Aggregating performance measures considerably reduces the complexity of a performance evaluation system. This dissertation analyzes the nature and characteristics of the aggregation of performance measures in institutional settings of multiple tasks and multiple periods. In multi-task settings, the number of tasks restricts the feasibility of a statistically sufficient aggregation and the nature of an economically sufficient aggregation. Statistical sufficiency of aggregation can be achieved only by multi-dimensional aggregate measures, whose minimum dimensionality is given by the number of tasks. If the number of aggregate measures is less than the number of tasks, an economically sufficient aggregation incurs loss of information even if there is no loss of information through the likelihood ratio. The results support the use of multi-dimensional aggregate measures to preserve the information content of performance measures in multi-task agencies. In a multi-period setting, the inter-temporal correlations among performance measures restrict the feasibility of a statistically sufficient aggregation and the nature of an economically sufficient aggregation. When performance measures are inter-temporally correlated, there is no statistically sufficient aggregation and an economically sufficient aggregation of the basic measures depends on the effort level to induce. The optimal aggregation is characterized by the agent's characteristics and the economic situation of the agency as well as the statistical properties of performance measures. In a long-term contract with multiple tasks, the inter-temporal covariance risk has a monotonic impact on the endogenous allocation of effort through the optimal relative incentive rate. The inter-temporal covariance risk, as well as the within-period risk premium, prevents the first best allocation of effort from being endogenously achieved even if the first best allocation is feasible. / Accounting

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