Relational contracts and the first-order approach

Relational Incentive Contracts and Performance Measurement

“…Hence we abstract away interesting problems of subjective measures such as leniency bias (MacLeod (2003)), favoritism(Prendergast and Topel (1996)), or influence activities(Milgrom (1988)).4 That is, our aim is not to provide a condition that ensures validity of FOA in the stationary environment of relational contracting, but to provide a condition under which the optimal bonus scheme has a simple hurdle structure as the FOA contract. A recent paper byHwang (2016) establishes a condition in the same environment as ours under which FOA is justified.…”

“…Our first main result on optimal bonus schemes in relational contracting complements the seminal work by Levin (2003), which characterizes an optimal incentive contract in the environment where FOA is valid and the univariate performance measure is exogenously given by the principal's objective (output). A recent paper by Hwang (2016) allows the principal to use alternative multivariate measures and establishes a sufficient condition on the signal's distribution and the agent's cost function under which the agent's expected payoff is globally concave and thus FOA is justified. Our approach is different from his in the aspect that instead of providing conditions that justify FOA, we seek conditions that ensure the optimal bonus scheme to take a simple form.…”

Relational Incentive Contracts and Performance Measurement

“…A recent paper by Hwang (2016) allows the principal to use alternative multivariate measures and establishes a sufficient condition on the signal's distribution and the agent's cost function under which the agent's expected payoff is globally concave and thus FOA is justified. Our approach is different from his in the aspect that instead of providing conditions that justify FOA, we seek conditions that ensure the optimal bonus scheme to take a simple form.…”

“…10 It is worthwhile to note that the generalized MLRP (GMLRP) is complementary to the condition of Hwang (2016), the local convexity of distribution function condition (LCDFC). As we have seen in the example above, when the additive noise has a small σ, the distribution of output does not satisfy LCDFC but obeys GMLRP.…”

“…A recent paper by Hwang (2016) establishes one sufficient condition for such global concavity that requires Pr(l(x, e † ) ≤ 0|c −1 (z)) to be convex in z, and shows that this condition (named LCDFC by the author, the local convexity of distribution function condition) is less restrictive than CDFC (the convexity of distribution function condition) first introduced by Mirrlees (1979). 16 However, as we will show shortly by an example, LCDFC does not hold and neither does the global constraint (1) in several interesting applications.…”