Operating leverage and bond yield spreads: Differentiating
between cash and accrual operating leverage

Abstract
The cost of debt is expected to increase with operating leverage. However, thisexpectation may not hold because of the countervailing effect of collaterals on the costof debt. Default risk is more likely to be associated with the cash component ofoperating leverage, which is less likely to be related to collateralized assets. Fixed cash
costs could exacerbate the risk associated with more volatile future cash flows anddownside cash flow risk, and thus increase the cost of debt. Consistent with ourconjecture, the evidence shows that bond yield spreads increase with cash operating
leverage but not with accrual operating leverage. The effect of cash operating leveragegets stronger as the bond market gets more efficient, when investors are more
pessimistic about a firm’s prospects, when the firm is operated less efficiently, andwhen the firm’s sales are volatile and difficult to predict.
Keywords: Cost of debt; Bond yield spreads; Cost structure; Operating leverage

Operating leverage and bond yield spreads: Differentiating
between cash and accrual operating leverage
Abstract
The cost of debt is expected to increase with operating leverage. However, thisexpectation may not hold because of the countervailing effect of collaterals on the costof debt. Default risk is more likely to be associated with the cash component ofoperating leverage, which is less likely to be related to collateralized assets. Fixed cash
costs could exacerbate the risk associated with more volatile future cash flows anddownside cash flow risk, and thus increase the cost of debt. Consistent with ourconjecture, the evidence shows that bond yield spreads increase with cash operating
leverage but not with accrual operating leverage. The effect of cash operating leveragegets stronger as the bond market gets more efficient, when investors are more
pessimistic about a firm’s prospects, when the firm is operated less efficiently, andwhen the firm’s sales are volatile and difficult to predict.

2

1. Introduction
We analyze the effects of operating leverage on the cost of debt. Given theessential role of the cost of capital in asset valuation, the financial economics literatureis replete with studies on risk and the cost of capital. One primary source of risk that isuniversally recognized by both academics and practitioners is leverage. While thediscussion is framed more often in terms of financial leverage, a firm’s operating risk
is also important for a credit analyst because, as Merkel (2016) notes, “if a firm has
high fixed costs and low variable costs (high operating leverage), its financial positionis less stable.” A high operating leverage essentially means a high ratio of fixed-tovariable cost, which is a very pervasive phenomenon that imposes constraints on
business operations and drives business risk. 1 However, surprisingly, while thedeterminants of the cost of debt has been exhaustively analyzed, there is thus far noevidence in the literature that operating leverage is a relevant factor.
One might speculate that operating leverage is simply an understudied
phenomenon. However, starting with Hamada (1972) and Lev (1974), who relate
operating leverage to systematic risk, financial economists have extensively analyzed
operating leverage. Several studies have analyzed the relation between operating
leverage and financial leverage (Ferri and Jones 1979, Mandelker and Rhee 1984;
Reinartz and Schmid 2016; Chen, Harford, and Kamara 2017; Mihov 2017). Others
have analyzed the effect of operating leverage on the cost of equity. Rubinstein (1973),
Mandelker and Rhee (1984), Booth (1991), and Ortiz-Molina and Phillips (2014)
suggest that operating leverage increases expected stock returns; and Kogan (2004),
Gomes, Kogan, and Zhang (2003), Carlson, Fisher, and Giammarino (2004), Cooper(2006); and Garcia-Feijoo and Jorgensen (2010) suggest that operating leverage
explains the value premium. Yet, the literature remains largely silent on the effectoperating leverage on the cost of debt.

1

Rubinstein (1973), Mandelker and Rhee (1984), Booth (1991), Gomes, Kogan, and Zhang (2003),
Carlson, Fisher, and Giammarino (2004), Kogan (2004), Zhang (2005), Cooper (2006), Garcia-Feijoo
and Jorgensen (2010), Ortiz-Molina and Phillips (2014), and Mihov (2017).
3

Our inquiry leads us to a more puzzling observation. Using corporate bondissues in the U.S. from 1981 to 2016, we find no evidence that bond yield spreads
significantly increase with operating leverage. Bond investors are generally institutions,which are sophisticated investors. Bond yield spreads are more likely to fully reflect
investors’ information and credit risk assessment of the borrower than loan yieldspreads (Liao 2015). 2 Therefore, if operating leverage is associated with the cost ofdebt, the effect should be reflected in bond yield spreads.
Our failure to document a significant association between operating leverageand bond yield spreads suggests that the literature is silent on the effect of operating
leverage on the cost of debt presumably because of a lack of corroborating evidence,
which presents a real challenge to the common wisdom that operating leverageincreases credit risk. Fixed costs reduce a firm’s ability to respond to changing businessenvironments, and are somewhat analogous to inside debt, which firms have to incureven when they are facing sales downturns. During persistent sales downturns, somefirms are burdened with large contractual expenses and unproductive assets that
constrain their degrees of freedom, reduce their operating profits, and impede theirability to avoid default. It appears then that both the cost of equity and the cost of debt
would increase with operating leverage.
We speculate that the failure to document a positive association between
operating leverage and bond yield spreads possibly relates to the countervailing effectof collaterals on the cost of debt. All else being equal, collaterals reduce the cost of debt
(Bester 1985; Besanko and Thakor 1987a, b; Chan and Kanatas 1985, 1987; Chan andThakor 1987; Benmelech and Bergman 2009). To the extent that fixed costs increase
with fixed assets, which can serve as collaterals, higher operating leverage would be
associated with lower cost of debt, neutralizing the positive effect of operating leverage.

2

Private lenders often use a wide range of non-price factors to protect themselves against downside risk
and have a monitoring advantage over bondholders (Diamond 1984). In contrast, bondholders’information is reflected almost exclusively in the prices that they are willing to pay for the bonds.
Moreover, loan contracts are often modified and restructured, and often include variable interest rates
and flexible refinancing options, which is not the case for bond contracts.
4Not every fixed cost is associated with tangible assets that are suitable to serveas collaterals. Costs associated with long-term contractual obligations are generally
fixed. For instance, as Donangelo, Gourio, Kehrig, and Palacio (2018) show, labor
expenses induce an important form of operating leverage. Labor expenses per se, which
are largely fixed costs, do not necessarily increase collateralizable assets. We also notethat fixed costs that are related to collateralizable assets are generally accruals (e.g.,
depreciation expenses), which do not jeopardize a firm’s default risk. In contrast, fixed
costs that are not related to collateralizable assets are generally cash expenditures thatcan jeopardize a firm’s default risk. Fixed contractual cash obligations are much moreconcerning to debtholders than depreciation and other accrual fixed costs. Exploiting
this important difference, we have devised a novel approach to capture the componentof operating leverage that is most likely to increase the cost of debt by sorting operating
leverage into cash operating leverage and accrual operating leverage.
Accrual accounting generates numbers, earnings in particular, that are bettermeasures of firm performance than cash flows (Dechow 1994), with revenue, costs, andprofits having both accrual and cash components. Operating leverage is calculated withaccounting data, which mix accruals and cash flows, though the two have very different
implications for debtholders. Operating leverage is traditionally measured from thecoefficient of the regression of operating costs on sales. Accordingly, we measure cash
operating leverage based on the coefficient of the regression of cash operating costs on
cash sales revenues, and accrual operating leverage based on the coefficient of theregression of accrual operating costs on accrual sales revenues. 3
We expect cash operating leverage to affect bond yield spreads through higher
variance of expected cash flows and specifically lower expected cash flows than usualin the case of an eventual downturn. This conjecture implies that cost structure contains
credit risk relevant information that is incremental to the information content of
3

Interest payments are a type of fixed costs. However, they are associated with financial leverage and notoperating leverage. Therefore, they are not included in our cash basis or accrual basis operating leveragecalculations.
5

observed (past) operating performance and volatility. Considering that (1) a firm’s
default risk decreases with its expected cash flows and increases with the variance of
its expected cash flows and (2) a bond’s payoff is an asymmetric function of aborrower’s economic performance, the implications of cost structure on bond prices
can be substantial.
The empirical evidence is consistent with our conjecture that cash operating
leverage increases the cost of debt. More specifically, we find strong evidence thathigher cash operating leverage results in higher bond yield spreads, consistent with cash
basis operating leverage increasing the risk of future profit streams and the cost of debt.
We find no such evidence for accrual operating leverage.
We address concerns that our finding might be biased due to the endogeneity ofa firm’s cost structure using two approaches. First, we use a firm fixed effects
estimation approach, where the potential omitted correlated variables are assumed to
be unobservable time-invariant firm characteristics. Second, we use a difference-indifferences research design that exploits a natural experiment created by the passage ofstate-level wrongful discharge laws as an exogenous increase in a firm’s operating
leverage. Our main results hold with both approaches. The evidence that the cash
operating leverage effect holds within firms is remarkable. Operating leverage is a firmspecific characteristic that is estimated using rolling time series regressions. Therefore,
most of the cash operating leverage variation is observed in the cross-section. The
significant within-firm effect provides strong evidence that bond yield spreads respondto changes in cash operating leverage.
To further assess whether the association between cash operating leverage andbond yield spreads is casual, we examine whether the association varies in predictableways. First, we posit that cash operating leverage is associated with bond yield spreads
because it contains incremental information about expected firm fundamentals. Under
this conjecture, the association between operating leverage and bond yield spreads
would weaken in periods when prices are less reflective of fundamentals. Accordingly,
6using Hu, Pan, and Wang’s (2013) measure of bond pricing noise, we find that the effectof cash operating leverage on bond yield spreads is attenuated when there is morepricing noise in the financial markets.
Second, we note that the effect of operating leverage should be particularly
concerning to debtholders if future sales are more volatile or difficult to predict, because
costs do not co-move with sales sufficiently for issuers with high operating leverage.
Operating leverage is high when firms make few sales but earn high margins on thesesales, exacerbating the risk associated with incorrect sales forecasts. When realized
sales are lower than forecasted sales, the impacts on cash flows and the firm’s operating
ability and debt servicing ability tend to be more severe. Accordingly, we find that theeffect of cash operating leverage on bond yield spreads increases with analysts’ earnings
forecast dispersion and sales volatility, both reflecting the difficulty to predict anissuer’s future sales and income.
Third, cash operating leverage increases cash flow volatility by pushing net
operating cash flows much higher (lower) when sales increase (decrease). However,bondholders are primarily concerned with downside risk. This concern is exacerbated(mitigated) when investors hold pessimistic (optimistic) views about a business’s
prospects. When a firm’s prospects are good, a sales decrease and thus the scenario of
insufficient cash flows (i.e., the downside risk) is less likely to materialize. Accordingly,
we also find that the association between cash basis operating leverage and bond yieldspreads is weaker when investors are optimistic about a firm’s future prospects.
Finally, we find that the effect of cash operating leverage on bond yield spreads
decreases with managerial ability to operate the firm efficiently. A more competentmanager is more likely to make the right adjustments regarding operating leverage andother business decisions to minimize the downside consequence. The effect is also lesspronounced if issuers operate in more competitive product markets, as product market
competition improves the efficiency of resource decisions by managers and mitigates7managerial slacks (Giroud and Mueller 2010, 2011). Because of the disciplinary role ofcompetition, managers are likely to respond more appropriately to potential sales
reductions and minimize the chance of continuing deterioration of future sales and theimpact on sales profit downturns.
Our study contributes to several streams of literature. First, it contributes to theliterature on operating leverage. Prior research largely focuses on the implications ofoperating leverage on equity markets. We investigate how operating leverage affects
debt pricing, filling in this large void in extant research. Second, we extend the literatureon the relations between the characteristics of firms’ real assets and securities pricing(e.g., Rubinstein 1973; Sagi and Seasholes 2007; Ortiz-Molina and Phillips 2014) byshowing that cash operating leverage affects bond yield spreads. Third, while theliterature is replete with studies on operating leverage, our study is the first to propose
measures of cash-based and accruals-based operating leverage, two components ofoperating leverage with very different implications for firm risk and stability. Finally,prior studies document that accruals are better performance measures than cash flows(Dechow 1994) or examine aspects of the accruals anomaly, where equity investors failto understand the properties of accruals versus cash flows (Sloan 1996). We show thatcash operating leverage and accrual operating leverage also have different pricing
implication for debt market investors.
The remainder of this paper is organized as follows. Section 2 discusses related
studies. Section 3 discusses our research design. Section 4 describes the sample. Section
5 reports the empirical results. Section 6 concludes.

2. The extant literature on operating leverage
Operating leverage is a measure of the combination of fixed costs and variable
costs in a company’s cost structure. A company with high fixed costs and low variable
costs has high operating leverage; whereas a company with low fixed costs and high8variable costs has low operating leverage. High operating leverage results in a strong
sensitivity of profits to changes in revenue and thus higher volatility of earnings. In
other words, a company with high operating leverage results in a low co-movement inrevenues and costs because of its cost structure of high fixed costs and low variable
costs (Garrison, Noreen, and Brewer 2017).
Studies have long been linking operating leverage with equity risk. Lev (1974)
provides early evidence that operating leverage is positively associated with systematic
risk. Gahlon and Gentry (1982) and Mandelker and Rhee (1984) demonstrate
theoretically and empirically that operating leverage explains a large proportion ofvariation in beta. Beta is a linear function of operating leverage in the model of capital
investment and asset prices of Carlson, Fisher, and Giammarino (2004), providing
further theoretical ground for the relation between operating leverage and systematic
equity risk. Consistent with that, Garcia-Feijoo and Jorgensen (2010) and Nova-Marx(2011) document that operating leverage is positively associated with stock returns andvalue premium.
Another strand of the literature examines the interactions between operating
leverage and financial leverage. Prezas (1987) demonstrates that operating leverage andfinancial leverage are simultaneously determined and often move in the same direction
pushing the firm into a new risk class. In contrast, Kahl, Lunn, and Nilsson (2016) argue
that firms with high operating leverage pursue more conservative financial policies (i.e.,lower financial leverage) because they face greater risk. Similarly, Chen, Harford, andKamara (2017) find that operating leverage is negatively associated with financial
leverage.
The literature also investigates determinants and consequences of operating
structure. Chen, Kacperczyk, and Ortiz-Molina (2011) find that unionization ispositively related to operating leverage and cost of equity. Donangelo (2014) andDonangelo et al. (2018) labor mobility and labor expenses lead to a form of operating
9

leverage and such leverage has asset pricing implications, being associated withexpected equity returns. Chang, Hall, and Paz (2017) find that firms with greater
customer concentration choose more rigid cost structure, i.e., high operating leverage,
and underperform those with low operating leverage. Holzhacker, Krishnan, andMahlendorf (2015) show that cost structure responds to regulatory changes. Aboody,
Levi, and Weiss (2018) show that managers reduce operating leverage when they facea reduction of option-based compensation and thus a decrease in risk-taking incentives.
To the best of our knowledge, none of the prior studies examines the effect of
cost structure on the cost of debt or bond pricing. Higher operating leverage gives rise
to greater expected volatility in earnings for the same amount of changes in revenues.
Operating leverage thus increases the likelihood of extreme earnings both on the upsideand on the downside. Because of their asymmetric payoff function, the extreme
negative earnings events induced by operating leverage concern bondholders but theextreme positive earnings events induced by operating leverage are irrelevant to them.
Therefore, bondholders cannot diversify away a bond’s downside risk with the upside
events of another bond as they do not share any upside gains. Consequently, operating
leverage represents a credit risk that should be priced.
While the above argument implies that operating leverage should be positively
associated with bond yield spreads, the relation depends on whether operating leverageis accrual driven or cash driven. In particular, bondholders do not concern as muchabout the accrual basis operating leverage. For example, depreciation expenses are aform of accrual basis fixed costs. A firm who has a high proportion of fixed costs andwhose fixed costs are mainly depreciation expenses would have high operating leverage.
However, its operating leverage is mainly driven by accruals. When this firm’s revenue
goes down, its costs do not go down much because of its high fixed costs. However, thefixed costs such as depreciation expenses do not involve any cash outflows. To theextent that the borrowing firms have to pay cash to bondholders on a regular basis,10

bondholders are more much more concerned about cash basis operating leverage than
accrual basis operating leverage.
It is difficult to gauge operating leverage from a single year’s financial
statements. Furthermore, discerning and analyzing cash flows and accruals requirereasonable accounting knowledge. However, bondholders are typically institutional
investors who are sophisticated and have access to more firm-specific information.
Therefore, they are more likely to understand the pricing implications of operating
leverage, cash flows, and accruals. The sophistication of bond investors implies thatthey are able to analyze the cash basis versus accrual basis operating leverage and pricethe cost structure accordingly. In sum, because a firm’s default risk increases as its
expected cash flow decreases or the variance of its expected cash flows increases, andthe bond market is dominated by sophisticated institutional investors, we posit thefollowing predictions. First, bond yield spreads increase with cash basis operating
leverage, but not with accrual basis operating leverage. Second, to the extent that
accruals drive operating leverage, the positive relation between bond yield spreads andoverall operating leverage is muted.

3. Research design
In this section, we describe our regression models and the measurement ofoperating leverage and other variables.
3.1 Modeling bond yield spreads
We model bond yield spreads (SPREAD) as a function of operating leverage(OL). The at-issue bond yield spreads represent the risk premium that firms must pay
to borrow money in the bond market and is a measure of a firm’s cost of debt (Shi 2003;
Qiu and Yu 2009). If a firm has more than one bond issue in a given fiscal year, we use
the proceeds-weighted spread, with the weight being the proceeds of each bond issue
relative to the total proceeds of all bonds issued by the firm during the year. We expect11

the coefficient on OL, our test variable, to be positive (i.e., the higher the operating
leverage, the higher the yield spread).
To ensure that we capture the impact of operating leverage on bond yieldspreads, we control for a number of firm- and bond-specific variables used in priorstudies (e.g., Bhojraj and Sengupta 2003; Khurana and Raman 2003; Klock, Mansi, andMaxwell 2005; Ortiz-Molina 2006; Francis, Hasan, John, and Waisman 2010). Morespecifically, we estimate the following model:
SPREADit =β0 + β1 OLit-1 +Controls +Industry effects +Year effects+εit(1)

SPREAD is the at-issue yield spreads, defined as the difference between thebond offering yield and the comparable Treasury bond yield with equivalent duration
as the bond issue. OL is our measure for operating leverage, which we describe in
Section 3.2.
Controls includes firm-specific and issue-specific characteristics that could
affect bond yield spreads. Specifically, we control for firm size (lnMV), measured as
the natural logarithm of market value of equity, because larger firms tend to be lessrisky. We control for leverage (LEV) because highly leveraged firms generally face ahigher probability of default. We include return on assets (ROA) in the model because
more profitable firms generally have lower default risk. We include interest coverage(INTCOV) which indicates a firm’s ability to generate income to cover its interest
expense and therefore is expected to be negatively associated with bond yield spreads.
We control for the intensity of a firm’s tangible assets (CAPINT) because it is viewed
as potential collateral and bond investors are expected to demand lower yields on firms
with more tangible assets. We control for the volatility of operating cash flows(STDOCF) and the volatility of firm stock returns (STDRET), and expect both to bepositively related to bond yield spreads. We control for a firm’s bond issuancefrequency (MULTI) because generally a frequent bond issuing and trading patternconveys information about the firm to investors and thus reduces adverse selection costs
12

(Francis et al. 2010). We include bond maturity (LnMAT) in the model because lenders
tend to issue long-term debts to larger and healthier firms, implying a negative
relationship between maturity and spreads. All else being equal, we expect the cost ofdebt to increase with the bond proceed (lnPROC), which captures the size of the debt.
However, as Shi (2003) notes, the size of a debt issue might proxy for greater
marketability of the debt, which could mitigate the risk-exposure effect associated witha large issuance. We include bond rating at the issue date (RATING) in the model tocontrol for information that the credit agencies possess about the issuers and the bondsthat are not captured by the other variables in the model.
Finally, we control for industry fixed effects because some industries are riskier
than others and, therefore, firms in these industries have larger spreads on their debts.
We also control for year fixed effects to isolate the impacts of time-series changes inbond yield spreads. Industry Effects are based on the Fama and French (1997) 48industry classification. Variables relating to firm characteristics are measured at the endof the fiscal year preceding the bond issues for the stock variables and over the fiscal
year preceding the bond issues for the flow variables, assuming a three-month reporting
lag. To reduce the effects of outliers, we winsorize all continuous variables at the 1st
and 99th percentiles. Please see the Appendix for detailed definitions of all variables
used in regressions.
3.2 Measuring operating leverage
To measure operating leverage, we estimate total operating leverage as theelasticity of a firm’s operating costs with respect to its sales, a method originated in Lev(1974) and widely used in subsequent studies. For example, Kallapur and Eldenburg(2005) and Aboody et al. (2018) all use a similar specification following Lev (1974) for
estimating time-series regressions of costs on revenue. Specifically, we estimate thefollowing time-series model for each firm i and year t, using the eight most recent yearly
13observations from year t-1 to year t-8.
OCik =𝛼𝛼0 + 𝛼𝛼it REVik +εit , 𝑘𝑘 = 𝑡𝑡 − 8, … , 𝑡𝑡 − 8(2)where OC is total operating costs, measured as the sum of cost of goods sold, generaland administrative expenses, as well as research and development expenses. REV is net
sales revenue. The coefficient 𝛼𝛼 is roughly the proportion of variable costs to total
costs, and a high 𝛼𝛼 can be interpreted as low operating leverage (Kallapur andEldenburg 2005). For ease of interpretation, we use 1-𝛼𝛼 as our proxy for operating
leverage (e.g., Lev 1974). If costs are primarily fixed, then the estimated 1-𝛼𝛼 is high,indicating a high ratio of fixed to variable costs, which results in a high operating
leverage.
In this study, we focus on the differential effect of cash-based versus accrualbased operating leverage on bond yield spreads. We decompose operating costs andrevenues into the cash-based operating costs and revenues and the accrual-based
operating costs and revenues, as in Ball, Gerakos, Linnainmaa, and Nikolaev (2015)
and Ball, Gerakos, Linnanmaa, and Nikolaev (2016). Cash-based operating leverage isestimated over the previous eight years using the coefficient of a time-series regressionof cash-based operating costs to cash-based revenues. The model is specified as follows:
CBOCik =𝛾𝛾0 + 𝛾𝛾it CBREVik +εik , 𝑘𝑘 = 𝑡𝑡 − 1, … , 𝑡𝑡 − 8(3)where CBOC is cash-based operating costs, measured as total operating costs pluschange in inventory and change in prepaid expenses minus change in trade accounts
payable and change in accrued expenses. CBREV is cash-based revenue, defined as net
sales revenue minus change in accounts receivable plus change in deferred revenue. Forease of interpretation, we refer to 1-𝛾𝛾 as cash-based operating leverage (COL).
Accrual-based operating leverage is estimated using a time-series regression of
accrual-based operating costs to accrual-based revenues, specified as follows:
ABOCik =𝜃𝜃0 + 𝜃𝜃it ABREVik +εik , 𝑘𝑘 = 𝑡𝑡 − 1, … , 𝑡𝑡 − 814(4)where ABOC is accrual-based operating costs, measured as total operating costs minus
cash-based operating costs. ABREV is accrual-based revenue, defined as reported sales
minus cash-based revenue. We refer to 1-𝜃𝜃 as accrual-based operating leverage (AOL).
With cash basis operating leverage and accrual basis operating leveragemeasured separately, we run the following regression, where all the variables aredefined as before.
SPREADit =β0 +β1 COLit-1 +β2 AOLit-1 +Controls +Industry effects+Year effects+εit(5)

In all the regression analyses, we use robust standard errors corrected forheteroskedasticity and clustered at the firm level.

4. Sample and descriptive statistics
We start with straight (nonconvertible) fixed-rate bond issues from U.S. firms
over 1981–2016 taken from the Securities Data Corporation (SDC) Global New Issues
database. We impose the following sample selection criteria: (1) the issuing firm is nota financial institution or a regulated utility (SIC codes 6000-6999 and 4900-4999,
respectively); (2) the issuing firm has the necessary accounting data on Compustat andreturn data on the Center for Research in Security Prices (CRSP) database; (3) data on
bond yield spreads, bond rating, bond proceeds, and bond time to maturity are available
on the SDC; and (4) sales and total operating costs are non-negative. Data used in the
cross-sectional tests are obtained from various sources, which are identified later in thepaper. For firms with multiple bond issues in a given year, following prior studies (e.g.,
Klock et al. 2005), we convert the multiple same-year issues into one observation
weighted by issue proceeds so that the unit of observation in our analyses is an issuing
firm and not a bond issue. These restrictions yield a final sample of 2,849 bond issues
conducted by 797 firms. The Appendix provides detailed definitions of variables.
Table 1 provides descriptive statistics for the variables used in the regression
15

analyses, including those used in the subsequent cross-sectional analyses. Operating
leverage has a mean value of 0.226. Cash basis operating leverage has a mean of 0.224
and accrual basis operating leverage has a mean of 0.837. All measures display
reasonable cross-sectional variations. The table also presents statistics on thecharacteristics of both the issuers and the bonds. All the statistics are similar inmagnitude to those reported in prior studies (e.g., Klock et al., 2005; Francis et al.,2010). Table 2 presents the correlations of variables appeared in Table 1. We note thatbond yield spread (SPREAD) is positively correlated with operating leverage (OL) andcash-based operating leverage (COL), but is not correlated with accrual-based operating
leverage (AOL). The correlations between SPREAD and other variables are generally
consistent with expectations. For example, SPREAD is negatively correlated withprofitability (ROA) and credit rating (RATING), and is positively correlated withvariables measuring performance volatility, such as standard deviations of operating
cash flows (STDOCF), returns (STDRET), and sales (STDSALE).

5. Empirical results
We start this section with an analysis of the association between bond yieldspreads and operating leverage. Next, in an effort to draw causal inferences, we address
the potential problems associated with the endogeneity of cost structure. Finally, tofurther strengthen our causal inferences, we conduct several cross-sectional analyses.
5.1 The main effect of operating leverage on the cost of debt
We report the results of the OLS regressions of yield spreads on operating
leverage in Table 3. Column (1) reports the results for the overall operating leveragemeasure OL. We find that the coefficient on OL is insignificant. In other words,
operating leverage is not associated with bond yield spreads.
In column (2), we put cash basis operating leverage COL and accrual basis
operating leverage AOL instead of the overall operating leverage in the regression. We
16find a significantly positive association between bond yield spreads and COL, but notsignificant relation between bond yield spreads and AOL. This finding suggests thatbondholders are primarily concerned with operating leverage induced by unpredictablesales revenues and operating costs that are cash based. The risk of cash shortage as aresult of a revenues decline but fixed cash outflows increases bond yield spreads. In
contrast, if the fixed costs are primarily accruals without immediate cash outflows, costof debt does not go up because while revenues decline but there is no committed fixedcash outflows to siphon off cash that could be used to pay back debtholders.
The coefficients on the control variables are also generally consistent with our
expectations and prior literature. More specifically, offering bond yield spreads
decrease with size, profitability, good credit ratings, and issuance frequency, andincrease with leverage, return volatility, and issuance size. The regression results incolumns (1) and (2) are consistent with our conjecture that bond yield spreads increase
with cash basis operating leverage but not accrual basis operating leverage, and that
operating leverage without separating the cash flow and accruals components fails toyield any insight regarding the impact of firms’ internal cost structure on cost of debt.

5.2 Potential endogeneity bias
Cost structure is a choice variable. Even though we control for the determinantsof bond yield spreads suggested in prior research, it is possible that operating leverageand cost of debt are both affected by the same unobserved firm characteristics. To
mitigate the endogeneity concern, we use two different approaches: (1) firm fixed
effects estimation; and (2) a difference-in-differences research design in a panel
regression using the adoption of state-level wrongful discharge laws as an exogenous
shock to a firm’s cost structure, i.e., an exogenous increase in the firm’s operating
leverage.

5.2.1 Firm fixed effect estimation
17

Operating leverage is a firm-specific characteristic, and a firm’s cost structure
is presumably relatively stable over time. In addition, it is estimated using rolling timeseries regressions. Accordingly, most of the cash operating leverage variation isobserved in the cross-section. Therefore, the operating leverage effect does not have tohold within firms, particularly in cross-sectional analyses. 4 Hence, we do not include
firm-fixed effects in our main analysis; we instead cluster the standard errors at the firmlevel. Nonetheless, if the unobservable firm-specific characteristics that potentiallyaffect both a firm’s cost structure and its bond yield spread are time invariant, then afirm fixed-effect estimation can account for the firm level heterogeneity and thus help
to address the endogeneity problem. Therefore, in this section, we conduct firm fixed
effects estimation, exploiting the within-firm, albeit weak, variation in cost structure.
The year-fixed-effect estimation also helps to assess whether transitory aggregate
economic conditions might be driving the bond yield spread effect.
We report the firm fixed-effect results in column (1) of Table 4. The coefficientson CBOL remain positive and statistically significant, and the coefficient on ABOL
remains insignificant. The firm fixed effects regressions give us the within-firm
estimation results with changes in operating leverage, both cash based and accruals
based, and changes in bond yield spreads. Therefore, to the extent that both cost
structure and credit spreads are affected only by time invariant unobservable firm
characteristics, the results suggest that the association between cash based and accruals
based operating leverage and credit spreads is unlikely to be driven by endogenous
forces.
5.2.2 Difference-in-differences research design
To further alleviate the endogeneity concern and strengthen causal inferences,4

A well-known caveat of fixed effects estimation is that it exacerbates the attenuation bias caused bymeasurement error (Griliches and Hausman 1986). This concern may be particularly relevant to our
setting because our operating leverage variables are estimated and thus potentially more prone tomeasurement error.
18

we also use a difference-in-differences research design. Specifically, we exploit thenatural experiment created by the passage of state-level wrongful discharge laws as an
exogenous increase in a firm’s operating leverage. These laws, passed by all 50 states
between 1967 and 1995, allow employees to sue employers for unjust dismissal and forlost earnings and punitive damages associated with such dismissals. As exceptions to
the practice that employers can terminate employees at will, these laws increase thecosts associated with dismissing employees (Jung 1997). After a wrongful discharge
law is passed in a state, it is more difficult for a firm in that state to adjust number of
employees and thus labor costs become more fixed. This represents an increase inoperating leverage. The adoption of wrongful discharge laws occurs at the state level
and in various years, and the change in operating leverage resulting from wrongful
discharge laws is exogenous with respect to any specific firm. To the extent that mostlabor costs are cash based, wrongful discharge laws affect mostly cash basis operating
leverage, the operating leverage measure of our focus.
There are three exceptions to the terminate-at-will rule and states can choose to
adopt none, any, or all three: good faith exception, implied contract exception, andpublic policy exception. 5 To analyze the effect of the adoption of the wrongful
discharge laws, we define an indicator variable WDL, which equals one if the state
where a firm is headquartered has adopted anyone of the three exceptions as of year t.
Hence, WDL takes the value 1 when a firm is subject to wrongful discharge laws and,
consequently, experiences an exogenous shock in labor operating leverage. We thenestimate the following panel regression:
SPREADit = β0 + β1WDLit + ∑ γ j Control jit + Firm Effects + Year Effects + ε it

(6)

j

5

For detailed discussions and legal definitions of these wrongful discharge laws (i.e., exceptions to theterminate-at-will rule), see Miles (2000), Kugler and Saint-Paul (2004), and Autor, Donohue, andSchwab (2006).
19

We include firm fixed effects in the difference-in-differences model to control
for fixed differences between treated firms (i.e., firms headquartered in states that are
affected by the laws in year t) and control firms (i.e., firms headquartered in states thatare unaffected by the laws in year t). The fixed effects ensure that β1 reflects average
within-firm changes in bond yield spreads from before to after the exogenous shock tooperating leverage, rather than simple cross-sectional correlations. We also include year
effects to account for potential omitted macro-economic factors that could affect bondyield spreads and the likelihood that a state adopts wrongful discharge laws. 6 The
identification strategy can be understood with an example. Suppose we wish to estimate
the impact of the wrongful discharge law (operating leverage) passed in Alabama in
1987. We calculate the difference in bond yield spreads before 1987 and after 1987 forall firms headquartered in Alabama. However, other economic factors in 1987 may haveaffected bond yield spreads and the before-after difference may not be due to a change
in operating leverage (associated with wrongful discharge laws). Choosing a control
state, for example Louisiana, would help to control for such factors. We calculate thesame difference between before and after 1987 for firms headquartered in Louisiana,
which does not pass any wrongful discharge laws in 1987. We can then compare thebond yield spread differences in Alabama and Louisiana before and after 1987. The
panel regression we use account for the staggered passage of wrongful discharge lawsby different states. As such, the control group is not just states that never pass such alaw; it consists of firms in all the states that do not pass a law in a particular year t, even
if the states have already passed a law before year t or will pass a law later on. Our
approach is similar to the difference-in-differences estimation used in the literature (e.g.,
Bertrand and Mullainathan 2003).

6

Acharya et al. (2013) argue that wrongful discharge laws affect innovation and Shi (2003) documentsthat R&D investment affects bond risk premium. Therefore, we control for R&D activity (RDINT) inregression (3). Moreover, the model also includes leverage (LEV), which controls for the effect ofwrongful discharge laws on capital structure (Serfling 2016).
20

We report the results from the difference-in-differences estimation in column (2)of Table 3. The coefficient on WDL, which measures the effect of increases in cash basis
operating leverage on bond yield spreads, is positive and statistically significant.
Wrongful discharge laws should increase the operating leverage of labor-intensive
issuers more than other issuers. Therefore, we expect the impact of the exogenous shock
to be stronger for firms with higher labor intensity. We test this conjecture in columns(3) and (4) of Table 3. Column (3) use a measure of labor intensity in terms of numberof employees per dollar of total sales (Serfling 2016). Specifically, we calculate theratio of the number of employees over sales for each firm-year and denote it EMPINT.
We find that the coefficient on WDL is insignificant, but that on WDL×EMPINT is
positive and statistically significant. These results indicate that the wrongful discharge
laws affect labor-intensive firms more and such an exogenous increase in the operating
leverage of those firms causes an increase in bond yield spreads. Column (4) uses an
expense-based measure of labor intensity. Specifically, we create a variable, PAYROLL,
as the ratio of labor and pension expenses to sales following Levine et al. (2015). Again,
we note that the coefficient on WDL is not statistically significant. However, thecoefficient on WDL×PAYROLL is positive and statistically significant, confirming thatthe labor laws affect labor-intensive firms more and that the exogenous increase in theoperating leverage of those firms causes an increase in bond yield spreads. Overall,although operating leverage can be a firm choice variable, the evidence suggests thatthe positive association between cash basis operating leverage and bond yield spreads
is not likely to be driven by some endogeneity bias.

5.3 Cross-sectional analyses
5.3.1 The effect of pricing noise
There are periods, such as during crises, when prices are less reflective of firm
fundamentals. If cash operating leverage is associated with bond yield spreads because21

it contains incremental information about expected firm fundamentals, then bond yieldspreads should be less reflective of the impact of cash operating leverage during such
periods. We proxy for the extent to which prices deviate from fundamentals using Hu,
Pan, and Wang’s (2013) measure of pricing noise (NOISE). This measure is computed
as the aggregate root mean squared error (in basis points) of the deviation of the market
yield on a bond at a given point in time from the yield predicted by a model of the yield
curve applied to all bonds at the same point in time (please see Hu et al., 2013 fordetails). 7 The pricing error reflects the relative scarcity of arbitrage capital and thus thebarriers to arbitrage, leading to asset mispricing. We augment Equation (5) with NOISE
and the interaction term between NOISE and operating leverage.
The results of the analysis are reported in Table 5. While the main effect of cash
operating leverage (COL) is significantly positive, the coefficient on the interaction
term between COL and NOISE is significantly negative. This evidence that bond yieldspreads reflect issuers’ cash operating leverage less as prices deviate from fundamentals
is consistent with the notion that cash operating leverage contains incremental
information about expected firm fundamentals.

5.3.2 The effect of analyst forecast dispersion and sales volatility
Operating leverage is high when firms make few sales but earn high margins onthese sales. Such firms face higher risk associated with incorrect sales forecasts. For
bondholders, lower actual sales relative to forecasted sales can give rise to insufficient
cash flows (relative to budgeted or forecasted cash flows), which hurts the borrower’s
debt repayment ability. Therefore, we expect that the effect of cash operating leverageon bond yield spreads to concentrate in firms whose sales revenues are volatile anddifficult to predict. We test this conjecture by conditioning our analysis on two variables.

7

We thank Jun Pan for making the data publicly available at http://www.mit.edu/~junpan/. The data areat daily frequency, starting at 01/02/1987. We use the monthly average in the month of bond issuance.
22

First, we use the dispersion of analysts’ forecast of earnings (DISP) to measure thedegree of difficulty in forecasting a firm’s future revenues and income. We also use thestandard deviation of sales revenues over the five years prior to the bond issuance(STDSALE) to measure the expected sales volatility of the firm.
The results of the analysis are presented in Table 5. The coefficients on theinteraction terms between COL and both DISP and STDSALE are positive andsignificant. Hence, the evidence is consistent with our expectations that, while an issuer
with higher operating leverage would tend to have higher bond yield spreads, theoperating leverage spread effect is exacerbated when the firm’s future sales is expected
to be volatile and difficult to predict.

5.3.3 The effect of investors’ expectations about future firm prospects
As explained earlier, we posit that the association between operating leverageand bond yield spreads would decrease with investors’ expectations about a firm’s
future prospects. We measure the market’s expectation of an issuer’s future prospects
in two ways. First, we use the one-year stock return prior to the bond issuance (RET).
Using returns to measure investors’ expectations of firms’ prospects is consistent withboth theoretical models and numerous prior empirical studies (e.g., Barberis, Shleifer,
and Vishny 1997; Collin-Dufresne, Goldstein, and Martin 2001). Second, we use
analysts’ expected growth in the issuer’s earnings (LTG). Specifically, LTG is expectedlong term growth rate of earnings per share, computed by IBES as the median of
analysts’ forecasts of earnings per share growth rate over the next five years as of the
fiscal year end before bond issuance. A higher RET or LTG is generally an indicationthat investors are optimistic about a firm’s future operating performance. We augment
Equation (5) with RET, LTG, and the interaction terms between these future prospectvariables and operating leverage. The results reported in Table 6 are consistent with our
expectations that the impact of cash basis operating leverage on bond yield spreads
23

would decrease with perceived firm prospects. The coefficients on the interaction terms
between COL and RET and LTG are both significantly negative. The results suggestthat while bondholders are concerned about cash basis operating leverage, such a
concern is abated if the market perceives the firm as having good prospects such thatthe likelihood for an extreme revenue decline, and thus for operating leverage tomagnify the reduction in cash flows, is lower.

5.3.2 The effect of operating efficiency and competition
We expect the association between operating leverage and bond yield spreads
to be weaker when bond investors have reasons to believe that a firm is managed
efficiently. With a given degree of operating leverage, an efficiently operated firm ismore likely to make adjustments to minimize the impact of declining revenues and turnthings around. Therefore, the effect of operating leverage on exacerbating cash shortfall
could be potentially mitigated with an effective manager. We proxy for the level of
efficiency with which a firm is operated by Demerjian, Lev, and McVay’s (2012)
managerial ability measure. Demerjian, Lev, and McVay’s (2012) measure, which welabel MGR_ABILITY, is a within-industry relative efficiency estimate based on dataenvelopment analyses (DEA). 8
It has long been argued that product market competition helps to ensure thatmanagers make more efficient operation decisions and do not waste corporate resources(e.g., Alchian 1950; Stigler 1958). Product market competition puts pressure on
managers to make value maximizing decisions and mitigate managerial slack (e.g.,
Giroud and Mueller 2010, 2011). 9 Therefore, in a competitive market, managers tend
to respond quickly and optimally to potential and ongoing revenue reductions, thus

8

Please refer to Demerjian, Lev, and McVay (2012) for detailed discussions about the measure. The
dataset containing the measure is available athttps://community.bus.emory.edu/personal/PDEMERJ/Pages/Download-Data.aspx.
9
The argument in Giroud and Mueller (2010, 2011) implies that competition can reduce agency problem.
We perform agency related cross-sectional analysis next, using additional measures of agency problem.
24

more likely to minimize (cash) losses suffered by the firm as a result of changing
business conditions. We measure competition with FLUIDITY, which is based on a
textual analysis of firms’ product descriptions in 10-K filings to capture changes inother firms’ products relative to a firm’s own products. Following Hoberg, Phillips, andPrabhala (2014), FLUIDITY measures the product threats faced by a firm and isretrieved from the Hoberg-Phillips Data Library. 10 A higher value of FLUIDITY
indicates more competition and higher operating efficiency. Consistent with Valta(2012), we expect bond spreads to increase with FLUIDITY because of the uncertainties
in cash flow and liquidation associated with product market competition. However,
because competition generally forces managers to make more efficient decisions and torespond faster to changing economic conditions, it is likely to mitigate the impact ofoperating leverage on bond spread.
The results are reported in Table 7. The coefficients on the interaction terms
between both COL and MGR_ABILTIY and FLUIDITY are significantly negative.
Hence, the evidence is consistent with our expectations that, while an issuer with higher
operating leverage would tend to have higher bond yield spreads, the operating leverage
spread effect is mitigated when the firm is operated with relatively efficient managers
or in a highly competitive product market.

6. Conclusion
It is generally assumed that operating leverage would increase default risk andconsequently the cost of debt. However, there is no empirical evidence of such a relation
between operating leverage and the cost of debt. We argue the expected relation may
not hold because of the countervailing effect of collaterals on the cost of debt. We
further note that default risk is more likely to be associated with cash operating leverage,

10

For details of this measure, please see Hoberg, Phillips, and Prabhala (2014). The Hoberg-Phillips
industry data web page is at: http://hobergphillips.usc.edu/
25

which is less likely to be related to collateralized assets. Accordingly, we sort operating
leverage into cash operating leverage and accrual operating leverage. We expect fixedcash costs to exacerbate the risk associated with more volatile future cash flows anddownside cash flow risk, and thus to increase the cost of debt.
Consistent with our expectations, the evidence shows that higher cash operating
leverage results in higher bond yield spreads. We find no such evidence for accrual
operating leverage. We find that the spread-increasing effect of cash operating leverage
is weaker in period of apparent financial market mispricing, when investors are
optimistic about the firm’s prospects, or when the firm is operated more efficiently. We
also find that the effect is stronger when a firm’s sales revenue is difficult to predict.
Our main results are robust to controlling for the endogeneity of firms’ cost structure.
The study integrates the credit spread research and the cost structure research andextends both literatures.

26

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Appendix: Variable definitions
Variable
SPREAD

OL

COL

AOL

LnMV
LEV
ROA
INTCOVCAPINT
STDOCF
STDRET
RDINT
MULTI
LnMAT

LnPROCRATINGWDL
EMPINT
PAYROLL
RET

Description
Bond yield spread, measured as the yield to maturity of the corporate bond at
issuance minus the yield to maturity of a Treasury security with a similar durationand expressed in basis points. If a firm issues multiple bonds in a year, thisvariable is calculated as a weighted average of all bond issues based on eachbond’s proceeds.
The operating leverage measure related to Lev (1974), defined as one minus thecoefficient estimate of 𝛼𝛼 from firm-specific regressions of the following modelusing the last 8 years of observations prior to the bond issuance:
OCik =𝛼𝛼0 +αit REVik + εik , k=t-1…t-8, where OC is total operating costs, measured
as the sum of cost of goods sold, general and administrative expenses, as well as
research and development expenses; REV is net sales revenue.
The cash-based operating leverage measure, defined as one minus the coefficientestimate of 𝛾𝛾 from firm-specific regressions of the following model using thelast 8 years of observations prior to the bond issuance:
CBOCik =γ0 +γit CBREVik + εik , k=t-1…t-8, where CBOC is cash-based operating
costs, measured as total operating costs plus change in inventory and change inprepaid expenses minus change in trade accounts payable and change in accrued
expenses. CBREV is cash-based revenue, defined as net sales revenue minuschange in accounts receivable plus change in deferred revenue.
The accrual-based operating leverage measure, defined as one minus thecoefficient estimate of 𝜃𝜃 from firm-specific regressions of the following modelusing the last 8 years of observations prior to the bond issuance:
ABOCik =θ0 +θit ABREVik + εik , k=t-1…t-8, where ABOC is accrual-based
operating costs, measured as total operating costs minus cash-based operating
costs. ABREV is accrual-based revenue, defined as reported net sales minus cashbased revenue.
The natural logarithm of market value of equity at fiscal year-end prior to thebond issuance.
Financial leverage defined as long-term debt over total assets at the fiscal year
end prior to the bond issuance.
Return on assets defined as income before extraordinary items divided by total
assets at the fiscal year end prior to the bond issuance.
Interest coverage defined as the ratio of net income plus interest expense tointerest expense at the most recent fiscal year end prior to the bond issuance.
Capital intensity calculated as the ratio of gross property, plant and equipment tototal assets at the fiscal year end prior to the bond issuance.
Standard deviation of operating cash flows divided by total assets over the five
years prior to the bond issuance.
Standard deviation of monthly stock returns over the year prior to the bondissuance.
R&D intensity defined as R&D expenses divided by net sales at the fiscal year
end prior to the bond issuance.
An indicator variable that equals one if a firm has multiple bond issues during thesample period, and zero otherwise.
The natural logarithm of the number of years until maturity on the issue. If a firm
issues multiple bonds in a year, this variable is calculated as a weighted averagebased on each bond’s proceeds.
The natural logarithm of the total amount of proceeds received from the issue. If
a firm issues multiple bonds in a year, this variable is calculated as a weighted
average based on each bond’s proceeds.
Standard & Poor’s credit rating on the bond issue, converted to numerical scaleranging from 1 (D or SD) to 22 (AAA). If a firm issues multiple bonds in a year,
this variable is calculated as a weighted average of the ratings based on eachbond’s proceeds.
An indicator variable that equals one if a state has passed any wrongful discharge
laws, and zero otherwise.
Employee intensity, defined as the number of employees divided by sales.
Labor payroll intensity, defined as the ratio of total labor expenses to sales.
Annual buy-and-hold-returns for the year prior to the bond issuance.
32

LTG

MGR_ABILITYFLUIDITYDISPSTDSALENOISE

The expected long-term growth rate of earnings per share, computed by IBES as
the median of analysts’ forecasts of earnings per share growth rate over the next
five years as of the fiscal year end before bond issuance.
The decile rank (by industry and year) of managerial ability from Demerjian etal. (2012).
A measure of product market threats faced by a firm, constructed by Hoberg,
Phillips, and Prabhala (2014) based on a textual analysis of firms’ productdescriptions in 10-K filings to capture changes in other firms’ products relative
to the firm’s own products. See Hoberg et al. (2014) for details.
Analyst earnings forecast dispersion is measured as the standard deviation of the
final analyst forecast prior to the firm’s annual earnings announcement, divided
by the absolute value of the mean forecast.
Standard deviation of net sales scaled by total assets over the five years prior to
the bond issuance.
The aggregate root mean squared error (in basis points) of the deviation of themarket yield on a bond at a given point in time from the yield predicted by amodel of the yield curve applied to all bonds at the same point in time (see Hu,
Pan, and Wang, 2013 for details). The data are publicly available
at http://www.mit.edu/~junpan/. The data are at the daily frequency, starting on
01/02/1987. We use the monthly average in the month of bond issuance.

33

Table 1: Summary statistics

Dependent
Variable
SPREAD
Main Variables
OL
COL
AOL
Control
Variables
LnMV
LEV
ROA
INTCOVCAPINT
STDOCF
STDRET
RDINT
MULTI
LnMAT
LnPROCRATING
Variables in
Cross-Sectional
AnalysisRET
LTG
MGR_ABILITYFLUIDITYDISPSTDSALENOISE
EMPINT
PAYROLLN

Mean

Std. Dev.

P25

MedianP75

2,849

162.754125.355

76.702121.000

200.000

2,849
2,849
2,849

0.226
0.224
0.837

0.160
0.187
0.820

0.111
0.092
0.437

0.197
0.195
0.856

0.303
0.310
1.207

2,849
2,849
2,849
2,849
2,849
2,849
2,849
2,849
2,849
2,849
2,849
2,849

8.714
0.244
6.2%
7.124
0.381
2.8%
8.5%
0.021
0.953
2.355
5.523
15.396

1.556
0.139
5.1%
11.193
0.227
2.9%
3.9%
0.037
0.211
0.562
0.926
3.093

7.726
0.146
3.5%
2.438
0.202
1.1%
5.9%
0.000
1.000
2.015
5.005
14.000

8.656
0.225
6.1%
4.230
0.338
2.0%
7.6%
0.004
1.000
2.303
5.520
16.000

9.763
0.320
9.0%
7.524
0.543
3.4%
10.3%
0.027
1.000
2.738
6.204
17.000

2,849
2,664
2,737
1,674
1,691
2,849
2,669
1,982
597

0.135
12.192
0.638
5.530
0.128
0.041
3.389
5.451
0.224

0.322
4.222
0.321
3.360
0.493
0.036
2.080
4.727
0.084

-0.067
9.750
0.300
3.183
0.015
0.019
2.218
2.532
0.175

0.109
11.800
0.700
4.642
0.034
0.030
2.942
4.406
0.211

0.301
14.150
1.000
7.031
0.089
0.051
4.058
6.906
0.260

The table reports summary statistics for the variables used in subsequent analyses. The
sample consists of nonconvertible bond issues in the U.S. from 1981 to 2016. The
variables are defined in the Appendix.

34

Table 2: Correlation Matrix
SPREADSPREAD
OL
COL
AOL
LnMV
LEV
ROA
INTCOVCAPINT
STDOCF
STDRET
RDINT
MULTI
LnMAT
LnPROCRATINGRET
LTG
MGR_ABILITYFLUIDITYDISPSTDSALENOISE
EMPINT
PAYROLL

0.090
0.082
-0.024-0.3360.304-0.299-0.304-0.0490.240
0.426-0.247-0.161
0.029
0.194-0.707-0.125-0.044-0.228
0.154
0.263
0.122
0.171
-0.148-0.028

OL

CBOL

ABOL

LNMV

LEV

ROA

INTCOV CAPINT STDROA STDRET RDINT

0.053

0.038
0.855

0.006

-0.3830.253
0.222-0.161

0.349-0.061-0.050-0.014-0.3260.284
0.220-0.007-0.148
0.270
0.238
0.002

-0.3230.424-0.3280.313-0.4040.551

0.849-0.0910.246-0.073
0.284
0.252
0.062
0.146
-0.047
0.282
0.026
0.018
0.132
0.193
-0.023
0.046
0.046
0.238

-0.1290.235-0.061
0.243
0.216
0.103
0.102
-0.069
0.226
0.006
0.012
0.122
0.175
-0.029-0.0020.074
0.221-0.090-0.443-0.005-0.055-0.419-0.025-0.109-0.032-0.098-0.070-0.107-0.132-0.167-0.031-0.003-0.027-0.151-0.0160.025
0.070-0.028-0.038-0.153-0.018-0.2580.427
0.470-0.095-0.139-0.324
0.244
0.218
0.005

-0.193-0.087-0.2630.268
0.040-0.051
0.274
0.467

0.112
0.058
0.209
-0.083

0.067
0.051
0.210
0.012

-0.061-0.081-0.026-0.478-0.066-0.437-0.0830.050-0.040-0.059-0.441-0.141-0.139-0.1550.026

0.049-0.0060.305
0.081-0.213-0.0650.121-0.323-0.162

0.042
0.179

-0.127-0.272

0.108
0.000
0.027

0.255

-0.248-0.101-0.0270.153
0.161-0.152-0.1040.276-0.075-0.1520.862-0.061-0.109-0.2770.182
0.068-0.0400.166
0.463-0.070-0.421-0.007-0.016

0.040
0.061
0.015

0.630
0.551

-0.334-0.5700.280-0.0470.106-0.3980.002-0.009-0.0130.002
0.026-0.3580.036
0.128-0.185-0.017
0.002

0.319
0.140
0.086
-0.023-0.1130.007-0.1470.039-0.0290.481-0.037-0.0480.037-0.3310.164-0.305-0.109
0.020
0.327

0.279
0.101
-0.094-0.0030.039-0.268-0.037-0.1080.009-0.095-0.102-0.088-0.046-0.366

MULTI LNMAT LNPROC RATING-0.153
0.374
0.298
0.002

-0.2180.023
0.011-0.0230.315-0.331
0.203
0.365

0.252
-0.033-0.0650.020-0.006-0.028-0.011-0.0130.069-0.046-0.048-0.053-0.3120.152-0.0450.037-0.117-0.132-0.0020.127-0.0160.025-0.097-0.021
0.044-0.0380.074
0.341-0.031

0.051
0.064
0.199
-0.027-0.116
0.127
0.018

0.123
0.147
0.131
-0.1200.592-0.0740.135
0.218-0.7100.155
0.130-0.0560.604-0.5010.454
0.265-0.1480.061-0.022-0.043-0.295-0.422
0.304
0.249
0.051
0.050

0.169
0.055
0.019

-0.024
0.064

0.020

-0.0520.074-0.0530.059-0.042-0.0200.047-0.073-0.1050.130
0.252-0.019
0.000

0.215

-0.1210.150-0.0520.114

0.175-0.078

0.374
0.150

0.370
0.183

-0.138-0.245-0.102-0.0630.068-0.045-0.101-0.053-0.351-0.178

0.090
0.076

-0.036-0.180

0.211
0.049

0.051
-0.015

0.122

0.139

0.381

-0.0420.018-0.016-0.167-0.414-0.3170.050-0.042-0.044

0.119
0.071
0.050

-0.1560.320-0.153

Table 2: Correlation Matrix (cont’d)
SPREAD
OL
COL
AOL
LnMV
LEV
ROA
INTCOVCAPINT
STDOCF
STDRET
RDINT
MULTI
LnMAT
LnPROCRATINGRET

LTG

MGR_ABILITYFLUIDITYDISPSTDSALENOISE

EMPINT

PAYROLL-0.105-0.037-0.035
0.029
0.008
0.013

-0.012-0.205
0.113
0.122
0.020

0.160
0.275
0.264

0.228
-0.034-0.034-0.0220.098-0.368-0.337-0.055-0.114-0.0980.045-0.087-0.1160.064-0.2020.182-0.2260.084
0.010-0.0090.000-0.031-0.023

0.049

0.254
0.016
-0.007-0.0140.013-0.0260.010-0.0200.013-0.0340.101-0.033-0.373-0.1590.065-0.0490.042-0.074-0.077

RET

0.064
0.025
0.059
-0.021-0.011-0.1420.090-0.1420.057-0.0950.095-0.2680.082-0.0660.199-0.103

0.067
0.080

-0.111
0.075-0.048-0.061

0.002-0.065

0.228
0.085

0.240
0.213

0.075
0.132

-0.084-0.0350.216
0.066-0.0340.234-0.041-0.086-0.044
0.128

0.101
0.303
-0.019-0.048-0.0490.010-0.007-0.190-0.021-0.033-0.0220.057-0.0410.018-0.180-0.111-0.013-0.181
0.062
0.032

0.096
-0.136-0.025-0.080

0.136

0.054

0.193
-0.161

0.055-0.0720.029-0.053-0.002-0.203-0.0050.109-0.237-0.056
0.091

0.013

-0.0040.032-0.1400.078

0.046-0.044-0.041
0.132
0.010

0.290

-0.1030.097-0.186
0.222

0.022

0.085
0.253
-0.1530.009

LTG
MGR_ABILITY

0.109-0.023

0.040
0.014

0.028

FLUIDITY
DISP-0.027

0.038

0.010

-0.126-0.134-0.0600.208

STDSALE
NOISE

0.060
-0.081

0.061
0.021

0.149
0.065

-0.110-0.083

0.126
0.118

0.066

EMPINT
PAYROLL

0.067-0.018

0.242
0.175

-0.142-0.197-0.3440.044-0.0670.214

0.057-0.102

0.117
0.024

0.175
0.151

-0.1180.105-0.1080.021-0.2940.075
0.081-0.103-0.0690.010

0.233-0.053-0.021-0.2810.001-0.1320.017-0.009

0.056

0.165
0.496

Pearson correlations are reported above diagonal and Spearman correlations are below diagonal. Correlations coefficients in bold indicate two-tailed
statistical significance at 10% or lower level. All variables are defined in the Appendix.
36

Table 3: Operating leverage and bond yield spreads

OL
COL
AOL
LnMV
LEV
ROA
INTCOVCAPINT
STDOCF
STDRET
RDINT
MULTI
LnMAT
LnPROCRATING
Industry fixed effects
Year fixed effects
Adjusted-R2
Observations

Dependent variable: SPREAD(1)(2)
11.853(0.95)
24.452**(2.46)-2.246(-1.14)-10.721***-11.062***(-5.17)(-5.33)40.582**39.510**(2.63)(2.57)-1.538***-1.575***(-3.49)(-3.63)0.119
0.105(0.64)(0.56)-2.252-5.450(-0.20)(-0.50)3.325***
3.366***(5.32)(5.40)***
4.651
4.641***(8.61)(8.60)
0.429
0.291(0.57)(0.39)-39.308***-39.009***(-5.02)(-4.98)5.007*
5.110*(1.73)(1.77)
9.942***
10.119***(3.70)(3.78)-20.524***-20.491***(-21.09)(-21.08)
Yes
Yes
Yes
Yes
0.709
0.710
2,849
2,849

This table reports the results of regressing the at-issue yield spread (in basis points) on
operating leverage and a vector of firm- and bond-specific control variables, withindustry fixed effects and year fixed effects. The industry indicators are defined basedon the Fama and French (1997) 48-industry classification. The other variables aredeﬁned in the Appendix. All the continuous variables are winsorized at the top andbottom one-percentiles. The t-statistics are calculated using robust standard errors
corrected for heteroskedasticity and clustered at the firm level. ***, **, and * denotestatistical significance at the 1%, 5%, and 10% levels, respectively, in a two-tailed test.

37

Table 4: Firm fixed effects and difference-in-differences estimations
Dependent variable: SPREAD
Firm Fixed Effects Model
Difference-in-differences
(1)
(2)
(3)
(4)
COL

15.387**(1.98)
17.418**(2.10)

WDL-14.660(-0.95)4.001**(2.30)-3.735**(-2.13)

WDL*EMPINT
EMPINT
WDL*PAYROLLPAYROLL
AOL
LnMV
LEV
ROA
INTCOVCAPINT
STDOCF
STDRETRDINT
LnMAT
LnPROCRATING
Firm fixed effects
Year fixed effects
Adjusted-R2
Observations-7.820(-1.48)-1.464(-0.66)-34.294***(-8.38)-6.720(-0.30)-1.861***(-3.61)0.356(1.37)
40.659(1.55)
2.334***(2.80)
2.893***(4.70)-2.983*(-1.89)12.085***(4.04)
3.215(1.10)-15.806***(-10.19)
Yes
Yes
0.854
2,849

-0.360(-0.17)-31.048***(-7.72)-2.214(-0.10)-187.013***(-3.68)0.333(1.28)
51.745**(1.99)
250.237***(3.06)
285.403***(4.71)-3.240**(-2.09)12.051***(4.07)
4.127(1.44)-15.647***(-10.29)
Yes
Yes
0.861
2,849

-0.473(-0.22)-30.513***(-7.60)-0.244(-0.01)-169.589***(-3.26)0.304(1.18)
47.344*(1.81)
297.300***(3.58)
303.622***(5.03)-3.204**(-2.08)11.446***(3.88)
3.534(1.23)-14.900***(-9.77)
Yes
Yes
0.813
1,982

470.995**(2.27)-640.765**(-2.43)-6.748(-0.20)-75.069***(-4.33)-20.836(-0.29)-63.955(-0.41)-0.056(-0.08)10.470(-0.15)-429.074(-1.21)135.466(0.67)-2.372(-0.47)20.146***(2.62)-4.443(-0.59)-10.483*(-1.94)
Yes
Yes
0.793
597

Column 1 of this table reports the firm fixed effects estimation results of regressing
the at-issue yield spread (in basis points) on operating leverage and a vector of firmand bond-specific control variables. Columns 2 and 3 of the table reports the resultof the difference-in-differences estimation. Other variables are deﬁned in the
Appendix. All the continuous variables are winsorized at the top and bottom one38

percentiles. The t-statistics are calculated using robust standard errors corrected forheteroskedasticity and clustered at the firm level. ***, **, and * denote statisticalsignificance at the 1%, 5%, and 10% levels, respectively, in a two-tailed test.

39

Table 5: Pricing noise and the effect of operating leverage on bond yield spreads
Dependent variable: SPREAD
COL

Coefficient
51.519***t-value
3.10

COL*NOISE-9.686**

-2.27

NOISE
AOL
AOL*NOISE
LnMV
LEV
ROA
INTCOVCAPINT
STDOCF
STDRET
RDINT
MULTI
LnMAT
LnPROCRATING

16.177***-4.8540.810-12.590***
36.539**-1.569***0.078-4.7013.519***
4.583***
0.197-38.484***5.545***
8.718***-20.257***

9.09
-1.29
0.82
-6.34
2.49
-3.76
0.44
-0.45
5.87
8.86
0.28
-5.16
2.01
3.41
-21.79Industry fixed effects
Year fixed effects
Adjusted-R2
Observations

Yes
Yes
0.730
2,669

This table reports the results of regressing the at-issue yield spread (in basis points) on
operating leverage and a vector of firm- and bond-specific control variables, with theinclusion of the interaction term of operating leverage with pricing error. NOISE is measured
as the aggregate root mean squared error (in basis points) of the deviation of the market yield
on a bond at a given point in time from the yield predicted by a model of the yield curve
applied to all bonds at the same point in time (see Hu, Pan, and Wang (2013) for details).
Industry fixed effects and year fixed effects are included. The industry indicators are defined
based on the Fama and French (1997) 48-industry classification. The other variables aredeﬁned in the Appendix. All the continuous variables are winsorized at the top and bottom
one-percentiles. The t-statistics are calculated using robust standard errors corrected forheteroskedasticity and clustered at the firm level. ***, **, and * denote statisticalsignificance at the 1%, 5%, and 10% levels, respectively, in a two-tailed test.

40

Table 6: Analyst forecast dispersion, sales volatility, and the effect of operating
leverage on bond yield spreads

COL
COL*DISP
DISP
AOL
AOL*DISPCOL*STDSALESTDSALEAOL*STDSALE
LnMV
LEV
ROA
INTCOVCAPINT
STDOCF
STDRET
RDINT
MULTI
LnMAT
LnPROCRATING
Industry fixed effects
Year fixed effects
Adjusted-R2
Observations

Dependent variable: SPREAD(1)(2)
15.089-6.797(1.26)(-0.46)74.824**(2.58)
4.864*(0.60)-2.010-2.551(-0.88)(-0.91)-3.250(-0.47)8.560**(2.34)-2.236***(-3.05)4.470(0.12)-10.378***-11.377***(-4.43)(-5.48)36.246**41.928***(2.05)(2.73)-1.462***-1.537***(-2.98)(-3.53)0.083
0.129(0.41)(0.69)-4.951-7.120(-0.39)(-0.64)2.866***
3.321***(4.08)(5.26)***
4.540
4.667***(7.34)(8.66)
0.131
0.374(0.16)(0.50)-40.316***-37.537***(-4.33)(-3.80)4.790
5.451*(1.47)(1.88)
8.661***
10.216***(2.89)(3.81)-20.251***-20.685***(-18.06)(-21.27)
Yes
Yes
Yes
Yes
0.724
0.701
1,691
2,849

This table reports the results of regressing the at-issue yield spread (in basis points) on
operating leverage and a vector of firm- and bond-specific control variables, with the41

inclusion of the interaction term of operating leverage with equity-based compensationand the volatility of sales. Industry fixed effects and year fixed effects are included. The
industry indicators are defined based on the Fama and French (1997) 48-industry
classification. The other variables are deﬁned in the Appendix. All the continuous
variables are winsorized at the top and bottom one-percentiles. The t-statistics arecalculated using robust standard errors corrected for heteroskedasticity and clustered at
the firm level. ***, **, and * denote statistical significance at the 1%, 5%, and 10%
levels, respectively, in a two-tailed test.

42

Table 7: Issuer’s future prospects and the effect of operating leverage on bondyield spreads

COL
COL*RETRETAOLAOL*RETCOL*LTGLTG
AOL*LTG
LnMV
LEV
ROA
INTCOVCAPINT
STDOCF
STDRET
RDINT
MULTI
LnMAT
LnPROCRATING
Industry fixed effects
Year fixed effects
Adjusted-R2
Observations

Dependent variable: SPREAD(1)(2)
29.845***65.549***(2.82)(2.74)-47.643*(-1.93)-16.591*(-1.78)-2.4075.425(-1.15)(0.94)
1.647(0.31)-3.664**(-2.00)0.519(0.70)-0.634(-1.46)-9.255***-8.781***(-4.41)(-4.22)37.634**37.059**(2.46)(2.37)-1.289***-1.298***(-2.95)(-2.99)0.082
0.127(0.44)(0.69)-5.699-9.714(-0.52)(-0.88)3.282***
3.036***(5.28)(4.81)***
4.716
4.960***(8.76)(8.86)
0.288
0.315(0.37)(0.29)-39.818***-31.299***(-5.11)(-3.79)5.750**
5.878**(1.99)(2.06)
9.127***
8.326***(3.41)(3.13)-21.416***-20.673***(-21.71)(-21.08)
Yes
Yes
Yes
Yes
0.714
0.699
2,849
2,664

This table reports the results of regressing the at-issue yield spread (in basis points) on
operating leverage and a vector of firm- and bond-specific control variables, with the43inclusion of the interaction term of operating leverage with firm future prospects. Industry
fixed effects and year fixed effects are included. The industry indicators are defined basedon the Fama and French (1997) 48-industry classification. The other variables are deﬁned in
the Appendix. All the continuous variables are winsorized at the top and bottom onepercentiles. The t-statistics are calculated using robust standard errors corrected forheteroskedasticity and clustered at the firm level. ***, **, and * denote statisticalsignificance at the 1%, 5%, and 10% levels, respectively, in a two-tailed test.

44

Table 8: Issuer’s operation efficiency and the effect of operating leverage on
bond yield spreads

COL
COL*MGR_ABILITY
MGR_ABILITYAOL
AOL*MGR_ABILITYCOL*FLUIDITYFLUIDITYAOL*FLUIDITY
LnMV
LEV
ROA
INTCOVCAPINT
STDOCF
STDRET
RDINT
MULTI
LnMAT
LnPROCRATING
Industry fixed effects
Year fixed effects
Adjusted-R2
Observations

Dependent variable: SPREAD(1)(2)
68.996***83.960***(3.26)(3.49)-66.344**(-2.35)8.688(0.91)-4.2212.393(-1.04)(0.53)
3.103(0.54)-9.341***(-3.03)5.359***(3.99)-0.472(-0.70)-11.374***-13.507***(-5.50)(-4.65)31.900**34.411(2.10)(1.63)-1.565***-1.581***(-3.59)(-2.76)0.173
0.130(0.93)(0.57)-5.374-10.883(-0.49)(-0.68)***3.651
3.513***(5.76)(4.63)***
4.776
4.626***(8.93)(6.63)
0.523-0.343(0.70)(-0.36)-28.680***-19.839*(-3.68)(-1.72)5.511*
3.670(1.93)(0.88)
10.874***13.385***(4.14)(3.50)-20.240***-22.358***(-20.99)(-16.37)
Yes
Yes
Yes
Yes
0.719
0.713
2,737
1,674

This table reports the results of regressing the at-issue yield spread (in basis points) on
operating leverage and a vector of firm- and bond-specific control variables, with the45

inclusion of the interaction term of operating leverage with firm operational efficiency.
Industry fixed effects and year fixed effects are included. The industry indicators aredefined based on the Fama and French (1997) 48-industry classification. The othervariables are deﬁned in the Appendix. All the continuous variables are winsorized at the
top and bottom one-percentiles. The t-statistics are calculated using robust standard errors
corrected for heteroskedasticity and clustered at the firm level. ***, **, and * denotestatistical significance at the 1%, 5%, and 10% levels, respectively, in a two-tailed test.

46