Electronic copy available at: http://ssrn.com/abstract=1726943

Do Rating Agencies Cater? Evidence from Rating-Based

Contracts

Pepa Kraft ∗

New York University

Stern School of Business

August 15, 2014

Abstract

I examine whether rating agencies cater to borrowers with rating-based performance-priced loan contracts (PPrating firms). I use data from Moody’s Financial Metrics on itsquantitative adjustments for off-balance-sheet debt and qualitative adjustments for soft fac-tors. In the cross-section and for borrowers experiencing adverse economic shocks, I find thatthese adjustments are more favorable for PPrating firms than for other firms, consistent withrating agencies catering to the PPrating borrowers. I find that this catering is muted in twocircumstances when rating agencies’ reputational costs are higher than usual: (1) near theinvestment grade and prime short-term rating thresholds and (2) when Fitch Ratings alsoprovides a rating.

Keywords: Rating agency, off-balance-sheet finance, soft information, debt contracting

∗I am very grateful to the members of my dissertation committee: Ray Ball (chair), Phil Berger, Doug Diamond,Christian Leuz, and Doug Skinner, as well as Ryan Ball, Mary Barth, Utpal Bhattacharya, Alexander Bleck, Mar-shall Blume, Fabrizio Ferri, Joseph Gerakos, SP Kothari, Mathias Kronlund, Valeri Nikolaev, Maarten Petermann,Joshua Ronen, Stephen Ryan, Regina Wittenberg, Joanna Wu, Sarah Zechman, and Jerry Zimmerman. I alsothank participants at the NBER summer session on credit rating agencies, Standard & Poor’s Academic CouncilMeeting, Notre Dame Conference on Current Topics in Financial Regulation, UNC/Duke Fall Camp, Quantita-tive Management Associates research meeting as well as participants at accounting workshops at Boston College,Columbia University, London Business School, McGill University, New York University, Northwestern University,University of Chicago, University of Michigan, University of Pennsylvania, University of Rochester, University ofToronto, Stanford, and Washington University for constructive suggestions, questions, and feedback. I thank An-drew Tan and Hui Lin Tan for excellent research assistance. I am grateful for the financial support provided by theNYU Stern School of Business, the University of Chicago Booth School of Business, and the Deloitte Foundation.This paper was previously circulated under the title: The Impact of the Contractual Use of Ratings on the RatingProcess - Evidence from Rating Agency Adjustments. To contact me, email pkraft@stern.nyu.edu.

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Electronic copy available at: http://ssrn.com/abstract=1726943

1 Introduction

Private loan agreements increasingly include performance-pricing provisions that raise loan inter-

est rates or trigger early payment of principal when the borrowers’ public credit ratings decrease

(Beatty and Weber 2003; Asquith et al. 2005), yielding direct and immediate adverse effects on

the borrowers’ cash flows (Nicholls 2005). Rating agencies say that they are concerned about the

potential adverse consequences of this contractual use of credit ratings for borrowers’ creditwor-

thiness (Moody’s 2001; Standard & Poor’s 2008). I test the ‘catering hypothesis’ that this concern

causes rating agencies to cater to borrowers with these loans by providing credit ratings that are

more favorable than the borrowers’ credit risk justifies.1 I further test whether reputational costs

for rating agencies limit this rating inflation. Reputational costs might lead rating agencies to treat

rating-based performance-pricing provisions in loan contracts as risk factors, due to the adverse

effects on borrowers’ cash flows when their credit ratings deteriorate.

To identify catering, I examine rating agencies’ hard and soft adjustments, which capture dif-

ferent dimensions of borrowers’ credit risk. Hard adjustments capture credit risk arising from

quantifiable factors such as off-balance-sheet debt (Moody’s 2006; Moody’s 2007; Kraft 2014).

Soft adjustments capture credit risk arising from qualitative factors such as management cred-

ibility. I infer catering when these adjustments are more favorable for borrowers with ratings-

performance pricing (PPrating firms) than for borrowers with accounting-ratio based performance

pricing (PPratio firms), all else being equal. In particular, because PPratio firms tend to be riskier

than PPrating firms, I replicate all primary analyses partitioning the sample into groups of firms

with homogeneous credit risk, and find the results are robust to this partition.

Using a sample of U.S.-domiciled, non-financial firms with information available on Moody’s

Financial Metrics and Dealscan for 2002 through 2008, I find that rating agency adjustments are

more favorable for PPrating firms than for PPratio firms, consistent with the catering hypothesis.

For example, the average adjustment for off-balance-sheet debt equals 14% of total assets for

1Borrowers may try to influence their credit ratings for reasons other than existing performance-priced loans,such as achieving better valuations or gaining access to more liquid markets. These considerations are outside thescope of this paper.

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PPrating firms versus 21% for PPratio firms. Similarly, the average soft adjustment for PPrating

firms is only a fifth of the soft adjustment for PPratio firms. Multivariate analysis confirms that the

use of credit ratings rather than accounting ratios in performance pricing is associated with more

favorable/less unfavorable estimates of off-balance-sheet debt and soft adjustments. I further find

that PPrating firms that experience adverse economic shocks receive significantly less unfavorable

rating agency adjustments than do PPratio firms receiving such shocks, again consistent with the

catering hypothesis.

I find evidence that catering is muted in two cases where rating agencies likely bear heightened

reputational costs from catering. First, I find no evidence that rating agencies cater to PPrating

firms with ratings close to the critical investment-grade and prime short-term thresholds that act

as gateways to lower priced and more liquid debt markets. Second, I find that rating agency

adjustments are less favorable for PPrating firms with Fitch ratings, which unlike Moody’s and

Standard & Poor’s ratings are not incorporated in PPrating contracts.

This paper contributes to several literatures. First, a sizeable literature examines whether

rating agencies’ business model of collecting fees from the issuers they rate creates a conflict of

interest that leads to upwardly biased ratings in general (Partnoy 1999; Beaver et al. 2006; Mason

and Rosner 2007; Cheng and Neamtiu 2009; Becker and Milbourn 2011; Bolton et al. 2012) and

in particular for structured finance products (Mason and Rosner 2007; Benmelech and Dlugosz

2009). In the latter case, a debate exists as to whether rating inflation is due to catering or

underestimation of the credit risk of these non-traditional products (Coval et al. 2009; Ashcraft

et al. 2010; He et al. 2011; Griffin and Tang 2012). This research has not empirically investigated

the effect of debt contracts features, such as performance pricing, on catering, although Nicholls

(2005) and Manso (2013) describes the feedback loop that result from the use of credit ratings

in contracts. This study thus provides the first empirical evidence on the effect of the use of

credit ratings in debt contracts on rating agencies’ incentives in the rating process. My results

are broadly consistent with the extensively studied debt covenant hypothesis, in that borrowers

attempt to influence measures specified in debt contracts to achieve better outcomes under those

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contracts (Watts and Zimmerman 1986; Beatty and Weber 2003; Dichev and Skinner 2002).

The study also contributes to the literature on the use of hard and soft information in con-

tracting (Stein 2002; Petersen 2004; Rajan et al. 2010). Hard information is reliable and would

evoke a consensus when presented to different parties; in contrast, soft information is generally

not verifiable for contracting purposes (Rajan and Reichelstein 2009). Consistent with Petersen

(2004)’s conjecture, I show in this paper and in Kraft (2014) that while credit ratings primarily

reflect quantitative information, they also reflect qualitative factors. These findings are broadly

complementary to Ashbaugh-Skaife et al. (2006)’s finding that borrowers with better corporate

governance receive more favorable credit ratings.

2 Hypothesis development

Ratings are benchmarks of issuers’ credit worthiness. A large proportion of private debt contracts

includes provisions that are based on issuers’ public ratings, such as rating triggers and perfor-

mance pricing. These provisions render debt contracts sensitive to rating changes. A rating trigger

is a provision in a loan agreement that initiates a specific action in the event of a rating change.

A rating downgrade might set off accelerated debt repayment or posting of collateral (SEC 2003;

Nicholls 2005).2 For a recent prominent example, the downgrade of AIG triggered some of its coun-

terparties to demand additional collateral or principal repayments.3 More generally, rating-based

performance pricing refers to rating-sensitive debt obligations whose interest payments depend

on the borrower’s public ratings. Rating-based performance pricing provisions increase contrac-

tual interest rates when borrowers’ ratings get downgraded and decrease contractual interest rates

when borrowers’ ratings get upgraded. Furthermore, parties to over-the-counter financial transac-

tions explicitly or implicitly restrict themselves to dealing with counterparties with ratings above

2Nicholls (2005) lists default and acceleration triggers in loan agreements, pricing grids, security/collateralenhancement triggers, benchmark for triggering restrictive negative covenants, calculation of borrowing base andspringing liens, and qualification of permitted assignees as rating triggers.

3See “AIG needs to address CDS portfolio to save ratings” by Reuters on February 27, 2009 and “AIG facescash crisis as stock dives 61%” by The Wall Street Journal on September 16, 2009, as well as “Downgrades andDownfall. How could a single unit of AIG cause the giant company’s near-ruin and become a fulcrum of the globalfinancial crisis?” by Washington Post staff writers Robert O’Harrow and Brady Dennis on December 31, 2009.

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minimum levels (Moody’s 2001).

When contracts use credit ratings to enforce restrictions, changes in ratings directly and im-

mediately impact firms’ cash flows. This motivates the issuer to ask for favorable treatment by

the rating agency. Under their business model, rating agencies collect fees from the very issuers

they rate, which creates a basic conflict between providing accurate ratings and upwardly biased

ratings (Partnoy 1999; Mason and Rosner 2007; Becker and Milbourn 2011, Bolton et al. 2012).

Such a business model leads to conflicts of interest similar to the tradeoff facing other information

intermediaries which receive income from their objects of investigation, such as audit firms and

investment bank affiliated equity analysts.4 The reasoning for catering mirrors that of the debt

covenant hypothesis: Watts and Zimmerman (1986) argue that debt contracts that make covenant

thresholds a function of financial ratios give borrowers incentives to change accounting methods to

avoid costly covenant violations. Accounting ratio-based performance pricing in loan agreements

creates a continuous link between accounting ratios and interest rates, and thus performance pric-

ing creates incentives for managers to engage in income-increasing earnings management. Beatty

and Weber (2003) find that borrowers whose debt contracts allow them to make accounting changes

choose accounting methods that increase earnings. Dichev and Skinner (2002) find that borrowers’

accounting ratios are substantially more likely to be just above critical covenant thresholds rather

than below, which is consistent with the debt covenant hypothesis. Similarly, rating-based per-

formance pricing creates incentives for borrowers to implore rating agencies to cater to borrower

demands.

Rating agencies are not immune to catering by providing inflated ratings in other contexts

(Benmelech and Dlugosz 2009).5 Credit rating agencies rely on issuers for fees both at the time of

4For audit firms a large literature examines the question of auditor independence (Antle 1984; Larcker andRichardson 2004). Analysts’ economic incentives are associated with earnings adjustments, growth forecasts andrecommendations (Lin and McNichols 1998; Baik et al. 2009; Ertimur et al. 2011).

5Ashcraft et al. (2010) find that although ratings of mortgage backed securities contain useful information,ratings exhibit time-variation in their risk adjustments consistent with rating inflation in 2005-2007 and for high-risk and low-documentation loans. Coval et al. (2009) point out that ratings of CDOs are highly unreliable due tomodels that are highly sensitive to even small errors in economic projections or losses and that underestimate thecorrelation of risks across various debt securities. Griffin and Tang (2012) find evidence of upward bias in subjectiveadjustments on AAA-rated CDO tranches relative to their own model.

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issuance and through periodic monitoring fees for as long as the issue is outstanding. In addition,

rating agencies offer related consulting services, such as pre-rating assessments (White 2002; Bolton

et al. 2012). Rating agencies may provide unduly favorable ratings, especially to issuers who

generate substantial revenues (He et al. 2011). However, it is outside the scope of this study to

disentangle the explicit mechanisms of catering. Indeed, the literature on ratings of structured

finance products has not resolved the debate whether rating inflation is due to explicit catering

for business reasons or whether credit risk is underestimated because of implicit catering due to

erroneous judgments (Coval et al. 2009; Ashcraft et al. 2010; He et al. 2011; Griffin and Tang

2012).6 In either scenario, catering – a rating process that is too favorable given the underlying

economics – would be observed.

Borrowers face changes in contractual interest rates under performance pricing that are signif-

icantly greater than the fees they pay to rating agencies. Asquith et al. (2005) find an average

increase in the contractual interest rate of 13.8 basis points for each step in the pricing grid, with an

average of 5.1 steps for interest rate increasing performance pricing loans. In contrast, borrowers

pay fees of three to four basis points of the face amount for rating agencies to rate borrowers’ cor-

porate debt.7 The potential for large interest rate movements provides borrowers with incentives

to try to influence the rating agency.

Lenders need to monitor their borrowers in order to uncover any potential catering in the rating

process. However, monitoring is not without cost. Ex ante, borrower and lender agree to contract

6There are several possible non-mutually exclusive mechanisms for catering. Under explicit catering for businessreasons, the borrower pays an inflated fee to the rating agency. The ongoing business relationship between theborrower and the rating agency results in one-way fee income paid from borrower to agency, for rating as wellas advisory business. The rating agency provides a more favorable assessment for those borrowers from which itreceives higher fees, holding reputational costs constant. Under implicit catering due to erroneous judgments, theborrower, possibly with the help of a rating advisory consultant, provides optimistic disclosures to the rating analyst,which the rating analyst processes ‘at face value’. This results in catering as well: the rating analyst, by providingtoo little effort, awards upwardly biased ratings to borrowers that provide such upwardly biased disclosures. Suchan outcome may be sustainable because certified rating agencies enjoy market power due to an SEC-granted quasi-monopoly, in which the rating analyst exerts minimal effort in information collection, and the borrower bears theonus of disclosure.

7Standard & Poor’s (2009) documents that up to 4.25 basis points are charged for corporate debt, with aminimum fee of USD 70,000. Partnoy (2006) documents fees of 3-4 basis points of the face amount for corporatedebt, which is subject to minimum fee amounts ranging from USD 30,000 to a maximum of USD 300,000. Moreis charged for complex deals (up to 10 basis points). High volume issuers receive discounts. Monitoring fees,cancellation fees, and initial confidential rating fees are in the range of USD 20,000 to USD 50,000.

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on performance pricing because lenders can readily observe a signal of credit risk in the form of

an accounting ratio or a rating. Ratings are viewed as less manageable than borrower-generated

accounting ratios. Nonetheless, the business relationship between the borrower and the rating

agency gives rise to a conflict of interest. Lender are not part of this relationship and would incur

additional monitoring costs to assess whether ratings are biased or not.

Reputational concerns provide incentives to rating agencies to resist catering to issuer’s de-

mands (Klein and Leffler 1981; Shapiro 1983; Gorton and Winton 2003; Strausz 2005). The

economic role of rating agencies is to provide independent assessments of credit risk because the

delegation of information processing to an intermediary saves on the duplication of such monitoring

costs by dispersed bond holders (Wakeman 1984). Their primary asset is their reputation, which

is the basis for their long-term business prospects. Rating agencies are likely to take extra care in

their assessment due to reputational concerns about long-term business prospects (Klein and Lef-

fler 1981; Shapiro 1983; Gorton and Winton 2003; Strausz 2005) or due to concerns about outside

political intervention (Beaver et al. 2006). Hence I would expect less catering when reputational

costs are high.

Ratings are commonly used in performance pricing provisions in loan contracts. A traditional

loan contract is priced using a fixed interest rate or a fixed spread over a risk-free interest rate,

such as LIBOR or prime. Rating-based performance pricing explicitly links the contractual loan

interest rate to borrower’s current ratings (Asquith et al. 2005). In such rating-sensitive debt

contracts, rating changes lead to immediate changes in the contractual interest rate (for discussions

of performance pricing in debt see Beatty and Weber 2003; Asquith et al. 2005.) Borrowers enter

into such contracts with lenders for several reasons. First, these contracts help mitigate adverse

selection problems (Asquith et al. 2005.) When asymmetric information between borrower and

bank is likely to result in a misclassification of credit risk, performance pricing reduces the adverse

selection problem because the borrower and bank stipulate ex ante that the borrower’s interest rate

decreases when the borrower’s credit risk improves. This reduces re-contracting costs.8 Second,

8In a model by Hermalin and Katz 1991 renegotiation allows the contracting parties to contract over variablesthat would otherwise be non-contractible. Performance pricing reduces the need for such renegotiation.

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rating-based performance pricing diminishes incentives to engage in claim dilution and moral

hazard problems (Asquith et al. 2005; Bhanot and Mello 2006.) Performance pricing leads to

higher contractual interest rates when the borrower’s credit risk deteriorates. Thus the threat of

ex post settling up diminishes borrowers’ incentives to engage in behavior such as claim dilution

that weakens their creditworthiness.

However, rating-based performance pricing imposes a circularity problem because credit ratings

themselves can affect the credit quality of the borrower (Manso et al. 2010; Manso 2013). A rating

downgrade leads to a higher contractual interest rate. Such interest rate step-ups exacerbate

liquidity strains at the precise moment when an issuer is least able to deal with them (Moody’s

2001). Furthermore, these step-ups can exacerbate a company’s ability to comply with its cash

flow-based covenants, such as covenants based on total debt/cash flow and senior debt/cash flow

ratios. Rating agencies say that they are concerned about the consequences of the contractual

use of ratings for borrowers’ creditworthiness (Standard & Poor’s 2008; Moody’s 2001.) If rating

agencies take into account feedback effects of rating changes, they may be slow to downgrade in

order to avoid the borrower’s inefficient liquidation. In the model in Manso (2013) rating analysts

take into account both the accuracy of the rating and the effects of the rating on the likelihood

of default arising from rating-based performance pricing because they know that rating-based

performance pricing in loans impacts the contractual interest rate, which affects the default risk

of the borrower.

3 Empirical approach

3.1 The rating process: rating agency adjustments

In this study I estimate the association between rating-based performance pricing (PPrating) and

rating agency adjustments (ADJ ). Rating agency adjustments include hard adjustments such as

estimates of off-balance-sheet debt as well as soft adjustments. Hard and soft adjustments cap-

ture quantitative and qualitative factors that impact issuers’ default risk, respectively (Moody’s

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2006; Kraft 2014). Hard adjustments primarily comprise adjustments to reported financial state-

ments. A prominent hard adjustment is the estimation and inclusion of off-balance-sheet debt

(Kraft 2014). Higher levels of off-balance-sheet debt imply higher levels of credit risk, everything

else equal. Soft adjustments capture the impact on credit risk by factors such as management

quality, aggressive accounting, weak controls, governance risk, industry structure, and managerial

bondholder friendliness (Moody’s 2007). Soft adjustments either increase or decrease the rating

agency’s estimate of credit risk.

See Appendix A for an illustration of the rating process by Moody’s Financial Metrics for

Airgas, Inc. Moody’s rating analysts assign each industry group a rating grid that consists of

mainly quantitative factors. For Airgas, the rating grid captures assessments of its competitive

position, size, stability, profitability, leverage, and financial strength. Airgas’ adjusted financials

indicate that leverage is higher than that inferred from its reported financials. Debt-book capital

and debt/EBITDA ratios calculated using adjusted financials are substantially greater (and thus

warrant lower ratings) than those calculated using reported financials. Similarly, the cash flow

to debt ratios calculated using adjusted financials are substantially lower (and thus also warrant

lower ratings) than those calculated using reported financials. Thus, Airgas’ indicated rating on

the basis of adjusted ratios is one notch lower than the rating that the reported financials imply.

Soft adjustments lower the rating by another two notches. This illustration is typical of Moody’s

adjustments. Kraft (2014) shows that the major hard adjustment includes off-balance-sheet debt,

leading to substantially higher leverage ratios. On average, credit-risk increasing hard and soft

adjustments have an association with lower credit spreads and higher bond yields (Kraft 2014).9

9Examining rating agency adjustments allows me to investigate where, if any, the conflict of interest manifestsitself. Soft adjustments are by construction less verifiable and thus more likely to be biased than quantitativeadjustments, because ex post detection for a single firm case is difficult due to the unverifiability. For example,Rajan et al. (2010) find that as incentives for decision makers to collect value-relevant information diminish, marketparticipants rely increasingly on hard factors rather than value-relevant soft factors in the pricing of securitizedsubprime mortgages, which ultimately leads to an under-prediction of default risk in this scenario. Despite the factthat hard adjustments are less subjective than soft adjustments, even they provide discretion to rating analysts,who have to choose how big a multiplier to use to capitalize operating lease rent expense or whether to classify asecuritization as non-recourse.

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3.2 Base model

In order to assess the association between rating-based performance pricing (PPrating) and rating

agency adjustments (ADJ ) I estimate the following model.

ADJt,i = α0 + α1PPratingt,i +∑n

βnfirmcharacteristicst,i

+∑l

θlloancharacteristicst,i (1)

Rating agency’s adjustments capture various dimensions of credit risk, but they are also subject

to bias or noise in the rating process. The bias is subject to the rating agency’s and borrower’s

incentives to provide a favorable credit risk assessment. Thus, I control for borrower characteristics

that determine credit risk and that would be reflected in the rating agency’s adjustments, such

as leverage, profitability, size, and short-term liquidity. Factors similar to those determining the

choice of debt, as well as financial reporting benefits, drive the use of off-balance-sheet finance

(Beatty et al. 1995; Mills and Newberry 2005). The proportion of debt in the capital structure

depends on the riskiness of the underlying cash flows and asset tangibility. Empirical studies on

the cross-sectional determinants of leverage find that leverage increases with fixed assets, non-

debt tax shields, growth opportunities, and firm size (Harris and Raviv 1991; Rajan and Zingales

1995). Leverage decreases with volatility, advertising expenditures, research and development

expenditures, bankruptcy probability, profitability, and product uniqueness. I expect the same

determinants to hold for off-balance-sheet debt. I focus on size, profitability, asset tangibility,

market-to-book ratio, and book-leverage. I represent the firm characteristics with size (logarithm

of revenues), leverage (total balance-sheet debt divided by total assets), interest coverage (ratio

of operating profit to interest expense), operating margin (ratio of operating profit to revenues),

return on assets (ratio of operating profit to total assets), tangibility (ratio of inventory and net

property, plant and equipment to total assets), and market-to-book ratio (market value of equity

to book value of shareholders’ equity).10 Loan characteristics include maturity and size of loan.

10Borrowers also choose off-balance-sheet debt to raise external finance because of its financial reporting treatment.

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I estimate the model using OLS (ordered probit), where the dependent variable is the rating

agency adjustment to debt (soft or total rating agency adjustment). The empirical proxy for

rating-based contracts is the presence of a loan with performance pricing that links the contractual

interest rate to changes in the issuer’s bond rating. The variable of interest is PPrating, which

equals one if, at fiscal year-end, the borrower has at least one active loan facility outstanding that

contains a rating-based performance pricing feature. Rating agency adjustments, firm, and loan

characteristics are measured at fiscal year-end.

3.3 Contracting choice

Borrowers choose to link their interest rates to future observable events, such as changes in ratings

and accounting ratios, to mitigate adverse selection and moral hazard problems (Asquith et al.

2005). Contracts that link either payments or the posting of collateral to a deterioration of credit

risk mitigate the incentives to engage in claim dilution (Bhanot and Mello 2006; Manso et al.

2010). Ex ante, firms that choose to contract on performance pricing are more opaque than firms

raising loans without performance pricing clauses.

Conditional on contracting on performance pricing, firms and their lenders choose between

ratings and accounting ratios. Ratings are a comprehensive measure of default risk, but accounting

ratios can be timelier (Beatty and Weber 2003; Ball et al. 2008; Doyle 2008). The inclusion

of restrictions on managers’ behavior helps mitigate agency conflicts between debtholders and

managers acting on the behalf of equity holders. For example, financing covenants can be written

that restrict the issue of senior debt, the initiation of leases, or the issue of debt-like obligations

to restrict managers’ ability to dilute existing claims (Smith and Warner 1979). Contractual

adjustments incorporating off-balance-sheet debt are difficult to write and enforce. Writing an

The off-balance-sheet treatment results in financial reporting benefits, such as reporting a lower balance sheet-basedleverage ratio to comply with covenants or to appear less risky (Beatty et al. 1995; Engel et al. 1999; Mills andNewberry 2005). The rating agency’s estimate of off-balance-sheet debt is based on the borrower’s disclosures.By definition, the rating agency adjustment for off-balance-sheet debt is the amount of debt as recognized by theagency, and hence the captured amount of off-balance-sheet debt does not confer any financial reporting benefitswith respect to the rating process. Hence no controls are necessary for expected financial reporting benefits in therating process. The amount of off-balance-sheet debt may, however, confer financial reporting benefits for complyingwith debt covenants.

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explicit contract that contains a negative covenant that prohibits the use off-balance-sheet debt

would lead to loopholes and financial engineering (Jensen and Meckling 1976; Doyle 2008). Leftwich

(1983) finds that according to best practice, lenders should consider the possibility of ‘creative

financial arrangements’ when writing debt contracts. If left unmonitored, the use of off-balance-

sheet financing allows borrowers to dilute claims of existing, on-balance-sheet debtholders, and

it leads to higher economic leverage. However, even the best-practice contracts documented in

Leftwich (1983) contain vague language that is difficult to enforce. Ratings are comprehensive

measures of credit risk that incorporate a range of factors, which renders them useful for inclusion

in contracts (Doyle 2008).

In the main analysis, I estimate the regression conditional on performance pricing, so I compare

firms with rating-based performance pricing to firms with accounting-based performance pricing.

The small minority of firms whose performance pricing is based on both accounting ratios and

ratings (hybrids) is excluded. Firms that use performance pricing based on ratings are more

similar to firms that use performance pricing based on accounting ratios compared to firms that

use neither (Asquith et al. 2005). When debt contracts rely on accounting-based covenants, debt

holders are likely to provide higher incentives for timely loss recognition to the firm’s management

(Ball and Shivakumar 2006). Public bondholders will have a greater demand for timely loss

recognition than banks or other private lenders (Nikolaev 2010). This raises the concern that

differences in timely loss recognition would affect the speed with which accounting ratios reflect

the underlying economics. However, all the firms in my sample have both private debt and public

debt, as firm-years are required to have ratings available from Moody’s and loan data available

from Dealscan. Furthermore, the regressions control for leverage, and in an additional analysis the

sample is partitioned by credit risk. In the interest of external validity, I also conduct the empirical

tests for firms that have private loans without any performance pricing features. Those firms are

likely to be less opaque and thus less suitable as a control group, but their inclusion allows me to

increase the sample size.

Any observed differences in rating agency adjustments can be caused by differences in under-

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lying firm characteristics. I employ two methods to address endogeneity concerns. First, I test

whether the hypothesized relationship between rating agency adjustments and PPrating holds for

firms matched by credit risk. Riskier firms are more likely to contract on PPratio, whereas firms

that are safer from a credit perspective are more likely to contract on PPrating. Yields spreads on

publicly traded bonds serve as a benchmark of firms’ credit risk. I partition the sample by their

issuers’ yields spreads into high, medium, and low credit risk and run equation [1] within these

partitions. The partition serves as a natural matching mechanism and ensures that firms with

similar credit risk are compared. The cost of this approach is diminished power due to smaller

sample size.

Second, I exploit variation from unexpected adverse economic shocks to investigate whether

there is a bias in rating agency adjustments. An adverse shock to companies’ cash flows, for

example, by a drop in consumer demand, decreases the value of total assets and increases a firm’s

default risk. I investigate the reaction of rating agencies to firms with adverse economic shocks by

testing whether the rating agency’s reactions differ for firms with rating-based contracts compared

with firms without such contracts. Controlling for the size of the shock, I investigate whether any

differential reaction exists for firms with rating-based contracts relative to other firms. Under the

catering hypothesis, I expect more favorable treatment for firms with rating-based contracts that

experience adverse economic shocks, all else being equal. This research design has the advantage

of allowing me to calculate a benchmark of the expected adjustment for firms that experience a

shock to their default risk. I estimate the following regression where Shockt−1 equals an extreme

change in the market value of public debt during a firm’s fiscal year:

ADJt,i = α0 + α1PPratingt,i + α2Shockt−1,i

+α3Shockt−1,i ∗ PPratingt,i +∑n

βnfirmcharacteristicst,i (2)

For each bond, I calculate daily returns during the firm’s fiscal year. Low (negative) bond

returns are a reflection of an adverse economic shock that increases credit risk. I measure the

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size of the shock by the bottom return decile.11 Shocks are measured as changes in the market

value of public debt rather than accounting cash flows because they are less subject to accounting

discretion and more timely. An adverse economic shock increases default risk and should be

reflected equivalently in the increase in the rating agency’s risk assessment if the rating agency is

neutral for both PPrating and PPratio (or other) firms.

The net increase in default risk, rather than the gross increase in default risk, is relevant to

calculate the benchmark for the expected adjustment. The decrease in the value of public bonds

reflects the market’s anticipation of the size of the shock and how well the firm is expected to

handle it. One might think that more favorable rating agency adjustments for PPrating firms

could also imply that PPrating firms self-select into PPrating contracts because they are better at

dealing with shocks, for whatever reason. In this case, the more favorable adjustments would be

economically justified and not consistent with the catering hypothesis. However, this concern is

not valid because I measure the priced (net) increase in default risk. If the PPrating firm is better

at dealing with the shock, then the net increase in default risk will be less than the gross increase.

Hence the priced shock already includes the market’s assessment of the PPrating firm’s reaction

to the shock.

See Appendix B for a hypothetical example. The net shock is relevant to the comparison. In

the first scenario where the treatment is random, a PPrating firm is compared to PPratio firm 1.

Both should have the same change in the rating agency’s assessment of default risk. In the second

scenario, where PPrating firms choose to contract on ratings rather than accounting ratios, the

priced reaction of the PPrating firm, the net shock, is smaller than the gross shock. Because the

market anticipates that the PPrating firm is better at dealing with adverse shocks, the market

value of public debt decreases by less. Hence PPrating firm is compared to PPratio firm 2. Both

should have the same change in the rating agency’s assessment of default risk. Because the net

shock is measured by the change in the market’s assessment of default risk, any self-selection issue

is already priced, and hence a comparison of the rating agency’s assessments is meaningful.

11The deciles are less likely to be subject to data errors than the minimum daily return during the year.

14

3.4 Institutional constraints on catering

I investigate whether catering is muted when institutional constraints are present. First, I examine

whether rating agency adjustments are less favorable for issuers with ratings close to important

thresholds. Then I examine whether ratings by Fitch act as a constraining force.

A large proportion of bond investors, such as mutual funds, pension funds, and insurance

companies, use ratings by certified rating agencies to comply with rating-contingent regulation

(Coval et al. 2009; Bolton et al. 2012; Opp et al. 2013).12 To comply with such rating-contingent

regulation, investors desire high ratings. The investment grade cutoff and prime short-term ratings

are particularly important thresholds. Prime short-term credit ratings, such as P1, determine

commercial paper issuance. Furthermore, many investors cannot hold non-investment grade bonds

due to restrictions. Hence, issuers desire investment grade ratings to obtain a large investor base

and reduce the liquidity component of their cost of debt. If the regulator is myopic in the short-

term, an equilibrium with inflated ratings is feasible. Even if managers understand that investors

see through inflated ratings, they might still demand these ratings to help bond investors comply

with regulation (Bolton et al. 2012; Opp et al. 2013).

Under the catering hypothesis, I predict a stronger association between rating-based contracts

and the rating agency’s adjustment for firms close to important rating thresholds and for firms

with prime short-term credit ratings, relative to firms close to these thresholds without a rating-

based contract. I conduct a difference-in-difference analysis in order to test whether closeness to

an important rating threshold strengthens the catering incentive. On the other hand, reputational

costs as well as adverse trigger effects from rating downgrades are substantially higher at these

threshold ratings. I estimate the following regression and include an additional indicator variable

12Ratings by certified (or NRSRO) agencies are used by the SEC, federal and state legislation, and other regulatorsin the context of portfolio restrictions and capital adequacy assessments ((SEC 2003; Standard & Poor’s 2006). Forexample, money market funds can only invest in investment grade bonds. State insurance codes rely on NRSROratings to determine appropriate investments for insurers. The Federal Reserve Board and the Federal Home LoanBank System allow their members (the Federal Reserve System and federally charted savings and loans associations,respectively) to invest in investment grade securities only. The Department of Labor requires pension funds to holdcommercial paper rated above A-3. Furthermore, broker-dealers which are subject to the net capital rule useratings by certified agencies in capital adequacy tests, where the percentage reduction from stated values (securitieshaircuts) for the purpose of stock margin requirements and for net capital requirements depend on ratings.

15

that equals one if the issuer has a threshold rating (BBB-, which is the last rating above the

investment grade cutoff) or a short-term rating (P1, a prime short-term credit rating, which is

necessary to access the commercial paper market) and a term that interacts the threshold rating

and the PPrating indicator variable:

ADJt,i = α0 + α1PPratingt,i + α2thresholdt,i + α3thresholdt,i ∗ PPratingt,i

+∑n

βnfirmcharacteristicst,i +∑l

θlloancharacteristicst,i (3)

Second, I expect Moody’s and S&P to cater less if there exists a third credit rating that is

likely to constrain opportunistic behavior. Cantor and Packer (1996) finds that the probability of

obtaining a third rating is not related to uncertainty over firm default probability. A firm’s decision

to obtain a third rating is largely determined by the firm’s age and size. The importance of the

determinants of age and size in the decision to obtain a third rating underscores the importance of

spreading fixed costs. Larger firms can more easily amortize the fixed cost of a Fitch rating, while

older firms are more likely to have additional ratings because of persistence. Uncertainty does not

appear to be a major factor affecting the likelihood of obtaining a third rating. The prevalence of

additional ratings is unrelated to firms’ financial ratios such as leverage and to uncertainty about

firms’ default risk. In the same vein, Cantor and Packer (1997) finds that frequent and large debt

issuers are the most likely to obtain additional ratings. The study does not find evidence that firms

obtain additional ratings to help clear regulatory hurdles or to resolve greater ex ante uncertainty

about default risk. Xia (2014) finds S&P rating quality improves when the issuer receives a rating

by a smaller rating agency.

I estimate the following regression and include an additional indicator variable that equals

one if the issuer has a Fitch rating (FITCH and a term that interacts FITCH and the PPrating

16

indicator variable:

ADJt,i = α0 + α1PPratingt,i + α2FITCHt,i + α3FITCHt,i ∗ PPratingt,i

+∑n

βnfirmcharacteristicst,i +∑l

θlloancharacteristicst,i (4)

4 Data

I collect reported and adjusted financial statements as well as hypothetical ratings from Moody’s

Financial Metrics database for fiscal years ending during the calendar years of 2002 to 2008. For

a random subset of Financial Metrics firms, hypothetical ratings based on reported and adjusted

financial numbers are available, which allows me to compute credit analysts’ soft and total adjust-

ments. Appendix A provides an illustration of the rating process for Airgas, Inc. The final pub-

lished rating (actual rating) is a function of the reported numbers in Airgas’ financial statements,

credit analysts’ adjustments to those reported financials, such as the inclusion of off-balance-sheet

debt, and credit analysts’ qualitative adjustments.

I calculate the total adjustment (TOTAL) as the difference between actual rating and the hy-

pothetical rating implied by reported financials (indicated reported rating). The indicated reported

rating is the output of the credit analyst’s matrix of accounting ratios. These matrices include

accounting ratios such as measures of profitability and leverage and are industry-specific. The

accounting ratios in the credit risk matrix for indicated reported rating are based on the values as

reported by Airgas on the face of its financial statements, such as the reported value of debt or

total assets. As of March 31, 2008, Airgas’actual rating of BB+ is three notches below its indicated

reported rating of BBB+. Hence the numerical value of TOTAL equals three. The combined ef-

fect of the rating analyst’s hard and soft adjustments increases the analyst’s assessment of Airgas

credit risk relative to the credit risk assessment based on accounting ratios calculated from Airgas’

reported financial statements.

Conceptually, TOTAL consists of both soft and hard adjustments. I calculate the soft ad-

17

justment (SOFT ) as the difference between actual rating and the hypothetical rating implied by

adjusted financials (indicated adjusted rating). In the case of Airgas, SOFT takes a value of two,

which means that actual rating is two notches lower than indicated adjusted rating. The effect of

credit analysts’ soft adjustments increases their assessment of Airgas’ credit risk.

The hard adjustment is determined by credit analysts’ adjustments to reported financial state-

ments. Off-balance-sheet debt (OFFBS ) is the major hard adjustment (Kraft 2014). To calculate

the rating agency’s estimate of OFFBS, I calculate the difference between adjusted debt and re-

ported debt and scale the difference by total reported assets, where debt equals the sum of short-

term and long-term debt. The credit risk matrix in Appendix A shows that Airgas is awarded

an indicated reported rating of Baa1 (or BBB+ in standardized form) based on unadjusted ratios

and an indicated adjusted rating of Baa2 (BBB) based on adjusted ratios. The decrease in rating

is primarily driven by the deterioration in Airgas’ leverage ratios, namely Debt/Book Capital,

Debt/EBITDA, Retained Cash Flow/Debt, and Free Cash Flow/Debt. This illustration for Air-

gas is typical of credit rating agency adjustments to financial statements. Most hard adjustments

increase leverage as they incorporate off-balance-sheet-debt and thus lead to greater credit risk

and lower ratings (Kraft 2014).

Dealscan reports whether loan contracts have performance pricing features and whether those

are based on ratings or accounting ratios. First I link Moody’s Financial Metrics dataset to

Compustat by matching the issuers in Financial Metrics to their respective Compustat gvkey

identifiers and issuer cusips by company name. Then I employ the Dealscan-gvkey linking data

set from Chava and Roberts (2008). The merging of firm-year observations from Financial Metrics

with loans from Dealscan by gvkey creates the Financial Metrics-Dealscan sample (FMDS ). For

each firm-year in this sample, I calculate the number of active loans and determine whether any

of those include performance pricing features.

Table 1 reports that the FMDS sample contains 1,193 issuers and 6,196 issuer years. Most of

the observations are evenly split over 2002-2008. The highest industry peer group concentrations

are energy (11.3% of all firm years) and electric utilities (8.9% of all firm years). Financial services

18

are not included because they are not part of Financial Metrics. Firms from the whole distribution

of ratings are included in the sample. Around 44% (54%) of all observations have an investment

grade (speculative) rating, but most firms are concentrated in BBB-, BB- and B-ratings.

Table 2 documents that on average FMDS sample firms have total assets USD 8.8 billion,

leverage of 0.38, coverage of 7.90, operating margin of 0.10, return on assets of 0.07, and tangibility

of 0.46. Coverage ratio and operating margin are winsorized at the 1st and 99th percentile.13

OFFBS equals 17%, which implies that for the average firm a significant proportion of total assets

is financed by off-balance-sheet debt. On average, both SOFT and TOTAL reflect increases in

credit risk. TOTAL lowers the rating by almost one notch (0.96), and SOFT lowers the rating by

0.55 notches.14

Table 2 also reports firm characteristics and rating agency adjustments by type of performance

pricing. The variables PPrating and PPratio indicate whether a firm’s loan contracts incorporate

performance pricing based on ratings or accounting ratios respectively. More specifically PPrating

equals one if the firm has an active loan facility outstanding with rating-based performance pric-

ing. PPratio equals one if the firm has an active loan facility outstanding with accounting-based

performance pricing. PPratio firms are smaller and more levered than PPrating firms but have

similar profitability and tangibility as PPrating firms. Specifically, compared with the control

group of PPratio firms, firms in the PPrating subsample are bigger, with average total assets of

USD 11.2 billion (versus USD 3.5 billion), and have lower average leverage (0.30 versus 0.46) and

higher average interest rate coverage (9.5 versus 5.9), higher average operating margin (0.11 versus

0.08), and similar returns on assets (0.08 versus 0.06), as well as a similar average tangibility (0.47

versus 0.45).

The total and soft adjustments for firms with rating-based contracts decrease their ratings by

less than those for firms with accounting-ratio-based performance pricing. PPrating firms have

smaller adjustments for OFFBS, SOFT, and TOTAL than PPratio firms. On average, credit

13Firm characteristics are based on reported numbers.14Each rating is assigned a number from 1.0 for AAA to 21.0 for C. Hence a value of 1.00 of the adjustment

reflects one rating notch.

19

analysts estimate OFFBS at 14% for PPrating firms, which is less than their estimate of 21% for

PPrating firms. Likewise, TOTAL reduces the average PPrating firm’s by 0.75 notches but by

1.30 notches for the average PPratio firm. The smaller estimates of OFFBS, SOFT, and TOTAL

for PPrating firms are consistent with a more favorable treatment by the credit rating agencies.

However, they might also be a function of lower credit risk because PPrating firms are bigger

and less levered. Table 2 also reports the loan characteristics by issuer year. The average loan

has an amount of USD 2,086 million, a maturity of 63 months, and a spread of 161 basis points.

PPrating firms tend to have larger loans with shorter maturities and lower all-in-drawn spreads

than PPratio firms. However both types of loans are very likely to include accounting-based

covenants (CovAccg).

I measure an adverse economic shock by a significant decrease in the market value of the

issuer’s traded bonds. I collect bond prices from Trace and extract issue characteristics from FISD

Mergent. The sample bonds have an average offering amount of USD 436 million and an offering

yield of 5.8% (not tabulated). For each bond’s fiscal year, I calculate the bottom decile return

on an equal-weighted basis and weighted by trading volume. Table 2 documents that the 10th

percentile daily return (Shock return) for the average issue amounts to -1.2%. Shock return is

negative for more than 75% of the observations, and its minimum is -41% (not tabulated). While

some bonds are actively traded, the average bond only trades 52 days a year, and conditional on

trading, only 4.62 times per day (Bessembinder et al. 2009). I recalculate the shock variable by

using daily bond returns weighted by transaction volume (Shock return w). The distribution of

Shock return is similar to that of Shock return w.

As shown in Table 3 Panel A, of all issuer years in the FMDS sample, 78% have a performance

pricing feature (PPfeature), which is higher than the proportion reported for 1998 in Beatty et al.

(2002). The use of ratings or accounting ratios is relatively evenly distributed. Out of the 4,831

firm-years with PPfeature, around 53% exhibit PPrating and around 56% exhibit PPratio. Despite

the criticism rating agencies received during the period, I find little evidence of variation in the use

of ratings versus accounting ratios. If anything, firms and banks were more likely to incorporate

20

ratings rather than accounting ratios at the end of the sample period. A small proportion of issuers

has contracts with both PPrating and PPratio. Those hybrids are excluded in the analysis.

Table 3 Panel B reports that in most rating-based performance contracts interest rates are

allowed to step up or down (PPutroque), 12% have interest rates with step-up provisions only

(PPincrease), and 14% have interest rates with step-down provisions only (PPdecrease). The sum

of the proportions is greater than 100% because each firm year can contain several facilities with

different performance pricing schedules. Interest-rate decreasing performance pricing automatically

decreases the interest rate charged when the issuer’s credit risk improves. This feature lowers

renegotiation costs and reduces adverse selection problems (Asquith et al. 2005). Interest-rate

increasing performance pricing automatically increases the interest rate spread charged when the

issuer’s credit risk deteriorates. This feature reduces moral hazard and adverse selection problems

(Asquith et al. 2005).

In addition, Table 3 Panel B reports the potential change in interest rate spreads over Libor

at time of loan inception. MaxLessInitial is the number of basis points between the interest rate

charged on the contract at inception of the loan agreement and the maximum rate in the pricing

grid. The average difference between the maximum interest rate charged and the initial interest

rate is 44 basis points (the maximum difference amounts to 743 basis points). InitialLessMin is

the difference in basis points between the initial interest rate spread and the minimum interest

rate spread in the pricing grid. The average potential interest rate reduction is 26 basis points

(the maximum reduction is 425 basis points). These numbers for the potential interest spread

movement are significantly larger than the fees paid to rating agencies on corporate debt of three

to four basis points.

Table 4 reports the correlations between the two types of performance pricing, rating agency

adjustments, and firm characteristics. PPrating shows a significant and negative correlation with

OFFBS, whereas PPratio exhibits a significantly positive correlation with OFFBS. PPrating has

a significant negative association with SOFT and TOTAL. The reverse is true for PPratio firms.

Consistent with the univariate evidence in Table 2, PPrating firms exhibit lower rating agency

21

estimates of off-balance-sheet-debt and lower risk assessments of qualitative factors.

5 Empirical Results

Table 5 documents the estimates for the parameters from the regressions of OFFBS, SOFT and

TOTAL on PPrating and firm characteristics. Columns 1 and 2 contain the OLS estimates from

the regressions of OFFBS, and columns 3 to 6 contain the ordered probit estimates from the

regressions of SOFT and TOTAL. Standard errors are clustered by firm and include fixed effects

for utilities (electric, public, and water utilities) and energy and fixed effects for years.

Table 5 Panel A reports the estimates for the subsample of issuers that have loans with per-

formance pricing. Conditional on the presence of PPfeature, I find OFFBS and PPrating have a

significantly negative correlation in both model specifications with various control variables (mod-

els 1 and 2). Column 1 reports the estimated coefficients for a full set of control variables but

has fewer observations due to variable restrictions. Column 2 reports the coefficients for the main

control variables Leverage, Opmargin, and Tangibility. Prior research on off-balance-sheet-finance

finds that credit-constrained firms are more likely to raise off-balance-sheet debt (Beatty et al.

1995; Mills and Newberry 2005). Consistent with this claim, I find OFFBS decreases in Opmargin

and increases in Tangibility. The results suggest less profitable and more tangible-asset-intensive

firms are more willing and able to raise off-balance-sheet finance, or the rating agency makes more

conservative adjustments for these types of firms. SOFT and TOTAL have a significantly negative

association withPPrating across all model specifications. Ceteris paribus, Leverage and M2B have

negative associations with SOFT and TOTAL, whereas size, Coverage, Opmargin, and Quick have

positive associations with SOFT and TOTAL.

The results are consistent with the catering hypothesis. Unless differences in adjustments are

driven by unobservable firm characteristics, the use of rating-based performance pricing has an

association with more favorable rating agency adjustments, namely significantly lower estimates

of OFFBS, SOFT, and TOTAL.

22

Table 5 Panel B reports the results for the total FMDS sample. The results for the main effect

are very similar to the results in Panel A. I find PPrating has a significantly negative association

with OFFBS, SOFT, and TOTAL. The results for the control variables are weaker because the

control sample now includes firms that are potentially less comparable in that not all of them have

performance pricing clauses in their loans.

Table 6 reports the results for the base regressions in sample partitions of three different levels

of yield spread. YS0 denotes firms with lowest yields spread, which range from zero to 139 basis

points. YS1 denotes firms with medium yield spreads, which range from 140 to 279 basis points.

Last, YS2 denotes firms with highest yield spreads, which range from 280 to 1,654 basis points.

Columns 1–3 report the OLS estimates from the regressions of OFFBS, and columns 4–9 contain

the ordered probit estimates from the regressions of SOFT and TOTAL. The sample contains

issuers that have loans with performance pricing and the required data for yield spreads. I find

OFFBS and PPrating have no significantly negative correlation in any of the three YS partitions.

Requiring observable yields spreads decreases the sample size from 4,389 firm-years to 693 firm-

years. The coefficients for firm controls are similar to those in the base regression. The sample size

for SOFT and TOTAL regressions drops from 842 to 130 firm-year observations. PPrating has a

negative significant association with SOFT in all three sample partitions (models 4–6). Similarly,

PPrating has a negative significant association with TOTAL in all three sample partitions (models

7–9). The coefficients for firm controls are weaker than in the base regressions. The results

are consistent with the catering hypothesis. The use of rating-based performance pricing has an

association with significantly lower estimates of SOFT and TOTAL, even in small samples with

more homogeneous credit risk.

Table 7 reports the regression results of rating agency adjustments on adverse economic shocks.

Among firms that experience adverse economic shocks, I expect more favorable rating agency

adjustments under the catering hypothesis for PPrating firms: lower estimates of off-balance-sheet

debt and lower soft and total adjustments. However, such shocks only affect those borrowers

whose contractual interest rates can increase under the stipulations of the performance pricing

23

grid. Hence the shock analysis excludes performance pricing decreasing contracts and includes

performance pricing increasing (PPrating up) only. Adverse economic shocks are measured by the

magnitudes of an issuer’s 10th percentile daily bond return over the year (Shock ret). I employ

the bond level approach to maximize the probability of identifying adverse economic shocks.

Table 7 Panel A reports the regression results of rating agency adjustments on adverse eco-

nomic shocks for all issuers in the FMDS sample with available bond returns. The significantly

negative coefficient of Shock ret for OFFBS in model 1 is consistent with the interpretation that

firms’ adjustments for off-balance-sheet debt increase as they experience more adverse shocks.

The significant negative association between Shock ret and SOFT and TOTAL supports the view

that adjustments decrease credit ratings because these firms experience adverse economic shocks

(models 2–3). The coefficient of the interaction term Shock ret*PPrating up is positive but not

significantly different from zero for OFFBS (model 1). The coefficient of the interaction term

Shock ret*PPrating up is significantly positive for SOFT and TOTAL (models 2–3). More adverse

shocks have an association with incrementally more favorable adjustments for PPrating firms,

which is consistent with catering to PPrating firms. It is possible that PPrating firms are better

able to deal with adverse economic shocks than other firms, thus warranting the favorableness of

rating agency adjustments. However, the market reaction to the shock prices this possibility; for

an equally detrimental shock the market reaction for a PPrating firm would be less severe than

the market reaction for a control firm. The association between favorable adjustments and the

contractual use of ratings is observed after controlling for the size of the market reaction. Empiri-

cally, firms that experience adverse economic shocks are not more likely to contract on ratings: the

correlation between Shock ret and PPrating is not significantly different from zero, which supports

the assumption that those shocks are exogenous to the setting (not tabulated). Models 4–6 reports

the results for the analysis based on trading volume-weighted bond returns. The results are very

similar to those for simple bond returns.

Table 7 Panel B reports the probit regression results of changes in rating agency adjustments

(OFFBS δ, SOFT δ, TOTAL δ) on Shock ret. This change specification constitutes a more

24

stringent test, albeit with a loss of data points. OFFBS δ (SOFT δ, TOTAL δ) is an indicator

variable that measures whether the change in the adjustment in the fiscal year surrounding the

adverse shock increases or decreases the rating agency’s estimate of OFFBS (SOFT, TOTAL.)

Shock ret is more negative when the shock is worse. The statistically negative association between

Shock ret and TOTAL in model 3 implies that firms experiencing adverse shocks receive lower

assessments of credit risk. However, the interaction term Shock ret*PPrating up is statistically

positive and has almost the same absolute value of magnitude as the coefficient of Shock ret, which

suggests that PPrating up firms do not suffer lower credit risk assessments when they experience

adverse shocks. The results are qualitatively similar for SOFT δ but not statistically significant.

Models 4–6 reports the results for the analysis based on trading volume-weighted bond returns.

The results are very similar to those for simple bond returns. Overall, the direction of the coeffi-

cients is consistent with the level results in Table 7 Panel A. Rating agency adjustments capture

increases in credit risk for firms that experience adverse economic shocks. PPrating up firms’

agency adjustments however do not worsen, compared to the group of firms that does not contract

on ratings.

In a robustness test, I use changes in credit default swap (CDS) spreads as measures of credit

risk. In this alternative specification, an adverse shock is measured by an increase of the CDS

spread. Following the literature, I collect five-year CDS spreads of contracts with Modified Re-

structuring clauses, which are the most common and liquid in the US, from Markit. When the sam-

ple is restricted to firm-years with available five-year CDS spreads, sample size drops substantially.

In untabulated analysis I find that for SOFT and TOTAL the inferences remain unchanged: the

coefficients for Shock CDS and the interaction of Shock CDS and PPrating up remain significant.

Greater increases in CDS spreads are associated with greater adjustments, but the effect is miti-

gated for PPrating firms. Please note that the signs of the coefficients switch directions relative to

bond returns. Adverse shocks are increases in CDS spreads, whereas adverse shocks are measured

by lower (more negative) returns. Furthermore, I find statistically significant positive associations

between SOFT δ (TOTAL δ) and PPrating up, which implies that firms experiencing adverse

25

shocks receive assessments of greater credit risk. The interaction term Shock CDS*PPrating up

is negative but not significant. Overall, the results are largely consistent with those using bond

returns.

Next, I conduct a difference-in-difference analysis to test whether closeness to an important

rating threshold strengthens rating agency’s catering or whether reputational concerns prevail.

The rating thresholds I consider are the BBB- ratings as well as the short-term rating P1. Table

8 reports the results from the multivariate analysis for the FMDS sample.15 PPrating has a

negative association with OFFBS. However, the coefficient of the interaction term BBB-*PPrating

is significantly positive (model 1). Similarly, the interaction term P1*PPrating is significantly

positive (model 2). This implies that firms with rating-based contracts receive lower estimates

of off-balance-sheet debt than firms without such contracts. However, those PPrating firms that

are close to the BBB- cutoff or enjoy a P1-rating experience greater adjustments to their off-

balance-sheet debt than their counterparts that are not close to these rating thresholds. As in the

base regressions, I find that PPrating has negative associations with SOFT and TOTAL (models

3–6). Both interaction terms BBB-*PPrating and P1*PPrating are positive, and the latter is

statistically significant. This suggests that closeness to rating threshold leads to lower credit risk

assessments: those PPrating firms that have P1-ratings experience statistically greater soft and

total adjustments. The coefficients of the controls for firm characteristics have similar signs and

levels of significance as those in the base regression. The multivariate evidence is not consistent

with increased catering for firms near important short-term rating thresholds. In contrast, the

use of ratings in contracts for firms close to rating thresholds has an association with a more

unfavorable assessment of credit risk. This can be explained with higher reputational costs for the

credit rating agency at these threshold ratings.

Next, I conduct a difference-in-difference analysis to test whether the existence of Fitch ratings

for a given issuer weakens rating agency’s catering. Table 9 reports the results. Columns 1, 3, and

15There is not sufficient data to include P1 rating interactions if the sample includes firms with performancepricing only. The untabulated results with respect to the BBB- interaction term are similar to the results for fullFMDS sample.

26

5 report the results for the sample of firms with PPfeature embedded in their loans. Columns 2, 4,

and 6 report results for the FMDS sample. Both FITCH and PPrating have negative associations

with OFFBS. The coefficient of the interaction term FITCH*PPrating is significantly positive

(model 1). This implies that while firms with rating-based contracts and firms with FITCH

ratings receive lower estimates of off-balance-sheet debt, those PPrating firms that have a FITCH

rating experience a greater adjustments to their off-balance-sheet debt than their counterparts

without FITCH ratings. The coefficients for the FMDS sample have the same signs but lower

statistical significance (model 2).

Similarly, the coefficients of the interaction term FITCH*PPrating are significantly positive in

both the subsample of PPfeature firms and the FMDS sample (models 3–4). Last, the interaction

term FITCH*PPrating is significantly positive in the FMDS sample (model 6). This suggests that

the existence of a FITCH rating acts as a constraint on catering as it is associated with assessments

of greater credit risk: thosePPrating firms that have FITCH ratings experience statistically greater

off-balance-sheet debt, soft, and total adjustments. This can be explained with higher reputational

costs for the credit rating agency when another credit rating agency provides ratings.16

6 Conclusion

This study examines whether rating agencies cater to issuers with rating-based contracts. Rating-

based contracts link cash payouts to changes in ratings and thus make issuers more sensitive to

their public debt ratings. I examine the relation between rating-based debt contracts and rating

agency adjustments: hard adjustments in the form of the agency’s estimate of off-balance-sheet

debt, as well as soft adjustments for qualitative factors. I find evidence that rating agencies provide

more favorable adjustments to issuers with rating-based contracts relative to issuers with similar

contracts based on accounting ratios and other issuers with private loan agreements. Furthermore,

the documented negative association between rating-based debt contracts and rating agency ad-

16However, these results can also be explained if competition among credit rating agencies leads to lower qualityratings (Becker and Milbourn 2011). The debate on the impact of competition on rating quality is still open.

27

justments continues to hold for subsamples with more homogeneous credit risk. Last, I examine

rating agency adjustments in response to unexpected adverse economic shocks to firms, and I

find a differential reaction by the rating agency, which is consistent with catering to firms with

rating-based contracts. Firms with rating-based contracts receive more favorable rating agency

adjustments after experiencing adverse shocks to credit risk than firms without such contracts.

The evidence from the difference-in-difference analysis for rating thresholds shows that impor-

tant rating thresholds, such as the investment grade rating and prime short-term ratings that allow

firms access to more liquid markets, do not result in catering for firms with rating-based contracts.

In contrast, the adjustments for firms are more unfavorable than for other firms near important

rating thresholds. The reputational costs for the rating agency are likely to be more substantial at

these important rating thresholds. Similarly, the existence of a third credit rating mutes catering

incentives.

A lot of unanswered questions remain. Performance pricing is prevalent among firms that

issue private debt. However, rating triggers that link the posting of collateral or trigger early

repayment result in an even greater sensitivity of firms’ cash flows to changes in ratings. So rating-

based performance pricing might not be the most powerful setting to study catering arising from

rating-based contracts; however, the performance pricing data are available for a large sample.

Furthermore, this study examines only one aspect that could give rise to catering. A higher

sensitivity to rating changes could also result from the dependence on the public markets to issue

debt in order to raise external financing, or the existence of a financial subsidiary that relies more

heavily on ratings for its business than a firm not active in financial services.

28

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32

Appendix AIllustration of rating process for Airgas, Inc.

FY Ending March 31, 2008

Rating 

(Moody's)

Rating 

(Standardized) Numerical value

Indicated Rating (based on reported financials) Baa1 BBB+ 8

Indicated Rating (based on adjusted financials) Baa2 BBB 9

Actual  Rating (based on adjusted financials and soft factors) Ba1 BB+ 11

SOFT (soft adjustment) 2

TOTAL (total adjustment) 3

 

Weight

Reported 

financials

Adjusted 

financials

BUSINESS PROFILE

  Business Position Assessment 8.3% A A

SIZE & STABILITY

  Revenues (USD Billion) 8.3% Baa Baa

  Number of Divisions of Equal Size 8.3% Baa Baa

  Stability of EBITDA 8.3% Caa Caa

COST POSITION

  EBITDA Margin (3‐yr Average) 8.3% A A

  EBIT/Average Assets (3‐yr Average) 8.3% A A

  Contingencies as % of Cash from Operations (3‐yr Average) 8.3% Aa Aa

MANAGEMENT QUALITY

  Debt / Book Capital 8.3% Baa Ba

  Debt / EBITDA (3‐yr Average) 8.3% Baa Ba

FINANCIAL STRENGTH

  EBITDA / Interest Expense (3‐yr Average) 8.3% Baa Baa

  Retained Cash Flow / Debt (3‐yr Average) 8.3% A Baa

  Free Cash Flow / Debt (3‐yr Average) 8.3% Baa Ba

WEIGHTED AVERAGE Baa1 Baa2

Source: Moody's Financial Metrics

Indicated Rating

33

Appendix BShock analysis (hypothetical example)

Firm type:  PPrating firm PPratio firm 1 PPratio firm 2

Shock, gross ‐100 ‐100 ‐70

If treatment is random

Shock, net ‐100 ‐100 ‐70

Rating agency adjustment X X Y < X

If PPrating firm is better at dealing with shock (self‐selection)

Shock, net ‐70 ‐100 ‐70

Rating agency adjustment Y X Y

34

Table 1Sample distribution

No. observations FMDS  Industries (top 25) % of sample

N (issuers) 1,193 Energy 11.3%

N (issuer years) 6,196 Electric Utilities 8.9%

Manufacturing 7.8%

Year N Retail 7.3%

2002 836 Services 6.7%

2003 892 Media 5.6%

2004 943 Consumer Products 5.2%

2005 976 Chemicals 4.5%

2006 917 Healthcare 4.2%

2007 875 Technology 4.0%

2008 757 Metals, Mining & Steel 2.7%

Total 6,196 Automotive 2.7%

Telecommunications 2.4%

Rating N Gaming / Lodging 2.4%

AAA 36 Aerospace / Defense 2.3%

AA 107 Pharmaceuticals 2.3%

A 854 Homebuilding 1.7%

BBB 1,723 Forest Products 1.6%

BB 1,758 Restaurants 1.6%

B 1,507 Packaging 1.4%

CCC 194 Wholesale Distribution 1.2%

CC 13 Wholesale Power 1.2%

C 4 Apparel 1.2%

Total 6,196 Rail Roads & Trucking 1.1%

Agriculture 1.1%

The table reports the sample distribution for the FMDS sample by year, rating and

industry. Rating is the long‐term Moody's issuer rating on filing date. Industries are

classified according to Moody's classification scheme. 

35

Table 2Sample summary statistics for subsamples

Sample means FMDS PPrating=1 PPratio=1

Firm characteristics (millions USD)

Total assets 8,789 11,236 3,505

Revenues 7,611 9,553 3,370

Ratios

Leverage 0.38 0.30 0.46

Coverage 7.90 9.50 5.90

Opmargin 0.10 0.11 0.08

ROA 0.07 0.08 0.06

Tangibility 0.46 0.47 0.45

Rating agency adjustment for off‐balance sheet debt (% of total assets)

OFFBS 17.0% 14.0% 21.0%

Implied rating agency adjustments (notches)

SOFT 0.55 0.23 1.10

TOTAL 0.96 0.75 1.30

Loan characteristics

Loan amount (millions USD) 2,086 2,644 1,905

Loan maturity (months) 63 60 67

Allindrawn spread (bps) 161.0 100.0 226.0

CovAccg 0.81 0.95 0.98

Adverse return (Shock_ret)

Shock_ret ‐1.2% ‐1.1% ‐1.4%

Shock_ret_w ‐1.2% ‐1.1% ‐1.4%

The table reports the statistics for the FMDS sample. PPrating equals 1 if issuer year has a facility that includes a performance

pricing clause based on a rating. PPratio equals 1 if issuer year has a facility that includes a performance pricing clause based

on an accounting ratio. Leverage is the ratio of total debt to total assets. Coverage is the ratio of operating profit to interest

expense, winsorized at 1%. Opmargin is the ratio of operating profit to revenues, winsorized at 1%. ROA is the ratio of

operating profit to total assets. Tangibility is the ratio of inventory and net PPE to total assets. OFFBS equals adjusted total

debt less reported total debt, divided by total assets. SOFT equals the difference between the actual rating and the implied

adjusted rating. TOTAL equals the difference between the actual rating and the implied reported rating. CovAccg equals 1 if

loan has an accounting‐based covenant. Shock_ret equals the tenth percentile of daily bond return by issuer‐year bond.

Shock_ret_w equals the tenth percentile of daily bond return by issuer‐year bond (return based on price weighted by trading

volume). 

36

Table 3Performance pricing by year and type

Panel A

Year Total obs N Pct of total N Pct of PPfeat N Pct of PPfeat N Pct of PPfeat

2002 836 603 72% 321 53% 340 56% 58 10%

2003 892 653 73% 315 48% 385 59% 47 7%

2004 943 733 78% 367 50% 424 58% 58 8%

2005 976 781 80% 398 51% 451 58% 68 9%

2006 917 749 82% 401 54% 422 56% 74 10%

2007 875 708 81% 395 56% 388 55% 75 11%

2008 757 604 80% 355 59% 311 51% 62 10%

Total 6,196 4,831 78% 2,552 53% 2,721 56% 442 9%

Panel B

Performance pricing ‐ by direction (proportion) Sensitivity to interest rate (bps over Libor)

PPrating PPratio mean min max

PP_increase 12% 19% MaxLessInitial 44 0 743

PP_decrease 14% 56% InitiallessMin 26 0 425

PP_utroque 90% 66%

This table reports the statistics for the FMDS sample. PPfeature equals 1 if issuer year has a facility that includes a performance pricing clause. 

PPrating equals 1 if issuer year has a facility that includes a performance pricing clause based on a rating. PPratio equals 1 if issuer year has a 

facility that includes a performance pricing clause based on an accounting ratio (including user conditions).  Hybrid equals 1 if issuer year has 

facilities with both performance pricing based on ratings and accounting ratios. PP_increase equals 1 if issuer year has performance pricing 

clause with initial interest rate equal to minimum interest rate in grid. PP_decrease equals 1 if issuer year has performance pricing clause with 

initial interest rate equal to maximum interest rate in grid. PP_utroque equals 1 if issuer year has performance pricing clause with initial interest 

rate between maximum and minium interest rate in grid. MaxLessInitial equals the number of basis points between the rate charged on the 

contract at the inception of the loan agreement and the maximum rate in the performance pricing grid. InitiallessMin equals the number of 

basis points between the rate charged on the contract at the inception of the loan agreement and the minimum rate in the performance pricing 

grid.

Hybrid=1PPfeature=1 PPrating=1 PPratio=1

37

Table 4Correlation matrix

(1) (2) (3) (4) (5) (6) (7) (8) (9)

PPrating (1) 1.0000

PPratio (2) ‐0.4672* 1.0000

OFFBS (3) ‐0.0279* 0.0315* 1.0000

SOFT (4) ‐0.1557* 0.2610* 0.0601* 1.0000

TOTAL (5) ‐0.0897* 0.1662* 0.1936* 0.8673* 1.0000

Ln(Revenues) (6) 0.4168* ‐0.4504* 0.0972* ‐0.0739* 0.0113 1.0000

Leverage (7) ‐0.2511* 0.3313* ‐0.0083 ‐0.0136 ‐0.1635* ‐0.3959* 1.0000

Coverage (8) 0.2936* ‐0.3271* ‐0.0971* 0.0531* 0.1669* 0.3977* ‐0.6669* 1.0000

Opmargin (9) 0.1319* ‐0.1580* ‐0.3550* 0.0135 0.0254* ‐0.0124 ‐0.1664* 0.5840* 1.0000

Tangibility (10) ‐0.0844* 0.0922* ‐0.1633* ‐0.1265* ‐0.1296* ‐0.0902* 0.1116* ‐0.0434* 0.0213*

This table reports the Spearman rank correlation coefficients for the FMDS sample. PPrating equals 1 if issuer year has a facility that

includes a performance pricing clause based on a rating. PPratio equals 1 if issuer year has a facility that includes a performance

pricing clause based on an accounting ratio. OFFBS equals adjusted total debt less reported total debt, divided by total assets. SOFT

(soft adjustment) equals the difference between the actual rating and the implied rating from adjusted financials. TOTAL (total

adjustment) equals the difference between the actual rating and the implied rating from reported financials. Leverage is the ratio of

total debt to total assets. Coverage is the ratio of operating profit to interest expense, winsorized at 1%. Opmargin is the ratio of

operating profit to revenues, winsorized at 1%. Tangibility is the ratio of inventory and net PPE to total assets. The * denotes

significance at the 5% level.

38

Table 5Panel A: Regression analysis

Model 1 2 3 4 5 6

Regression type OLS OLS Oprobit Oprobit Oprobit Oprobit

Dependent variable OFFBS OFFBS SOFT SOFT TOTAL TOTAL

PPrating ‐0.084** ‐0.082** ‐1.096** ‐1.103** ‐0.915** ‐0.924**

[‐3.790] [‐3.748] [‐7.016] [‐7.543] [‐5.976] [‐6.421]

Ln(revenues) 0.021** 0.036** 0.159* 0.168** 0.155* 0.176**

[2.618] [3.995] [2.434] [2.920] [2.330] [3.061]

Leverage ‐0.053 0.106 ‐0.559+ ‐0.777** ‐1.188** ‐1.230**

[‐0.792] [1.157] [‐1.707] [‐3.069] [‐3.367] [‐4.592]

Coverage 0.000 0.008* 0.008*

[0.745] [2.160] [2.032]

Opmargin ‐0.186** ‐0.185** 0.787 0.937* 0.644 0.824+

[‐4.430] [‐4.951] [1.557] [2.001] [1.233] [1.729]

Tangibility 0.129** 0.184** ‐0.309 ‐0.457+ ‐0.235 ‐0.283

[3.202] [4.134] [‐1.104] [‐1.832] [‐0.819] [‐1.128]

Quick ‐0.057** 0.331** 0.253**

[‐4.773] [3.888] [2.900]

M2B 0.000 ‐0.001** ‐0.001**

[‐0.426] [‐7.092] [‐5.468]

Loan amount ‐0.000** ‐0.000** 0.000 0.000 0.000 0.000

[‐5.088] [‐5.093] [1.295] [0.554] [0.871] [0.158]

Loan maturity ‐0.001* ‐0.001* 0.001 0.001 0.001 0.001

[‐2.476] [‐2.496] [0.376] [0.385] [0.372] [0.280]

0.008 ‐0.351*

Constant [0.063] [‐2.360]

Observations 3,787 4,389 747 842 743 838

(Pseudo) R‐squared 0.120 0.120 0.068 0.052 0.055 0.045

The estimates are for the OLS (columns 1‐2) and ordered probit (columns 3‐6) parameters using the FMDS

sample, conditional on having PPfeature and excluding hybrids. OFFBS equals adjusted total debt less reported

total debt, divided by total assets. SOFT equals the difference between the actual rating and the implied rating

from adjusted financials. TOTAL equals the difference between the actual rating and the implied rating from

reported financials. PPrating equals 1 if issuer year has a facility that includes a performance pricing clause based

on a rating. Leverage is the ratio of total debt to total assets. Coverage is the ratio of operating profit to interest

expense, winsorized at 1%. Opmargin is the ratio of operating profit to revenues, winsorized at 1%. Tangibility is

the ratio of inventory and net PPE to total assets. Quick is the ratio of cash, marketable securities, and accounts

receivable to current liabilities, winsorized at 1%. M2B is the ratio of market value of equity to book value of

equity. Loan amount is the amount in millions USD. Loan maturity is the maturity of the loan in months. Industry

fixed effects for utilities and energy and year fixed effects are included. Robust t ‐ and z‐statistics in brackets.

Standard errors clustered by firm. The + indicates significance at 10%; the * significance at 5%; the **

significance at 1%.

39

Table 5 (continued)Panel B: Regression analysis

Model 1 2 3 4 5 6

Regression type OLS OLS Oprobit Oprobit Oprobit Oprobit

Dependent variable OFFBS OFFBS SOFT SOFT TOTAL TOTAL

PPrating ‐0.035** ‐0.030* ‐0.436** ‐0.480** ‐0.359** ‐0.400**

[‐2.709] [‐2.182] [‐3.817] [‐4.230] [‐3.200] [‐3.589]

Ln(revenues) 0.006 0.025* ‐0.061 ‐0.052 ‐0.026 ‐0.012

[0.932] [2.147] [‐1.221] [‐1.110] [‐0.520] [‐0.252]

Leverage ‐0.014 0.194 ‐0.218 ‐0.401+ ‐0.780** ‐0.842**

[‐0.265] [1.416] [‐0.861] [‐1.864] [‐2.680] [‐3.613]

Coverage 0.001 0.001 0.001

[1.034] [0.387] [0.308]

Opmargin ‐0.231** ‐0.210** 0.788+ 0.773+ 0.673 0.714+

[‐5.905] [‐5.556] [1.684] [1.859] [1.397] [1.685]

Tangibility 0.154** 0.193** ‐0.303 ‐0.417+ ‐0.197 ‐0.237

[4.115] [4.683] [‐1.228] [‐1.893] [‐0.779] [‐1.067]

Quick ‐0.046** 0.256** 0.214**

[‐4.141] [3.338] [2.880]

M2B 0.000 ‐0.001** ‐0.001**

[0.432] [‐6.058] [‐5.213]

Loan amount ‐0.000** ‐0.000** 0.000* 0.000+ 0.000 0.000

[‐5.134] [‐4.061] [2.167] [1.654] [1.164] [0.617]

Loan maturity 0.000 0.000 0.003 0.003 0.003 0.003

[‐0.557] [‐0.648] [1.100] [1.354] [1.307] [1.417]

0.141 ‐0.277

Constant [1.324] [‐1.300]

Observations 4,884 5,721 928 1,035 922 1,029

(Pseudo) R‐squared 0.110 0.120 0.035 0.027 0.031 0.025

The estimates are for the OLS (columns 1‐2) and ordered probit (columns 3‐6) parameters using the FMDS

sample, excluding hybrids. OFFBS equals adjusted total debt less reported total debt, divided by total assets.

SOFT equals the difference between the actual rating and the implied rating from adjusted financials. TOTAL

equals the difference between the actual rating and the implied rating from reported financials. PPrating equals

1 if issuer year has a facility that includes a performance pricing clause based on a rating. Leverage is the ratio of

total debt to total assets. Coverage is the ratio of operating profit to interest expense, winsorized at 1%.

Opmargin is the ratio of operating profit to revenues, winsorized at 1%. Tangibility is the ratio of inventory and

net PPE to total assets. Quick is the ratio of cash, marketable securities, and accounts receivable to current

liabilities, winsorized at 1%. M2B is the ratio of market value of equity to book value of equity. Loan amount is

the amount in millions USD. Loan maturity is the maturity of the loan in months. Industry fixed effects for utilities

and energy and year fixed effects are included. Robust t‐ and z‐statistics in brackets. Standard errors clustered by

firm.  The + indicates significance at 10%; the * significance at 5%; the ** significance at 1%.

40

Table 6Sample partition by credit risk

Model 1 2 3 4 5 6 7 8 9

Dependent variable OFFBS OFFBS OFFBS SOFT SOFT SOFT TOTAL TOTAL TOTAL

Regression type OLS OLS OLS oprobit oprobit oprobit oprobit oprobit oprobit

PARTITION BY YS YS0 YS1 YS2 YS0 YS1 YS2 YS0 YS1 YS2

PPrating 0.038 0.017 ‐0.045 ‐13.833** ‐0.904 ‐1.367** ‐11.058** ‐0.868+ ‐1.546**

[1.285] [0.615] [‐1.399] [‐9.523] [‐1.571] [‐2.851] [‐3.995] [‐1.705] [‐3.113]

Ln(revenues) 0.013 0.002 0.004 0.626 ‐0.080 0.052 ‐0.050 ‐0.129 0.121

[0.846] [0.177] [0.314] [0.745] [‐0.394] [0.194] [‐0.075] [‐0.580] [0.478]

Leverage ‐0.240* ‐0.128 ‐0.145 1.927 ‐2.144 3.607* ‐10.131+ ‐3.799** 3.718*

[‐2.591] [‐1.487] [‐1.378] [0.454] [‐1.440] [1.990] [‐1.804] [‐2.644] [2.096]

Coverage 0.000 ‐0.001 0.000 0.058 0.008 0.080+ ‐0.032 0.003 0.113**

[‐0.259] [‐1.347] [0.195] [0.532] [1.635] [1.705] [‐0.271] [0.598] [2.869]

Opmargin ‐0.195+ ‐0.224* ‐0.061 2.319 ‐2.368 1.743 ‐4.378 ‐3.076 0.821

[‐1.883] [‐2.343] [‐0.725] [0.240] [‐1.189] [0.984] [‐0.477] [‐1.477] [0.469]

Tangibility 0.116 0.046 0.167* ‐2.776 ‐0.634 ‐2.163* ‐0.531 ‐1.671+ ‐1.373

[1.601] [0.751] [1.983] [‐0.887] [‐0.598] [‐2.466] [‐0.208] [‐1.662] [‐1.624]

Loan amount 0.000 0.000 ‐0.000* 0.000 0.000+ 0.000 0.000 0.000* ‐0.000+

[‐1.213] [‐0.219] [‐2.097] [‐0.343] [1.737] [‐1.209] [0.029] [2.087] [‐1.736]

Loan maturity 0.000 0.000 0.001 0.002 0.007 0.035** 0.022 0.012 0.035**

[1.192] [‐1.383] [0.972] [0.140] [0.705] [3.496] [1.439] [1.259] [2.869]

Constant 0.003 0.174 0.337

[0.012] [1.206] [1.152]

Observations 202 242 249 24 59 47 24 59 47

R‐squared 0.250 0.140 0.230 0.364 0.124 0.182 0.322 0.156 0.197

The estimates are for the OLS (columns 1‐3) and ordered probit (columns 4‐9) parameters using the FMDS sample, conditional on having PPfeature

and excluding hybrids. The sample is partioned by YS. YS equals the difference between offering yield and yield on a comparable treasury security

(in basis points). OFFBS equals adjusted total debt less reported total debt, divided by total assets. SOFT equals the difference between the actual

rating and the implied rating from adjusted financials. TOTAL equals the difference between the actual rating and the implied rating from reported

financials. PPrating equals 1 if issuer year has a facility that includes a performance pricing clause based on a rating. Leverage is the ratio of total

debt to total assets. Coverage is the ratio of operating profit to interest expense, winsorized at 1%. Opmargin is the ratio of operating profit to

revenues, winsorized at 1%. Tangibility is the ratio of inventory and net PPE to total assets. Loan amount is the amount in millions USD. Loan

maturity is the maturity of the loan in months. Industry fixed effects for utilities and energy and year fixed effects are included. Robust t‐ and z‐

statistics in brackets. Standard errors clustered by firm. The + indicates significance at 10%; the * significance at 5%; the ** significance at 1%.

41

Table 7Panel A: Regression analysis with adverse shocks

Model 1 2 3 4 5 6

Regression type OLS Oprobit Oprobit OLS Oprobit Oprobit

Dependent variable OFFBS SOFT TOTAL OFFBS SOFT TOTAL

PPrating_up ‐0.003 0.162 0.256 ‐0.003 0.157 0.244

[0.21] [1.01] [1.52] [0.17] [1.00] [1.46]

Shock_ret ‐0.924+ ‐21.471** ‐26.711** ‐1.003* ‐20.038** ‐25.432**

[1.92] [3.62] [4.37] [2.05] [3.36] [4.09]

Shock_ret*PPrating_up 0.817 21.209** 27.830** 0.867 20.774** 26.757**

[1.52] [2.64] [3.35] [1.61] [2.62] [3.22]

Ln(revenues) ‐0.003 ‐0.053 ‐0.043 ‐0.003 ‐0.055 ‐0.045

[0.45] [0.92] [0.72] [0.45] [0.96] [0.75]

Leverage 0.008 ‐0.382 ‐0.881+ 0.008 ‐0.397 ‐0.903+

[0.14] [1.01] [1.89] [0.14] [1.05] [1.95]

Coverage ‐0.001** ‐0.001 ‐0.004 ‐0.001** ‐0.001 ‐0.004

[3.44] [0.26] [1.17] [3.44] [0.27] [1.19]

Opmargin ‐0.110* 0.162 ‐0.234 ‐0.109* 0.148 ‐0.249

[2.27] [0.26] [0.41] [2.26] [0.24] [0.44]

Tangibility 0.097** ‐0.877** ‐0.756+ 0.097** ‐0.872** ‐0.750+

[2.71] [2.78] [1.95] [2.71] [2.76] [1.93]

Constant 0.118

[1.08] [1.08]

Observations 5,459 1,301 1,294 5,459 1,301 1,294

(Pseudo) R‐squared 0.110 0.033 0.030 0.110 0.023 0.029

Bond returns (Shock_ret)

Bond returns, weighted by 

volume (Shock_ret_w)

The estimates are for the OLS (columns 1, 4) and ordered probit (columns 2‐3, 5‐6) model

parameters using the FMDS‐Trace sample, excluding hybrids. OFFBS equals adjusted total debt less

reported total debt, divided by total assets. SOFT (soft adjustment) equals the difference between

the actual rating and the implied rating from adjusted financials. TOTAL (total adjustment) equals

the difference between the actual rating and the implied rating from reported financials.

PP_rating_up equals 1 if issuer year is classified as PP_increase or PP_utroque. Shock_ret equals

the tenth percentile of daily bond return by issuer‐year bond. Shock_ret_w equals the tenth

percentile of daily bond return by issuer‐year bond (return based on price weighted by trading

volume). The interaction terms Shock_ret*PPrating_up (Shock_ret_w*PPrating_up) measure the

diff‐in‐diff. Leverage is the ratio of total debt to total assets. Coverage is the ratio of operating

profit to interest expense, winsorized at 1%. Opmargin is the ratio of operating profit to revenues,

winsorized at 1%. Tangibility is the ratio of inventory and net PPE to total assets. Robust t‐ and z‐

statistics in brackets. Industry fixed effects for utilities and energy and year fixed effects are

included. Standard errors clustered by firm. The + indicates significance at 10%; the * significance

at 5%; the ** significance at 1%.

42

Table 7Panel B: Regression analysis with adverse shocks for changes in adjustments

Model 1 2 3 4 5 6

Regression type Probit Probit Probit Probit Probit Probit

Dependent variable OFFBS_δ SOFT_δ TOTAL_δ OFFBS_δ SOFT_δ TOTAL_δ

PPrating_up ‐0.073 ‐0.125 ‐0.027 ‐0.073 ‐0.099 ‐0.01

[0.75] [0.43] [0.10] [0.75] [0.34] [0.04]

Shock_ret 2.736 ‐12.123 ‐28.776** 2.756 ‐17.363 ‐31.432**

[1.47] [0.92] [2.72] [1.49] [1.31] [2.92]

Shock_ret*PPrating_up ‐2.28 6.459 23.854+ ‐2.338 9.628 26.395+

[0.69] [0.40] [1.75] [0.72] [0.60] [1.94]

Ln(revenues) 0.023 ‐0.025 ‐0.087 0.023 ‐0.021 ‐0.086

[0.61] [0.26] [1.00] [0.61] [0.22] [0.99]

Leverage ‐0.442+ ‐1.607* ‐1.367* ‐0.443+ ‐1.681* ‐1.450*

[1.84] [2.30] [2.38] [1.85] [2.38] [2.49]

Coverage 0.004 ‐0.007 ‐0.006 0.004 ‐0.008 ‐0.007

[1.40] [1.25] [1.01] [1.40] [1.32] [1.06]

Opmargin 0.497 ‐0.093 ‐1.293 0.498 ‐0.066 ‐1.287

[1.26] [0.10] [1.46] [1.26] [0.07] [1.45]

Tangibility 0.689** ‐0.36 ‐0.664 0.689** ‐0.362 ‐0.655

[2.64] [0.63] [1.21] [2.65] [0.63] [1.20]

Constant 0.338 0.891 1.748 0.337 0.797 1.737

[0.53] [0.53] [1.17] [0.53] [0.47] [1.17]

Observations 5,287 697 683 5,287 697 683

Pseudo R‐squared 0.068 0.070 0.142 0.068 0.049 0.144

The estimates are for the probit model parameters using the FMDS‐Trace sample (excluding hybrids).

OFFBS_delta equals 1 if OFFBS at fiscal‐year‐end is greater than OFFBS at prior fiscal‐year end and 0

if it is smaller. SOFT_delta equals 1 if SOFT at fiscal year‐end is greater than SOFT at prior fiscal year‐

end and 0 if it is smaller. TOTAL_delta equals 1 if TOTAL at fiscal year‐end is greater than TOTAL at

prior fiscal year‐end and 0 if it is smaller. PPrating_up equals 1 if issuer‐year is classified as

PP_increase or PP_utroque. Shock_ret equals the tenth percentile of daily bond return by issuer‐year

bond. Shock_ret_w equals the tenth percentile of daily bond return by issuer‐year bond (return

based on price weighted by trading volume). The interaction term Shock*PPrating_up measures the

diff‐in‐diff. Leverage is the ratio of total debt to total assets. Coverage is the ratio of operating profit

to interest expense, winsorized at 1%. Opmargin is the ratio of operating profit to revenues,

winsorized at 1%. Tangibility is the ratio of inventory and net PPE to total assets. Industry fixed

effects for utilities and energy and year fixed effects are included. Robust z‐statistics in brackets.

Standard errors clustered by firm. The + indicates significance at 10%; the * significance at 5%; the **

significance at 1%.

Bond returns (Shock_ret)

Bond returns, weighted by 

volume (Shock_ret_w)

43

Table 8Regression analysis for rating thresholds

Model 1 2 3 4 5 6

Regression type OLS OLS Oprobit Oprobit Oprobit Oprobit

Dependent variable OFFBS OFFBS SOFT SOFT TOTAL TOTAL

PPrating ‐0.041** ‐0.037+ ‐0.459** ‐0.640** ‐0.390** ‐0.472*

[‐3.032] [‐1.934] [‐3.816] [‐2.861] [‐3.273] [‐2.269]

BBB‐ ‐0.042* ‐0.422 ‐0.319

[‐2.034] [‐1.511] [‐1.458]

BBB‐*PPrating 0.057* 0.428 0.394

[2.265] [1.282] [1.347]

P1 ‐0.088** ‐1.336** ‐1.163**

[‐3.450] [‐4.480] [‐4.392]

P1*PPrating 0.064* 0.850** 0.636*

[2.426] [2.692] [2.189]

Ln(revenues) 0.006 0.020** ‐0.059 ‐0.041 ‐0.025 0.000

[1.016] [2.619] [‐1.159] [‐0.523] [‐0.484] [0.006]

Leverage ‐0.017 ‐0.008 ‐0.253 ‐0.151 ‐0.814** ‐0.794+

[‐0.321] [‐0.147] [‐0.989] [‐0.413] [‐2.751] [‐1.676]

Coverage 0.001 0.001 0.001 0.005 0.001 0.003

[1.052] [1.136] [0.421] [1.221] [0.367] [0.727]

Opmargin ‐0.230** ‐0.195** 0.776+ 1.014+ 0.673 0.894

[‐5.907] [‐3.553] [1.670] [1.778] [1.408] [1.612]

Tangibility 0.153** 0.148** ‐0.308 ‐0.701+ ‐0.201 ‐0.406

[4.105] [3.055] [‐1.243] [‐1.782] [‐0.794] [‐1.015]

Quick ‐0.046** ‐0.033* 0.262** 0.345** 0.217** 0.160

[‐4.143] [‐2.189] [3.434] [2.637] [2.921] [1.158]

M2B 0.000 0.000 ‐0.001** 0.003 ‐0.001** 0.002

[0.428] [0.115] [‐6.135] [1.027] [‐5.266] [0.555]

Loan amount ‐0.000** ‐0.000** 0.000* 0.000* 0.000 0.000

[‐5.138] [‐4.140] [2.115] [2.474] [1.136] [0.991]

Loan maturity 0.000 0.000 0.003 0.007+ 0.003 0.007+

[‐0.554] [‐0.683] [1.065] [1.818] [1.285] [1.946]

Constant 0.138 ‐0.090

[1.302] [‐0.776]

Observations 4,884 2,043 928 398 922 394

(Pseudo) R‐squared 0.110 0.130 0.036 0.072 0.031 0.045

The estimates are for the OLS (columns 1‐2) and ordered probit (columns 3‐6) model parameters using

the FMDS sample, excluding hybrids. OFFBS equals adjusted total debt less reported total debt, divided

by total assets. SOFT equals the difference between the actual rating and the implied rating from

adjusted financials. TOTAL equals the difference between the actual rating and the implied rating from

reported financials. PPrating equals 1 if issuer year has a facility that includes a performance pricing

clause based on a rating. BBB‐ equals 1 if rating = BBB‐, and 0 otherwise. P1 equals 1 if rating = P1, and 0

otherwise. The interaction term THRESHOLD*PPrating measures the diff‐in‐diff. Leverage is the ratio of

total debt to total assets. Coverage is the ratio of operating profit to interest expense, winsorized at 1%.

Opmargin is the ratio of operating profit to revenues, winsorized at 1%. Tangibility is the ratio of

inventory and net PPE to total assets. Quick is the ratio of cash, marketable securities, and accounts

receivable to current liabilities, winsorized at 1%. M2B is the ratio of market value of equity to book

value of equity. Loan amount is the amount in millions USD. Loan maturity is the maturity of the loan in

months. Industry fixed effects for utilities and energy and year fixed effects are included. Robust t‐ and z‐

statistics in brackets. Standard errors clustered by firm. The + indicates significance at 10%; the *

significance at 5%; the ** significance at 1%.

44

Table 9Interaction with FITCH

Model 1 2 3 4 5 6

Dependent variable OFFBS OFFBS SOFT SOFT TOTAL TOTAL

Regression type OLS OLS oprobit oprobit oprobit oprobit

Sample PPfeature FMDS PPfeature FMDS PPfeature FMDS

PPrating ‐0.083** ‐0.032+ ‐1.181** ‐0.648** ‐0.979** ‐0.526**

[‐3.469] [‐1.734] [‐7.492] [‐4.917] [‐6.345] [‐4.033]

FITCH ‐0.116** ‐0.067** ‐0.219 ‐0.317+ ‐0.175 ‐0.229

[‐3.061] [‐3.001] [‐1.089] [‐1.830] [‐0.869] [‐1.473]

FITCH*PPrating 0.068+ 0.027 0.392+ 0.593** 0.280 0.444*

[1.819] [1.144] [1.678] [2.835] [1.190] [2.254]

Ln(revenues) 0.041** 0.029** 0.137* ‐0.051 0.149* ‐0.013

[4.579] [2.715] [2.371] [‐1.043] [2.556] [‐0.274]

Leverage 0.121 0.207 ‐0.596* ‐0.368+ ‐1.091** ‐0.819**

[1.282] [1.473] [‐2.288] [‐1.693] [‐4.003] [‐3.486]

Coverage 0.001 0.001 0.011** 0.003 0.010** 0.003

[1.116] [1.329] [3.088] [0.913] [2.847] [0.868]

Opmargin ‐0.207** ‐0.236** 0.683 0.726+ 0.589 0.658

[‐4.963] [‐5.991] [1.441] [1.657] [1.223] [1.476]

Tangibility 0.192** 0.197** ‐0.506* ‐0.409+ ‐0.333 ‐0.230

[4.297] [4.776] [‐1.990] [‐1.831] [‐1.312] [‐1.023]

Loan amount ‐0.000** ‐0.000** 0.000 0.000+ 0.000 0.000

[‐4.919] [‐4.103] [0.763] [1.733] [0.380] [0.650]

Loan maturity ‐0.001* 0.000 0.002 0.003 0.001 0.004

[‐2.215] [‐0.526] [0.530] [1.518] [0.370] [1.529]

Constant ‐0.437** ‐0.345+

[‐2.943] [‐1.690]

Observations 4,389 5,721 842 1,035 838 1,029

R‐squared 0.130 0.130 0.059 0.031 0.049 0.028

The estimates are for the OLS (columns 1‐2) and ordered probit (columns 3‐6) model parameters. Columns 

1,3,5 are based on the FMDS sample, conditional on PPfeature. Columns 2,4,6 are based on FDMS sample. 

OFFBS equals adjusted total debt less reported total debt, divided by total  assets. SOFT equals the difference 

between the actual rating and the implied rating from adjusted financials. TOTAL equals the difference 

between the actual rating and the implied rating from reported financials. PPrating equals 1 if issuer year has 

a facility that includes a performance pricing clause based on a rating. FITCH equals 1 if issuer‐year has Fitch 

rating, and 0 otherwise. The interaction term FITCH*PPrating measures the diff‐in‐diff. Leverage is the ratio of 

total debt to total assets. Coverage is the ratio of operating profit to interest expense, winsorized at 1%. 

Opmargin is the ratio of operating profit to revenues, winsorized at 1%. Tangibility is the ratio of inventory 

and net PPE to total assets. Loan amount is the amount in millions USD. Loan maturity is the maturity of the 

loan in months. Industry fixed effects for utilities and energy and year fixed effects are included. Robust t‐ and 

z‐statistics in brackets. Standard errors clustered by firm. The + indicates significance at 10%; the * 

significance at 5%; the ** significance at 1%.

45