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Journal of Business Finance & Accounting
Journal of Business Finance & Accounting, 37(5) & (6), 538–559, June/July 2010, 0306-686X
doi: 10.1111/j.1468-5957.2010.02194.x
Corporate Debt Financing and Earnings
Quality
Aloke (Al) Ghosh and Doocheol Moon∗
Abstract: Our study establishes linkages between two extensively researched areas, debt financing and the quality of earnings. Debt can have a ‘positive influence’on earnings quality because managers are likely to use their accounting discretion to provide private information about the firms’ future prospects to lower financing costs. For high debt, it can also have a
‘negative influence’ on earnings quality as managers use accruals aggressively to manage earnings to avoid covenant violations. Using accruals quality as a proxy for earnings quality, we document a non-monotonic (cur vilinear) relation between debt and earnings quality. The relationship is positive at low levels of debt and negative at high debt levels with an inflection point around
41%. Our results suggest that firms that rely heavily on debt financing might be willing to bear higher costs of borrowing from lower earnings quality because the benefits from avoiding potential debt covenant violations exceed the higher borrowing costs.
Keywords: debt financing, earnings quality, accruals quality
1. INTRODUCTION
Our study establishes linkages between two extensively researched areas, corporate debt financing and earnings quality, where earnings quality refers to the ability of earnings to predict future cash flows. Although few studies, if any, empirically examine whether debt financing is associated with earnings quality, some researchers often presume that such a relationship exists. For instance, Pope (2003, p. 281) claims that:
the balance between debt and equity financing will produce demands for accounting information and may explain differences in disclosure patterns.
Similarly, O’Brien (1998, p. 1253) posits that:
if financial reporting exists to ser ve the needs of external capital providers, then we should expect differences in accounting to coincide with differences in the arrangements for providing capital.
∗The first author is from Stan Ross Department of Accountancy, Baruch College, The City University of New York. The second author is Associate Professor of Accounting, School of Business, Yonsei University, Seoul, Korea. They are greatly indebted to Peter Joos, Darius Miller, Steve Young, and an anonymous referee for their numerous comments and suggestions that improved our thinking on this topic. They also thank Val Dimitrov, John Elliott, Larr y Harris, Prem Jain, Bill Ruland, Jonathan Sokobin and the participants at the
2006 Annual AAA Meetings and 2006 FMA Meetings for their comments. (Paper received December 2008, revised version accepted December 2009, Online publication Februar y 2010)
Address for correspondence: Aloke (Al) Ghosh, Stan Ross Department of Accountancy, Baruch College, The City University of New York, Box B12-225, One Bernard Baruch Way, New York, NY 10010, USA. e-mail: Aloke.Ghosh@baruch.cuny.edu
 C 2010 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK
and 350 Main Street, Malden, MA 02148, USA. 538
Earnings are often considered better predictors of future cash flows than current cash flows because accounting accruals, a key component of earnings (the other being cash flows), are informative about future cash flows. However, accruals may also ser ve as noisy predictors of future cash flows because of manipulation and biases. Because debt affects managerial incentives and reporting choices, the linkages between debt and earnings quality depend on accruals quality. A key contribution of our study is that we posit a non-linear relationship between debt and earnings quality; earnings quality first increases and then declines with debt. Our tests are based on accruals quality which is used as a basis for drawing inferences on earnings quality.
One contracting view suggests a positive association between debt and the quality of reported earnings. Debt holders demand higher quality information, especially earnings, to assess the continued creditworthiness of borrowers (Grossman and Hart,
1982; and Jensen, 1986). When earnings predict future cash flows more accurately, creditors have lower risk because they can estimate solvency risk, liquidity risk and bankruptcy risk more precisely. Debt also bonds management to pre-commit to high quality information because of lower borrowing costs (Diamond, 1991). Since debt reduces various agency conflicts (Jensen, 1986; and Stulz, 1990), managers have few reasons to mask economic performance using their accounting discretion. Thus, debt has a ‘positive influence’ on earnings quality through its effect on accruals, and earnings are better predictors of future cash flows, because (1) accruals are less prone to managerial manipulations, and (2) managers acting in the interests of debt holders can use their accounting discretion to provide private information about the future prospects of the firm thereby lowering the cost of borrowing (Feltham et al., 2007).
A contrasting viewpoint is that debt has a ‘negative influence’ on earnings quality. When debt is relatively high, managers have strong incentives to make accounting choices and reporting decisions that reduce the likelihood of possible debt covenant violations (Watts and Zimmerman, 1986).1 Opportunistic managers are more likely to use their financial reporting discretion because (1) financial leverage frequently ser ves as a proxy for closeness to accounting-based covenant violations (Billett et al., 2007; Dichev and Skinner, 2002; Press and Weintrop, 1990; and Smith, 1993), and (2) the cost of violating debt covenants is large (Beneish and Press, 1993). Therefore, when debt is high, accounting numbers may not represent faithfully the underlying future economic performance because of the aggressive use of accruals to manage earnings in an effort to avoid covenant violations (Sweeney, 1994; and DeFond and Jiambalvo,
1994). One implication is that accruals are noisy predictors of future performance, which suggests a negative relationship between debt and earnings quality.
The distinctive dual role of debt suggests that the interactions of the positive and negative influence of debt ultimately determine earnings quality. For low debt, firms have incentives to reduce the cost of debt by reporting high quality earnings. Concurrently, firms are less likely to manage earnings because the risk of a covenant breach is either low or non-existent. Therefore, for low debt, debt and earnings quality are positively associated because the positive influence of debt dominates the negative influence. In sharp contrast, for high debt, debt and earnings quality are negatively associated because the negative influence dominates the positive influence. Because
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GHOSH
 
AND
 
MOON
1 Corporate debt typically includes financial covenants that restrict the borrowers’ activities. The principal purpose of financial covenants is to manage the conflicts of interests between lenders and borrowers (Smith and Warner, 1979).
 
C
 
 
2010
 
Blackwell
 
Publishing
 
Ltd.
the risk of breaching a covenant is large for highly leveraged firms, earnings are prone to being manipulated to avoid covenant violations. Thus, when the costs of violating covenants are sufficiently large, managers are willing to forego lower borrowing costs from reporting high quality to avoid even costlier covenant violations.
Our proxy forearnings quality is based on the measure developed by Dechow and Dichev (2002), McNichols (2002) and Francis et al. (2005). These studies determine accruals quality based on the mapping between current period working capital accruals and operating cash flows in the prior, current, and future periods while controlling for changes in revenues, and property, plant and equipment. Accruals, and therefore earnings, quality is negatively related to the extent that working capital accruals do not map into the explanator y variables because of estimation errors and/or managerial opportunism.
The contracting literature suggests that private debt is more effective as a monitoring device than public debt and that private debt has more restrictive covenants (Smith,
1993; Smith and Warner, 1979; and Bharath et al., 2008). Therefore, we restrict our sample to firms with private debt only. Based on a large sample of US nonfinancial firms for the period 1992 to 2004, our preliminar y evidence is consistent with a non-monotonic (cur vilinear) relationship between earnings quality and private debt financing. Partitioning the sample into quintiles based on debt (total debt to total assets), we find that earnings quality improves monotonically for the first four quintiles and then declines for the fifth quintile.
In the multivariate regressions, we use a non-linear specification that includes debt and debt squared in the same regression. After controlling for other determinants of earnings quality such as length of operating cycle, firm size, standard deviation of sales, standard deviation of operating cash flows, frequency of negative earnings, the cost of debt, the probability of default, market to book, and R&D investments, we find that the coefficient on debt is negative while the coefficient on the debt squared term is positive with an inflection point around 41%. Our results are ver y similar when we use an alternative spline functional form with breakpoints of 40% or 45% to model the non-linear relation.
The results suggest that for the most part (almost 80% of the sample), earnings quality improves with higher private debt levels. However, once debt levels are high, earnings quality declines with higher debt. Private creditors have stringent and detailed covenants when debt is high. Because the likelihood of covenant violation is large at high debt levels, managers’ immediate concern is to avoid costly debt covenant violations rather than report earnings that are more informative about future cash flows.
We conduct additional analyses to assess the robustness of our results. First, to address concerns that earnings quality and debt are simultaneously determined, we use a two- stage least squares model to jointly estimate earnings quality and debt. Second, instead of only relying on a balance sheet measure of debt, we also consider interest expense deflated by sales as an alternative measure of leverage based on the income statement. Another advantage of analyzing interest coverage ratio is that this measure is often used to construct covenants. Finally, we also use debt to equity ratio as an alternative balance sheet measure of debt. Our results are robust to these supplementar y tests.
In a related analytical study, Feltham et al. (2007) suggest that accounting precision (or quality) improves with debt when debt is high or when firms are close to violating debt covenants. However, debt has little effect on accounting quality when firm value is either ver y high or ver y low relative to debt covenant thresholds. While our study and
the Feltham et al. (2007) study are related, there are key differences. First, they examine the conflicts between equity holders and managers, while we concentrate on conflicts between debt holders and managers. Second, we draw inferences about the relationship between earnings quality and debt based on contracting reasons, while Feltham et al. (2007) derive inferences based on the considerations of equity holders. Third, our nonlinear prediction is the outcome of debt- and equity-holder considerations. At low debt levels, managers are able to reduce the cost of debt financing when earnings are more informative about future cash flows. As residual owners, equity holders also benefit from lower debt costs. At high debt levels, managers’ immediate concern is to avoid costly debt covenant violations even though their actions might destroy equity values because earnings are less informative.
Our research provides an interesting insight. Several studies emphasize the role of earnings quality in reducing the cost of external financing (e.g., Easley and O’Hara,
2004; Francis et al., 2005; and Diamond, 1991). However, our results suggest that firms that rely heavily on debt financing might be willing to bear higher costs of borrowing from lower earnings quality because the benefits from avoiding potential debt covenant violations exceed the higher costs of borrowing.
The rest of the paper is organized as follows. Section 2 links debt and earnings quality. Section 3 outlines the research design, and Section 4 reports the sample description. Section 5 discusses the empirical results, and Section 6 concludes.
2. LINKS BETWEEN DEBT FINANCING AND EARNINGS QUALITY
(i) Positive Influence of Debt on Earnings Quality
In large, diffusely held public corporations, managers have incentives to expropriate wealth from shareholders and bondholders (Jensen and Meckling, 1976). Atomistic shareholders have few reasons to monitor managerial actions because the costs of monitoring are high while the benefits are low. In contrast, private debt holders have the incentives to monitor and limit potential managerial wealth expropriation. In order to be willing to risk capital, private lenders including commercial banks require the ability to continuously monitor client firms throughout the maturity period and demand high quality information because of the need to assess the risk of their loans (Slovin et al.,
1990). In markets with limited capital, firms also have incentives to supply high quality information to reduce the cost of borrowing (Diamond, 1991).
In a related study, Grossman and Hart (1982) consider debt as an example of a
‘precommitment’ or ‘bonding’ device. Debt bonds managers to act in the interest of shareholders because of the desire to avoid bankruptcy, which in turn increases market value. Grossman and Hart (1982) offer three reasons why self-interested managers have incentives to issue debt to increase firm value. First, managers’ salaries are often dependent on firm value through incentive schemes. Second, the probability of a takeover is low for firms with high market value because acquiring firms have to pay more. A third reason is that it is easier to raise capital for managers when firm value is high, which increases the opportunities for perquisite consumption.
Similarly, Jensen (1986) views debt as a disciplinar y instrument. Because contractual debt payments absorb free cash flows and reduce internal cash flows available for unprofitable investments, managers are unable to invest excess cash in negative net present value projects.
Collectively these arguments suggest that debt has a positive influence on earnings quality. Other things remaining constant, debt holders have lower credit risk when firms report earnings that are more informative about future economic performance. Managers have influence over earnings because of their discretion over accruals which are based on accounting choices, assumptions, and estimates. Managers acting in the interest of debt holders and equity holders have incentives to use their accounting discretion to report more informative earnings to reduce the firms’ cost of borrowing. As residualclaimants, equity holders benefit when firms lower their financing costs. Thus, earnings quality improves with debt because accruals are more informative about future cash flows.
Both the bonding and the agency arguments suggest that the incentives to report high quality earnings are stronger for private debt than for public debt because private lenders are better as monitoring and bonding agents. Managers are less likely to use their accounting discretion to mislead stakeholders about the economic value of the firm when firms have private debt; rather, they are more likely to devote their energy towards maximizing firm value (Warfield et al., 1995).
(ii) Negative Influence of Debt on Earnings Quality
Debt can also have a negative influence on earnings quality. Because of various agency conflicts between managers and bondholders, debt holders resort to contractual arrangements, many of which are based on financial accounting ratios, to reduce expropriation of wealth by managers (Watts and Zimmerman, 1986). Bond covenants are contractual arrangements that protect the lender and restrict the actions of the borrower.
Bondholders are likely to rely more heavily on the use of covenants as debt increases to mitigate agency conflicts. Since the cost of default is high (e.g., Beneish and Press,
1993; and Chen and Wei, 1993),2 opportunistic managers have incentives to use accounting methods that reduce the likelihood of debt covenant violations (Dichev and Skinner, 2002; and Beatty and Weber, 2003). Managerial opportunism is expected to increase with financial leverage because prior research provides evidence that leverage is associated with closeness to debt constraints on earnings, retained earnings, leverage, tangible net worth and working capital (Billett et al., 2007; Press and Weintrop, 1990; Duke and Hunt, 1990; and Christie and Leftwich, 1990).
Under the debt covenant perspective, the extent of accounting manipulation is expected to increase with debt as firms tr y to avoid potential covenant violations.3
Further, prior studies document that loan covenants are much more stringent in private debt than in public debt (Smith, 1993; and Smith and Warner, 1979). Low renegotiation costs provide private lenders with incentives to write detailed and tailor-made contracts and stringent covenants (Bharath et al., 2008). Thus, firms with private debt are more
2 Beneish and Press (1993) find that borrowing terms change for 48 of the 91 firms reporting technical default in their sample. Interest rates increase for 31 of the 48 firms, decrease for 3 firms, and remain unchanged for 14 firms. The mean change in interest costs is large, nearly 1% of the market value of equity. Chen and Wei (1993) also report that violations generally result in serious consequences rather than waivers.
3 Lenders could respond to covenant violations in a number of ways: (1) terminate lending agreements, (2) demand immediate repayment, (3) increase collateral, (4) increase interest rates, (5) impose additional debt covenants, and (6) waive the violation (Dichev and Skinner, 2002; and Gopalakrishnan and Parkash,
1995).
likely to use accounting choices to avoid covenant violations when debt is high (Dichev and Skinner, 2002).
A key implication is that the quality of reported numbers is low when private debt is high because managers make accounting choices that do not reflect the firms’ underlying economic performance. A higher level of managerial inter vention with respect to accounting choices, whether it is income-increasing or income-decreasing, erodes accruals, and earnings, quality because accruals are noisy predictors of future cash flows.4 Hence, when debt is high, the association between the quality of reported earnings and debt is negative.
(iii) Relationship Between Debt Financing and Earnings Quality
The two conflicting perspectives of debt suggest that the relationship between debt financing and earnings quality is ultimately determined by the interactions of the positive and negative influence of debt. For low debt, firms are expected to have fewer and less restrictive debt covenants which diminish the risk of a covenant breach. Therefore, managers are less likely to manipulate and report low quality earnings when the risk of breaching a covenant is low or non-existent. On the other hand, firms have strong incentives to reduce the cost of debt by reporting high quality earnings. Therefore, for low debt, debt and earnings quality are positively associated because the positive influence of debt outweighs the negative influence.
In contrast, when firms have substantial debt, debt and earnings quality are negatively associated because the negative influence of debt dominates the positive influence. Although firms continue to have incentives to report high quality earnings to reduce the cost of debt when debt is high, firms also have high risk of violating covenants. Because the cost of breaching a debt covenant is large (e.g., higher borrowing costs, immediate repayment of principal, reputation costs), managers are expected to use their accounting discretion to avoid breaching such covenants. Thus, managers may be willing to forgo the benefits of high earnings quality because the potential costs of violating covenants are even larger. Thus, at sufficiently high debt levels, the negative influence of debt is expected to be the dominant factor.
Accordingly, we hypothesize that earnings quality first increases and then declines with increasing debt levels. Because there is no theor y guiding us on the inflection point, we let the data inform us on the debt level when the negative influence of debt outweighs the positive influence.
3. RESEARCH DESIGN
(i) Construct for Earnings Quality
Accounting accruals, a cornerstone of financial reporting, recognize revenues earned and expenses incurred regardless of whether cash is exchanged contemporaneously (Wild et al., 2007). The Financial Accounting Standards Board (FASB) states that earnings based on accrual accounting provide a better indication of a firm’s ability
4 Some examples of managerial judgments with regard to accounting decisions and choices include estimates for uncollectible receivables, useful lives of assets, future healthcare benefits, write-offs, restructuring charges, and inventor y methods choices.
to generate future cash flows than a measure based on cash receipts and payments
(SFAC No. 1; and FASB 1978).
While cash flows are more reliable, earnings are more informative about future cash flows or more relevant for capital market participants (e.g., Dechow et al., 1998; and Dechow, 1994).5 Because accruals involve accounting choices and judgments, managers have more flexibility in manipulating earnings through accruals than using cash flows. Consequently, the quality of earnings is expected to var y through the use of accruals depending on the incentives and the nature of the business contracts.
The accruals quality metric developed by Dechow and Dichev (2002) with the modifications suggested by McNichols (2002) is frequently used as a measure for earnings quality. For instance, Francis et al. (2005) use this modified version of the Dechow and Dichev (2002) model to examine the relation between earnings quality and the cost of debt. In this framework, working capital accruals (Accruals) are regressed
on operating cash flows (Cash flow) in the current (t ), prior (t −1), and future (t+1)
periods, changes in revenues ( Revenue ), and property, plant and equipment (Fixed
assets) as follows:
Accrualst = β0 + β1 Cash flowt −1 + β2 Cash flowt + β3 Cash flowt +1
+ β4 Revenue t + β5 Fixed assetst + εt(1)
where Accruals is measured as Accounts receivable + Inventor y − Accounts payable − Taxes payable + Other assets (net). All the variables in equation (1)
are scaled by average total assets.
Equation (1) is estimated each year and for each industr y, where industr y is defined using Fama and French (1997) 48 industr y groups with at least 20 firms in each year. Annual cross-sectional estimations of equation (1) yield year- and firm-specific residuals (ε). Residuals for a firm in any given year are the standard deviation of its residuals (ε)
from equation (1) computed over five years from years t −4 to t . Larger Residuals indicate
poorer earnings quality.6
The earnings quality measure estimated from equation (1) is ideally suited for our tests because our objective is to capture the degree of the mapping of accruals on future cash flows while controlling for growth because growth is associated with accruals and debt financing. Changes in revenues and fixed assets are proxies for growth in the Dechow and Dichev (2002) specification.
(ii) Debt Financing and Earnings Quality
We estimate the relationship between earnings quality and debt financing using the following regression:
Residuals = β0 + β1 Debt + β2 Debt 2 + β3 Operating cycle + β4 Size + β5 Sales
σ
+ β6 C as h flowσ + β7 Losses + β8 Cost of debt + β9 Z -Score
+ β10 Growth + β11 R& D + ε (2)
5 The quality of earnings is often considered to be higher than the quality of cash flows because accruals result in earnings being more persistent, less volatile, more strongly associated with future cash flows, and more strongly associated with current stock price than cash flows.
6 Consistent with prior studies (e.g., Francis et al., 2005), we winsorize the extreme values of the Residuals
distribution to the 1 and 99 percentiles.
where Debt is defined as the ratio of total (long-term+short-term) debt to total assets;
Operating cycle is the logarithmic transformation of the sum of days accounts receivable
and days inventor y outstanding; Size is the logarithmic transformation of the average of the beginning and ending total assets; Salesσ and Cash flowσ are the standard deviation of sales and operating cash flows, respectively; Losses are the proportion of firm-years
with negative earnings from years t − 4 to t ; Cost of debt is interest expense deflated by
average total debt; Z-Score is Altman’s Z-score; Growth is the sum of the market value of
equity and the book values of preferred stock and debt scaled by the book value of total assets; and R&D is research and development expenditures scaled by the book value of total assets.7
Since Residuals are computed using a five-year rolling window from years t − 4 to t ,
we use the same time period to compute the independent variables. Therefore, Debt ,
Operating cycle, Size , Cost of debt , Z-Score , Growth and R&D are measured as the average of the five annual values for years t − 4 to t . The two standard deviation variables (Salesσ
and Cash flowσ ) and Losses are also computed using five annual obser vations over the same time period.
As in Dechow and Dichev (2002) and Francis et al. (2005), we include operating cycle, firm size, volatility of sales and cash flow, and proportion of losses to capture the innate or non-discretionar y components of earnings quality. The innate factors capture the influence of operating environment and business model on earnings quality, which is different from the discretionar y component. We also control for cost of debt and Altman’s Z-score because debt might be correlated with the cost of debt financing and financial distress (Francis et al., 2005). Further, we include market to book ratio and R&D expenditures to proxy for growth opportunities which affect both the accrual generating process and debt.
4. DATA AND SUMMARY STATISTICS
Our sampling frame consists of all firms covered on the 2006 Compustat (active and inactive). Cash flow and variables to compute Accruals are from the cash flow statement and they are available from 1987. Because Residuals are constructed based on five-
annual residuals (t − 4 to t ) estimated from equation (1), which includes lead and
lag Cash flow, the sample is restricted to firms with at least seven consecutive years of
accounting data (we include firm-year obser vations with five-year lag and one-year lead data).8 Therefore, the first year we examine the effect of debt on earnings quality is
1992. The 2006 Compustat covers accounting data up to 2005 and thus the final year included in the sample is 2004. Data on debt and other firm characteristics are also obtained from Compustat files. We confine our sample to firms with private debt and also exclude firms in the financial sector (6000s SICs) and the public sector (9000s
7 Days accounts receivable is 360/(Sales/Average accounts receivable), days inventor y outstanding is
360/(Cost of goods sold/Average inventor y), and Z-score is estimated as 1.2×(Working capital/Total assets) + 1.4×(Retained earnings/Total assets) + 3.3×(EBIT/Total assets) + 0.6×(Market value of equity/Total liabilities) + (Sales/Total assets).
8 This restriction is likely to introduce a sur vivorship bias in our sample with firms being larger and more
successful than the population. However, as Francis et al. (2005) conclude, this restriction reduces the variation in Residuals thereby introducing a downward bias.
SICs). Similar to Faulkender and Petersen (2006), firms without available S&P debt ratings are considered as having private debt.9
Consistent with prior research, we winsorize the top and bottom one percent of obser vations for the variables included in the regression analyses with the exception of Operating cycle , Size and Losses to remove the influence of outlier obser vations.10
This sample selection procedure results in 8,240 firm-year obser vations used to test the relationship between earnings quality and debt over a thirteen-year period from 1992 to 2004.
Table 1 reports descriptive statistics on Residuals and other firm characteristics. Some of the variables (such as Debt , Operating cycle , Size , Cost of debt , Z-Score , Growth and R&D) are first averaged using a five-year rolling window to be consistent with Residuals and the rest of the variables. The mean (median) Residuals are 0.058 (0.045), which is similar to the findings in Francis et al. (2005). Although Dechow and Dichev (2002) also provide descriptive statistics for Residuals, our results are comparable to Francis et al. (2005) because we follow their procedure to estimate Residuals. Using 91,280 firm-year obser vations extending over 32-years from 1970 to 2001, Francis et al. (2005) report a mean (median) Residuals of 0.044 (0.031).
The mean total debt to total assets ratio is 0.248, with a standard deviation of 0.181. The mean (median) operating cycle transformed in logarithmic values is 4.865 (4.930). The mean and median of Size , Losses, and two volatility measures appear to be similar to those in Dechow and Dichev (2002) and Francis et al. (2005). A mean (median) cost of debt is 10.5% (8.8%). Firms in our sample tend to be financially healthy, with mean (median) Z-Score of 3.809 (3.336). The mean (median) market to book ratio is 1.953 (1.409), whereas the mean (median) R&D investments relative to total assets are 4.7% (0.5%).
Table 2 provides Pearson correlations for variables in equation (2). The variable with the highest correlation with Debt is Z-Score −the correlation is −0.50. Among the
other variables, Cash flowσ has high correlations with Size , Salesσ , Losses, Growth and R&D. These correlations are −0.47, 0.48, 0.55, 0.48 and 0.46, respectively. Correlations acrossmost other variables are relatively low. Overall, multicollinearity does not seem to be severe.11
5. RESULTS
Petersen (2009) compares different methods of addressing the issue of cross-sectional and time-series dependence of the residuals for panel data, and shows that clustered standard errors from pooled regressions provide unbiased estimates of the true standard errors. Following his recommendation, we report pooled regression results
9 This sample selection procedure excludes firms with S&P debt ratings. There are substantial size differences between firms with and without S&P debt ratings. The median firm with S&P debt ratings has total assets of $2,016 million, while that without debt ratings has total assets of only $69 million. Although larger firms tend to be excluded from the sample, it is balanced against the closer monitoring and tighter covenants in private debt.
10 Because we use the logarithmic transformation of operating cycle and firm size, and Losses lies between
0 and 1, the outlier problems for these variables are not as severe as those for other variables.
11 Our multivariate findings are unlikely to be affected by multicollinearity. The condition indexes (Belsley et al., 1980) in the last two regressions in Table 4 are 24.51 and 28.78, which are lower than the cutoff of 30 suggested by Belsley et al. Further, when we exclude from the regressions Cash flowσ that is highly correlated with other explanator y variables, our results in Table 4 remain unchanged.
Descriptive Statistics
Variables Mean Standard Deviation Lower Quartile Median Upper Quartile
Earnings Quality
Residuals 0.058 0.044 0.027 0.045 0.074
Private Debt
Debt 0.248 0.181 0.110 0.218 0.346
Innate Factors
Operating cycle 4.865 0.659 4.563 4.930 5.275
Size 4.270 1.699 3.041 4.233 5.355
Salesσ 0.215 0.174 0.094 0.165 0.283
Cash flowσ 0.087 0.074 0.038 0.065 0.109
Losses 0.212 0.313 0.000 0.000 0.400
Cost of debt 0.105 0.071 0.072 0.088 0.113
Financial Health
Z-Score 3.809 4.334 2.058 3.336 5.055
Growth Opportunities
Growth 1.953 1.678 1.098 1.409 2.150
R&D 0.047 0.080 0.000 0.005 0.065
Notes:
The sample contains 8,240 firm-year obser vations over the periods 1992 to 2004 that do not have an S&P debt rating. Residuals are the standard deviation of the residuals from years t -4 to t from the regression of working capital accruals on operating cash flows in the prior, current, and future periods,
changes in revenues, and property, plant and equipment. Debt is the ratio of total (long-term+short-term)
debt to total assets. Operating cycle is the log of the sum of days accounts receivable and days inventor y
outstanding, where days accounts receivable is 360/(Sales/Average accounts receivable) and days inventor y outstanding is 360/(Cost of goods sold/Average inventor y). Size is the log of the average of the beginning and ending total assets. Salesσ and Cash flowσ are the standard deviation of sales and operating cash flows scaled by average total assets from years t -4 to t , respectively. Losses is the proportion of firm-years with negative earnings from years t -4 to t . Cost of debt is interest expense deflated by average total debt. Z-Score
is Altman’s Z-score, where Z-score is estimated as 1.2×(Working capital/Total assets) + 1.4×(Retained earnings/Total assets) + 3.3×(EBIT/Total assets) + 0.6×(Market value of equity/Total liabilities) +
(Sales/Total assets). Growth is the sum of the market value of equity and the book value of preferred stock
and debt scaled by the book value of total assets. R&D is research and development expenditures scaled by the book value of total assets. Debt , Operating cycle , Size , Cost of debt , Z-Score , Growth and R&D are the average of the five annual values for years t -4 to t .
with statistical significance based on Rogers (1993) clustered standard errors that account for within-firm and within-year correlations.
(i) Debt Financing and Earnings Quality
Table 3 provides preliminar y insights into the relationship between earnings quality and debt financing using Residuals as a proxy for earnings quality. The sample is sorted annually into five portfolios based on Debt . Debt 1 consists of firms with the lowest levels of debt (mean Debt is around 4%), while Debt 5 includes firms with the highest debt levels (mean Debt is around 53%). Panel A reports mean and median values for Residuals across debt quintiles.
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Our results in Panel A suggest a non-monotonic relation between earnings quality and debt financing; the mean and median Residuals first decline and then increase across increasing levels of debt. The mean Residuals monotonically decrease from 0.060 in Debt 1 to 0.051 in Debt 4. For higher levels of Debt , we find a reversal in pattern for Residuals; the mean Residuals increase to 0.063 for the fifth debt portfolio. The median
Pearson Correlation Matrix
1 2 3 4 5 6 7 8 9 10 11
1. Residuals 1.00
2. Debt 0.08 1.00
3. Operating cycle 0.18 −0.09 1.00
4. Size −0.47 −0.04 −0.14 1.00
5. Salesσ 0.46 0.10 −0.06 −0.37 1.00
6. Cash flowσ 0.64 0.04 0.16 −0.47 0.48 1.00
7. Losses 0.53 0.13 0.23 −0.45 0.33 0.55 1.00
8. Cost of debt 0.22 −0.13 0.07 −0.20 0.14 0.26 0.25 1.00
9. Z-Score −0.21 −0.50 0.01 0.15 −0.14 −0.16 −0.28 −0.01 1.00
10. Growth 0.36 −0.01 0.11 −0.23 0.23 0.48 0.41 0.16 0.25 1.00
11. R&D 0.37 −0.16 0.28 −0.25 0.14 0.46 0.47 0.18 −0.03 0.50 1.00
Notes:
This table provides Pearson correlation matrix for the sample. Residuals are the standard deviation of the residuals from years t −4 to t from the regression of working capital accruals on operating cash flows in
the prior, current, and future periods, changes in revenues, and property, plant and equipment. Debt is the ratio of total (long-term+short-term) debt to total assets. Operating cycle is the log of the sum of days accounts
receivable and days inventor y outstanding, where days accounts receivable is 360/(Sales/Average accounts receivable) and days inventor y outstanding is 360/(Cost of goods sold/Average inventor y). Size is the log of the average of the beginning and ending total assets. Salesσ and Cash flowσ are the standard deviation
of sales and operating cash flows scaled by average total assets from years t − 4 to t , respectively. Losses is the proportion of firm-years with negative earningsfrom years t − 4 to t . Cost of debt is interest expense deflated by average total debt. Z-Score is Altman’s Z-score, where Z-score is estimated as 1.2×(Working capital/Total assets) + 1.4×(Retained earnings/Total assets) + 3.3×(EBIT/Total assets) + 0.6×(Market value of equity/Total liabilities) + (Sales/Total assets). Growth is the sum of the market value of equity and
the book value of preferred stock and debt scaled by the book value of total assets. R&D is research and
development expenditures scaled by the book value of total assets. Debt , Operating cycle , Size , Cost of debt ,
Z-Score , Growth and R&D are the average of the five annual values for years t − 4 to t .
numbers also indicate a similar pattern. The median Residuals decrease from 0.049 in
Debt 1 to 0.037 in Debt 4, and then increase to 0.046 for Debt 5.
Panel B shows the differences in mean and median Residuals and their statistical significance between debt quintiles. The difference in mean (median) Residuals
between Debt 4 and Debt 1 is −0.009 (−0.012), which is statistically significant at the
1% level. The difference appears to be economically meaningful; the decline in mean
(median) Residuals is about 15 (24) percent of the mean (median) Residuals in Debt 1. The magnitude of the differences in mean and median Residuals between Debt 4 and Debt 5 is also large and economically significant; the difference in mean (median) Residuals is 0.012 (0.009) which is about 24 (24) percent of the number in Debt 4. Since Residuals are inversely related to earnings quality by construction, the univariate results in Table 3 suggest that earnings quality first improves and then declines with higher debt.
Figure 1 illustrates the non-linearity of the relationship between Residuals and Debt . It shows the mean and median Residuals across the debt quintiles. The upper line plots the mean Residuals and the bottom line plots the median Residuals. Since earnings quality is inversely related to Residuals, debt and earnings quality are positively associated between Debt 1 and Debt 4, which is consistent with the debt monitoring view. However, between Debt 4 and Debt 5, the two variables are negatively associated, which is consistent with the managerial opportunism perspective.
 Debt Financing and Earnings Quality (n = 8,240) 	
Debt Residuals
Debt Quintiles Mean Mean Median
Panel A: Residuals Across Debt Quintiles
Debt 1 (Low) 0.043 0.060 0.049
Debt 2 0.131 0.058 0.048
Debt 3 0.218 0.057 0.045
Debt 4 0.318 0.051 0.037
Debt 5 (High) 0.529 0.063 0.046
Panel B: Differences in Residuals
Debt 4 – Debt 1 −0.009 −0.012
(t -statistic; z-statistic) (−5.88)∗∗ (−8.33)∗∗
Debt 5 – Debt 4 0.012 0.009
(t -statistic; z-statistic) (6.54)∗∗ (5.46)∗∗
Notes:
The sample is sorted into five portfolios based on Debt from 1992 to 2004. Residuals are the standard deviation of the residuals from years t − 4 to t from the regression of working capital accruals on operating
cash flows in the prior, current, and future periods, changes in revenues, and property, plant and equipment.
Debt is a five-year average of the ratio of total (long-term+short-term) debt to total assets for years t − 4 to t .
∗∗ denotes statistical significance at the 0.01 level for a two-tailed test.
Figure 1
Debt Financing and Earnings Quality
0.07
0.06
0.05
0.04
R
e
s
i
d
u
a
ls0.03
0.02
0.01
0
1 2 3 4 5
Debt Quintile
Mean Residuals Median Residuals
Notes:
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This figure reports the mean and median Residuals across increasing debt levels. Residuals are the standard deviation of the residuals from the regression of working capital accruals on operating cash flows in the prior, current, and future periods, changes in revenues, and property, plant and equipment. Earnings quality is inversely related to Residuals by construction. Debt is the sum of long-term and short-term debt deflated by total assets. The sample is sorted into five portfolios based on Debt .
 Debt Financing and Earnings Quality: Multivariate Results (n = 8,240) 	
Dependent Variable: Residuals
Variables (1) (2) (3) (4)
Intercept 0.053 (56.09)∗∗ 0.067 (59.00)∗∗ 0.016 (5.08)∗∗ 0.021 (5.97)∗∗
Private Debt
Debt 0.019 (5.27)∗∗ −0.103 (−12.25)∗∗ −0.019 (−3.23)∗∗ −0.024 (−3.59)∗∗
Debt 2 0.175 (13.11)∗∗ 0.035 (3.80)∗∗ 0.029 (3.21)∗∗
Innate Factors
Operating cycle 0.004 (8.11)∗∗ 0.004 (7.65)∗∗
Size −0.003 (−15.90)∗∗ −0.003 (−15.79)∗∗
Salesσ 0.043 (13.62)∗∗ 0.043 (13.84)∗∗
Cash flowσ 0.222 (22.36)∗∗ 0.200 (18.65)∗∗
Losses 0.026 (15.62)∗∗ 0.020 (10.81)∗∗
Cost of debt 0.010 (1.70)
Financial Health
Z-Score −0.001 (−5.96)∗∗
Growth Factors
Growth 0.001 (3.40)∗∗
R&D 0.014 (1.74)
Adjusted R 2 0.6% 4.6% 50.5% 51.2%
Notes:
Residuals are the standard deviation of the residuals from years t −4 to t from the regression of
working capital accruals on operating cash flows in the prior, current, and future periods, changes in revenues, and property, plant and equipment. Debt is the ratio of total (long-term+short-term) debt to
total assets. Operating cycle is the log of the sum of days accounts receivable and days inventor y outstanding, where days accounts receivableis 360/(Sales/Average accounts receivable) and days inventor y outstanding is 360/(Cost of goods sold/Average inventor y). Size is the log of the average of the beginning and ending total assets. Salesσ and Cash flowσ are the standard deviation of sales and operating cash flows scaled by
average total assets from years t −4 to t , respectively. Losses is the proportion of firm-years with negative earnings from years t −4 to t . Cost of debt is interest expense deflated by average total debt. Z-Score is Altman’s Z-score, where Z-score is estimated as 1.2×(Working capital/Total assets) + 1.4×(Retained earnings/Total assets) + 3.3×(EBIT/Total assets) + 0.6×(Market value of equity/Total liabilities) + (Sales/Total assets).
Growth is the sum of the market value of equity and the book value of preferred stock and debt scaled by the
book value of total assets. R&D is research and development expenditures scaled by the book value of total assets. Debt , Operating cycle , Size , Cost of debt , Z-Score , Growth and R&D are the average of the five annual values
for years t −4 to t . We report the coefficients and the corresponding t -statistics in parenthesis. Statistical
significance of the reported coefficients is based on Rogers (1993) clustered standard errors correcting for
within-firm and within-year correlations.
∗∗ denotes statistical significance at the 0.01 level for a two-tailed test.
Table 4 presents our main regression results. In column 1, we only include Debt which ser ves as a benchmark case for the relation between earnings quality and debt financing;
the coefficient on Debt is positive (0.019, t -statistic = 5.27). A positive relationship is
consistent with the view that earnings quality decreases with debt.
Because univariate results suggest a non-linear relation between debt financing and earnings quality, we include Debt and Debt 2 in column 2. Although the regression model includes only one more variable, the explanator y power jumps substantially; an adjusted R 2 increases to 4.6% in column 2 from 0.6% in column 1. Further, the coefficient on Debt
is negative and significant (−0.103, t -statistic = −12.25), while the coefficient on Debt 2
is positive and significant (0.175, t -statistic = 13.11), confirming the non-monotonic
relationship documented in Table 3. Consistent with our hypothesis, we find a nonlinear
relationship between debt and earnings quality. When debt is relatively low, the positive
influence of debt dominates the negative influence of debt because managers are less likely to manipulate earnings when the probability of violating a covenant is not high. However, at high debt levels the risk of breaching a covenant becomes so high that it is more valuable to the firm to impair earnings quality in order to avoid the covenant breach than to continue to offer reliable views of the future prospects of the firm.
Results from the reduced model excluding control variables might be overstated because they do not account for the other factors that explain the variation in earnings quality. Therefore, in column 3 we include a number of other factors associated with earnings quality, as in Dechow and Dichev (2002) and Francis et al. (2005). Although the magnitude of the coefficients becomes smaller, the results in column 3 are
virtually unchanged; the coefficient on Debt remains negative and significant (−0.019,
t -statistic = −3.23), and the coefficient on Debt 2 also remains positive and significant
(0.035, t -statistic = 3.80).12
In column 4, we include four more control variables in addition to the control
variables in column 3. We add Cost of debt since Francis et al. (2005) find a significant association between earnings quality and the cost of debt, and Z-Score to control for the probability of bankruptcy that might be associated with Debt . Finally, we include Growth and R&D because growth opportunities are a key determinant of debt financing. The inclusion of the four additional variables does not change our results. The coefficient
on Debt is negative and significant (−0.024, t -statistic = −3.59), and the coefficient on
Debt 2 is positive and significant (0.029, t -statistic = 3.21).13 Our point estimates from
column 4 results suggest that the relationship between earnings quality and debt is
cur vilinear with an inflection point around 41%.14
Results for the control variables are consistent with findings in prior studies and with our expectations. Coefficient estimates on Operating cycle , Salesσ , Cash flowσ , Losses and Growth are all positive and significant, which suggests that earnings quality is lower for firms with longer operating cycles, firms with higher volatility of sales and operating cash flows, firms with more incidence of negative earnings realizations, and firms with higher growth opportunities. Coefficient estimates on Size and Z-Score are negative and significant, indicating that earnings quality is higher for larger firms with less financial difficulty.
(ii) Sensitivity Analysis
(a) Alternative Research Design: Spline Regressions
Our regression results are based on a non-linear specification that includes Debt and
Debt 2 in the same regression. We also use an alternative research design based on spline
12 While we include all the control variables in Francis et al. (2005), and Dechow and Dichev (2002) in our tabulated results, we also tr y including several combinations of these control variables to analyze the robustness of our results. We find that the results on the coefficients on Debt and Debt 2 from these specifications are ver y similar to those reported in Table 4.
13 Since financial health might also affect the association between Debt and Residuals, we also interact
Altman’s Z-score with Debt and Debt 2 . In unreported results, we find that none of the interaction terms,
Debt ×Z-Score and Debt 2 ×Z-Score , are significant. More important, coefficient estimates on Debt and Debt 2
continue to be highly significant.
14 Since the relation between Debt and Residuals is cur vilinear, the inflection point is the level of Debt at which Residuals is the minimum. Thus, δResiduals/δDebt = −0.024 + 2×0.029×Debt = 0, which implies that
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Residuals is the lowest when Debt is about 41%.
regressions. In particular, we estimate the following model:
Residuals = β0 + β1 Debt (0,0.40) + β2 Debt (0.40,1) + β3 Operating cycle
+ β4 Si z e + β5 Salesσ + β6 Cash flowσ + β7 Losses + β8 Cost of debt
+ β9 Z -Score + β10 Growth + β11 R& D + ε. (3)
We divide Debt into low and high range categories using 40% as a breakpoint. Specifically, Debt (0,0.40) equals Debt if Debt lies between 0 and 40%, and 40% other wise (low range). Debt (0.40,1) equals Debt minus 40% if Debt is greater than 40%, and 0 other wise (high range). All the other variables are as previously defined.
Table 5 reports results of estimating equation (3). Consistent with the results in Table 4, we again document a non-linear relation between earnings quality and debt. When we include all control variables in column 3, the coefficient on Debt (0,0.40) is
negative (−0.011, t -statistic = −3.24), but that on Debt (0.40,1) is positive (0.023, t -statistic =
3.05). In untabulated results, we find that when we use 45% as an alternative breakpoint
to divide Debt into low and high range, our results remain unchanged.
In sum, consistent with the earlier findings, results in Table 5 also suggest that debt has a positive influence on earnings quality at low levels of debt but that this effect reverses at high debt levels.
(b) Endogeneity: Using Simultaneous Equations
One concern with ordinar y least square regressions is that the results might bebiased and inconsistent if debt financing is an endogenous variable (Wooldridge, 2002). We address possible endogeneity concerns using a two-stage least squares (2SLS) estimation procedure that jointly estimates the relationship between debt and earnings quality. In particular, we estimate equation (2) in conjunction with the following equation, where Residuals and Debt are endogenous variables:
Debt = β0 + β1 Residuals + β2 Size + β3 Cash flowσ + β4 Z −Score + β5 Growth + β6 R& D
+ β7 Fixed assets + β8 Cash flow + β9 ITC + β10 NOI + Industr y/Year dummies + ω
(4)
where ITC and NOL are indicator variables for investment tax credits and net operating loss carr y for wards, respectively. To be consistent with the time period used to compute
Residuals, the above variables are the average of the five annual values for years t − 4
to t . All the other variables are as previously defined.
The empirical specification for debt in equation (4) is largely based on the functional form used in Fama and French (2002). Size , Cash flowσ , Fixed assets and Cash flow measure expected bankruptcy cost, which is predicted to be negatively associated with debt. Z- Score measures financial health. Growth and R&D represent investment opportunities, which are also expected to be negatively associated with debt. ITC and NOL capture non-debt tax shields and are again expected to be negatively associated with debt.
In columns 1 and 2 of Table 6, we report results based on 2SLS estimation for all sample firms. Once again, our results confirm earlier findings that the relationship between Debt and Residuals is non-monotonic. Coefficient estimates on Debt and Debt 2
in column 1 are −0.059 (t -statistic = −5.79) and 0.027 (t -statistic = 3.78), respectively.
Table 5
 Debt Financing and Earnings Quality: Spline Regression Estimation (n = 8,240) 
Dependent Variable: Residuals
Variables (1) (2) (3)
	Intercept
Private Debt
Debt (0,0.40)
	0.063 (67.06)∗∗
−0.038 (−9.42)∗∗
	0.015 (4.79)∗∗
−0.009 (−2.78)∗∗
	0.020 (5.71)∗∗
−0.011 (−3.24)∗∗
	Debt (0.40,1)
	0.127 (12.61)∗∗
	0.024 (3.66)∗∗
	0.023 (3.05)∗∗
	Innate Factors
	
	
	
	Operating cycle
	
	0.004 (8.09)∗∗
	0.004 (7.63)∗∗
	Size
Salesσ
Cash flowσ
Losses
	
	−0.003 (−15.79)∗∗
0.043 (13.56)∗∗
0.223 (22.46)∗∗
0.026 (15.61)∗∗
	−0.003 (−15.68)∗∗
0.043 (13.79)∗∗
0.200 (18.66)∗∗
0.020 (10.76)∗∗
	Cost of debt
	
	
	0.011 (1.85)
	Financial Health
	
	
	
	Z-Score
Growth Factors
Growth
	
	
	−0.001 (−5.93)∗∗
0.001 (3.45)∗∗
	R&D
	
	
	0.014 (1.78)
	Adjusted R 2
	4.6%
	50.5%
	51.2%
Notes:
Residuals are the standard deviation of the residuals from years t −4 to t from the regression of
working capital accruals on operating cash flows in the prior, current, and future periods, changes in
revenues, and property, plant and equipment. Debt is measured as a spline variable: (1) Debt (0,0.40) equals Debt if Debt lies between 0 and 40%, and 40% other wise; and (2) Debt (0.40,1) equals Debt minus 40% if Debt is greater than 40%, and 0 other wise. Operating cycle is the log of the sum of days accounts receivable and days inventor y outstanding, where days accounts receivable is 360/(Sales/Average accounts receivable) and days inventor y outstanding is 360/(Cost of goods sold/Average inventor y). Size is the log of the average of the beginning and ending total assets. Salesσ and Cash flowσ are the standard deviation of sales and
operating cash flows scaled by average total assets from years t −4 to t , respectively. Losses is the proportion of firm-years with negative earnings from years t −4 to t . Cost of debt is interest expense deflated by average total debt. Z-Score is Altman’s Z-score, where Z-score is estimated as 1.2×(Working capital/Total assets)
+ 1.4×(Retained earnings/Total assets) + 3.3×(EBIT/Total assets) + 0.6×(Market value of equity/Total liabilities) + (Sales/Total assets). Growth is the sum of the market value of equity and the book value
of preferred stock and debt scaled by the book value of total assets. R&D is research and development
expenditures scaled by the book value of total assets. Debt , Operating cycle , Size , Cost of debt , Z-Score , Growth
and R&D are the average of the five annual values for years t −4 to t . We report the coefficients and the
corresponding t -statistics in parenthesis. Statistical significance of the reported coefficients is based on
Rogers (1993) clustered standard errors correcting for within-firm and within-year correlations.
∗∗denotes statistical significance at the 0.01 level for a two-tailed test.
Thus, accounting for simultaneous relationship between debt and earnings quality, our results suggest that earnings quality first increases and then decreases with higher debt levels. In column 2, we find that the coefficient on Residuals is not significant (0.287,
t -statistic = 1.27).
(c) Interest Expense and Earnings Quality
Accounting-related covenants are frequently based on leverage, net worth, current ratio, and interest coverage ratio (Sweeney, 1994). We focus on leverage because of the distinctive dual role of debt in influencing earnings quality (i.e., from a theoretical
Table 6
Debt Financing and Earnings Quality: Two-Stage Least Squares Estimation
 (n = 8,240) 	
Residuals Debt
Dependent Variables = (1) (2) Intercept 0.038 (7.63)∗∗ 0.225 (9.10)∗∗
Endogenous Variables
Debt −0.059 (−5.79)∗∗
Debt 2 0.027 (3.78)∗∗
Residuals 0.287 (1.27)
Instrumental Variables
Operating cycle 0.004 (7.00)∗∗
Size −0.003 (−13.69)∗∗ 0.002 (1.50)
Salesσ 0.044 (18.43)∗∗
Cash flowσ 0.193 (27.29)∗∗ −0.210 (−3.47)∗∗
Losses 0.021 (13.61)∗∗
Cost of debt −0.004 (−0.70)
Z-Score −0.002 (−8.57)∗∗ −0.023 (−37.95)∗∗
Growth 0.003 (8.13)∗∗ 0.030 (19.48)∗∗
R&D −0.021 (−2.38)∗ −0.660 (−23.61)∗∗
Fixed assets 0.020 (3.03)∗∗
Cash flow −0.071 (−4.11)∗∗
ITC −0.113 (−4.73)∗∗
NOL 0.014 (3.02)∗∗
Industr y/Year dummyIncluded
Adjusted R 2 49.7% 38.7%
Notes:
Residuals are the standard deviation of the residuals from years t −4 to t from the regression of
working capital accruals on operating cash flows in the prior, current, and future periods, changes in revenues, and property, plant and equipment. Debt is the ratio of total (long-term+short-term) debt to
total assets. Operating cycle is the log of the sum of days accounts receivable and days inventor y outstanding, where days accounts receivable is 360/(Sales/Average accounts receivable) and days inventor y outstanding is 360/(Cost of goods sold/Average inventor y). Size is the log of the average of the beginning and ending total assets. Salesσ and Cash flowσ are the standard deviation of sales and operating cash flows scaled by
average total assets from years t −4 to t , respectively. Losses is the proportion of firm-years with negative
earnings from years t -4 to t . Cost of debt is interest expense deflated by average total debt. Z-Score is Altman’s Z-score, where Z-score is estimated as 1.2×(Working capital/Total assets) + 1.4×(Retained earnings/Total assets) + 3.3×(EBIT/Total assets) + 0.6×(Market value of equity/Total liabilities) + (Sales/Total assets).
Growth is the sum of the market value of equity and the book value of debt scaled by the book value of total
assets, R&D is research and development expenditures scaled by the book value of total assets, Fixed assets is property, plant, and equipment divided by average total assets, Cash flow is operating cash flows scaled by average total assets, and ITC and NOL are indicator variables for investment tax credits and net operating loss carr y for wards, respectively. Debt , Operating cycle , Size , Cost of debt , Z-Score , Growth, R&D, Fixed assets, Cash flow, ITC, and NOL are the average of the five annual values for years t -4 to t . We report the coefficients and the corresponding t -statistics in parenthesis. Statistical significance of the reported coefficients is based on Rogers (1993) clustered standard errors correcting for within-firm and within-year correlations.
∗∗and ∗ denote statistical significance at the 0.01 and 0.05 level, respectively, for a two-tailed test.
standpoint debt can have a positive or a negative influence on earnings quality). Most other measures do not have this dual characteristic with the notable exception of interest coverage ratio. While financial leverage is a balance sheet measure of debt, interest coverage ratio can be considered as an income statement or ‘flow’ measure of debt.
Although interest coverage is often defined as operating income before taxes to interest expense, we avoid using that definition because the ratio is difficult to interpret for firms with losses. One potential solution is to delete firm-year obser vations with negative interest coverage ratio but that might result in firms that are central to our hypothesis being discarded. Instead, we use interest expense deflated by revenues (Interest expense ) as a proxy for debt financing. This measure avoids problems associated with loss firms and can be interpreted in the same way as Debt .
In Table 7, we report the results using Interest expense as an alternative proxy for debt financing. As before, Interest expense is measured as the five-year average computed over
years t − 4 to t . Consistent with the Table 4 results, we find that the relation between
Interest expense and Residuals is also non-monotonic; when we include all control variables
Table 7
 Relationship Between Interest Expense and Earnings Quality (n = 8,240) 	
Dependent Variable: Residuals
Variables (1) (2) (3) (4)
Intercept 0.055 (102.02)∗∗ 0.054 (84.43)∗∗ 0.016 (5.04)∗∗ 0.016 (5.04)∗∗
Debt
Interest expense 0.093 (7.35)∗∗ −0.151 (−5.79)∗∗ −0.044 (−2.50)∗ −0.114 (−6.15)∗∗
Interest expense 2 0.113 (2.71)∗∗ 0.043 (2.22)∗ 0.110 (3.33)∗∗
Innate Factors
Operating cycle 0.004 (7.81)∗∗ 0.004 (8.23)∗∗
Size −0.003 (−15.38)∗∗ −0.003 (−14.81)∗∗
Salesσ 0.042 (13.09)∗∗ 0.040 (12.60)∗∗
Cash flowσ 0.226 (22.57)∗∗ 0.199 (18.51)∗∗
Losses 0.029 (16.01)∗∗ 0.023 (11.95)∗∗
Cost of debt 0.022 (3.53)∗∗
Financial Health
Z-Score −0.001 (−8.49)∗∗
Growth Factors
Growth 0.002 (5.23)∗∗
R&D 0.006 (0.86)
Adjusted R 2 1.3% 1.5% 50.4% 51.5%
Notes:
Residuals are the standard deviation of the residuals from years t −4 to t from the regression of
working capital accruals on operating cash flows in the prior, current, and future periods, changes in
revenues, and property, plant and equipment. Interest expense is the ratio of interest expense to revenues. Operating cycle is the log of the sum of days accounts receivable and days inventor y outstanding, where days accounts receivable is 360/(Sales/Average accounts receivable) and days inventor y outstanding is
360/(Cost of goods sold/Average inventor y). Size is the log of the average of the beginning and ending total assets. Salesσ and Cash flowσ are the standard deviation of sales and operating cash flows scaled by
average total assets from years t −4 to t , respectively. Losses is the proportion of firm-years with negative earnings from years t −4 to t . Cost of debt is interest expense deflated by average total debt. Z-Score is Altman’s Z-score, where Z-score is estimated as 1.2×(Working capital/Total assets) + 1.4×(Retained earnings/Total assets) + 3.3×(EBIT/Total assets) + 0.6×(Market value of equity/Total liabilities) + (Sales/Total assets).
Growth is the sum of the market value of equity and the book value of preferred stock and debt scaled by the
book value of total assets. R&D is research and development expenditures scaled by the book value of total assets. Interest expense , Operating cycle , Size , Cost of debt , Z-Score , Growth and R&D are the average of the five
annual values for years t −4 to t . We report the coefficients and the corresponding t -statistics in parenthesis.
Statistical significance of the reported coefficients is based on Rogers (1993) clustered standard errors
correcting for within-firm and within-year correlations.
∗∗and ∗ denote statistical significance at the 0.01 and 0.05 level, respectively, for a two-tailed test.
in column 4, the coefficient on Interest expense is negative (−0.114, t -statistic = −6.15), while that on Interest expense 2 is positive (0.110, t -statistic= 3.33). Findings indicate that
earnings quality first increases and then decreases with higher interest expense levels.
(d) Alternative Deflator for Debt
In Tables 1 to 6, we deflate total debt by total assets (Debt ). As a robustness check, we also use the book value of common equity as an alternative deflator. As before, we measure
debt to equity (DE) as the average of the five annual values from years t − 4 to t .
Table 8 reports the multivariate regression results using DE as an alternative proxy
for debt financing. Consistent with the Table 4 results, the relation between DE and
Table 8
 Debt Financing and Earnings Quality: Debt-to-Equity Ratio (n = 8,240) 	
Dependent Variable: Residuals
Variables (1) (2) (3) (4)
Intercept 0.060 (97.43)∗∗ 0.060 (99.30)∗∗ 0.017 (5.18)∗∗ 0.018 (5.43)∗∗
Private Debt
DE −0.004 (−8.22)∗∗ −0.010 (−12.82)∗∗ −0.003 (−4.35)∗∗ −0.002 (−3.97)∗∗
DE2 0.003 (11.85)∗∗ 0.001 (3.99)∗∗ 0.001 (3.15)∗∗
Innate Factors
Operating cycle 0.004 (7.98)∗∗ 0.005 (7.77)∗∗
Size −0.003 (−15.87)∗∗ −0.003 (−15.86)∗∗
Salesσ 0.043 (13.63)∗∗ 0.043 (13.78)∗∗
Cash flowσ 0.222 (22.32)∗∗ 0.201 (18.56)∗∗
Losses 0.025 (14.79)∗∗ 0.020 (10.62)∗∗
Cost of debt 0.009 (1.55)
Financial Health
Z-Score −0.001 (−5.27)∗∗
Growth Factors
Growth 0.001 (2.91)∗∗
R&D 0.014 (1.78)
Adjusted R 2 1.3% 4.0% 50.1% 50.7%
Notes:
Residuals are the standard deviation of the residuals from years t −4 to t from the regression of
working capital accruals on operating cash flows in the prior, current, and future periods, changes in revenues, and property, plant and equipment. DE is the ratio of total (long-term+short-term) debt to
common shareholders’ equity. Operating cycle is the log of the sum of days accounts receivable and days inventor y outstanding, where days accounts receivable is 360/(Sales/Average accounts receivable) and days inventor y outstanding is 360/(Cost of goods sold/Average inventor y). Size is the log of the average of the beginning and ending total assets. Salesσ and Cash flowσ are the standard deviation of sales and
operating cash flows scaled by average total assets from years t −4 to t , respectively. Losses is the proportion of firm-years with negative earnings from years t −4 to t . Cost of debt is interest expense deflated by average total debt. Z-Score is Altman’s Z-score, where Z-score is estimated as 1.2×(Working capital/Total assets)
+ 1.4×(Retained earnings/Total assets) + 3.3×(EBIT/Total assets) + 0.6×(Market value of equity/Total liabilities) + (Sales/Total assets). Growth is the sum of the market value of equity and the book value
of preferred stock and debt scaled by the book value of total assets. R&D is research and development
expenditures scaled by the book value of total assets. DE , Operating cycle , Size , Cost of debt , Z-Score , Growth
and R&D are the average of the five annual values for years t −4 to t . We report the coefficients and the
corresponding t -statistics in parenthesis. Statistical significance of the reported coefficients is based on
Rogers (1993) clustered standard errors correcting for within-firm and within-year correlations.
∗∗denotes statistical significance at the 0.01 level for a two-tailed test.
Residuals is also non-monotonic. In the last regression when we include all control variables, the coefficients on DE and DE2 are −0.002 (t -statistic = −3.97) and 0.001 (t -statistic = 3.15), respectively, indicating that earnings quality first increases and then
decreases with higher debt-to-equity ratio.
6. CONCLUSIONS
Our study investigates the association between private debt financing and the quality of earnings. We posit that the relationship between private debt and earnings quality is positive when debt is low. Since private lenders rely on high quality information to assess the creditworthiness of borrowers, they are likely to demand better quality earnings. Thus, increased monitoring from capital market participants is expected to lead to accounting accruals (a key component of earnings) that are more informative about future cash flows. Firms also benefit from reporting informative earnings from a lower cost of debt.
In contrast, we hypothesize that the relationship between private debt and earnings quality is negative when debt is sufficiently high. Contracting theor y suggests that private debt holders impose stringent accounting-based contractual constraints to limit expropriation of wealth by managers. The stringency of the covenants is expected to increase with debt. Therefore, for high debt, there is a trade-off between benefits from reporting high quality earnings and benefits from avoiding covenant breaches. Managers are more likely to use the latitude in accounting choices to manage earnings around covenant limits because the costs of violating debt covenants are large for high debt. The use of accounting discretion to avoid covenant violations lowers earnings quality because accruals are noisy predictors of future performance.
Our measure of earnings quality is based on the accruals quality metric developed by Dechow and Dichev (2002), modified by McNichols (2002), and implemented by Francis et al. (2005). Using accruals quality as a proxy for earnings quality, we find that earnings quality first increases and then declines across increasing levels of debt with an inflection point around 41%.
We conduct a number of additional analyses. First, we use a piecewise linear specification instead of including debt and a squared-term of debt in the same regression to test for non-linearity. Further, since debt is likely to be an endogenous variable, earnings quality and debt might be jointly determined. Therefore, we also use a two-stage least squares model to jointly estimate the relation between earnings quality and debt. Third, because accounting covenants are also based on interest coverage ratio, we use a variation of the interest coverage ratio (interest expense to revenues) as an alternative income statement based measure of debt financing. Finally, we deflate debt by equity as an alternative balance sheet measure of debt. We find that our results remain unchanged for these additional analyses.
Our results suggest that the positive relationship between debt and earnings quality is descriptive for the most part of debt (almost 80% of the sample). However, the reverse is