Three Essays on Executive Compensation - KOPS - Universität ...

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Executive Compensation and Firm Performance in Germany performance sensitivities quantitatively. The sub-period specification with industry fixed effects misses executive characteristics such as age, education or bargaining power, which are captured by executive fixed effects, but not by industry fixed effects. Columns 1 and 2 of Table 1.11 show the results for the pre-crisis and crisis periods, respectively, with total compensation as the dependent variable. The payperformance sensitivities are very similar in the two periods and the pay-performance sensitivities are decreasing in firm risk in both sub-periods. Columns 3 and 4 of Table 1.11 display the results for the two sub-periods with cash bonuses as the dependent variable. Pay-performance sensitivities are again very similar in the two periods. As before in the full sample analysis, the payperformance sensitivities of total compensation and cash bonuses are of similar magnitude. This indicates that cash bonuses drive most of the pay-performance sensitivity of total compensation also in the sub-periods. We skip the results for long-term compensation because neither EBIT nor stock returns are significant in the sub-periods. Splitting the sample does not change the results for stock returns. Neither in the 2005-2007 nor in the 2008-2009 period stock returns have explanatory power for total compensation or cash bonuses. Together with the EBIT results of the subperiod analysis, this gives more support for Hypothesis 1. Our results suggest that compensation of German executives is based on accounting performance and not on stock returns. Hence, it is not surprising that German executives received bonus payments in the crisis despite negative stock returns. 1.6.3 Quantile Regression Summary statistics in Tables 1.1 to 1.5 indicate that compensation as well as firm performance variables are skewed. This raises the question whether observations in the right tale of the distributions drive mean estimates of pay-performance sensitivities. An obvious method to address this concern is quantile regression. This allows us to estimate the impact of the explanatory variables at different quantiles of the compensation distribution instead of estimating the mean effect over the whole distribution. Applying quantile regression to panel data is not straightforward. Koenker (2004) argues that simply including dummies for every group in the panel delivers biased coefficient estimates. He proposes a method to estimate coefficents that 39
Executive Compensation and Firm Performance in Germany vary for different quantiles, but fixed effects that are constant over the whole range of quantiles. However, the discussion in Bache et al. (2011) shows that this estimator only delivers unbiased results if either the periods or the groups (executives in our case) in the panel are sufficiently large. Our sample is small in terms of both time periods and groups, which makes quantile regression with a panel structure infeasible. Instead we run a quantile regression model without fixed effects. The results from quantile regression without fixed effects are not comparable to the results of the original fixed effects specificition. Hence, we compare the results from the quantile regression with coefficient estimates for the mean effects from an ordinary least squares regression, also without fixed effects. This gives us an indication whether the skewness of the variables has an impact on mean estimates. Table 1.12 shows the results from the ordinary least squares regression in column 1. The sum of the first two lines of Table 1.12 is the pay-performance sensitivity. With 1.105 the estimate for the pay-performance sensitivity from the ordinary least squares regression is considerably larger compared to the 0.433 estimate from the panel regression (Table 1.7 ). This difference arises because we do not control for executive-specific variation in this regression. Note that without executive fixedeffects firm size has significant explanatory power for total compensation. Columns 2 to 10 of Table 1.12 show the effects at different quantiles of the total compensation distribution. The pay-performance sensitivity is strictly increasing from 0.584 at the lowest to 1.979 at the highest quantile. At the median the payperformance sensitivity of 1.125 is very similar to the mean estimate of 1.105 from the ordinary least squares regression. Executives at the top of the compensation distribution earn more for generating EBIT than their colleagues at the bottom of the compensation distribution. The same effect occurs for the CEO status. CEOs earn higher total compensation and the gap between CEOs and non-CEOs grows when we look at higher percentiles of the distribution. The effect of firm size on the other hand is almost constant over the whole distribution. The estimates for the quantiles are largely in line with the results from the ordinary least squares regression. The mean pay-performance sensitivity is very similar to the pay-performance sensitivity at the median. The two regression approaches also deliver very similar estimates for the effect of firm size on total pay. Only the mean effect of being a CEO is quite different from the effect at the median. Overall results from the quantile regression suggest that the mean estimates for the pay- 40
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Erklärung Ich versichere hiermit,
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