Optimal relational bonus contracts when the first-order approach fails

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  • Optimal relational bonus contracts when the first-order approach fails
  • Abstract:
    It is well known that a scheme where the agent is paid a bonus if her performance exceeds some hurdle is optimal in relational contracting when the first-order approach (FOA) can be applied. The paper shows that if the monotone likelihood ratio property (MLRP) holds, then such a scheme is optimal also when FOA fails, but with an adjusted hurdle. In a setting where effort is measured with additive noise, I show that a more precise signal leads to higher equilibrium effort and surplus, and that effort converges to the full-information optimal effort when the uncertainty vanishes