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We propose a theory of variance analysis that is based on disappointment aversion. Extending a principal-agent model, we assume that the agent perceives corporate standards as reference points to which he compares his performances and feels disappointed if the performances fall short of the standards. We show that the agent’s disappointment aversion leads to the optimal design of variance analysis that is characterized by the control limit policy: Variance investigation takes place when the agent’s performance falls significantly out of range. The theory explains well the commonly observed conditional variance investigation, in particular, why firms often engage in lower-tail monitoring.
Hoa Ho, Ludwig Maximilian University of Munich
Christian Hofmann, Ludwig Maximilian University of Munich