treated as 0.
Revolver Indicator An indicator variable taking value 1 if the loan is a revolver loan, and 0 otherwise.
# Total Covenants The number of total covenants including both financial and general covenants.
# Financial Covenants The number of financial covenants based on accounting numbers. .
# General Covenants The number of general covenants including dividend restrictions and sweeps.
Financial Covenants Use The number of financial covenants based on accounting numbers divided by the
number of total covenants.
(Tangible) Net Worth Slack The difference between (Tangible) net worth at the quarter prior to loan
origination and (tangible) net worth threshold specified in debt agreement scaled
by assets.
Build-up Indicator An indicator variable taking value 1 if the deal has a build-up provision for
(tangible) net worth covenant, and 0 otherwise. Missing values are treated as 0.
35
Appendix: Description of Variables (Continued)
Conservatism Measures
Book-to-Market -1 times the book value of assets divided by the market value of equity plus the
book value of debt.
Balance Sheet Conservatism To measure Balance Sheet Conservatism, we regress Book-to-Market Ratio on a set
of variables that proxy for rents, growth, distress, and market sentiment, with
industry and year fixed effects. The residuals are our measure of Balance Sheet
Conservatism. See Table 3 Panel A for details.
Timely Loss Recognition To measure Timely Loss Recognition, we estimate Basu's (1997) market-based
model at industry level (three-digit sic codes) for each year using prior ten years of
data: NI=α+βR+ηDR+γRDR+ε. NI is Income before Extraordinary Items for firm i
in the fiscal year t deflated by the market value of equity at the beginning of the year
and adjusted by the average Income before Extraordinary Items for all firms in year
t, R is the 12-month return on firm i ending three months after the end of the fiscal
year less the corresponding CRSP equal-weighted market return, and DR is an
indicator variable equal to 1 if the firm's R is negative and 0 otherwise.
Observations with NI and R falling to the top and bottom 1 percent are excluded.
(β+γ) is the measure of Timely Loss Recognition.
Non-Operating Accruals Following Beatty et al. (2008), Non-Operating Accruals is the average of non-
operating accruals (COMPUSTAT #172 + #14 - #308 + #302 + #303 + #304 +
#305) scaled by assets over a period with a maximum of 5 years and a minimum of
2 years.
Conditional Conservatism A composite measure computed as the principal component of Timely Loss
Recognition and Non-Operating Accruals.
Other
Default Spread Difference between the yields of BAA and AAA corporate bonds.
Term Spread Difference between the yields of 10-year T-bills and 2-year T-bills.
Note
All variables are measured at or for the fiscal year-end prior to loan origination date except for the ones that are
indicated otherwise.
36
Table 1 Sample Description
The sample contains all loans originated from 1996 through 2006 with available loan data and control variables.
Panel A: Sample Distribution by Industry
Year # Loans Percent
1996 445 9.20
1997 507 10.49
1998 426 8.81
1999 375 7.76
2000 422 8.73
2001 459 9.49
2002 423 8.75
2003 439 9.08
2004 526 10.88
2005 472 9.76
2006 341 1.05
Total 4835 100
Panel A: Sample Distribution by Year
Industry # Loans Percent
Chemicals 160 3.31
Computers 522 10.80
Durable mfrs 1,303 26.95
Extractive 289 5.98
Food 146 3.02
Mining & Construction 149 3.08
Pharmaceuticals 118 2.44
Retail 918 18.99
Services 663 13.71
Textiles & Printing 390 8.07
Transportation 177 3.66
Total 4835 100
37
Table 2 Descriptive Statistics
The sample contains 4,835 loans originated from 1996 through 2006. All variables are described in the Appendix.
Variable N Mean Q1 Median Q3 Std Dev.
Firm Characteristics
Assets ($ millions) 4835 3063 243 694 2127 9623
Log(Assets) 4835 6.62 5.49 6.54 7.66 1.63
Profitability 4835 0.16 0.11 0.16 0.22 0.12
Loss Years 4835 0.16 0 0 0.2 0.24
Leverage 4835 0.35 0.17 0.35 0.51 0.23
Credit Rating 4835 13.74 12 14 16 3.40
Standard Deviation of Returns 4835 2.96 1.84 2.55 3.63 1.66
Long-Term Growth Forecasts 4835 16.83 12 15 20 7.57
Asset Tangibility 4835 0.47 0.39 0.48 0.54 0.12
Loan Characteristics
Spread 4835 153.96 62.5 125 225 111.96
Facility-to-Assets 4835 0.26 0.11 0.21 0.35 0.22
Deal-to-Assets 4835 0.32 0.13 0.25 0.42 0.30
Maturity 4835 48.64 36 59 60 18.44
Log(Maturity) 4835 3.79 3.58 4.08 4.09 0.47
Collateral Indicator 4835 0.46 0 0 1 0.50
Performance Pricing Indicator 4835 0.62 0 1 1 0.48
Revolver Indicator 4835 0.85 1 1 1 0.36
# Total Covenants 4835 3.51 1 3 5 2.80
# Financial Covenants 4835 2.13 1 2 3 1.56
# General Covenants 4835 1.38 0 1 2 1.68
Financial Covenants Use 3686 0.58 0.40 0.60 0.75 0.24
Net Worth Slack 826 0.11 0.05 0.09 0.14 0.09
Tangible Net Worth Slack 789 0.16 0.07 0.13 0.22 0.12
Build-up Indicator 1798 0.59 0 1 1 0.49
Conservatism Measures
Balance Sheet Conservatism 4835 -0.03 -0.15 -0.02 0.12 0.21
Book-to-Market 4835 -0.68 -0.86 -0.66 -0.48 0.28
Conditional Conservatism 4835 -0.02 -0.58 -0.12 0.35 0.97
Timely Loss Recognition 4835 0.26 0.17 0.24 0.31 0.16
Non-Operating Accruals 4835 0.02 0 0.01 0.03 0.05
38
Table 3 Measuring Balance Sheet Conservatism
Panel A: First Stage Regression
Table 3 Panel A displays results of regressing Book-to-Market on a set of variables that proxy for rents, growth,
distress, and market sentiment, with industry and year fixed effects. The residual for the regression is our measure of
balance sheet conservatism. The sample consists of 21,330 firm-year observations from 1995 to 2005. Book-to-
Market is computed as -1 times the book value of assets divided by the market value of equity plus the book value of
debt. Long-Term Growth Forecasts is the median of all long-term growth estimates by analysts obtained from IBES.
Sales Growth is sales at year t+1 divided by sales at year t. Industry Concentration is the Herfindahl index calculated
by summing the squares of the individual firm market shares based on sales for the four largest companies in an
industry (four-digit sic code) scaled by 10,000. Indicator of Top Four Companies is 1 if the company is among the
top four companies based on sales in an industry (four-digit sic code) and 0 otherwise. Consumer Sentiment Index is
the index of the consumer sentiment from University of Michigan. S&P Index is the level of the S&P's Composite
Index (NYSE/AMEX only) from CRSP. Profitability is EBITDA scaled by the lag of assets. For those firms have
credit rating information from Compustat, Credit Rating is S&P LT Domestic Issuer Credit Rating. Otherwise,
Credit Rating is estimated using a method similar to Barth et al. (2008) and Beatty et al. (2008). First, we regress
ratings on Log(Assets), ROA, Debt-to-Assets, Dividend Indicator, Subordinated Debt Indicator, and Loss Indicator,
with industry and year fixed effects for rated firms. We then use the estimated coefficients from the first regression
and the firm's financial information to compute a rating for each firm in each year. The computed rating values are
winsorized at 2 and 27. Standard Deviation of Returns is the standard deviation of the daily return less the
corresponding decile returns for the fiscal year. Industry is defined according to Barth et al. (1998). All variables
except for Sales Growth (defined above) are measured at or for the fiscal year-end corresponding to the year end
when Book-to-Market is measured. Compustat variables are truncated at 1% level for both top and bottom tails.
***, **, * denote significance at 1%, 5% and 10% levels respectively. Figures in parentheses are t-statistics based
on OLS standard errors.
Variables Predicted Sign
Long-Term Growth Forecasts + 0.0108 ***
(47.85)
Sales Growth + 0.0822 ***
(10.27)
Industry Concentration ? -0.0372 **
(2.21)
Industry Concentration × Indicator of Top Four Companies + 0.0429 ***
(3.96)
Consumer Sentiment Index + 0.0038 ***
(6.88)
S&P Index + 0.0002 ***
(9.07)
Profitability + 0.6067 ***
(44.78)
Credit Rating - -0.0085 ***
(13.20)
Standard Deviation of Returns - -0.0157 ***
(11.64)
Intercept -1.4655 ***
(28.78)
Industry Fixed Effects Yes
Year Fixed Effects Yes
Number of Observations 21,330
Adj R-squared 33.85%
Book-to-Market
39
Table 3 Measuring Balance Sheet Conservatism (Continued)
Panel B: Validation based on Alternative Conservatism Measures
Table 3 Panel B compares results of regressing the residual value and fitted value respectively on alternative
conservatism measures. The residual value and fitted value are from the first-stage regression shown in Panel A. The
residual value is our measure of balance sheet conservatism. LIFO Reserve Indicator is 1 if LIFO Reserve is positive
and 0 otherwise. Accelerated Depreciation Indicator is 1 if the footnote shows that the firm only uses accelerated
depreciation and 0 otherwise. Advertising Reserve is amortized advertising expenses using a sum-of-the-years-digits
method over two years. R&D Reserve is amortized R&D expenditures using a sum-of-the-years-digits method over
five years. To measure Asymmetric Timeliness and Timely Loss Recognition, we estimate Basu's (1997) market-
based model at industry level (three-digit sic codes) for each year of the sample period using prior ten years of data:
NI=α+βR+ηDR+γRDR+ε. γ is the measure of Asymmetric Timeliness and (β+γ) is the measure of Timely Loss
Recognition. Non-Operating Accruals is measured following Beatty et al. (2008), which is the average of non-
operating accruals scaled by assets over a period with a maximum of 5 years and a minimum of 2 years. All
variables are measured at or for the fiscal year-end corresponding to the year end when Book-to-Market is
measured. ***, **, * denote significance at 1%, 5% and 10% levels respectively. Figures in parentheses are t-
statistics based on robust standard errors clustered at both firm and year levels.
Variables Predicted Sign
LIFO Reserve Indicator + 0.009 -0.035 ***
(1.04) (6.17)
Accelerated Depreciation Indicator + 0.0329 -0.0018
(1.29) (0.09)
Advertising Reserve + 0.4247 *** -0.2202 ***
(5.11) (3.79)
R&D Reserve + 0.6051 *** 0.0025
(5.85) (0.05)
Asymmetric Timeliness + 0.0354 ** -0.0341
(1.97) (1.20)
Non-Operating Accruals + 0.1237 * -0.0579 *
(1.91) (1.89)
Intercept -0.0419 *** -0.6282 ***
(5.82) (27.82)
Number of Observations 21,330 21,330
R-squared 5.15% 0.99%
Variables Predicted Sign
LIFO Reserve Indicator + 0.0088 -0.0342 ***
(1.01) (6.13)
Accelerated Depreciation Indicator + 0.0325 -0.0011
(1.27) (0.05)
Advertising Reserve + 0.4235 *** -0.2225 ***
(5.11) (3.92)
R&D Reserve + 0.6074 *** -0.0099
(5.85) (0.22)
Timely Loss Recognition + 0.0281 -0.0894 ***
(1.43) (2.80)
Non-Operating Accruals + 0.1247 * -0.0578 **
(1.92) (1.88)
Intercept -0.0405 *** -0.6129 ***
(5.12) (24.72)
Number of Observations 21,330 21,330
R-squared 5.13% 1.46%
Residual Value Fitted Value
Residual Value Fitted Value
40
Table 3 Measuring Balance Sheet Conservatism (Continued)
Panel C: Coefficients from Basu Regressions by Groups
Table 3 Panel C compares Basu coefficients for firms over the periods of t-2 to t and of t+1 to t+3 ranked by the
residual value and fitted value respectively. The residual value and fitted value are from the first-stage regression
shown in Panel A. The residual value is our measure of balance sheet conservatism. The following pooled regression
is estimated in each two period for each group: NI=α+βR+ηDR+γRDR+ε. Figures in parentheses are t-statistics
based on OLS standard errors.
Ranked by Residual Value
Low Medium High High - Low Low Medium High High - Low
Asymmetric Timeliness γ 0.14 0.16 0.22 0.08 0.32 0.20 0.21 -0.11
(19.58) (24.19) (28.42) (8.23) (27.86) (28.31) (31.58) (8.80)
Timely Loss Recognition β + γ 0.14 0.16 0.20 0.06 0.30 0.19 0.18 -0.12
(24.12) (26.30) (25.98) (6.68) (26.37) (25.79) (29.38) (9.97)
Number of Observations 19,656 19,459 19,038 17,106 17,307 17,735
Adj. R-squared 5.37% 6.56% 6.83% 7.61% 7.05% 7.43%
Ranked by Fitted Value
Low Medium High High - Low Low Medium High High - Low
Asymmetric Timeliness γ 0.22 0.09 0.10 -0.12 0.39 0.20 0.13 -0.26
(26.41) (15.21) (19.14) (11.78) (32.86) (27.57) (23.84) (21.01)
Timely Loss Recognition β + γ 0.19 0.09 0.10 -0.09 0.35 0.18 0.12 -0.22
(26.60) (18.08) (21.03) (9.35) (29.84) (25.41) (23.14) (18.37)
Number of Observations 19,434 19,296 19,423 17,328 17,470 17,350
Adj. R-squared 0.06% 0.04% 0.04% 0.09% 0.07% 0.06%
Pre-Period: t-2 to t Post-Period: t+1 to t+3
Pre-Period: t-2 to t Post-Period: t+1 to t+3
41
Table 4 Balance Sheet Conservatism and Loan Spreads
The full sample contains 4,835 loans between 1996 and 2006 with all control variables available. The dependent
variable is Spread. Specification 1 reports the results of the full sample, while specifications 2 and 3 are subsamples
excluding high-tech industries (Computers and Pharmaceuticals) and young firms (lowest quintile of age),
respectively. Standard errors are clustered using the two-way methodology at the firm level and the year level and t-
statistics are reported. All variables are described in the Appendix. ***, **, * denote significance at 1%, 5% and
10% levels respectively.
Dependent Variable =
Spread in b.p.
ttt
Balance Sheet Conservatism -39.61 *** -6.51 -45.34 *** -6.72 -41.30 *** -5.46
Log of Assets -14.62 *** -14.24 -13.82 *** -11.77 -14.02 *** -10.56
Profitability -102.22 *** -7.22 -105.71 *** -6.04 -120.22 *** -6.61
Loss Years 42.69 *** 5.86 45.49 *** 5.59 34.69 *** 4.34
Leverage 66.23 *** 7.14 70.35 *** 6.58 64.87 *** 6.20
Credit Rating 4.00 *** 5.08 4.15 *** 4.40 3.65 *** 4.55
Standard Deviation of Returns 13.73 *** 10.34 13.02 *** 9.33 13.37 *** 8.37
Long-Term Growth Forecasts -0.48 ** -2.20 -0.31 -1.35 -0.54 ** -2.48
Asset Tangibility -29.23 ** -2.05 -1.78 -0.23 1.13 0.13
Facility-to-Assets -19.82 *** -2.88 -16.54 * -1.97 -15.60 ** -2.31
Log of Maturity -19.75 *** -4.06 -20.65 *** -4.39 -20.40 *** -4.11
Collateral Indicator 46.94 *** 9.75 45.59 *** 8.93 50.39 *** 8.72
Default Spread 23.09 1.63 28.31 *** 1.91 23.12 * 1.67
Term Spread 10.86 *** 3.57 10.48 *** 3.35 11.20 *** 4.50
Revolver Indicator -64.20 *** -9.81 -67.41 *** -9.65 -64.88 *** -10.76
Intercept 263.60 *** 10.49 240.49 *** 8.60 258.80 *** 9.83
Deal Purpose Fixed Effects Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Number of Observations 4,835 4,191 4,110
R-squared 0.629 0.619 0.636
Coefficient Coefficient Coefficient
123
Full Sample Exclude High Tech Firms Exclude Young Firms
42
Table 5 Balance Sheet Conservatism and Access to Capital
The full sample contains 4,385 loans between 1996 through 2006 with all control variables available. The dependent
variable is Deal-to-Assets which is a proxy for the borrower’s access to bank loans. Specification 1 reports the
results of the full sample , while specifications 2 and 3 are sub-samples excluding high-tech industries (Computers
and Pharmaceuticals) and young firms (lowest quintile of age), respectively. Standard errors are clustered using the
two-way methodology at the firm level and the year level and t-statistics are reported. All variables are described in
the Appendix. ***, **, * denote significance at 1%, 5% and 10% levels respectively.
Dependent Variable =
Deal-to-Assets
ttt
Balance Sheet Conservatism 0.06 *** 3.62 0.06 *** 2.98 0.07 *** 3.20
Log of Assets -0.08 *** -15.57 -0.08 *** -17.19 -0.08 *** -13.70
Profitability 0.29 *** 5.83 0.33 *** 6.79 0.26 *** 4.19
Loss Years -0.06 *** -3.63 -0.06 *** -3.42 -0.08 *** -4.69
Leverage 0.13 *** 7.56 0.13 *** 6.38 0.12 *** 6.46
Credit Rating 0.00 -0.95 0.00 -1.34 0.00 -0.74
Standard Deviation of Returns -0.01 ** -1.98 -0.01 *** -2.26 0.00 -1.19
Long-Term Growth Forecasts 0.00 ** -1.66 0.00 * -1.41 0.00 -1.41
Asset Tangibility -0.12 *** -3.03 -0.11 *** -2.54 -0.11 *** -2.52
Log of Maturity 0.15 *** 10.95 0.15 *** 9.80 0.14 *** 12.91
Collateral Indicator 0.04 *** 3.71 0.05 *** 3.57 0.04 *** 4.33
Revolver Indicator -0.03 -1.60 -0.03 -1.53 -0.04 * -1.68
Intercept 0.28 *** 5.29 0.28 *** 4.63 0.30 *** 5.53
Deal Purpose Fixed Effects Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Number of Observations 4835 4195 4113
R-squared 0.40 0.39 0.40
Coefficient
12 3
Full Sample Exclude High Tech Firms Exclude Young Firms
Coefficient Coefficient
43
Table 6 Balance sheet conservatism and Covenant Intensity
The sample contains 3,833 loans between 1996 through 2006 with covenant information available on Dealscan and
all control variables available. The dependent variable in specification 1 is the total number of financial covenants
and in specification2, it is the number of General Covenants. Standard errors are clustered using the two-way
methodology at the firm level and the year level and t-statistics are reported. All variables are described in the
Appendix. ***, **, * denote significance at 1%, 5% and 10% levels respectively.
tt
Balance Sheet Conservatism -0.65 *** -6.82 -0.78 *** -7.08
Log of Assets -0.23 *** -7.45 0.01 0.36
Profitability 1.13 *** 7.61 0.27 *** 1.23
Loss Years -0.48 *** -3.67 0.42 ** 2.05
Leverage 0.61 *** 4.26 0.92 *** 6.19
Credit Rating 0.03 *** 2.67 0.08 3.52
Standard Deviation of Returns -0.04 ** -3.03 0.00 0.12
Long-Term Growth Forecasts 0.00 0.10 -0.01 * -1.82
Asset Tangibility -0.71 ** -2.05 -1.21 *** -5.29
Deal-to-Assets 0.04 0.63 1.19 *** 8.15
Log of Maturity 0.01 0.15 0.35 *** 4.36
Revolver Indicator -0.32 *** -4.69 -1.21 *** -7.06
Intercept 4.30 *** 7.66 -0.57 -0.71
Deal Purpose Fixed Effects Yes Yes
Industry Fixed Effects Yes Yes
Number of Observations 3833 3833
R-squared 0.179 0.324
Coefficient Coefficient
12
# Financial Covenants # General Covenants
44
Table 7 Balance Sheet Conservatism and (Tangible) Net Worth Covenant Slack
The samples contain 826 and 789 loans between 1996 and 2006 with all control variables available and with either
net worth covenant or tangible net worth covenant. The dependent variables are (Tangible) Net Worth Covenant
Slack as described in Appendix. Standard errors are clustered using the two-way methodology at the firm level and
the year level and t-statistics are reported. All variables are described in the Appendix. ***, **, * denote
significance at 1%, 5% and 10% levels respectively.
tt
Balance Sheet Conservatism 0.06 *** 3.41 0.05 ** 2.51
Log of Assets 0.01 1.63 0.00 -0.34
Profitability 0.04 1.52 0.07 * 1.73
Loss Years 0.02 1.41 0.03 * 1.76
Leverage -0.13 *** -5.30 -0.15 *** 5.45
Credit Rating 0.00 0.59 0.00 -0.16
Standard Deviation of Returns 0.00 -0.93 0.00 -0.66
Long-Term Growth Forecasts 0.00 * 1.84 0.00 ** 2.33
Asset Tangibility -0.08 -1.60 -0.23 ** -2.45
Deal-to-Assets -0.02 -1.38 -0.03 -1.21
Log of Maturity 0.00 0.20 -0.01 -0.86
Build-up Indicator -0.03 *** -3.70 -0.02 *** 5.09
Revolver Indicator -0.07 -0.72 -0.01 -0.63
Intercept 0.17 *** 3.99 0.37 1.25
Deal Purpose Fixed Effects Yes Yes
Industry Fixed Effects Yes Yes
Number of Observations 826 789
R-squared 0.202 0.219
12
Net Worth Covenant Slack Tangible Net Worth Covenant Slack
Coefficient Coefficient
45
Table 8 Spread and the Interaction between Conditional and Balance Sheet Conservatism
Panel A: Regression Results
Panel A reports the results of the regressions. The sample contains 4,835 loans between 1996 through 2006 with all
control variables available. The dependent variable is Spread. Firms are independently sorted into three groups each
based conditional conservatism (using the Overall CC measure) and balance sheet conservatism. Specification 1
reports the results for conditional conservatism and specification 2 reports the interaction groups. Standard errors are
clustered using the two-way methodology at the firm level and the year level and t-statistics are reported. All
variables are described in the Appendix. ***, **, * denote significance at 1%, 5% and 10% levels respectively.
Dependent variable = Spread
tt
Conditional Conservatism -4.00 *** -2.71
Low CC & Med BC -9.62 *** -3.08
Low CC & High BC -25.02 *** -4.39
Med CC & Low BC -4.23 -1.34
Med CC & Med BC -17.40 *** -6.48
Med CC & High BC -21.62 *** -4.66
High CC & Low BC -9.66 ** -1.84
High CC & Med BC -16.65 *** -5.78
High CC & High BC -29.72 *** -6.07
Log of Assets -14.70 *** -14.50 -14.62 *** -14.72
Profitability -115.03 *** -9.04 -98.80 *** -6.97
Loss Years 41.67 *** 5.80 45.64 *** 6.08
Leverage 69.98 *** 7.85 64.05 *** 7.44
Credit Rating 4.10 *** 4.85 3.92 *** 4.85
Standard Deviation of Returns 14.02 *** 10.70 13.81 *** 10.61
Long-Term Growth Forecasts -0.49 ** -2.29 -0.47 ** -2.16
Asset Tangibility -34.40 ** -2.32 -31.11 ** -2.11
Facility-to-Assets -21.73 *** -3.04 -20.32 *** -2.86
Log of Maturity -20.36 *** -4.21 -20.25 *** -4.10
Collateral Indicator 49.48 *** 10.69 47.56 *** 10.15
Default Spread 32.05 * 1.95 32.98 ** 2.06
Term Spread 5.27 ** 1.98 5.45 ** 2.17
Revolver Dummy -64.02 *** -9.72 -64.16 *** -9.82
Intercept 262.81 *** 9.74 274.76 *** 10.57
Deal Purpose Fixed Effects Yes Yes
Industry Fixed Effects Yes Yes
Number of Observations 4,835 4,835
R-squared 0.623 0.628
12
Conditional Conservatism Interactions
Coefficient Coefficient
46
Table 8 Spread and the Interaction between Conditional and Balance Sheet Conservatism
(Continued)
Panel B: Coefficients by Groups and F Tests
Panel B reports the coefficients, differences in coefficients across the nine groups of conservatism and the associated
F-statistics. ***, **, * denote significance at 1%, 5% and 10% levels respectively.
Diff of Coeff F Test of Dif
f
Low Medium High High - Low Low vs. High
Low Intercept -4.23 -9.66 -9.66 3.37*
Medium -9.62 -17.40 -16.65 -7.03 2.63
High -25.02 -21.62 -29.72 -4.70 1.35
Diff of Coeff
High - Low -25.02 -17.39 -20.06
F Test of Diff Low vs. High 19.31*** 10.59*** 22.16***
Balance Sheet
Conservatism
Conditional Conservatis
m
47
Table 8 Spread and the Interaction between Conditional and Balance Sheet Conservatism
(Continued)
Panel C: Spread Results Conditioning on Monitoring
Panel C compares the spread results between loans with above-median Financial Covenants Use and loans with
below-median Financial Covenants Use. Coefficients across the nine groups and the differences between high and
low groups are reported. ***, **, * denote significance of F tests at 1%, 5% and 10% levels respectively.
Sample with above-median Financial Covenants Use (N=1562)
Diffof Coeff
F Test of Diff
Low Medium High High - Low Low vs. High
Low Intercept -0.03 -7.19 -7.19 3.57*
Medium -1.73 -11.22 -9.00 -7.27 1.82
High -12.58 -15.55 -19.35 -6.77 1.26
Diff of Coeff
High - Low -12.58 -15.52 -12.16
F Test of Diff Low vs. High 4.96** 7.04*** 8.29***
Sample with below-median Financial Covenants Use (N=2124)
Diffof Coeff
F Test of Diff
Low Medium High High - Low Low vs. High
Low Intercept -2.56 -3.73 -3.73 0.22
Medium -8.31 -14.91 -19.02 -10.71 2.42
High -25.86 -16.73 -31.60 -5.74 0.76
Diff of Coeff
High - Low -25.86 -14.17 -27.87
F Test of Diff Low vs. High 11.00*** 6.08** 11.49***
Conditional Conservatism
Balance Sheet
Conservatism
Conditional Conservatism
Balance Sheet
Conservatism
48
Table 9 Financial Covenants Use and the Interaction between Conditional and Balance
Sheet Conservatism
Panel A: Regression Results
Panel A reports the results of the regressions. The sample contains 3,686 loans between 1996 through 2006 with all
control variables available and with at least 1 financial covenant. The dependent variable is Financial Covenants Use
as described in Appendix. Firms are independently sorted into three groups each based conditional conservatism
(using the Overall CC measure) and balance sheet conservatism. Specification 1 reports the results for conditional
conservatism and specification 2 reports the interaction groups. Standard errors are clustered using the two-way
methodology at the firm level and the year level and t-statistics are reported. All variables are described in the
Appendix. ***, **, * denote significance at 1%, 5% and 10% levels respectively.
Dependent variable =
Financial Covenant Use
tt
Conditional Conservatism 0.01 ** 2.41
Low CC & Med BC 0.03 *** 2.81
Low CC & High BC 0.06 *** 3.44
Med CC & Low BC 0.01 1.04
Med CC & Med BC 0.05 *** 3.83
Med CC & High BC 0.08 *** 11.32
High CC & Low BC 0.02 * 1.67
High CC & Med BC 0.04 *** 2.86
High CC & High BC 0.06 *** 5.80
Log of Assets 0.00 0.54 0.00 0.42
Profitability 0.13 *** 4.67 0.10 *** 3.10
Loss Years -0.11 *** -3.98 -0.12 *** 3.99
Leverage -0.12 *** -5.71 -0.10 *** -4.43
Credit Rating -0.02 *** -5.49 -0.02 *** -5.49
Standard Deviation of Returns -0.02 *** -5.07 -0.02 *** -4.74
Long-Term Growth Forecasts 0.00 ** 2.52 0.00 *** 2.58
Asset Tangibility 0.10 *** 3.32 0.10 *** 3.15
Deal-to-Assets -0.11 *** -7.18 -0.12 *** -7.75
Log of Maturity -0.04 *** -3.00 -0.04 *** -3.12
Revolver Dummy 0.08 *** 6.31 0.08 *** 6.32
Intercept 1.04 *** 11.20 1.00 *** 11.03
Deal Purpose Fixed Effects Yes Yes
Industry Fixed Effects Yes Yes
Number of Observations 3,686 3,686
R-squared 0.283 0.291
12
Conditional Conservatism Interactions
Coefficient Coefficient
49
Table 9 Financial Covenants Use and the Interaction between Conditional and Balance
Sheet Conservatism (Continued)
Panel B: Coefficients by Groups and F Tests
Panel B reports the coefficients, differences in coefficients across the nine groups of conservatism and the associated
F-statistics. ***, **, * denote significance at 1%, 5% and 10% levels respectively.
Diff of Coeff
F Test of Diff
Low Medium High High - Low Low vs. High
Low Intercept 0.01 0.02 0.02 2.78*
Medium 0.03 0.05 0.04 0.01 0.36
High 0.06 0.08 0.06 0.00 0.00
Diff of Coeff
High - Low 0.06 0.07 0.04
F Test of Diff Low vs. High 11.84*** 19.31*** 10.10***
Conditional Conservatis
m
Balance Sheet
Conservatism
