2003
DOI: 10.1007/s00199-001-0250-y
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Distributions for the first-order approach to principal-agent problems

Abstract: Summary. The first-order approach is a technical shortcut widely used in agency problems. The best known set of sufficient conditions for its validity are due to Mirrlees and Rogerson and require that the distribution function is convex in effort and has a likelihood ratio increasing in output. Only one nontrivial example was so far known to satisfy both properties. This note provides two rich families of examples displaying both properties.

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Cited by 44 publications
(28 citation statements)
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“…In particular, Jewitt (1988) observes that few distributions satisfy both the MLRP and CDFC conditions. One distribution was provided by Rogerson (1985) (attributed to Steve Matthews) and later two classes of di¤erentiable examples were provided by Licalzi and Spaeter (2003).…”
Section: Proposition 3: (I) Mlrp Implies That the Critical Ratio ( ; mentioning
confidence: 99%
“…In particular, Jewitt (1988) observes that few distributions satisfy both the MLRP and CDFC conditions. One distribution was provided by Rogerson (1985) (attributed to Steve Matthews) and later two classes of di¤erentiable examples were provided by Licalzi and Spaeter (2003).…”
Section: Proposition 3: (I) Mlrp Implies That the Critical Ratio ( ; mentioning
confidence: 99%
“…To verify the consistency of Equation (26) with the first-order approach, we now consider an example of distribution that satisfies the two properties that are sufficient for the first-order condition to be valid when the conditional distribution of loss is a function of e: the convexity of the distribution function with respect to effort F ee > 0 and the monotone likelihood ratio property (MLRP) (Milgrom 1981;Rogerson 1985;LiCalzi and Spaeter 2013).…”
Section: Optimal Retention Ratementioning
confidence: 99%
“…This distribution was proposed by Rogerson (1985). For two other distributions with the MLRP, see LiCalzi and Spaeter (2013). First, we need to compute the MLRP to make sure that the optimality conditions are satisfactory.…”
Section: Appendix B Specific Distribution and Constant Risk Aversionmentioning
confidence: 99%
“…He maintains the MLRC, but relaxes the CDFC by imposing other restrictions. 8 Later, LiCalzi and Spaeter (2003) show that some reasonable classes of distributions actually satisfy both the MLRC and the CDFC.…”
mentioning
confidence: 99%