Early versus late effort in dynamic agencies with learning about productivity

In this paper we analyze a dynamic agency problem where contracting parties learn about the agent´s future productivity over time. We consider a two period model where both the agent and the principal observe the agent´s second period performance productivity at the end of the first period. This observation is assumed to be non verifiable information. We compare long-term contracts to short-term contracts with respect to their suitability to motivate effort in both periods. On the one hand short-term agreements allow for a better fine-tuning of second period incentives as they can be aligned to the observation of the agent´s second period performance productivity. On the other hand in short-term agreements the effect of early effort on future performance is ignored as contracts have to be sequentially optimal. Hence, the difference between long-term and short-term agreements is characterized by a trade-off between inducing effort in the first and in the second period. We analyze the determinants of this trade-off and demonstrate its implications for performance easurement and information system design (e.g. we compare accrual to cash-accounting).