Difference in pay sensitivity between owners and non-owners

In this section, we are going to study the difference in pay sensitivity between owners and non-owners. We first present two models with EBIT and ROA as a measure of performance. The effect of performance on change in CEO pay for owners compared to non-owners can be picked up by the interaction terms. In this case, we have multiplied the different measures of performance with the non-owner dummy. Note that we now look at the percentage change in CEO pay, denoted as ∆CEO pay.

Table 10 – The sensitivity of CEO pay related to performance

Growth EBIT 16 -0.00325 0.00171

(-1.58) (0.98)

All regressions are robust to heteroskedasticity t statistics in parentheses

* p < 0.1, ^** p < 0.05, ^*** p < 0.01

Table 10 shows the results of the models describing pay sensitivity. Model 1 uses EBIT as a measure of performance, and Model 2 uses ROA. Growth in EBIT from 2015 to 2016 does not have a significant impact on changes in CEO pay from 2017 to 2018 in general, but the growth for non-owners in the same period is significant at a five percent level. This finding supports our second hypothesis and suggests that the compensation of non-owners is more sensitive to performance. The changes in EBIT from 2016 to 2017 does not have a significant effect on changes in CEO pay from 2017 to 2018, but the sign of the coefficients does again support our hypothesis. Model 1 might also indicate that a change in performance takes some time to influence the pay. This seems plausible, as one year’s financial performance may not be available before a couple of months into the following year (e.g., through annual reports). Also,

renegotiation of compensation is unlikely done in “real-time”, leading to a lagged increase in compensation.

ROA variables are insignificant for both years, and the signs of the coefficients are inconsistent.

Summary statistics of the ROA variables (Table 7) showed much variation. The correlation matrix (Table 15 in the appendix) shows some correlation between ROA and growth in EBIT.

However, the correlation is not very strong. Thus, it is not surprising that we see different results when we use these different variables as a proxy for performance.

Model 3 and Model 4 examines the same as Model 1 and Model 2 but jumps back one year on both the dependent variable and the independent variables. Growth in EBIT from 2015 to 2016 is insignificant and with different signs. Model 3 does not support Hypothesis 2. In Model 4, CEO duality is significant at a five percent level, and ROA in 2016 is significant at a ten percent level. The effect is though ambiguous, as the correlation is positive in 2016 and negative in 2015. Board size is significant at a ten percent level in Model 1, 2, and 4. However, the signs of the coefficient are negative when using 2018 change in compensation and positive using 2017 change.

We observe a positive relationship between the increase in EBIT and pay. However, an issue with EBIT as a performance measure is that without controlling for exogenous shocks, the CEO could be rewarded and punished for events that is out of his or her control. For example, the shipping industry is highly dependent on the shipping rates, which is determined by the market (e.g., look at the OSLO Shipping Index which we mentioned in Section 6.1.1). If the industry is doing well, most firms experience growth in EBIT. If the company in question increases EBIT, but lower than the industry in general, the CEO should not be rewarded. On the other side, the CEO should be rewarded if the company experience a higher increase in EBIT than the industry in general. Furthermore, if the industry is doing well, this might increase the portability, and firms are more obliged to offer a bonus for the increased EBIT in order to retain their manager, even though the performance is below the industry average.

Figure 4 confirms what we mentioned earlier about ROA and EBIT growth not following each other that well. This can be one reason we observe inconsistent results regarding pay-performance sensitivity. Inconsistencies motivate us to do the same analysis using a different measure of performance, liquidity.

Table 11 – The sensitivity of CEO pay related to performance, using liquidity ratio

(1) (2)

Liquidity 16 -0.00528 -0.00512

(-0.96) (-1.36)

NO_Liquidity 16 -0.00343 -0.0273^**

(-0.15) (-2.51)

Both regressions are robust to heteroskedasticity t statistics in parentheses

* p < 0.1, ^** p < 0.05, ^*** p < 0.01

The first model in Table 11 does not support our second hypothesis. In Model 2, the compensation of non-owners is negatively correlated using liquidity as a measure of firm performance, and the coefficient is significant at a five percent level. This does not support our

-40-2002040G_EBIT16

-200 -100 0 100 200

ROA16

Figure 4 – Scatterplot of EBIT growth and ROA

hypothesis, as we expected a positive relationship. Numbers for 2015 show a positive relationship between performance and change in pay, but the coefficients are not significant.

Like Model 4 in Table 10, we observe a positive correlation between CEO duality and board size for the change in pay in 2017. This effect is statistically significant at a ten percent and five percent level respectively. Liquidity ratio as a proxy of performance provides us with no evidence of higher pay-performance sensitivity for non-owners.

Result 3: We find evidence in favor of Hypothesis 2 using EBIT as a performance measure, but no evidence using ROA and liquidity ratio.