This paper investigates whether the current level of tournament incentives for top executives is related to the firm’s future credit rating. Greater pay dispersion (our proxy for tournament incentives) has been found to be associated with both greater firm performance and greater firm riskiness. Taking the bondholders’ perspective, credit rating agencies would view increases in performance favorably and increases in riskiness unfavorably, leading to the empirical question of how pay dispersion affects a firm’s credit rating, if at all. We find strong evidence that pay dispersion is negatively associated with credit ratings. We also find that the negative impact of pay dispersion on credit ratings is stronger when firms have greater default risk. Finally, we find weak evidence that strong shareholder rights accentuate the negative relation between pay dispersion and credit ratings.
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For example, creditors are more interested in evaluating a firm’s default risk, whereas equity investors are more interested in the firm’s growth opportunities.
They also provide an explanation for the reason why all firms do not practice good governance. They show that CEOs of firms with weaker corporate governance receive more excess compensation, and firms with lower credit ratings are more likely to overcompensate their CEOs. More importantly, CEOs of firms with lower credit ratings have far more excess compensation than their share of the additional debt costs.
In developing our first hypothesis, we also discuss the case that the negative impact of increases in riskiness on credit ratings could be lessened if rating agencies do not view all increases in firm risk as problematic.
The measure is referred to as CEO pay slice in Bebchuk et al. (2011).
In this paper we use CEO entrenchment and CEO power interchangeably.
On the other hand, Yu and Luu (2016) find evidence inconsistent with the tournament theory, showing that executive pay dispersion has a non-linear effect of bank performance.
For example, Douglas et al. (2016) demonstrate that firms with higher cash flow risk have higher bond yield spreads.
The rating data is available monthly at the end of each month. To match with our firm-year observations, we take the rating at the end of a given fiscal year. Alternatively, when we average monthly ratings over the entire fiscal year the results are qualitatively similar.
Following Ashbaugh-Skaife et al. (2006), S&P debt rating of AAA is assigned the value of 7; AA + , AA, and AA- is 6; A + , A, and A- is 5; BBB + , BBB, and BBB- is 4; BB + , BB, and BB- is 3; B + , B, and B- is 2, and CCC + and lower is 1. The empirical results are qualitatively similar when we code S&P ratings by assigning a number to each rating level as in Kuang and Qin (2013).
Untabulated results show that controlling for delta and vega of the top 5 executive officers does not change our inferences.
Alternatively, we define an indicator variable, POSTCRISIS, to control for the fact that credit-rating agencies have been more cautious and giving lower ratings since the financial crisis of 2008, as they were blamed for playing a critical contributing role to the meltdown of the financial system. Replacing year indicators with POSTCRISIS generates results that are qualitatively similar to those reported in the tables.
Alternatively, if we include firm fixed effects to control for time-invariant firm effects, or estimate two-way cluster-adjusted standard errors by firm and by year, our inferences do not change.
For a robustness check, we divide the sample into two groups based on the value of HIGH_RISK, run separate regressions on the groups, and use Chi square statistics to test the significance of the difference between the coefficients on PAY_DISP across the two groups. The results of these tests are qualitatively similar to those reported in Table 4. We use a similar across-group approach for a robustness check on the test of H3 and the results are qualitatively similar to those reported in Table 5. Allison (1999) demonstrates that this type of across-group test can lead to the concern that finding a difference in two coefficients “may be an artifact of differences in the degree of residual variation (unobserved heterogeneity) in the models” for the two groups (Allison 1999, p. 189). Hence we use the interaction approach with Allison’s delta in our main tests of H2 and H3.
G scores are only available every 2 or 3 years during the period 1992–2006. As a result, it is not included as an independent variable in tests of H1 and H2 to prevent the loss of observations from years 2008–2013.
Alternatively, using the G scores from the most adjacent year generates qualitatively similar results.
Kini and Williams (2012) use EBITDA to proxy for free cash flows whereas we employ reported operating cash flows.
It is likely that the positive simple correlation is driven by the fact that LOG_DIF_TOTAL is highly correlated with SIZE (coefficient = 0.565).
Although the simple linear correlation between LOG_DIF_TOTAL and CASH_VOL is negative, the correlation becomes significantly positive (coefficient = 0.113) after controlling for SIZE.
The significance of CEO_VEGA disappears when G scores are controlled for (as shown later in Table 5), indicating that the positive association between CEO_VEGA and RATING could be driven by the omission of the G score variable. We did not include G scores in this model since its inclusion would result in a significant decline in the number of observations as discussed earlier in the paper.
When G-Score is introduced into the model in Table 5, CEO_DUALITY is no longer statistically significant.
A replication of tests of H2 using this smaller sample and controlling for G scores show results qualitatively unchanged from those in Table 4.
Using the across-group approach, we compare coefficients from the second-stage regressions for high-risk and low-risk groups to test H2 and H3. These Chi-square tests lead to qualitatively similar results.
In sensitivity analyses, we also control for two additional measures of CEO entrenchment, which are CEO tenure and the percentage of common shares owned by CEO. Although both measures are negatively related to firm credit ratings, our main results of the negative association between pay dispersion and credit ratings remain unchanged.
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Appendix: Variable definition and predicted sign
Appendix: Variable definition and predicted sign
|RATING||Monthly S&P long-term issuer credit ratings at the end of the following fiscal year as coded in Ashbaugh-Skaife et al. (2006)|
|CO_VAR_TOTAL||±||Coefficient of variation of top five executives’ total compensation paid at the end of the current fiscal year|
|LOG_DIF_TOTAL||±||Natural log of difference between CEO total compensation and the median of total compensation paid to the next four highest paid top executives at the end of the current fiscal year|
|CPS||±||CEO total compensation over the sum of total compensation of all top five highest paid executives at the end of the current fiscal year|
|ROAa||+||Net income before extraordinary items divided by total assets|
|CASH_VOL||−||Standard deviation of net operating cash flows for the most recent 5 years|
|SIZE||+||Natural log of total assets|
|LEVER||−||Total liabilities divided by total assets|
|INTCOV||+||Operating income before depreciation divided by interest expense|
|LOSS||−||One if net income before extraordinary items is negative in the current and prior fiscal year, zero otherwise|
|CAP_INTEN||+||Gross PPE divided by total assets|
|CEO_DUALITY||−||One if the firm’s CEO is also the chairman of the board, zero otherwise|
|SUBORD||−||One if the firm has subordinated debt, zero otherwise|
|FIN_UTILITY||+||One if the firm is in a regulated financial or utility industry (SIC codes 4900 s and 6000 s), zero otherwise|
|CEO_DELTA||−||The pay-performance sensitivity of the firm’s CEO as in Core and Guay (2002)|
|CEO_VEGA||−||The pay-risk sensitivity of the firm’s CEO as in Core and Guay (2002)|
|G_SCORE||−||G-score as in Gompers et al. (2003) during the period 1992–2007, if missing, the previous year G-score is used for a given firm|
|E_INDEX||−||Entrenchment index as in Bebchuk et al. (2009) during the period 1992–2007, if missing, the previous year G-score is used for a given firm|
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Bannister, J.W., Newman, H.A. & Peng, E.Y. Top management tournament incentives and credit ratings. Rev Quant Finan Acc 55, 769–801 (2020). https://doi.org/10.1007/s11156-019-00859-8
- Executive compensation
- Tournament incentives
- Credit ratings
- G10, 24, 34