We study a procurement problem where the distribution of types is determined by a productive, yet unobservable, prior effort of the agent. Productivity enhancing investments and learning are discussed as canonical examples. Our analysis explains unusually high levels of production for the most efficient firms, that is premia at the top. Distortions and overproduction at the top arise if the top is informative about investments. Distortions and premia at the top are the larger the more informative the top about unobserved investments. In the limiting case where the inference at the top is almost exact, optimal contracts can be approximated arbitrarily closely by standard procurement contracts adjusted by a reward for the top. In the context of learning we explain the emergence of extreme reward schemes when the principal is a quasi risk lover and the emergence of equalized incentive schemes when the principal is a quasi risk averter. These results are derived within a first order approach. Sufficient conditions validating the approach are derived and found less restrictive than those in pure moral hazard problems: contracts under asymmetric information ex post are necessarily monotonic while this has to be ensured in settings of pure moral hazard.
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Publisher Info
Paper provided by Sonderforschungsbereich 504, Universität Mannheim & Sonderforschungsbereich 504, University of Mannheim in its series Sonderforschungsbereich 504 Publications with number
01-49.
Length: 29 pages Date of creation: 19 Nov 2001 Date of revision: Handle: RePEc:xrs:sfbmaa:01-49
Note: Special thanks are due to Martin Hellwig. I also thank Sudipto Bhattacharya, Jacques Crémer, Christian Laux, Benny Moldovanu for very useful dicussions and comments. Financial support from the Deutsche Forschungsgemeinschaft, SFB 504, at the University of Mannheim, is gratefully acknowledged. Contact details of provider: Postal: D-68131 Mannheim Phone: (49) (0) 621-292-2547 Fax: (49) (0) 621-292-5594 Email: Web page: http://www.sfb504.uni-mannheim.de/ More information through EDIRC