Abstract: This paper empirically assesses the impact of human capital acquisition from the target firm through a merger or acquisition on post-acquisition CEO turnover in the acquiring firm. Using a sample of 236 mergers during 1996 to 2000 in the US, I find: (1) 46% of CEOs of acquiring firms are replaced within 5 years, 28% leave voluntarily, and 18% are forced to step down; (2) if the acquiring firm acquired the target firm's top executive through merger and retained as a top executive in the newly combined firm, the acquiring firm's CEO is more likely to leave; (3) if top executives of the target firm are retained as board directors in the merged entity, the acquiring firm's CEO is less likely to leave voluntarily, but no change occurs in the probability of being forced out.

Abstract: I investigated the finite sample performance of several estimators proposed for the panel data Tobit regression model with individual effects, including the Honor?estimator, the continuously updating GMM estimator, and the empirical likelihood estimator. The continuously updating GMM estimator and the empirical likelihood estimator are based on more conditional moment restrictions than the Honor?estimator, and consequently are more efficient than the Honor?estimator for large samples. My simulation study shows that the continuously updating GMM estimator, which is shown to have similar finite sample performance as the empirical likelihood estimator, performs not better, but in most cases is worse than the Honor?estimator for small samples. The reason for this finding is that all three estimators are based on the moment restrictions that require discarding observations, and in my design, over sixty percent of observations were discarded. The too few observations lead to an imprecise weighting matrix estimate, which in turn leads to an unreliable best GMM estimator. This study calls for an alternative estimation method that does not rely on trimming.

"Estimation of the Panel Data Censored Regression Model: A Simulation Study"