We study benefits received by target chief executive officers (CEOs) in completed mergers and acquisitions. Certain target CEOs negotiate large cash payments in the form of special bonuses or increased golden parachutes. These negotiated cash payments are positively associated with the CEO's prior excess compensation and negatively associated with the likelihood that the CEO becomes an executive of the acquiring company. Regression estimates suggest that target shareholders receive lower acquisition premia in transactions involving extraordinary personal treatment of the CEO. Target CEOs experience very high turnover rates both at the time of acquisition and, for those who remain employed, for several years thereafter.

We document that backfilling in the ExecuComp database introduces a data-conditioning bias that can affect inferences and make replicating previous work difficult. Although backfilling can be advantageous due to greater data coverage, if not addressed, the oversampling of firms with strong managerial incentives and higher subsequent returns leads to a significant upward bias in abnormal compensation, pay-for-performance sensitivity, and the magnitudes of several previously established relations. The bias also can lead to one misinterpreting the appropriate functional form of a relation and whether the data support one compensation theory over another. We offer methods to address this issue.

We study benefits received by target chief executive officers (CEOs) in completed mergers and acquisitions. Certain target CEOs negotiate large cash payments in the form of special bonuses or increased golden parachutes. These negotiated cash payments are positively associated with the CEO's prior excess compensation and negatively associated with the likelihood that the CEO becomes an executive of the acquiring company. Regression estimates suggest that target shareholders receive lower acquisition premia in transactions involving extraordinary personal treatment of the CEO. Target CEOs experience very high turnover rates both at the time of acquisition and, for those who remain employed, for several years thereafter.

We document that backfilling in the ExecuComp database introduces a data-conditioning bias that can affect inferences and make replicating previous work difficult. Although backfilling can be advantageous due to greater data coverage, if not addressed, the oversampling of firms with strong managerial incentives and higher subsequent returns leads to a significant upward bias in abnormal compensation, pay-for-performance sensitivity, and the magnitudes of several previously established relations. The bias also can lead to one misinterpreting the appropriate functional form of a relation and whether the data support one compensation theory over another. We offer methods to address this issue. Received May 12, 2014; editorial decision May 10, 2016 by Editor David Hirshleifer.