Affiliated Banker on Board and Conservative Accounting
David H. Erkens
K.R. Subramanyam
Jieying Zhang
Marshall School of Business
University of Southern California
July 2013
Acknowledgments: This study has benefited from helpful comments by Sarah Bonner, Alon Kalay,
Joseph Weber, and workshop participants at Boston College, Columbia University, Michigan State
University, Ohio State University, University of California at Berkeley, University of California at Irvine,
University of California at Los Angeles, University of Miami, University of Southern California, and
Washington University at St. Louis.
Affiliated Banker on Board and Conservative Accounting
ABSTRACT
We examine the effect of lender monitoring from board representation (affiliated banker on board,
or AFB) on conservative accounting. We argue that lender monitoring reduces the information
asymmetry that underlies the agency problem of debt and thus reduces lenders’ demand for
conservatism-facilitated monitoring. We find that AFB firms have markedly lower conservative
accounting than non-AFB firms after propensity-score matching on observable confounding
factors. We use an instrumental variable analysis, a difference-in-difference design, and the
contrast between AFB and unaffiliated banker on board to alleviate the concern that unobservable
confounding factors drive our results. We perform three additional analyses to substantiate our
hypothesis. First, we show that AFB renegotiations are more likely to occur in the absence of
covenant violations, suggesting that AFBs use private information to renegotiate in a timelier
manner. Second, we find that AFB firms have a weaker association between conservatism and
leverage/covenants, suggesting that they have a lower demand for conservatism-facilitated control
transfer. Third, we find that relationship lending has a similar effect on conservatism, suggesting
that lender monitoring, not AFB per se, decreases conservatism.
JEL classification: G3; G21; M41
Keywords: affiliated banker on board; conservative accounting; debt contracting; lender
monitoring
Data Availability: All data are publicly available from sources identified in the text.
I. INTRODUCTION
Debt contracting has evolved to mitigate the agency problem of debt by transferring
control rights to lenders during adversity via covenant violations (Black and Cox 1976; Jensen
and Meckling 1976). Conservative accounting, in turn, has evolved to facilitate debt contracting
by triggering timely covenant violations through timely loss recognition (Watts 2003; Ball et al.
2008). Debt contracting, however, is potentially not the only mechanism that could mitigate the
agency problem of debt. Because borrower-lender information asymmetry is central to the agency
problem of debt, any monitoring mechanism that is effective in reducing this information
asymmetry (henceforth, lender monitoring) can ameliorate the agency problem, and therefore
reduce the need for debt contracting and conservative accounting. In this paper, we examine
whether a particularly effective lender monitoring mechanism, i.e., lender participation in
management through board representation or having an affiliated banker on board (henceforth,
AFB), reduces the demand for, and therefore the supply of, conservative accounting. 1
We hypothesize that the presence of an AFB will reduce the demand for conservative
accounting. Our rationale is the following. Board representation provides the lender access to
private information, and therefore allows the lender to closely monitor the firm and renegotiate
whenever there are signs of adversity. These renegotiations could occur either voluntarily, or
through triggering of catch-all type covenants, such as the Material Adverse Change clause
(MAC), that allocate control rights to lenders to address unforeseen risks not captured by financial
1
We use the terms affiliated (unaffiliated) banker on board to describe a board member who is a top executive of a
commercial bank that belongs to a syndicate that has (does not have) a concurrent lending relationship with the
firm. Analogously, we also use the terms affiliated bank and affiliated lender to refer to the members of the AFBs’
syndicate. However, unaffiliated lender refers to a lender that does not have a concurrent board tie with the firm.
We also refer to the firm that has an AFB as the AFB firm. Finally, we use syndicate membership as the basis for
affiliation because under the principle of "collective action" the rights and responsibilities of syndicate members
are inexorably tied together (Taylor and Sansone 2007).
1
covenants.2 In turn, this will reduce affiliated lenders’ need for the timely control transfer enabled
through conservatism-facilitated covenant violations. Because debt contracting and conservative
accounting are costly, a firm that already has an AFB will reduce the use of debt contracting and
conservative accounting to save associated costs.3 Accordingly, we hypothesize that firms with an
AFB will have less conservative accounting.
There are several reasons why AFB may not lead to less conservative accounting, thus
adding tension to our hypothesis. First, AFBs’ lender liability and fiduciary duty to shareholders
could discourage AFBs from exploiting their information advantage to protect their interests at
the expense of other stakeholders (Kroszner and Strahan 2001). 4 Second, AFB could demand
more conservative accounting to enhance lenders’ renegotiation power, or influence management
to abandon risky projects, resulting in asset write-offs that are consistent with conservative
accounting. Third, unaffiliated lenders may continue to demand conservative accounting to
protect themselves against the affiliated lenders’ information advantage. 5 Accordingly, whether
AFB firms have less conservative accounting is an empirical question.
We test our hypothesis using a unique hand-collected dataset of board ties and lending
relationships between non-financial firms and commercial banks. Our primary sample comprises
1,293 S&P 1500 firms over the 2000-2006 period (6,481 firm-year observations). We measure
accounting conservatism as the asymmetric timeliness of earnings using the Basu (1997) model,
2
Consistent with the wider protection offered by private information-based renegotiations, Roberts and Sufi (2009b)
find that over 90% of loans are renegotiated before their maturity and over 80% of renegotiations occur in the
absence of covenant violations.
3
The costs of conservatism include the adverse effects that covenant violations have on firms, which are exacerbated
by covenant violations that are false alarms, and inefficiencies for equity valuation.
4
Lender liability refers to the potential loss of seniority and liability for losses to other claimants, if a senior lender is
found to be active and inequitable prior to a borrower’s bankruptcy.
5
We do not expect unaffiliated lenders to influence the extent of conservative accounting for the following reasons.
Affiliated banks tend to be influential lenders to the firm and thereby have a disproportionate effect on firms’
accounting choices. For example, affiliated banks’ syndicates on average hold more than 80% of AFB firms’
private debt commitments and more than 50% of their total debt commitments. Also lender liability protects
unaffiliated lenders from the self-serving actions by affiliated lenders, thereby reducing unaffiliated lenders’
demand for protection from conservative accounting.
2
to which we add firm fixed effects (Ball et al. 2013). To control for bias arising from observable
confounding factors, all our empirical tests are conducted using propensity-score matching
techniques (PSM), wherein treatment (AFB) observations are matched to control (non-AFB)
observations based on propensity scores derived from models that include an extensive list of firm
and corporate governance characteristics associated with both conservatism (Ahmed and
Duellman 2007; LaFond and Watts 2008) and the presence of commercial bankers on corporate
boards (Gilson 1990; Kroszner and Strahan 2001). As hypothesized, we find that AFB firms have
significantly less conservative accounting than matched non-AFB firms—in fact, AFB firms have
an insignificant asymmetric timeliness coefficient. Notably, this result arises through both lower
timeliness of bad news and greater timeliness of good news for AFB firms vis-à-vis matched nonAFB firms.
Next, we conduct various tests to address bias arising from unobservable confounding
factors (i.e., endogeneity) while still controlling for observable confounding factors using PSM.
First, we use an instrumental variable analysis to extract exogenous variation in AFB. We find
that observations with a high exogenous level of AFB have significantly lower conservatism than
matched observations with a low exogenous level of AFB. Second, we use a “difference-indifference” design to control for time-invariant unobservable endogenous variation in AFB. We
examine the extent of conservatism for a small subset of our sample firms before and after they
initiate an AFB relationship during our sample period. We find that firms experience a significant
decrease in conservatism after they initiate the AFB relationship, while matched non-AFB firms
exhibit no significant change in conservatism. 6 Third, we use the presence of unaffiliated bankers
6
An alternative explanation for this finding is that the AFB influences firm policies, particularly by reducing risktaking, which results in lower conservatism after the AFB initiation. We examine and find—consistent with
Kroszner and Strahan (2001)—that a number of firm-characteristics, especially risk measures, do not change after
3
on board as a pseudo experiment. While the presence of affiliated and unaffiliated bankers on
corporate boards may largely be driven by the same factors, our theory predicts that only the
presence of affiliated bankers should lead to lower conservatism because unaffiliated bankers do
not have investments in the firm that they need to monitor. On the other hand, if unobservable
confounding factors shared by affiliated and unaffiliated bankers drive our results, we should find
that unaffiliated bankers are also associated with a decrease in conservatism. Consistent with our
theory, we find that the presence of unaffiliated bankers on corporate boards is not associated
with a decrease in conservatism. Thus, the results from all three analyses suggest that our primary
results are unlikely to be attributable to endogeneity. 7
We also perform additional analyses that directly test key elements in our hypothesis. First,
we test whether private information allows AFBs to renegotiate in a timelier manner by
comparing AFB and non-AFB renegotiations that are borrower unfavorable. We find that while
every AFB renegotiation precedes a covenant violation, a considerable proportion of non-AFB
renegotiations follows covenant violations. These results support our premise that monitoring by
AFBs allows lenders to renegotiate in a timely manner based on private information, prior to
covenant violations. Second, we examine whether the presence of an AFB reduces lenders’
reliance on conservatism-facilitated debt contracting. We find that AFB firms have a weaker
relation between conservatism and debt covenants/leverage, which suggests that AFB firms
supply less conservatism to facilitate timely control transfers, ostensibly because there is less
lender demand for such accounting. Finally, we triangulate our results using relationship lending
AFB initiation. It is therefore unlikely that our results are driven by changes in firms’ underlying economics
initiated by the AFB.
7
Our matched AFB and non-AFB subsamples do not significantly differ with respect to the observable confounding
factors that we explicitly control for using PSM. In addition, these two subsamples also do not significantly differ
with respect to various measures of firms’ exposure to mandatory impairment charges, such as the incidence of
negative returns and the lagged book-to-market ratio. The latter finding does not only rule out mandatory
conservatism as an alternative explanation for our findings, but also substantiates the underlying premise of our
hypothesis that firms apply within GAAP discretion to be more conservative for the purpose of debt contracting.
4
as an alternative proxy for lender monitoring. Relationship lending provides lenders with private
information accumulated through repeated lending and through a close relationship with the
borrower (Boot 2000), and therefore can be argued to be an alternative proxy for lender
monitoring. We find that relationship lending is also associated with a significant reduction in
conservatism, the magnitude of which is similar to that associated with the presence of an AFB.
Finding similar results with an alternative proxy reinforces our hypothesis that any effective
lender monitoring mechanism, not AFB per se, reduces the extent of conservative accounting.
Our study makes important contributions to the literature on conservative accounting. First,
we suggest that borrower-lender information asymmetry is an important precondition for the
agency problem of debt, which is the basis for the existence of debt-contracting motivated
conservative accounting. Because of this, any lender monitoring mechanism that decreases this
information asymmetry—such as AFB or relationship lending—could significantly reduce, or
even eliminate, the need for debt-contracting motivated conservatism. Our study therefore
suggests that conservative accounting facilitated debt-contracting is not the only solution to the
agency problem of debt, and that there could be alternative and equally effective lender
monitoring mechanisms that could be used instead. Second, we find that only a minority of U.S.
firms choose these alternative lender monitoring mechanisms. While there could be cultural
factors involved, this does suggest that these alternative lender monitoring mechanisms are costly,
and that debt-contracting/conservative accounting is a cost-effective solution to reducing the
agency problem of debt for the vast majority of firms. Our study, therefore, suggests that debtcontracting motivations are an important factor for the existence of conservative accounting.
Overall, we contribute to the debate on the efficiency and/or desirability of conservative
5
accounting in a debt contracting context (Gigler et al. 2009; Caskey and Hughes 2012). 8
Our study also contributes to the growing finance literature on lender monitoring. While most
of the extant research has examined the economic and financial consequences of lender
monitoring mechanisms (e.g., Bharath et al. 2011; Ciamarra 2012), our study is the first to
examine their implications to a firm’s accounting choices. Our study shows that lender
monitoring not only reduces lending frictions but also shapes financial reporting. 9
II. HYPOTHESIS DEVELOPMENT
Conservative Accounting, Debt Contracting and the Agency Problem of Debt
The agency problem of debt arises from the inherent conflict of interests between
shareholders and debtholders. Because managers’ interests are more closely aligned with those of
shareholders, borrowing firms take actions that potentially transfer wealth from lenders to
shareholders. Debt contracting has evolved to mitigate the agency problem of debt (Black and
Cox 1976; Jensen and Meckling 1976). A key role of debt contracting is to allocate control rights
between lenders and borrowers, and in particular, to transfer control rights to lenders during
adversity through covenant violations so that lenders have the option to proactively protect their
interests.
An important precondition for the existence of the agency problem of debt is lender-borrower
information asymmetry, that is, borrowers are better informed than lenders (e.g., Garleanu and
Zwiebel 2009). In absence of information asymmetry, lenders can write contracts in a more
8
We note that our paper is agnostic regarding the informational efficiency of conservatism to debt contracting, which
cannot be gauged only from the lender’s perspective and is beyond the scope of this paper. Our goal is modest, that
is, to identify lender monitoring mechanisms such as AFB that could ameliorate the information asymmetry and
thus reduce the underlying demand for conservatism. However, our results suggest that conservatism may not be a
first-best solution for the lender-borrower conflict, and lender monitoring mechanisms such as AFB could be
superior to debt-contracting and conservatism, but come at other costs.
9
Our findings also support the use of the asymmetric timeliness coefficient in the Basu model as a proxy for crosssectional variation in conservatism, thus adding to the debate on the validity of this measure (Patatoukas and
Thomas 2011; Ball et al. 2013).
6
complete manner ex ante and renegotiate ex post as soon as new information arrives. In the
presence of information asymmetry, however, the effectiveness of debt contracting critically
depends on the availability and timeliness of the information that can be contracted upon.
Financial information is an important and readily available source of contractible
information, and is therefore extensively used in debt contracts. Conservative accounting, in turn,
has evolved naturally to facilitate debt contracting by triggering timely covenant violations
through timely loss recognition (Watts 2003). In support, the extant literature documents that
conservatism reduces the cost of debt (Ahmed et al. 2002; Zhang 2008) and that debt-contracting
influences the extent of conservative accounting (Beatty et al. 2008; Nikolaev 2010).10
While conservatism in conjunction with debt contracting can mitigate the agency problem of
debt, it is costly to implement. For example, covenant violations have significant adverse effects
on borrowing firms (Beneish and Press 1993; Nini et al. 2009; Roberts and Sufi 2009a).
Conservatism could also trigger unwarranted covenant violations, i.e., false alarms, and thus
impose unnecessary costs on the contracting parties (Gigler et al. 2009). Finally, conservatism
creates inefficiencies for equity valuation (Barth et al. 2001), where unbiased accounting is
preferable.
Monitoring Can Mitigate the Agency Problem of Debt
Debt contracting, however, is not the only mechanism for mitigating the agency problem of
debt. Any monitoring mechanism that decreases lender-borrower information asymmetry will
10
There is a theoretical debate on whether conservatism improves debt-contracting efficiency. One view is that
conservatism may decrease contracting efficiency if covenants can be written optimally to transfer control rights to
lenders only when the future adversity appears to be sufficiently bad (Gigler et al. 2009). Another view challenges
this assumption of covenant optimality and suggests that the contracting role of accounting measures cannot be
replicated by altering covenant thresholds (Caskey and Hughes 2012). Our paper seeks to establish the
substitutability between alternative monitoring mechanisms, i.e., AFB and conservatism-facilitated debt
contracting, and thus is agnostic regarding the informational efficiency of conservatism to debt contracting.
7
allow lenders to contract more efficiently ex ante and renegotiate in a timely manner ex post. Such
a mechanism, in turn, will reduce lenders’ need for the control-transfer facilitated by covenant
violations. Because contracting is costly, we should therefore see less reliance on debt contracting
when alternative monitoring mechanisms are present. This intuition is formally modeled in
Garleanu and Zwiebel (2009), who show that lower information asymmetry leads to less control
rights allocated to lenders in the form of less restrictive covenants. Consistent with their theory,
the literature finds less restrictive debt covenants when lender-borrower information asymmetry is
mitigated due to acquisition of private information by lenders through various channels, such as
lender board representation (Ciamarra 2012), relationship lending (Bharath et al. 2011), and
personal connections between employees of banks and firms (Engelberg et al. 2012).
To understand how alternative monitoring mechanisms could reduce the need for control
transfer empowered by covenants, it is important to understand how lenders exert control rights to
protect their interests. Upon covenant violations, the most common approach lenders take to
protect themselves is to renegotiate loan terms (Chen and Wei 1993; Beneish and Press 1993).
Alternatively, lenders can—and do—renegotiate loans in the absence of covenant violations. In
fact, Roberts and Sufi (2009b) show that 90% of borrower unfavorable renegotiations are not
triggered by covenant violations but by the arrival of new information. These renegotiations occur
either voluntarily, or through triggering of catch-all type covenants, such as the Change of
Business Clause or the Material Adverse Change Clause (MAC), that allocate control rights to
lenders to address unforeseen risk not captured by financial covenants. Thus, superior private
information obtained through alternative monitoring mechanisms allows lenders to renegotiate in
a timelier manner, and well before any covenant violation. This timely renegotiation negates the
need for control transfer afforded by covenant violations.
8
AFB is an Effective Monitoring Mechanism
One effective lender monitoring mechanism is their participation in management through
board representation, i.e., having an affiliated banker on board (AFB). Many U.S. firms have
bankers on their board.11 While a majority of these bankers represent banks that do not lend to the
firm (i.e., unaffiliated), some bankers represent banks that have a concurrent lending relationship
with the firm (i.e., affiliated) (Kroszner and Strahan 2001). Unaffiliated bankers primarily provide
financial expertise, but affiliated bankers also enhance the lending relationship. This enhanced
lending relationship increases borrowers’ access to credit and possibly lowers borrowing cost.
Borrowers can also leverage bankers’ industry-specific financial expertise as bankers usually lend
to other firms in the same industry. On the other hand, AFB can be potentially costly to
borrowers. First, borrowing firms may have to endure a reduction in operating flexibility due to
enhanced monitoring. Second, represented banks could use their information advantage for
possible rent extraction from the borrowers (Rajan 1992).
Lenders’ board representation confers at least two advantages to the represented banks. First,
it improves lender monitoring by increasing the flow of information from borrower to the
affiliated bank. Private information obtained through board representation is superior to financial
information obtained through the regular loan negotiation process (Baysinger and Butler 1985;
Hoshi et al. 1990, 1991; Stearns and Mizruchi 1993). Specifically, private information from board
representation is arguably more qualitative, more detailed, more forward-looking and timelier
compared to periodic (quarterly) financial information. 12 Moreover, it is less subject to
11
For example, Santos and Rumble (2006) find that 25% of non-financial S&P 500 firms have bankers on their
boards while Kroszner and Strahan (2001) find that this proportion is 32% in their sample of larger firms. Using a
more broad-based sample, we find that the incidence of bankers on board is about 11%, suggesting that bankers are
more widely represented on boards of larger companies.
12
Consistent with private information obtained through board representation being timelier than quarterly reports, we
find that firms in our sample held on average eight full board meetings per year. This excludes committee meetings
and informal meetings that board members have with each other and with management at other times during the
9
managerial manipulation than financial information, and is therefore more reliable (Stearns and
Mizruchi 1993). Consistent with the improved monitoring, prior literature finds that AFB is
associated with more access to private debt, lower borrowing cost, less covenants, valueenhancing acquisitions, etc. (Byrd and Mizurchi 2005; Gϋner et al. 2008; Ciamarra 2012; Hilscher
and Ciamarra 2013). The improved monitoring through board membership allows the affiliated
bank to anticipate adverse events earlier than relying on financial statements—despite the timely
loss recognitions through conservative accounting—and therefore take protective actions in a
timelier manner than afforded through covenant violations.
Second, board representation also allows the affiliated banks to accumulate industry-specific
knowledge, learn about potential borrowers, and connect with other executives on the board.
Kroszner and Strahan (2001) show that banks tend to lend more to industries in which the bank
has board representation, suggesting that the need to collect information motivates banks’ board
presence and banks translate their information into actual lending. The improved industry
knowledge also helps lenders in monitoring the borrower and anticipating adverse events prior to
their occurrence.
Board representation also imposes costs on the affiliated bank because of lender liability
created by U.S. legal doctrine, which is geared towards strong shareholder rights (Kroszner and
Strahan 2001). If a bank is found to be active and inequitable prior to a borrower’s bankruptcy,
the bank can lose seniority in its claim against the bankrupt firm; the bank potentially also faces
liability for losses to other claimants that can be attributed to its actions. Lender liability imposes
a non-trivial risk on banks as senior claimholders and can be a major consideration in their
year that can further increase the quality and timeliness of private information obtained through board
representation (Conger et al. 1998).
10
decision to serve on corporate boards.13 In addition, banker directors’ fiduciary duty also creates
inherent conflict of interest with their responsibility to further the interests of the banks that they
represent (Gϋner et al. 2008). Because of these regulatory costs, we observe a low incidence of
affiliated bankers on board in the U.S., but more frequent occurrences of unaffiliated bankers on
board.
AFB Can Reduce Demand for Conservatism
We note the following points in our preceding discussion. First, conservatism has evolved to
facilitate debt contracting in mitigating the agency problem of debt. Second, borrower-lender
information asymmetry is central to the agency problem of debt and any lender-monitoring
mechanism that is effective in reducing this information asymmetry mitigates the agency
problem. Third, AFB is a particularly effective, albeit costly, lender monitoring mechanism.
Finally, debt contracting and conservative accounting are costly and therefore, if a firm already
has an AFB in place, it would seek to save costs by reducing its reliance on debt contracting and
conservatism. Ciamarra (2012) finds evidence consistent with this notion in that AFB firms rely
less on debt covenants. In order for this to happen, however, the following two conditions are
necessary: (1) costly conservatism: implementing conservative accounting should be costly for the
firm; and (2) substitutability of benefits: there should be some redundancy in the benefits arising
from the monitoring through AFB and that through debt contracting and conservatism.
We
extend this idea to the accounting domain, and propose the following hypothesis (in alternate
form):
H1A: Firms with an affiliated banker on board have less conservative accounting than
firms without an affiliated banker on board.
13
http://www.abiworld.org/committees/newsletters/financebank/vol7num6/lender.pdf provides a list of cases filed
under lender liability in bankruptcies.
11
Before we conclude this section, it is necessary to clarify certain issues. First, it is important
to note that our hypothesis is a conditional statement, i.e., we make predictions regarding the
extent of conservative accounting conditional on the presence or absence of an AFB. Choosing to
have an AFB is a broad-based decision on the part of both the borrower and the lender that
considers factors that are beyond the narrow realm of the agency problem of debt. Accordingly, in
our hypothesis development, we assume the appointment of the AFB is exogenous, and derive
predictions regarding the extent of conservative accounting conditional on the presence (or
absence) of an AFB. Of course, in our empirical analysis we do consider the possibility that AFB
and conservatism may be jointly determined, and conduct several tests to ensure our results do not
arise because of endogeneity.
Second, we need to more clearly understand how the presence of an AFB reduces the demand
for conservative accounting from lenders. Our hypothesis is pretty intuitive in a single lender
setting: monitoring by the AFB eschews the need for the control transfer arising from covenant
violations, and therefore results in reduced demand for conservative accounting. However,
extending this logic to a multi-lender scenario is not straightforward. That is, for a firm to reduce
the extent of conservatism, it is necessary that all lenders demand less conservatism, including
unaffiliated lenders who do not have the direct benefits of AFB monitoring. Prima facie, it is not
clear why the presence of AFB should reduce demand for conservative accounting from the
unaffiliated lenders. While we acknowledge this problem, we do not expect unaffiliated lenders’
demands to affect the extent of accounting conservatism for two reasons. First, affiliated lenders
are the most important lenders for AFB firms, partly because their informational advantage over
other lenders makes it unattractive for other lenders to compete for AFB firms’ loan business
(Rajan 1992). Thus, unaffiliated lenders are not a critical source of financing for AFB firms, and
12
so these lenders are unlikely to materially influence AFB firms’ accounting. 14 Second, lender
liability provides affiliated lenders with a strong incentive to ensure that all lenders are protected
(Krozner and Strahan 2001). Because of this, unaffiliated lenders may not demand conservative
accounting as they are protected from the effects of any self-serving actions by the affiliated
lenders.
Finally, we acknowledge that several arguments can work against finding lower conservatism
for AFB firms. First, AFBs have fiduciary responsibilities to protect shareholder interests and
they are also subject to lender liability (Kroszner and Strahan 2001). This can discourage
affiliated lenders from actively monitoring the firm and taking protective actions when the need
arises. Second, it is possible that affiliated lenders use their information advantage to demand
more conservative accounting to enhance lenders’ renegotiation power or influence borrowers to
more quickly discontinue risky project once an initial set-back occurs, which would manifest into
an increase in conservative accounting. Third, unaffiliated lenders may demand conservative
accounting despite the protection from lender liability, and firms may accede to their demands.
These arguments add tension to our hypothesis and make it an empirical question whether AFB is
associated with lower accounting conservatism.
III. SAMPLE AND RESEARCH DESIGN
Sample Selection
The sample consists of 1,293 non-financial firms and 6,481 firm-year observations that were
included in the S&P 1500 index from 2000 to 2006. We drop all financial firms (SIC 6000-6900)
because we are interested in lending relationships between commercial banks and non-financial
14
Untabulated analyses show that, in our sample, affiliated banks’ syndicates on average hold more than 80% of AFB
firms’ private debt commitments and more than 50% of their total debt commitments. In addition, for half of these
firms affiliated banks’ syndicates are the sole provider of private debt and for a quarter of these firms affiliated
banks’ syndicates provide almost 80% of total committed long-term debt. Moreover, we find that once an affiliated
banker joins a board the affiliated bank is involved in the vast majority of new private loans issued to a firm (91%).
13
firms. Our sample firms have to satisfy the following additional criteria. First, firms must have
biographic information on their board members available from the BoardEx database.15 Second,
firms must have loan information available from the DealScan database. We merge our sample
with the DealScan database using the link file used in Chava and Roberts (2008).16 Finally, firms
must have data available for the variables used in our analyses from Compustat, CRSP, Corporate
Library, Thomson Reuters, and Risk Metrics. We discuss these variables in detail below.
Variable Definitions
Affiliated Bankers on Board (AFB)
Following Krozner and Strahan (2001), we classify directors as being bankers when they are
executives of commercial banks. Using biographic information from BoardEx and information on
commercial banks from FDIC’s institution directory and Hoover’s online, we classify 11% of
firm-year observations (711 observations) as having at least one commercial banker on their
board.17,18 Further, we classify banker directors as affiliated when their bank belongs to a loan
syndicate that has an outstanding loan agreement with the firm during the fiscal year. Because
DealScan overwrites the history of a lender’s parent and ultimate parent after mergers and a large
number of commercial banks have been involved in mergers, we supplement loan data from
DealScan with ownership data from the Federal Reserve’s National Information Center to
determine whether a banker director’s bank had an outstanding loan agreement with a firm during
15
The BoardEx database contains biographic information on over 400,000 executives and board members, as well as
data on board composition and committee appointments of public and private firms from all major countries across
the world. This database is relatively new in the literature and has been used, for example, by Cohen et al. (2010).
16
We thank Sudheer Chava and Michael Roberts for sharing their Dealscan-Compustat link file with us.
17
We exclude banks that are not primarily commercial banks. For example, we exclude banks that primarily engage
in non-commercial banking activities such as Merrill Lynch.
18
For a variety of reasons this percentage is lower than the 31.6% reported by Kroszner and Strahan (2001). First,
Krozner and Strahan limit their sample to firms that appeared in the Forbes 500 list in 1992. Second, Krozner and
Strahan do not exclude banks that primarily engage in non-commercial banking activities. Our percentage of
commercial bankers on corporate boards is similar to the 23% found by Booth and Deli (1999) for S&P 500 firms
in 1990 – 19% of our sample firms have a commercial banker on their board when we restrict our sample to S&P
500 firms in 2000.
14
the fiscal year.19 We also cross-check the ownership data from the Federal Reserve National
Information Center with other information sources such as companies’ own websites. Using this
methodology we classified 4.8% of firm-year observations (311 observations) as having AFB.20
Measuring Conservatism
We measure conservatism using Basu’s (1997) specification:
Earnings = α0 + β1DR + β2Ret + β3Ret × DR + γZ + ε
(1)
where Earnings is earnings before extraordinary items scaled by market value of equity at the
beginning of the fiscal year; Ret is the 12-month buy-and-hold return over the fiscal year; DR is
an indicator variable that equals one when Ret is negative, and zero otherwise; and Z equals a
vector of firm fixed effects. In this specification, β2 measures the timeliness of earnings in
incorporating good news (hereafter, timely gain recognition), and β2+β3 measures the timeliness
of earnings in incorporating bad news (hereafter, timely loss recognition). Our primary interest is
on β3, the asymmetric timeliness of earnings in incorporating bad news compared to incorporating
good news (hereafter, asymmetric timeliness coefficient). To ensure that our results are not driven
by the scale-induced bias in Basu estimates identified by Patatoukas and Thomas (2010), we
include firm fixed effects (Ball et al. 2013).21
Control Variables
We include three sets of control variables (see Appendix A for variable definitions). First,
19
For example, FleetBoston was acquired by Bank of America in 2004 and DealScan codes all FleetBoston loans,
including those from before 2004, to be from Bank of America. Thus, if we would rely on ownership data from
DealScan, we would misclassify a firm with an unaffiliated banker from Bank of America on its board as having an
affiliated banker, even though there was no lending relationship between the firm and Bank of America at that time.
20
Our hypothesis hinges on significant overlap between AFBs’ tenure on the board and the maturity of affiliated
loans. Thus, it is important that lender-directors do not relinquish their seat shortly after a loan is extended. We
manually check the data and find that (1) banker-directors never leave the firm within a year after issuing loans to
the firm; and (2) in over 90% of the cases a banker-director stays on the board until the end of the maturity date of
the loans during our sample period.
21
As explained in Ball et al. (2013), when firm-specific effects in earnings are taken into account, estimates of the
asymmetric timeliness coefficient in the Basu specification do not exhibit the bias.
15
we include firm-characteristics associated with conservatism, including market-to-book, leverage,
litigation risk, and firm size (LaFond and Watts 2008). Second, we include corporate governance
characteristics associated with conservatism, including board independence, board size, separation
of CEO and chairman roles, average outside directorships, inside director ownership, outside
director ownership, institutional ownership, and G-Score (Ahmed and Duellman 2007). Finally,
we include factors that are associated with the probability of having commercial bankers on
corporate boards, such as stock return volatility, squared stock return volatility, asset tangibility,
commercial credit ratings, capital structure, bankruptcy risk, and industry membership (Gilson
1990; Kroszner and Strahan 2001). Panel A of Appendix B presents descriptive statistics on these
control variables. The panel shows that our sample firms have relatively high market-to-book
ratios (4.629), low litigation risk (1.2% likelihood of being sued), high asset tangibility (30.2% of
total assets), and low bankruptcy risk (0.2% probability of going bankrupt). Overall, our sample
firms appear to be large and financially healthy.
Empirical methodology
We use the propensity-score matching technique (PSM) to control for confounding factors.
Specifically, for each firm-year we estimate the conditional odds of having AFB using a logistic
regression model where AFB is the dependent variable and the control variables discussed above
are the independent variables. Subsequently, we match AFB to non-AFB observations based on
these odds and retain matches that do not significantly differ on these odds. The resulting sample
(PSM sample, hereafter) contains AFB and non-AFB observations that do no significantly differ
on control variables. We use this PSM sample to examine the influence of AFB on conservatism.
Thus, in essence we use PSM to create conditions that approximate those of a randomized control
group design - a quasi-randomized experiment.
16
Using PSM has at least two advantages over standard regression approaches in our setting.
First, PSM is currently the most robust approach for achieving an unbiased estimate of the effect
of the treatment variable (treatment effect, hereafter). Methods that attempt to achieve economic
equivalence by adding control variables to the regression specification will only result in an
unbiased estimate of the treatment effect if the functional relationship between control variables
and the outcome is known and correctly specified (Armstrong et al. 2010). In contrast, PSM does
not rely on these assumptions because it achieves economic equivalence by exclusively retaining
matches that do not significantly differ on control variables. Second, adding control variables to
the Basu model results in significant multicollinearity and results that are difficult to interpret
because these control variables have to be interacted with DR, RET, and RET×DR. Because PSM
ensures that our AFB and non-AFB samples do not significantly differ on control variables, it
allows us to control for observable confounding factors without adding control variables to the
Basu model. Thus, in our setting, for both theoretical and practical reasons, PSM is a superior
method for controlling for confounding factors.
Panel B of Appendix B presents the logistic regressions used to derive propensity scores.
The dependent variable in column (1) is the AFB indicator.22 The panel shows that AFB firms
tend to be larger than non-AFB firms, but do not differ with respect to other determinants of
conservatism such as the market-to-book ratio, leverage and litigation risk.23 Consistent with
Kroszner and Strahan (2001) we find that AFB firms have higher asset tangibility and are more
likely to have a commercial credit rating. In addition, we find that AFB firms have lower inside
22
Columns (2) through (8) present propensity score models for various analyses that use the same control variables,
but some alternative variation of AFB as the treatment variable of interest. We discuss these alternative treatment
variables in their corresponding sections.
23
This suggests that litigation risk is unlikely to explain the potential difference in conservatism between AFB and
non-AFB firms. Moreover, consistent with litigation risk in our sample being too small to be a significant source of
conservatism we find that litigation risk does not explain conservatism in our sample (results not tabulated).
17
director ownership, higher outside director ownership, and lower institutional holdings. Overall,
the regression results suggest that our control variables are potentially important confounding
factors.24
Using propensity scores derived from the logistic model presented in column (1) of Panel B
from Appendix B, we match each AFB observation to at most four non-AFB observations that
have the closest odds of having AFB. We require matches to have a maximum caliper difference
of 0.001 (odds ratio). This matching procedure reduces our sample from 6,481 to 1,416 firm-year
observations, of which 300 are from AFB firms and 1,116 are from non-AFB firms. We use this
matched sample to test our hypothesis.
IV. AFB AND CONSERVATISM
Our hypothesis predicts that AFB firms have less conservative accounting than non-AFB
firms. We first test our hypothesis using our PSM sample. While using PSM ensures that our
results are not driven by observable confounding factors, it is still possible that unobservable
confounding factors drive our results. Accordingly, we subsequently perform various analyses to
alleviate the concern that our results are driven by unobservable confounding factors. Finally, we
examine mandatory conservatism as an alternative explanation.
Primary Results
To test our hypothesis, we allow β3 in model (1) to take different values for AFB and nonAFB firms. Specifically, using the PSM sample we estimate the following model:
Earnings = α0 + β1DR + β2AFB + β3DR × AFB + β4Ret + β5 DR × Ret + β6Ret × AFB
+ β7 DR × Ret × AFB + γZ + ε
24
(2)
We use the area under the ROC (Receiver Operating Characteristic) curve (AUC) as a measure of how accurate our
logistic regression models are at discriminating between treatment and control firms (Hosmer and Lemeshow
2000). Our main propensity score model provides an AUC of 0.86, which far exceeds the acceptable level of
discrimination used by previous research (0.7). Thus, the ROC analysis presented in column (1) of Panel B from
Appendix B indicates that the covariates included in our propensity score model predict the presence of AFB with
reasonable accuracy.
18
where AFB equals one when firms have affiliated bankers on their board, and zero otherwise; all
other variables are as defined in model (1). Our hypothesis predicts that β7 is negative, that is,
AFB firms have a lower asymmetric timeliness coefficient than matched non-AFB firms. As
discussed above, we estimate model (2) after adding firm fixed effects. In addition, we cluster
standard errors at the firm and year level to control for residual dependence. To remove outliers,
we estimate the model after dropping the top 1% of absolute standardized residuals.
Panel A of Table 1 presents descriptive statistics on the variables included in model (2)
separately for AFB and non-AFB firms. Among others, the panel shows that AFB firms and
matched non-AFB firms do not differ significantly with respect to average stock returns and the
incidence of negative stock returns (DR), suggesting that any difference in conservatism is
unlikely driven by differences in stock returns, particularly the incidence of negative stock returns
(i.e., bad news).25
Panel B of Table 1 presents the results of estimating model (2). Consistent with our
hypothesis the panel reports that β7 is negative (-0.133) and significant at p < 0.01, suggesting that
AFB firms have significantly lower asymmetric timeliness than non-AFB firms. Panel C
reconstructs estimates of timely loss recognition (column 1), timely gain recognition (column 2),
and two asymmetric timeliness measures, i.e., timely loss recognition minus timely gain
recognition (column 3) (Basu 1997) and timely loss recognition divided by timely gain
recognition (column 4) (Bushman and Piotroski 2006).26 Consistent with our hypothesis, the
panel shows that both asymmetric timeliness measures are significant for non-AFB firms, but not
for AFB firms; and that the difference is statistically significant at p < 0.05 for both measures.
25
Although Ret and DR are not included in our propensity score models, it appears that matching on a comprehensive
list of control variables also balances Ret and DR between our matched AFB and non-AFB subsamples.
26
For consistency with prior literature and also brevity, in subsequent analyses we focus only on the asymmetric
timeliness coefficient.
19
Moreover, the panel shows that AFB firms have lower timely loss recognition and higher timely
gain recognition than non-AFB firms, which confirms that AFB firms use less conservative
accounting (Guay and Verrechia 2006). Thus, overall the results presented in Panels B and C of
Table 1 are consistent with AFB firms using less conservative accounting than non-AFB firms. 27
In order to ensure that our results are not driven by the economic characteristics that we
attempt to control for using PSM, we verify that the matched AFB and non-AFB subsamples do
not significantly differ with respect to variables included in our propensity score model. Twosample t-tests and a likelihood ratio test suggest no systematic differences in the distribution of
these variables (results not tabulated). Thus, our results are not driven by the economic
characteristics that we control for using PSM.28
Addressing Unobservable Confounding Factors
Thus far, we have used PSM to ensure that our results are not driven by a comprehensive list
of observable factors that prior literature has shown to be associated with conservative accounting
or the presence of AFB. However, it is possible that other unobservable factors that are omitted
from our propensity score model drive both the affiliated bankers’ decision to serve on the board
and the firm’s level of conservatism. In order to enhance our ability to draw causal inferences, we
perform analyses that use: (1) instrumental variables to extract exogenous variation in AFB; (2) a
difference-in-difference design to control for time-invariant unobservable endogenous variation in
AFB; and (3) the presence of unaffiliated bankers on corporate boards as a pseudo experiment. As
in our primary analysis we use PSM to ensure that our results are not driven by observable
confounding factors.
27
Consistent with our results not being driven by the use of stock returns as a proxy for economic news (Dietrich et
al. 2007; Givoly et al. 2007), we find similar results when we use changes in operating cash flows as a proxy for
economic news (Ball and Shivakumar 2006).
28
We find that the treatment (e.g., AFB) and control (e.g., non-AFB) samples do not significantly differ on control
variables in all PSM samples (results not tabulated).
20
Instrumental Variables
Instrumental variable analysis exploits exogenous variation in the treatment condition to
create a quasi-randomized experiment that allows researchers to draw inferences about causal
relationships. In order to extract exogenous variation in AFB and control for observable
endogenous variation in AFB, we combine an instrumental variable analysis that classifies
observations as having a high or low exogenous level of AFB with a propensity score analysis
that eliminates imbalances in control variables between these two groups of observations. The
resulting PSM sample contains observations that exhibit significant exogenous variation in AFB,
but insignificant observable endogenous variation in AFB.
Specifically, we estimate the following logit model to classify observations as exhibiting a
high or low exogenous level of AFB:
AFB = α0 + β1 Industry importance to primary lender + β2 Primary lender within 50 mile radius
+ β3 Number of commercial banks within 50 mile radius + ε
(3)
where Industry importance to primary lender equals the fraction of the primary lender’s loan
portfolio issued to other firms in the same industry (Fama-French 48-industry classification)
during the past five years; Primary lender within 50 mile radius equals one when the primary
lender's headquarter is within a 50 mile radius of the firm's headquarter, and zero otherwise; and
Number of commercial banks within 50 mile radius equals the number of commercial banks that
are headquartered within a 50 mile radius of the firm's headquarter.29 Because lenders’ industry
focus and geographic proximity are unlikely to influence firm-specific financial reporting choices,
we expect our instruments to influence conservatism only through their relationship with AFB.
Moreover, we expect our instruments to be directly related to AFB. Particularly, we expect bank
29
We designate lenders as being primary lenders when they hold the largest fraction of a firm’s private debt
outstanding at the end of the fiscal year. Because data on lender shares is typically not available we assume that all
syndicate members hold an equal share of the loan. When our approach for identifying primary lenders identifies
multiple lenders we take the maximum of each measure across all primary lenders.
21
executives to be more likely to serve on corporate boards of firms they lend to when acquiring
information about these firms’ industry is more important (Industry importance to primary
lender), their physical proximity makes it less costly to serve on boards (Primary lender within 50
mile radius), and when they have less competition for board seats (Number of commercial banks
within 50 mile radius). Thus, overall we believe that our three measures are valid instruments.
Panel A of Table 2 reports descriptive statistics on our instruments. On average, a primary
lender issues 6% of its loans to other firms in the same industry, 21% of firms have a primary
lender that is headquartered in the same geographic area, and 4.89 commercial banks are
headquartered in firms’ geographic area. Panel B of Table 2 presents the estimation results of
model (3). Consistent with our prediction, Industry importance to primary lender and Primary
lender within 50 mile radius are positively associated and Number of commercial banks within 50
mile radius is negatively associated with AFB.30 We use the fitted values from this regression to
classify observations as exhibiting a high or low exogenous level of AFB. Specifically, we drop
observations that have a predicted AFB value in the second and third quartile of our sample to
increase the strength of our instruments (Baiocchi et al. 2010) and create an indicator variable
(AFB IV) that equals one when an observation’s predicted AFB value is in the top quartile of our
sample, and zero otherwise. Subsequently, we use the same PSM approach as used in our primary
analysis to eliminate imbalances in control variables between observations that exhibit a high
(AFB IV = 1) and low (AFB IV = 0) exogenous level of AFB. The results of our propensity score
model are presented in column (2) in Panel B from Appendix B.
Finally, we use the resulting PSM sample to estimate the following model:
30
Our model of three instruments provides an AUC of 0.7, which is considered to be an acceptable level of
discrimination (Hosmer and Lemeshow 2000). We also find that an AUC of 0.7 is significantly different from 0.5
at p < 0.01. Thus, the ROC analysis indicates that our instruments are able to predict the presence of AFB with
reasonable accuracy.
22
Earnings = α0 + β1DR + β2AFB IV + β3DR × AFB IV + β4Ret + β5 DR × Ret + β6Ret × AFB IV
+ β7 DR × Ret × AFB IV + γZ + ε
(4)
If our results are not driven by endogenous variation in AFB we should again find that β7 is
negative. Panel C of Table 2 presents descriptive statistics on the variables included in model (4)
separately for observations that exhibit a high (AFB IV = 1) and low (AFB IV = 0) exogenous
level of AFB. Among others, the panel shows that the two sets of observations do not differ with
respect to average stock returns and the incidence of negative stock returns. The results of
estimating model (4) are presented in Panel D of Table 2. Consistent with our results not being
driven by endogenous variation in AFB we find that β7 is negative (-0.159) and significant at p <
0.01.
One could argue that our instruments are not exogenous if lenders’ industry focus and
geographic proximity directly affect conservatism by reducing lender-borrower informational
asymmetries. To ascertain that our instruments are exogenous we perform an over-identifying
restrictions test (Larcker and Rusticus 2010). A standard over-identifying restrictions test
regresses the residuals from the second stage regression on the instruments—significant
coefficients on the instruments suggest that they are not exogenous. However, such a test is
meaningless given that the focus in the Basu model is on the interaction between DR and Ret.
Accordingly, we conduct an equivalent, and for our purpose, more suitable test that shows that
our instruments affect conservatism only through their relationship with AFB (for a similar
approach, see Leuz and Oberholzer-Gee 2006). We estimate a Basu-model where we interact the
Basu variables with our three instruments and AFB. Including AFB as one of the interactions
allows us to examine whether our instruments have a direct effect on conservatism that does not
go through their association with AFB. Consistent with our instruments being exogenous,
23
untabulated results indicate that none of the three instruments are significantly associated with
conservatism after controlling for AFB.
Difference-in-Difference Design
In our next analysis, we control for time-invariant unobservable endogenous variation in
AFB using a difference-in-difference design as follows. First, we identify firms for which the
AFB relationship was initiated during our sample period and drop all AFB firm-years for which
the lending relationship was not initiated in the focal year. Second, we drop all firm-year
observations that have missing financial and market data for the period t-3 through t+3, where t is
the focal year. This results in a sample of 5,243 firm-year observations of which 51 observations
are from AFB initiators (one firm-year per AFB initiator), and 5,192 are from non-AFB firms.
Third, using this reduced sample, we match the 51 AFB initiators to non-AFB observations using
the same PSM approach as previously used (column (3) of Panel B from Appendix B). This
matching procedure results in a PSM sample of 251 firm-year observations, of which 51 are from
AFB initiators (AFB Initiator = 1) and 200 are from non-AFB firms (AFB Initiator = 0). Fourth,
we retrieve for each firm-year observation financial and market data for the period t-3 through
t+3, where t is the focal year, and drop the focal year. This results in a sample of 1,506 firm-year
observations, of which 306 (6 per AFB initiator) are from AFB initiators (AFB Initiator = 1) and
1,200 (6 per non-AFB firm-year) are from non-AFB firms (AFB Initiator = 0). Finally, using this
sample, we estimate the following difference-in-difference model:
Earnings = β1DR + β2DR × AFB Initiator + β3Ret+ β4Ret × DR + β5Ret × AFB Initiator
+ β6Ret × DR × AFB Initiator + β7Post Initiator + β8DR × Post Initiator
+ β9 AFB Initiator × Post Initiator + β10DR × AFB Initiator × Post Initiator
+ β11Ret × Post Initiator + β12Ret × DR × Post Initiator + β13Ret × AFB Initiator × Post Initiator
+ β14Ret × DR × AFB Initiator × Post Initiator + γZ+ε
(5)
24
where Post Initiator equals one for firm-years t+1 through t+3 (for both AFB initiators and nonAFB firms), and zero otherwise; all other variables are as defined in model (1) and above.
Consistent with our hypothesis we expect β14 to be negative, that is, relative to non-AFB firms,
AFB initiators experience a decrease in conservatism after initiating an affiliated lending
relationship.
Panel A of Table 3 presents descriptive statistics on the measures used in the estimation
separately for AFB initiators and non-AFB firms. Among others, the table shows that these two
sets of firms do not significantly differ with respect to average stock returns and the incidence of
negative stock returns. Panel B of Table 3 reports the result of estimating model (5). Consistent
with our prediction, the panel shows that β14 is negative (-0.166) and significant at p < 0.10. Thus,
also our difference-in-difference analysis suggests that our results are not driven by endogenous
variation in AFB.
An alternative explanation for our difference-in-difference results is that affiliated lenders
coerce firms into lowering operational risk, which mechanically leads to a decrease in
conservatism by lowering the probability of future negative outcomes. 31 This could occur,
because formal power derived from having a board seat, informal power derived from having
financial expertise (French and Raven 1959), and quasi monopoly power derived from their
informational advantage over other lenders may give affiliated lenders significant influence over
firms’ operational decisions (Sharpe 1990; Rajan 1992).32 To rule out this alternative explanation,
we examine whether firms exhibit a significant decrease in commonly used measures of risk31
A related but different alternative explanation is that bankers choose to serve on boards of less risky firms. Our
analysis on unaffiliated bankers alleviates this concern.
32
Evidence suggests that lenders do use their power on the board to coerce borrowers into adopting lender-friendly
policies. For example, once on board, lenders exercise downward pressure on debt ratios in a manner divergent
from shareholder interests (Byrd and Mizruchi 2005), coerce firms without financial constraints and few
investment opportunities (but low default risk) to obtain large loans (Gϋner et al. 2008), and promote value
enhancing acquisitions (Hilscher and Ciamarra 2013).
25
taking, including cash flow volatility, earnings volatility, return volatility, idiosyncratic return
volatility, market-to-book ratio, and R&D intensity, after initiating an AFB relationship. Panel C
of Table 3 presents results from this analysis. We do not find significant changes in these
measures. This is consistent with the finding in Kroszner and Strahan (2001) that economic
fundamentals do not change after a banker joins the board. Thus, it is unlikely that the differencein-difference results are mechanically driven by changes in firm fundamentals, particularly risktaking.
Using Unaffiliated Bankers on Board as a Pseudo Experiment
Next, we exploit the presence of unaffiliated bankers on corporate boards to create a pseudo
experiment that allows us to draw inferences about the causal relationship between AFB and
conservatism. An unaffiliated banker is a board member who is a top executive of a commercial
bank that does not have a concurrent lending relationship with the firm. Both affiliated and
unaffiliated bankers may be asked to serve on corporate boards because their financial expertise is
valued by firms (Mizruchi 1996) and the benefits they receive for serving on boards are very
similar (e.g., industry expertise, board member experience, networking opportunities, influence).
Because of this, the presence of both affiliated and unaffiliated bankers on corporate boards may
be largely driven by the same set of factors, including unobservable factors. However, in contrast
to affiliated bankers, unaffiliated bankers do not have investments in the firm to monitor. Thus,
while the presence of affiliated and unaffiliated bankers on corporate boards may largely be
driven by the same factors, our theory suggests that only affiliated bankers should be associated
with a decrease in conservatism. This presents us with a unique setting to examine whether our
results are driven by unobservable confounding factors shared by affiliated and unaffiliated
bankers. That is, if our results are driven by these common factors, unaffiliated bankers should
26
also be associated with a similar decrease in conservatism as AFBs. 33
Accordingly, in this section we examine the effect of unaffiliated bankers on conservative
accounting. We first match unaffiliated banker firms (UNAFB) to no banker control firms using
the same PSM as previously discussed.34 We note that consistent with our motivation for
exploiting the presence of unaffiliated bankers on corporate boards, column (4) of Panel B from
Appendix B shows that UNAFB is associated with many of the same observable determinants as
AFB, such as return volatility, tangibility, and institutional ownership. Thus, it is likely that
UNAFB and AFB share unobservable confounding factors.
Using this PSM sample we estimate the following regression model:
Earnings = α0 + β1DR + β2UNAFB + β3DR × UNAFB + β4Ret + β5 DR × Ret + β6Ret × UNAFB
+ β7 DR × Ret × UNAFB + γZ + ε
(6)
Table 4 reports the regression results of estimating model (6). Panel A of Table 4 presents
descriptive statistics on the measures used in the estimation. Panel B of Table 4 reports the results
of estimating model (6). We find an insignificant β7, which suggests that firms with unaffiliated
bankers on their board do not differ from firms with no bankers on their board with respect to
their asymmetric timeliness. This is in sharp contrast with the significantly lower conservatism
associated with affiliated bankers and suggests that unobservable determinants, such as lower firm
risk, shared by affiliated and unaffiliated bankers do not explain our results.
Controlling for Mandatory Conservatism
Because GAAP explains a fair amount of the cross-sectional variation in conditional
conservatism (Lawrence et al. 2012), we also separately examine whether our subsamples differ
with respect to their exposure to mandatory conservatism; i.e., declines in asset values that are
33
For example, they may both serve on boards of less risky firms that have lower conservatism due to a lower
probability of negative outcomes.
34
In order to isolate the impact of unaffiliated bankers on board we drop AFB firm-years from the control group.
Thus, the number of observations used for the propensity score estimation reduces from 6,481 to 6,170.
27
subject to impairment charges as mandated by GAAP. Ruling out this alternative explanation is
important for two reasons. First, although our propensity score model already includes proxies
that are closely related to firms’ exposure to mandatory impairment charges (e.g., market-tobook), it is possible that our matched AFB and non-AFB subsamples may still differ with respect
to this economic characteristic. Second, the underlying premise of our theory is that firms apply
within GAAP allowed discretion to be more conservative in response to debt contracting.35 Thus,
ensuring that our results are not driven by mandatory conservatism not only rules out an
alternative explanation for our findings, but also substantiates our hypothesis.
It is important to note that several of the previously reported results already suggest that it is
unlikely that our results are driven by mandatory conservatism. For instance, Panel A of Table 1
shows that our matched AFB and non-AFB subsamples do not differ with respect to the incidence
of negative stock returns (DR), which can be viewed as a measure of firms’ exposure to declines
in asset value changes that are subject to impairment charges as mandated by GAAP. Moreover,
the greater timely gain recognition of AFB firms presented in Panel C of Table 1 also suggests
that the lower conservatism cannot exclusively be driven by mandatory conservatism. In addition,
consistent with our PSM approach effectively controlling for mandatory conservatism we find
that our matched AFB and non-AFB subsamples do not differ with respect to two measures of
mandatory conservatism used in Lawrence et al. (2012), specifically the lagged book to market
ratio (BTMt-1), and an indicator that captures whether firms have a lagged book to market ratio
larger than one (BTMt-1 >1) (results not tabulated). Thus, it is unlikely that our findings are driven
by mandatory conservatism.
35
Discretionary conservatism can arise when firms choose to report bad news earlier than specified by GAAP (such
as through a timely write-down or a restructuring charge) or delay the incorporation of good news (such as through
conservative revenue recognition). Consistent with this, Lawrence et al. (2012) find that 22% of firms report writedowns when GAAP does not require them to do so, that is when market values are well above book values.
28
Nevertheless, we perform two additional analyses to verify that our results are not driven by
mandatory conservatism. First, we exclude firms that have lagged book to market ratios larger
than one (6% of the sample). Second, we further match firms on their lagged book to market
partition as defined by Lawrence et al. (2012). The results from these analyses are similar to those
presented in Table 1 (results not tabulated). Overall, these supplemental analyses that essentially
assess the economic equivalence of our matched AFB and non-AFB subsamples with respect to
their exposure to mandatory conservatism provide assurance that our results are not driven by
mandatory conservatism.
V. ADDITIONAL ANALYSES
Thus far, we have documented a negative relation between AFB and conservatism, and
performed various analyses that suggest that this relation is not driven by observable and
unobservable endogenous variation in AFB. In this section, we provide direct evidence for the
three key arguments made in our hypothesis. Our hypothesis is that lender monitoring
mechanisms, such as AFB, allow lenders to renegotiate in a timelier manner based on private
information, thus reducing lenders’ need for conservatism-facilitated control transfer. First, we
test whether private information allows AFBs to renegotiate in a timelier manner. Second, we test
whether AFB reduces lenders’ need for, and thereby borrowers’ supply of conservatism to
facilitate control transfers. Third, we test whether lender monitoring, not AFB per se, lowers the
demand for conservatism.
Renegotiation Timeliness
We examine the relative timeliness of loan renegotiations by examining whether loans
extended by affiliated lenders are more likely to be renegotiated in the absence of covenant
violations. Renegotiations trigged by covenant violations are by definition based on public
29
information and generally occur after a significant deterioration in creditworthiness of the
borrower. In contrast, renegotiations in the absence of covenant violations are more likely to be
based on private information that anticipates adversity. Thus, we classify renegotiations as being
timely when they are not preceded by covenant violations within a year of the renegotiation date.
Further, because our theory focuses on how affiliated lenders protect their interests, we focus on
borrower unfavorable renegotiations.
Following Roberts and Sufi (2009b), we classify a renegotiation as borrower unfavorable
if it results in a loan amount decrease without a decrease in the interest spread, or an interest
spread increase without an increase in the loan amount. We obtain loan renegotiation data from
Nini et al. (2009) and DealScan’s loan amendment database. To appear in our sample we require
renegotiations to have original loan terms and renegotiated loan terms available from DealScan.
Further, we require renegotiations to have covenant violation data available from the covenant
violation database constructed by Roberts and Sufi (2009a) and require renegotiations to occur
during our sample period (2000-2006).36
Panel A of Table 5 compares the frequency of borrower unfavorable renegotiations that
occur in the absence of covenant violations between loans extended by affiliated and unaffiliated
lenders. It is striking that none of the AFB renegotiations is preceded by covenant violations,
while 9.64% of the non-AFB renegotiations are preceded by covenant violations. 37 A likelihood
ratio χ2-test indicates that the difference between AFB and non-AFB renegotiations is significant
at p < 0.10. Note that this result is based on a rather small sample in particular for AFB loans,
36
We thank Yiwei Dou for sharing with us the renegotiation data and Michael Roberts and Amir Sufi for making the
covenant violation data publicly available.
37
The statistics of non-AFB borrower unfavorable renegotiations preceded by covenant violations are comparable to
that in Roberts and Sufi (2009b), which reports that on average 9.6% of renegotiations are preceded by covenant
violations (Table 5, p.171). This percentage is small because many of the renegotiations are not triggered by
borrower-specific changes in credit risk, but by changes in macro-economic conditions such as interest rates, GDP
growth and the supply of credit.
30
partly because we impose a very restrictive definition of borrower unfavorable renegotiations that
understates their presence. Thus, we also use an alternative definition of borrower unfavorable
renegotiations in which we exclude lender favorable renegotiations. Untabulated results show that
our findings are robust to this alternative definition. 38
Panel B of Table 5 repeats our frequency test after using PSM to ensure that our AFB and
non-AFB renegotiation samples do not significantly differ on the control variables used in
previous analyses. Specifically, we use control variable data for the fiscal year closest to the
renegotiation date and match AFB renegotiations to non-AFB renegotiations after making one
modification to our PSM approach. Because of the small sample size of AFB renegotiations, we
use a maximum caliper difference of 0.01 instead of 0.001 (control variables are still balanced).
Panel B shows that the documented effect in Panel A is larger after PSM. Specifically, Panel B
shows that none of the AFB renegotiations, but 32% of the non-AFB renegotiations is preceded
by covenant violations. Overall, this analysis directly supports our argument that AFBs use their
private information to renegotiate in a timelier manner than non-AFBs.
Supply of Conservatism to Facilitate Control Transfers
Next, we examine whether AFB reduces lenders’ need for, and therefore borrowers’
supply of conservatism to facilitate control transfers. Prior literature argues that firms are more
conservative when they are more highly leveraged and/or use more covenants, because
conservatism facilitates timely control transfers (LaFond and Watts 2008; Nikolaev 2010).
Accordingly, we use the strength of the relation between leverage/covenants and conservatism as
a proxy for the supply of conservatism to facilitate control transfers (LaFond and Watts 2008;
38
The implication of this alternative definition is that renegotiations that cannot be clearly classified as being
borrower unfavorable or favorable based on DealScan data are classified as borrower unfavorable. A manual
search of the EDGAR filings of a small random sample of these unclassified cases suggests that only half of them
are borrower unfavorable. Thus, while our main definition understates the number of borrower unfavorable
renegotiations, our alternative definition overstates this number.
31
Nikolaev 2010).39 If AFB lowers the supply of conservatism to facilitate control transfers we
expect AFB to attenuate the relation between leverage/covenants and conservatism. As mentioned
in our hypothesis development section, affiliated lenders may use their information advantage to
demand more conservative accounting to enhance their renegotiation power. If this is more likely
when agency conflicts are more severe (high leverage/covenants), the relation between
leverage/covenants and conservatism may be more pronounced for AFB firms. Thus, the tension
in our hypothesis may make it difficult to find support for the key argument that we test in this
analysis.
Panel A and B of Table 6 presents descriptive statistics on firm leverage and debt
covenants.40 We report the comparison between AFB and non-AFB firms for both unmatched and
matched samples. Before PSM, AFB firms have higher leverage but fewer covenants than nonAFB firms, consistent with AFB firms relying less on covenants even when their leverage is
higher (Ciamarra 2012) and, more generally, having a lower demand for debt contracting. After
PSM, AFB firms and non-AFB firms have similar leverage and covenant intensity, which ensures
that any difference in supply of conservatism to facilitate control transfers is not driven by a
difference in debt contracting. Next, we formally test how the relation between
leverage/covenants and conservatism changes by the presence of AFB. To test this, we add an
indicator that captures whether firms have high leverage/high covenant intensity to model (2) and
estimate the following regression:
39
We do not compare the frequency of covenant violations between AFB and non-AFB firms because such a test
only captures the ex post realization of control transfers without addressing the fundamental question of whether
firms supply less conservatism to facilitate control transfers ex ante.
40
We measure the use of debt covenants as the sum of six dummy variables representing the presence of the
following covenants: debt issuance sweep, equity issuance sweep, asset sale sweep, dividend restriction, at least two
financial covenants, and a secured covenant (Bradley and Roberts 2004). All covenants are at the package level
except the secured covenant which is at the facility level. We define a loan package to have the secured covenant as
long as one of the facilities has the secured covenant. Since firms may have multiple packages we aggregate the
package data to the firm-level by weighting the resulting covenant index by the loan amount.
32
Earnings = β1DR + β2AFB + β3DR × AFB + β4Ret+ β5Ret × DR + β6Ret × AFB + β7Ret × DR × AFB
+ β8High Agency Conflict + β9DR × High Agency Conflict + β10 AFB × High Agency Conflict
+ β11DR × AFB × High Agency Conflict + β12Ret × High Agency Conflict
+ β13Ret × DR × High Agency Conflict + β14Ret × AFB × High Agency Conflict
+ β15Ret × DR × AFB × High Agency Conflict + γZ+ε
(7)
where High Agency Conflict equals either an indicator that captures whether a firm’s leverage is
in the top quartile of our sample (High Leverage) or an indicator that captures whether a firm’s
covenant intensity is in the top quartile of our sample (High Covenants); all other variables are as
defined in model (1).
In this model, the relation between leverage/covenants and conservatism is captured by β13
for non-AFB firms and β13 + β15 for AFB-firms. Thus, a negative β15 is consistent with AFB firms
supplying less conservatism to facilitate control transfers than non-AFB firms. For ease of
interpretation, Panels C and D of Table 6 present reconstructed coefficients from the above
regression in a 2×2 table representing asymmetric timeliness along the AFB and
leverage/covenant dimensions. Consistent with our key argument that AFB firms supply less
conservatism to facilitate control transfers than non-AFB firms, β15 is negative and significant at p
< 0.05 in both panels. Moreover, consistent with prior literature (e.g., Nikolaev 2010), for nonAFB firms, higher leverage/covenants is associated with greater asymmetric timeliness.41 In
contrast, for AFB firms, the asymmetric timeliness coefficients in both the low and the high
leverage/covenant partitions are insignificant, suggesting that AFB firms do not supply
conservatism to facilitate control transfers and that affiliated lenders do not demand more
conservative accounting to enhance their renegotiation power even when agency conflicts are
41
We note that Nikolaev (2010) uses bond covenants. We use bank loan covenants because we study bankers on
board and bank loans are directly tied to bankers’ incentives. Our finding of a positive association between
conservatism and loan covenants complements Nikolaev (2010).
33
more severe (i.e., high leverage and/or high covenant intensity).42 Overall, these findings are not
only a natural extension of Ciamarra (2012), but also directly support our hypothesis that the
reduction in conservatism in AFB firms is driven by a lower lender demand, and thus a lower
borrower supply of conservatism to facilitate control transfers.
Relationship Lending as an Alternative Proxy for Lender Monitoring
In this section we triangulate our results using relationship lending as an alternative proxy
for lender monitoring. Relationship lending refers to close and influential ties between private
lenders and borrowers that facilitates monitoring by lenders (e.g., Petersen and Rajan 1994;
Bharath et al. 2011). Relationship lending is usually characterized as “[when] a bank invests in
obtaining customer-specific information, often proprietary in nature; and evaluates the
profitability of these investments through multiple interactions with the same customer over time
and/or across products” (Boot 2000). Thus, relationship lending can be argued to have similar
effects on conservative accounting as AFB.
Our measure of relationship lending is based on those used by Bharath et al. (2011).43
Similar to Bharath et al. we classify a lender as being a relationship lender when the lender was
retained as a lead bank for a loan outstanding at the end of the fiscal year and the lender was also
a lead bank for at least one other loan issued to the firm in the prior five years. 44Our relationship
lending measure consists of three components, which are defined as follows: (1) Number of loans
equals the number of private loans issued to a firm in the prior five years for which the firm’s
relationship bank was retained as a lead bank; (2) Relative frequency of loans equals Number of
42
We find similar results when we use a measure of a firm’s information environment (probability of informed trade)
as a proxy for agency costs; i.e., agency costs are higher when firms’ information environment is less transparent
(results not tabulated).
43
Because we need a firm-year level measure of relationship lending, we compute the three relationship lending
measures that are computed at the loan level in Bharath et al. (2011) at the firm-year level.
44
Following Bharath et al. (2011), we classify a bank as lead bank if lead arranger credit is ‘Yes”, or the bank is
coded as either “agent”, “administrative agent”, “arranger” or “lead bank” in DealScan.
34
loans scaled by the number of private loans issued to a firm in the prior five years; and (3)
Relative amount of loans equals the loan amount corresponding to Number of loans scaled by the
total amount of private loans issued to a firm in the prior five years. Whenever multiple lenders
are designated as relationship banks we take the maximum of each measure across all relationship
banks. Because all three measures capture different but related aspects of relationship lending, we
create an indicator variable, RLB, that equals one when the values of all three relationship lending
measures (i.e., Relative frequency of loans, Number of loans and Relative amount of loans) are in
the top quartile of the sample, and zero otherwise.
Panel A of Table 7 provides descriptive statistics on the three components of our
relationship lending measure for the firms with RLB equal to one. The panel shows that these
firms typically have lenders that were retained as a lead bank on at least five loans issued in the
prior five years and that these loans generally represent all private loans that were issued during
this time period. Thus, clearly firms with RLB equal to one have many interactions with their
lenders through repeated lending. Not surprisingly, there is certain overlap between relationship
lending and AFB. We find that 63 out of 311 firm-year observations with AFB are classified as
having RLB equal to one. To ensure that our results are not driven by relationship lending we
construct a revised PSM sample that excludes RLB firms (AFB excl. RLB) (see column (6) of
Panel B from Appendix B for PSM regression results). Column (1) of Panel B from Table 7
shows that we find similar results using this revised PSM sample (β7 =0.146, p=0.002).
Subsequently, we examine whether lender monitoring, not AFB per se, reduces
conservatism by estimating the effect of relationship lending on conservatism after dropping AFB
firms (RLB excl. AFB). Column (6) of Panel B from Appendix B presents the PSM model for RLB
excl. AFB. A comparison between columns (5) and (6) shows that even though AFB and RLB are
35
both lender monitoring mechanisms they are driven by different economic factors. Specifically,
while larger firms are more likely to have either AFB or RLB, highly leveraged firms are more
likely to have RLB but not AFB, which is consistent with concerns about lender liability
influencing AFB (Kroszner and Strahan 2001). Moreover, AFB and RLB also differ with respect
to their association with return volatility, board independence, and institutional ownership.
Consistent with lender monitoring, not AFB per se, reducing conservatism, column (2) of Panel B
from Table 7 shows that relationship lending (RLB excl. AFB) is negatively associated with
conservatism (β7 = -0.128, p=0.000).
As the previous results show that lender monitoring, not AFB per se, reduces conservatism
we next examine the overall effect of lender monitoring by combining our AFB and RLB
measures into an overall measure of lender monitoring (AFB or RLB). Consistent with our
previous results column (3) shows that our overall measure of lender monitoring is negatively
associated with conservatism (β7 = -0.066, p=0.039).45
Finally, to compare the effect of AFB vs. relationship lending on conservatism, we study
whether the intersection between AFB and relationship lending (AFB and RLB) has an enhanced
ability in reducing conservatism, compared to AFB alone or relationship lending alone.
Consistent with AFB and RLB not having synergistic/interactive effects, column (4) shows that
combining the two does not lead to an additional reduction in conservative accounting (β7 =
-0.218, p=0.227).46 Taken together, the results in Table 7 provide direct support for our argument
that lender monitoring, not AFB per se, lowers the demand for conservatism.
45
Note that it is difficult to compare the magnitude of β7 across columns because each column is based on a different
PSM sample.
46
We acknowledge that the small sample size used in column (4) prevents us from drawing strong conclusions about
synergistic/interactive effects of AFB and RLB on conservatism.
36
VI. CONCLUSION
This study examines how affiliated bankers on corporate boards influence the use of
conservative accounting. We document that firms that have affiliated bankers on board use less
conservative accounting. This result is robust to controlling for an extensive list of observable
confounding factors using propensity score matching. In addition, an instrumental variable
analysis, a difference-in-difference analysis, and a pseudo experiment contrasting AFB firms to
firms with unaffiliated bankers on board suggest that the lower level of conservatism for AFB
firms is unlikely to be caused by unobservable confounding factors. Moreover, additional
analyses substantiate our hypothesis. First, we find that AFB renegotiations are more likely to
occur in the absence of covenant violations, suggesting that AFBs use private information to
renegotiate in a timelier manner than non-AFBs. Second, we find that AFB firms have a weaker
association between conservatism and leverage/covenants, suggesting that they have a lower
demand for conservatism-facilitated control transfer. Third, we find that relationship lending has a
similar effect on conservatism, suggesting that lender monitoring, not AFB per se, decreases
conservatism.
Our primary contribution is finding that lender monitoring mechanisms that reduce
borrower-lender information asymmetry decrease the debt contracting demand for conservative
accounting. Our results suggest that borrower-lender information asymmetry is an important
precondition for the agency problem of debt, which is the basis for the existence of debtcontracting motivated conservative accounting. Because of this, any lender monitoring
mechanism that decreases this information asymmetry—such as AFB or relationship lending—
could significantly reduce, or even eliminate, the need for debt-contracting motivated
conservatism.
37
This study has several limitations that can be addressed by future research. First, despite our
attempts to address observable and unobservable confounding factors, we acknowledge that
perfect economic equivalency is impossible to achieve and unidentified economic factors can be
an alternative explanation. Second, our paper is agnostic regarding the information efficiency of
conservatism for debt contracting. The relative information efficiency of conservatism can be
gauged only by considering the costs and benefits to all providers of capital. Such an analysis is
beyond the scope of our paper. Our goal is modest, that is, to identify alternative lender
monitoring mechanisms, such as AFB, that could ameliorate the information asymmetry and thus
reduce the underlying demand for conservatism. Overall, this study improves our understanding
of the various monitoring mechanisms that can be used to mitigate the agency cost of debt.
38
APPENDIX A
Variable Definitions
Variable
AFB
.
Earnings
Ret
DR
Accruals
Market-to-book
Leverage
Litigation risk
Log(assets)
Stock return volatility
Stock return volatility2
PP&E
Commercial credit rating
Short-term debt
Bankruptcy risk
Board independence
Log(board size)
CEO/Chair separated
Average outside
directorships
Inside director ownership
Outside director ownership
Institutional ownership
G-Score
Industry Importance to
primary lender
Primary lender within 50
mile radius
Number of commercial
banks within 50 mile radius
Definition
.
Equals one when a firm has an affiliated banker on its board, and zero
otherwise.
Earnings before extraordinary items scaled by market value of equity at the
beginning of the fiscal year.
12-month buy-and-hold return over the fiscal year.
Equals one when Ret is negative, and zero otherwise.
Earnings before extraordinary items minus operating cash flow, scaled by
average total assets.
Market value of equity scaled by book value of equity.
Source .
BoardEx,
DealScan
Compustat,
CRSP
CRSP
CRSP
Compustat
Compustat
CRSP
Sum of long-term debt and the current portion of long-term debt scaled by Compustat
total assets.
Probability of litigation from Rogers and Stocken (2005).
Compustat,
CRSP
Logarithmic transformation of total assets.
Compustat
Standard deviation of monthly stock returns over the prior 3 years.
CRSP
Square of Stock return volatility.
Property, Plant & Equipment scaled by total assets.
Compustat
Equals one when a firm has a commercial credit rating, and zero otherwise. Compustat
Short-term debt scaled by total debt.
Compustat
Probability that a firm will go bankrupt from Shumway (2001).
Compustat,
CRSP
Number of outside directors scaled by total number of directors.
Corporate
Library
Logarithmic transformation of number of directors.
Corporate
Library
Equals one when the CEO is not the chairman of the board, and zero Corporate
otherwise.
Library
Total outside directorships held by the board scaled by the number of Corporate
directors.
Library
Common shares held by inside directors scaled by total common shares Corporate
outstanding.
Library
Common shares held by outside directors scaled by total common shares Corporate
outstanding.
Library
Common shared held by institutional investors scaled by total common Thomson
shares outstanding.
Reuters
Governance index from Gompers et al. (2003).
Risk Metrics
Fraction of loans issued by a firm’s primary lender in the firm’s industry DealScan
during the prior 5 years, excluding the loans issued to the focal firm. We
designate lenders as being primary lenders when they hold the largest
fraction of a firm’s private debt outstanding at the end of the fiscal year.
An indicator variable equal to one when the primary lender's headquarter is DealScan
within a 50 mile radius of the firm's headquarter, and zero otherwise.
The number of commercial banks that have a headquarters within a 50 mile Compustat
radius of the firm's headquarter. Commercial banks have a three digit SIC
code equal to 602 in Compustat.
39
APPENDIX B
Propensity Score Models
Variable
.
Market-to-book
Leverage
Litigation risk
Log(assets)
Stock return volatility
Stock return volatility2
PP&E
Commercial credit rating
Short-term debt
Bankruptcy risk
Board independence
Log(board size)
CEO/Chair separated
Average outside directorships
Inside director ownership
Outside director ownership
Institutional ownership
G-Score
Panel B: Propensity Score Models
(1)
Explanatory Variable
Intercept
Market-to-book
Leverage
Litigation Risk
Log(assets)
Stock return volatility
Stock return volatility2
PP&E
Commercial credit rating
Short-term debt
Bankruptcy risk
Board independence
Log(board size)
CEO/Chair separated
AFB
-20.436
(0.000)
-0.006
(0.693)
-1.155
(0.310)
-4.557
(0.242)
0.354
(0.002)
29.125
(0.016)
-140.210
(0.025)
2.789
(0.000)
0.947
(0.004)
-1.977
(0.150)
-5.614
(0.716)
-0.530
(0.585)
-0.067
(0.876)
-0.125
(0.492)
Mean
4.629
0.227
0.012
7.678
0.118
0.017
0.302
0.124
0.056
0.002
0.680
2.375
0.922
1.217
0.051
0.009
0.749
9.416
Std. Dev.
70.720
0.160
0.019
1.489
0.059
0.020
0.221
0.330
0.086
0.009
0.150
0.341
0.268
0.851
0.116
0.049
0.194
2.546
(2)
Q1
1.646
0.093
0.004
6.638
0.078
0.006
0.128
0.000
0.001
0.000
0.571
2.079
1.000
0.615
0.002
0.000
0.624
8.000
(3)
(4)
AFB
AFB IV Initiator UNAFB
0.298
-7.135
-2.188
(0.793) (0.003)
(0.082)
-0.046
-0.000
0.000
(0.003) (0.684)
(0.405)
0.623
-0.503
-0.353
(0.245) (0.506)
(0.599)
2.872
-3.506
-1.866
(0.227) (0.639)
(0.667)
-0.160
0.405
0.065
(0.011) (0.000)
(0.513)
-3.915
-5.759
10.445
(0.233) (0.298)
(0.043)
2.272
13.246 -42.063
(0.791) (0.537)
(0.017)
3.197
1.363
0.872
(0.000) (0.157)
(0.082)
-0.249
0.253
0.310
(0.280) (0.538)
(0.304)
0.275
0.981
-0.015
(0.752) (0.488)
(0.982)
9.221
14.724 -13.197
(0.211) (0.016)
(0.476)
0.040
0.319
0.243
(0.931) (0.874)
(0.721)
0.124
-0.039
0.321
(0.625) (0.962)
(0.264)
-0.158
-0.480
-0.161
(0.243) (0.075)
(0.288)
40
Median
2.379
0.226
0.007
7.508
0.104
0.011
0.240
0.000
0.021
0.001
0.700
2.398
1.000
1.000
0.009
0.001
0.763
9.000
(5)
AFB excl.
RLB
-17.913
(0.000)
-0.017
(0.289)
-2.282
(0.029)
-6.488
(0.324)
0.205
(0.092)
25.335
(0.027)
-108.022
(0.042)
3.533
(0.000)
1.201
(0.003)
-3.696
(0.014)
-22.306
(0.290)
-1.401
(0.162)
-0.168
(0.754)
-0.054
(0.811)
Q3
3.696
0.339
0.012
8.594
0.143
0.020
0.440
0.000
0.077
0.002
0.800
2.639
1.000
1.625
0.040
0.004
0.878
11.000
(6)
(7)
(8)
RLB excl. AFB or AFB and
AFB
RLB
RLB
-9.245
-6.699 -14.383
(0.000)
(0.000)
(0.001)
-0.011
-0.010
-0.061
(0.567)
(0.518)
(0.289)
1.738
0.988
2.472
(0.001)
(0.020)
(0.303)
1.185
-1.657
-5.220
(0.800)
(0.672)
(0.585)
0.381
0.312
0.426
(0.000)
(0.000)
(0.015)
-10.518
-1.415
19.494
(0.085)
(0.767)
(0.460)
14.172 -12.629 -131.020
(0.413)
(0.322)
(0.376)
0.084
0.730
0.359
(0.839)
(0.047)
(0.670)
0.635
0.678
0.841
(0.001)
(0.000)
(0.110)
1.976
1.176
0.171
(0.010)
(0.078)
(0.945)
-50.191 -29.608
63.596
(0.065)
(0.025)
(0.255)
3.018
1.462
2.175
(0.000)
(0.001)
(0.213)
0.230
0.182
1.567
(0.309)
(0.426)
(0.012)
0.025
0.020
-0.097
(0.842)
(0.783)
(0.810)
(Continued on next page)
APPENDIX B (continued)
Average outside
directorships
Inside director ownership
Outside director
ownership
Institutional ownership
G-Score
-0.046
(0.695)
-3.079
(0.081)
1.393
(0.077)
-1.543
(0.036)
0.085
(0.091)
Yes
-0.164
(0.054)
-1.589
(0.010)
0.384
(0.738)
-0.777
(0.028)
0.035
(0.261)
Yes
-0.269
(0.127)
-0.139
(0.909)
-1.214
(0.705)
-0.545
(0.583)
0.007
(0.891)
Yes
-0.262
(0.036)
-0.324
(0.606)
-0.291
(0.769)
-2.154
(0.000)
0.017
(0.686)
Yes
-0.080
(0.564)
-3.403
(0.135)
1.270
(0.119)
-1.881
(0.023)
0.104
(0.079)
Yes
-0.052
(0.628)
0.569
(0.522)
-2.052
(0.138)
0.764
(0.052)
0.041
(0.221)
Yes
0.005
(0.952)
0.058
(0.927)
-0.241
(0.807)
-0.193
(0.579)
0.052
(0.056)
Yes
0.200
(0.342)
-0.259
(0.890)
2.778
(0.545)
0.563
(0.650)
-0.013
(0.854)
Yes
Industry indicators
Sample:
Dependent measure=1
311
1,620
51
400
248
561
872
63
Dependent measure=0
6,170
1,518
5,192
5,770
5,609
5,609
5,609
809
Total number of obs.
6,481
3,138
5,243
6,170
5,857
6,170
6,481
872
AUC
0.860
0.738
0.786
0.723
0.874
0.816
0.768
0.788
Test AUC=0.50
45.520
27.090
9.039
16.220
44.210
43.180 34.710
10.370
Z-statistic
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
p-value
Matched sample:
Treated
300
720
51
373
234
520
809
43
Control
1,116
1,503
200
1,399
875
1,901
2,876
133
Total matched sample
1,416
2,223
251
1,772
1,109
2,421
3,685
176
This table presents the results of propensity score models that are used in the analyses presented in Table 1
through 7. Panel A provides descriptive statistics on the explanatory variables (covariates) included in the propensity
score models. See Appendix A for variable definitions. Panel B presents the results of logistic regressions from which
we derive propensity scores. The dependent variables in each column are defined as follows. (1) AFB equals one
when a firm has an affiliated banker on its board, and zero otherwise. (2) AFB IV equals one when the probability of
having AFB based on three instrumental variables (Industry importance to primary lender, Primary lender within 50
mile radius, Number of commercial banks within 50 mile radius) is in the top quartile of the sample, and zero
otherwise (see Panel B of Table 2 for estimation results of the instrumental variable regression). We drop firms in the
second and third quartiles to increase the strength of our instruments (Baiocchi et al. 2010). (3) AFB Initiator equals
one when a firm initiated an affiliated lending relationship in a given year (AFB Initiator), and zero otherwise. For
firms that have an affiliated lending relationship we retain only the year in which the lending relationship was
initiated. Further, we require firm-year observations to have non-missing financial and market data for the years t-3
through t+3. (4) UNAFB equals one when a firm has a board member who is a top executive of a commercial bank
that does not belong to a syndicate that has a concurrent lending relationship with the firm, and zero otherwise.
Because firms can simultaneously have AFBs and UNAFBs we drop all AFB firm-years when we examine the
influence of UNAFB on conservative accounting. (5) AFB excl. RLB equals AFB after dropping firms that have RLB.
RLB equals one when the values of all three relationship lending measures (i.e., Relative frequency of loans, Number
of loans and Relative amount of loans) are in the fourth quartile of the sample, and zero otherwise. Definitions of
these measures are in Appendix A. (6) RLB excl. AFB equals RLB after dropping firms that have AFB. (7) AFB or
RLB equals one when firms have AFB or RLB, and zero otherwise (8) AFB and RLB equals one when firms have both
AFB and RLB, and zero otherwise, after dropping firms that have neither AFB nor RLB. The models are estimated
after dropping the top 1% of absolute standardized residuals. In addition, Z-statistics are computed using firm and
year level cluster robust standard errors (two-tailed p-values are presented in parentheses). We match each
observation that is exposed to the treatment of interest (e.g., AFB) to at most four control observations and require
matches to have a maximum caliper difference of 0.001 (odds ratio). Two sample t-tests performed after matching
indicate that all covariates are balanced between treatment and control group (p >10% for all covariates).
For our logistic models, we use the area under the ROC (Receiver Operating Characteristic) curve (AUC) as a
measure of how accurate our logistic regression models are at discriminating between treatment and control firms
(Hosmer and Lemeshow 2000, p.160-164). A greater AUC indicates better predictive ability of the model—Hosmer
and Lemeshow classify anything above 0.7 as an “acceptable” level of discrimination. We test whether the AUC is
statistically different from chance (0.50) using the Z-statistic which is equal to (AUC-0.50)/((standard error (AUC))
(see Zhou et al. 2002).
41
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44
TABLE 1
Affiliated Bankers on Board and Conservative Accounting
Panel A: Descriptive Statistics on Measures used for Basu (1997) Model – AFB Firms versus Non-AFB Firms
P-value for
AFB Firms (N=300)
Non-AFB Firms (N=1,116)
Difference in
Variable Mean Median Std. Dev.
Mean
Median Std. Dev.
Mean Distribution
Earnings 0.056
0.061
0.117
0.051
0.057
0.066
0.551
0.090
DR
0.280
0.000
0.450
0.310
0.000
0.463
0.405
0.977
Ret
0.186
0.147
0.400
0.149
0.107
0.373
0.204
0.065
Panel B: Main Regression Results
Earnings = β1DR + β2AFB + β3DR × AFB + β4Ret+ β5Ret × DR + β6Ret × AFB + β7Ret × DR × AFB + γZ+ε
β1
0.004
(0.457)
β2
-0.012
(0.080)
β3
0.005
(0.563)
β4
0.040
(0.000)
β5
0.105
(0.010)
β6
0.033
(0.033)
β7
-0.133
(0.003)
R2
0.311
N
1,416
Panel C: Accounting Conservatism – AFB versus non-AFB Firms
(1)
(2)
(3)
(4)
Bad news
Good news
Conservatism
Conservatism
Conservatism .
DR=1 l
DR=0 .
(1) – (2) .
(1)/(2)
.
AFB
β4+ β5 + β6+ β7
β4 + β6
β5 + β7
(β4+ β5 + β6+ β7)/( β4 + β6)
Estimate
0.045
0.073
-0.028
0.612
p-value
(0.030)
(0.000)
(0.517)
(0.511)
No AFB
β4+ β5
β4
β5
(β4+ β5)/( β4)
Estimate
0.145
0.040
0.105
3.642
p-value
(0.000)
(0.000)
(0.010)
(0.046)
AFB – No AFB
Estimate
-0.100
0.033
-0.133
-3.030
p-value
(0.023)
(0.033)
(0.003)
(0.015)
This table presents our main results of the relation between AFB and conservatism. The sample is constructed
using the propensity score model presented in column (1) of Panel B from Appendix B, in which each AFB firm-year
is matched to at most four non-AFB firm-years with a maximum caliper difference of 0.001 (odds ratio). Panel A
provides descriptive statistics on the measures used for estimating the Basu (1997) model separately for AFB and
non-AFB Firms. Two-sample t-tests are used to test differences in means, and Kolmogorov-Smirnov two-sample
tests are used to test differences in distributions. See Appendix A for variable definitions. Panel B presents the results
of our main analysis on the relation between AFB and conservatism. We estimate our model after including firm
fixed effects and dropping the top 1% of absolute standardized residuals. In addition, to control for residual
dependence in our pooled time-series cross-sectional regression, we cluster standard errors at the firm and year level
(two-tailed p-values are presented in parentheses). Panel C presents reconstructed coefficients from Panel B. We use
the delta method to compute the standard errors of the conservatism measure presented in column (4).
45
TABLE 2
Instrumental Variables
Panel A: Descriptive Statistics on Instrumental Variables
Variable
.
Industry importance to primary lender
Primary lender within 50 mile radius
Number of commercial banks within 50 mile radius
Mean
0.062
0.213
4.885
Std. Dev.
0.107
0.409
4.361
Q1
0.004
0.000
2.000
Median
0.027
0.000
4.000
Q3
0.069
0.000
7.000
Panel B: First-Stage Instrumental Variable Logit Model
AFB = α0 + β1 Industry importance to primary lender + β2 Primary lender within 50 mile radius
+ β3Number of commercial banks within 50 mile radius + ε
α0
-3.169
(0.000)
β1
1.901
(0.000)
β2
0.992
(0.000)
β3
-0.115
(0.001)
N
6,481
AUC
0.700
Test AUC=0.50
Z-statistic
p-value
13.370
0.000
Panel C: Descriptive Statistics: AFB IV versus non-AFB IV Firms
Variable
Earnings
DR
Ret
AFB IV Firms (N=720)
Mean Median Std. Dev.
0.041
0.052
0.091
0.365
0.000
0.482
0.158
0.104
0.460
Non-AFB IV Firms (N=1,503)
Mean Median Std. Dev.
0.031
0.047
0.104
0.379
0.000
0.485
0.166
0.105
0.606
P-value for
Difference in
Mean Distribution
0.142
0.059
0.754
1.000
0.600
0.384
Panel D: Regression Results using AFB IV
Earnings = β1DR + β2AFB IV + β3DR×AFB IV + β4Ret + β5Ret×DR + β6Ret×AFB IV + β7Ret×DR×AFB IV + γZ + ε
β1
β2
β3
β4
β5
β6
β7
R2
N
-0.013
-0.020
-0.001
-0.038
0.149
0.043
-0.159
0.110
2,223
(0.132)
(0.020)
(0.873)
(0.044)
(0.000)
(0.073)
(0.003)
This table presents the results of an analysis that combines instrumental variables with propensity score matching
(Baiocchi et al. 2010). Panel A presents descriptive statistics on the three instrumental variables included in the firststage logit model presented in Panel B. See Appendix A for variable definitions. We use the fitted values from this
regression to classify observations as exhibiting a high or low exogenous level of AFB. Specifically, we drop
observations that have a predicted AFB value in the second and third quartile of our sample to increase the strength of
our instruments (Baiocchi et al. 2010) and create an indicator variable (AFB IV) that equals one when an
observation’s predicted AFB value is in the top quartile of the sample, and zero otherwise. Subsequently, we use the
propensity score model presented in column (2) of Panel B from Appendix B to match each AFB IV firm-year (AFB
IV = 1) to at most four non-AFB IV firm-years (AFB IV = 0) with a maximum caliper difference of 0.001 (odds ratio).
Panel C provides descriptive statistics on the measures used for estimating the Basu (1997) model separately for AFB
IV firms and non-AFB IV firms. Two-sample t-tests are used to test differences in means, and Kolmogorov-Smirnov
two-sample tests are used to test differences in distributions. Panel D presents the results of estimating the Basu
(1997) model after including firm fixed effects and dropping the top 1% of absolute standardized residuals. In
addition, to control for residual dependence in our pooled time-series cross-sectional regression, we cluster standard
errors at the firm and year level (two-tailed p-values are presented in parentheses).
46
TABLE 3
Difference-in-Difference Design
Panel A: Descriptive Statistics: AFB Initiators versus non-AFB Firms
Variable
Earnings
DR
Ret
AFB Initiators (N=306)
Mean
Median
Std. Dev.
0.062
0.060
0.071
0.310
0.000
0.463
0.147
0.117
0.380
Non-AFB Firms (N=1,200)
Mean
Median
Std. Dev.
0.040
0.057
0.126
0.347
0.000
0.476
0.122
0.093
0.401
P-value for
Difference in
Mean Distribution
0.008 0.007
0.350 0.912
0.513 0.133
Panel B: Regression Results Difference-in-Difference AFB Initiation
Earnings = β1DR + β2DR × AFB Initiator + β3Ret+ β4Ret × DR + β5Ret × AFB Initiator
+ β6Ret × DR × AFB Initiator + β7Post Initiator + β8DR × Post Initiator + β9 AFB Initiator × Post Initiator
+ β10DR × AFB Initiator × Post Initiator + β11Ret × Post Initiator + β12Ret × DR × Post Initiator
+ β13Ret × AFB Initiator × Post Initiator+ β14Ret × DR × AFB Initiator × Post Initiator + γZ+ε
β1
β2
β3
β4
β5
β6
β7
β8
β9
β10
β11
β12
β13
β14
0.007 0.011 0.027 0.082 0.015 0.073 0.013 -0.009 -0.012 0.005 -0.046 0.089 0.037 -0.166
(0.475) (0.324) (0.172) (0.052) (0.357) (0.117) (0.161) (0.550) (0.256) (0.789) (0.082) (0.217) (0.269) (0.083)
Panel C: Difference-in-Difference of Primary Firm-Characteristics
P-value for
AFB Initiators (N=51)
Non-AFB Firms (N=200)
Difference-inDifference
Difference
Difference
Variable
. Mean Median Std. Dev.
Mean Median Std. Dev. Mean Distribution
Cash flow volatility
0.000 -0.001
0.037
-0.005 -0.003
0.033
0.524 0.140
Earnings volatility
0.000 -0.002
0.044
0.002 0.000
0.036
0.903 0.399
Stock return volatility
-0.016 -0.017
0.043
-0.021 -0.020
0.065
0.990 0.645
Idiosyncratic returns volatility -0.021 -0.015
0.038
-0.025 -0.023
0.055
0.861 0.464
Market-to-book
-0.770 -0.121
2.282
0.450 -0.129
9.105
0.345 0.991
R&D intensity
0.000 0.000
0.009
-0.002 0.000
0.010
0.534 0.947
Litigation risk
0.002 0.002
0.006
0.001 0.001
0.012
0.778 0.590
This table presents the results of a difference-in-difference analysis based on a propensity-matched sample
derived from the model presented in column (3) of Panel B from Appendix B, in which firms that initiated an AFB
relationship during our sample period (AFB Initiator=1) are matched to at most four non-AFB firm-years (AFB
Initiator=0) with a maximum caliper difference of 0.001 (odds ratio). This matching procedure results in a sample of
51 AFB initiator firms and 200 non-AFB firm-years. We obtain measures included in the Basu (1997) model for both
the AFB initiator firms and the matched non-AFB firm-years for the period t-3 through t+3, where t is the focal year,
and drop the focal year. This expands the sample to 306 AFB Initiator firm-years (6 per AFB Initiator) and 1,200
non-AFB firm-years (6 per non-AFB firm-year). AFB Initiator equals one for AFB Initiators, and zero for non-AFB
firms. Post Initiator equals one for firm-years t+1 through t+3 (for both AFB initiators and non-AFB firms), and zero
otherwise. All other measures are as defined in Appendix A. Panel A provides descriptive statistics on the measures
used for estimating the Basu (1997) model separately for AFB Initiators and non-AFB Firms. Two-sample t-tests are
used to test differences in means, and Kolmogorov-Smirnov two-sample tests are used to test differences in
distributions. Panel B presents the results of estimating the Basu (1997) model after including firm fixed effects and
dropping the top 1% of absolute standardized residuals (R2 = 0.134 and N = 1,506). Note that we do not include the
main effect of AFB Initiator as this measure is time-invariant. The coefficient of interest in this analysis is β14. A
negative coefficient estimate indicates that AFB initiators experience a significant decrease in asymmetric timeliness
after AFB initiation relative to matched non-AFB firms. In addition, to control for residual dependence in our pooled
time-series cross-sectional regression, we cluster standard errors at the firm and year level (two-tailed p-values are
presented in parentheses).
(Continued on next page)
47
TABLE 3 (Continued)
Panel C presents the results of a difference-in-difference analysis based on propensity scores that examines
whether firms exhibit a significant change in risk-taking and other firm-characteristics that are potentially associated
with conservative accounting after an AFB initiation. Differences in firm-characteristics are computed using data
from the three years prior to AFB initiation and the three years after AFB initiation, where Cash flow volatility is the
three-year standard deviation of operating cash flows scaled by total assets; Earnings volatility is the three-year
standard deviation of earnings before extraordinary items scaled by total assets; Stock return volatility is the 36
months standard deviation of stock returns; Idiosyncratic returns volatility is the 36 months standard deviation of
regression residuals from a regression of stock returns on market returns; Market-to-book is the three-year average of
the market value of equity scaled by the book value of common equity; R&D intensity is the three-year average of
R&D expenditures scaled by total assets; Litigation risk is the three-year average of the probability of litigation from
Rogers and Stocken (2005). Two-sample t-tests are used to test differences in means, and Kolmogorov-Smirnov twosample tests are used to test differences in distributions.
48
TABLE 4
Using Unaffiliated Bankers on Board as a Pseudo Experiment
Panel A: Descriptive Statistics: UNAFB versus No Banker Firms
UNAFB Firms (N=373)
Mean Median Std. Dev.
0.048
0.053
0.078
0.362
0.000
0.481
0.127
0.079
0.374
Variable
Earnings
DR
Ret
No Banker (N=1,399)
Mean Median Std. Dev.
0.041
0.053
0.094
0.331
0.000
0.471
0.168
0.127
0.426
P-value for
Difference in
Mean Distribution
0.382
0.357
0.413
0.970
0.214
0.053
Panel D: Regression Results using UNAFB
Earnings = β1DR + β2UNAFB + β3DR × UNAFB + β4Ret+ β5Ret × DR + β6Ret × UNAFB + β7Ret × DR × UNAFB
+ γZ+ε
β1
-0.009
(0.059)
β2
-0.017
(0.077)
β3
0.019
(0.094)
β4
0.029
(0.005)
β5
0.048
(0.106)
β6
0.003
(0.782)
β7
0.084
(0.163)
R2
0.194
N
1,772
This table presents the results of an analysis that examines whether having unaffiliated bankers on corporate
boards (UNAFB) has a similar negative effect on asymmetric timeliness as AFB. UNAFB equals one when firms have
board members who are top executives of commercial banks that do not belong to a syndicate that has a concurrent
lending relationship with the firm, and zero otherwise. The sample is based on propensity scores derived from the
model presented in column (4) of Panel B from Appendix B, in which each UNAFB firm-year (UNAFB=1) is
matched to at most four no banker firm-years (UNAFB=0) with a maximum caliper difference of 0.001 (odds ratio).
Panel A provides descriptive statistics on the measures used for estimating the Basu (1997) model separately for
UNAFB firms and no banker firms. Two-sample t-tests are used to test differences in means, and KolmogorovSmirnov two-sample tests are used to test differences in distributions. Panel B presents the results of estimating the
Basu (1997) model after including firm fixed effects and dropping the top 1% of absolute standardized residuals. In
addition, to control for residual dependence in our pooled time-series cross-sectional regression, we cluster standard
errors at the firm and year level (two-tailed p-values are presented in parentheses).
49
TABLE 5
Timely Renegotiations
Panel A: Borrower Unfavorable Renegotiations in the Absence of Covenant Violations
Relationship
No AFB .
AFB
No Covenant Violation
253
14
(90.36%)
(100.00%)
Covenant Violation
27
0
( 9.64%)
( 0.00%)
Mean Difference (AFB - No AFB)
-0.088
0.070
Median Difference (AFB - No AFB)
0.000
0.071
Test Statistic
p-value
Likelihood Ratio χ2-Test
2.767
0.096
.
Panel B: Borrower Unfavorable Renegotiations in the Absence of Covenant Violations with PSM
Relationship
No AFB .
AFB
.
No Covenant Violation
17
8
(68.00%)
(100.00%)
Covenant Violation
8
0
(32.00%)
( 0.00%)
Mean Difference (AFB - No AFB)
-0.320
0.070
Median Difference (AFB - No AFB)
0.000
0.084
Test Statistic
p-value
Likelihood Ratio χ2-Test
5.211
0.022
This table presents analyses that examine whether borrower unfriendly loan renegotiations are more likely to
occur in the absence of covenant violations when loans are extended by affiliated lenders. We obtain loan
renegotiation data from Nini et al. (2009) and DealScan’s loan amendment database. To appear in our sample we
require renegotiations to have original loan terms and renegotiated loan terms available from DealScan. Further, we
require renegotiations to have covenant violation data available from the covenant violation database constructed by
Roberts and Sufi (2009a) and require renegotiations to occur during our sample period (2000-2006). Following,
Roberts and Sufi (2009b) we classify loan renegotiations as borrower unfriendly when they result in a loan amount
decrease without a decrease in the interest spread, or an interest spread increase without an increase in the loan
amount. We drop all other renegotiations. Loans are classified as being renegotiated in the absence of covenant
violations when renegotiations are not preceded by covenant violations within a year of the renegotiation date. Panel
A provides a likelihood ratio χ2-test, a two-sample t-test to test the difference in means, and a Wilcoxon two-sample
test to test the difference in medians. Panel B repeats these tests after matching AFB renegotiations to non-AFB
renegotiations based on propensity scores derived from a first-stage logit regression (results not tabulated) in which
we regress AFB on the same explanatory variables used in the propensity score models presented in Panel B of
Appendix B (AUC=0.904). Specifically, we match each AFB renegotiation to at most four non-AFB renegotiations
and require matches to have a maximum caliper difference of 0.01 (odds ratio). Covariates included in the first-stage
model are balanced between AFB and non-AFB renegotiations. Moreover, AFB and non-AFB renegotiations do not
differ with respect to the number of covenants included in loan contracts.
50
TABLE 6
Supply of Conservatism to Facilitate Control Transfers
Panel A: Descriptive Statistics Leverage
Variable
N
Not matched 311
Matched
300
AFB Firms
Mean Median Std. Dev.
N
0.266
0.262
0.137
6,170
0.265
0.260
0.139
908
Non-AFB Firms
Mean
Median Std. Dev.
0.225
0.225
0.161
0.254
0.257
0.148
P-value for
Difference in
Mean
Distribution
0.000
0.000
0.664
0.238
Panel B: Descriptive Statistics Covenant Intensity
AFB Firms
Non-AFB Firms
Variable
N Mean Median Std. Dev.
N
Mean
Median Std. Dev.
Not matched 311 0.861
0.485
1.110
6,170
1.288
0.802
1.521
Matched
300 0.889
0.506
1.120
908
1.008
0.375
1.336
Panel C: AFB and Relation between Leverage and Conservatism
P-value for
Difference in
Mean
Distribution
0.000
0.000
0.628
0.105
Earnings = β1DR + β2AFB + β3DR × AFB + β4Ret+ β5Ret × DR + β6Ret × AFB + β7Ret × DR × AFB
+ β8High Leverage + β9DR × High Leverage + β10 AFB × High Leverage
+ β11DR × AFB × High Leverage + β12Ret × High Leverage + β13Ret × DR × High Leverage
+ β14Ret × AFB × High Leverage + β15Ret × DR × AFB × High Leverage + γZ+ε
No AFB
Δ AFB
β5
β7
Low Leverage
0.035
-0.014
(0.266)
(0.759)
β13
β15
Δ Leverage
0.128
-0.261
(0.061)
(0.012)
β5 + β13
β7 + β15
High Leverage
0.163
-0.275
(0.004)
(0.003)
Panel D: AFB and Relation between Covenant Intensity and Conservatism
Conservatism .
AFB .
β 5 + β7
0.021
(0.468)
β13 + β15
-0.134
(0.051)
β5+β7+β13+β15
-0.112
(0.189)
Earnings = β1DR + β2AFB + β3DR × AFB + β4Ret+ β5Ret × DR + β6Ret × AFB + β7Ret × DR × AFB
+ β8High Covenants + β9DR × High Covenants + β10 AFB × High Covenants
+ β11DR × AFB × High Covenants + β12Ret × High Covenants + β13Ret × DR × High Covenants
+ β14Ret × AFB × High Covenants + β15Ret × DR × AFB × High Covenants + γZ+ε
Conservatism .
Low Covenants
Δ Covenants
High Covenants
Δ AFB
β7
-0.102
(0.047)
β15
-0.200
(0.023)
β7 + β15
-0.302
(0.000)
No AFB
β5
0.075
(0.083)
β13
0.219
(0.020)
β5 + β13
0.293
(0.000)
51
AFB .
β 5 + β7
-0.027
(0.515)
β13 + β15
0.019
(0.745)
β5+β7+β13+β15
-0.009
(0.891)
(Continued on next page)
TABLE 6 (Continued)
This table examines whether AFB moderates the by prior literature documented influence of leverage and
covenant intensity on accounting conservatism. A firm’s covenant intensity is computed by taking the sum of six
covenant indicators: collateral, dividend restriction, more than two financial covenants, asset sales sweep, equity
issuance sweep, and debt issuance sweep at the loan package level and weighting the resulting covenant index by the
loan amount of loans issued in the prior 5 years. Firm year observations are classified as High (Low)
leverage/covenants if their leverage/covenant intensity score is in the top quartile (below the top quartile) of our
sample. All other measures are as defined in Appendix A.
Panels A and B present descriptive statistics on leverage and covenant intensity measures separately for AFB
firms and non-AFB firms both before and after propensity score matching (see Table 1 for further details on how the
matched sample was constructed). Two-sample t-tests are used to test differences in means, and KolmogorovSmirnov two-sample tests are used to test differences in distributions.
Panels C and D present reconstructed asymmetric timeliness coefficients from a regression model that examines
whether AFB moderates the relation between respectively leverage and accounting conservatism and covenant
intensity and accounting conservatism. The models in both panels are estimated using the matched sample used in our
main analysis (see Table 1 for further details). Further, we estimate each model after including firm fixed effects and
dropping the top 1% of absolute standardized residuals. In addition, to control for residual dependence in our pooled
time-series cross-sectional regression, we cluster standard errors at the firm and year level (two-tailed p-values are
presented in parentheses).
52
TABLE 7
Relationship Lending as an Alternative Proxy for Lender Monitoring
Panel A: Descriptive Statistics on Lending Relationship in Past Five Years
Measures
.
N.
Mean Std. Dev.
Number of loans
624
5.144 1.465
Relative frequency of loans
624
0.949 0.080
Relative amount of loans
624
0.984 0.032
Q1 .
4.000
0.857
0.984
Median
5.000
1.000
1.000
Q3 .
6.000
1.000
1.000
Panel B: Asymmetric-Timeliness Regressions—AFB versus RLB
(1)
(2)
(3)
(4)
AFB excl. RLB
RLB excl. AFB
AFB or RLB
AFB and RLB
0.006
0.008
-0.002
0.030
(0.450)
(0.118)
(0.559)
(0.317)
Treatment
-0.003
-0.003
-0.004
-0.023
(0.674)
(0.252)
(0.356)
(0.080)
DR× Treatment
0.004
-0.012
-0.000
-0.031
(0.660)
(0.145)
(0.969)
(0.356)
Ret
0.036
0.036
0.020
0.077
(0.009)
(0.000)
(0.000)
(0.065)
Ret×DR
0.162
0.086
0.070
0.186
(0.004)
(0.003)
(0.004)
(0.259)
Ret× Treatment
0.034
0.021
0.028
-0.075
(0.035)
(0.052)
(0.054)
(0.052)
Ret×DR× Treatment
-0.146
-0.128
-0.066
-0.218
(0.002)
(0.000)
(0.039)
(0.227)
R2
0.308
0.213
0.157
0.255
N
1,109
2,421
3,685
176
This table presents the results of analyses that examine whether relationship lending (RLB), an alternative lender
monitoring mechanism, has a negative association with conservatism similar to AFB. Panel A provides descriptive
statistics on the three measures we use to determine whether a firm has a high level of relationship lending (RLB=1).
Number of loans equals the number of private loans issued to a firm in the prior five years for which the firm’s
relationship bank was retained as a lead bank. We classify a bank as lead bank if lead arranger credit is ‘Yes”, or the
bank is coded as either “agent”, “administrative agent”, “arranger” or “lead bank” in DealScan. Relative frequency of
loans equals Number of loans scaled by the number of private loans issued to a firm in the prior five years. Relative
amount of loans equals the loan amount corresponding to Number of loans scaled by the total amount of the private
loans issued to a firm in the prior five years. Whenever multiple lenders are designated as relationship banks we take
the maximum of each measure across all relationship banks. RLB equals one when the values of all three relationship
lending measures (i.e., Relative frequency of loans, Number of loans and Relative amount of loans) are in the fourth
quartile of the sample, and zero otherwise. We match each firm-year that is exposed to the treatment of interest to at
most four control firm-years and require matches to have a maximum caliper difference of 0.001 (odds ratio). Two
sample t-tests performed after matching indicate that all covariates are balanced between treatment and control group
(p >10% for all covariates). Panel B presents the results of estimating the Basu (1997) model for each propensity
score matched sample, constructed using the corresponding propensity score developed in columns (5) through (8) of
Panel B from Appendix B. We estimate these models after including firm fixed effects and dropping the top 1% of
absolute standardized residuals. In addition, to control for residual dependence in our pooled time-series crosssectional regression, we cluster standard errors at the firm and year level (two-tailed p-values are presented in
parentheses).
Variables
DR
.
53