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Two New Conditions Supporting the First‐Order Approach to Multisignal Principal–Agent Problems
Author(s) -
Conlon John R.
Publication year - 2009
Publication title -
econometrica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.7
H-Index - 199
eISSN - 1468-0262
pISSN - 0012-9682
DOI - 10.3982/ecta6688
Subject(s) - generalization , convexity , principal (computer security) , simple (philosophy) , space (punctuation) , function (biology) , computer science , order (exchange) , state space , mathematical economics , state (computer science) , mathematics , signal (programming language) , calculus (dental) , mathematical optimization , algorithm , economics , epistemology , statistics , mathematical analysis , medicine , philosophy , dentistry , finance , evolutionary biology , financial economics , biology , operating system , programming language
This paper presents simple new multisignal generalizations of the two classic methods used to justify the first‐order approach to moral hazard principal–agent problems, and compares these two approaches with each other. The paper first discusses limitations of previous generalizations. Then a state‐space formulation is used to obtain a new multisignal generalization of the Jewitt (1988) conditions. Next, using the Mirrlees formulation, new multisignal generalizations of the convexity of the distribution function condition (CDFC) approach of Rogerson (1985) and Sinclair‐Desgagné (1994) are obtained. Vector calculus methods are used to derive easy‐to‐check local conditions for our generalization of the CDFC. Finally, we argue that the Jewitt conditions may generalize more flexibly than the CDFC to the multisignal case. This is because, with many signals, the principal can become very well informed about the agent's action and, even in the one‐signal case, the CDFC must fail when the signal becomes very accurate.