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Dynamic Quotas with Learning

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Author Info
Larry Karp (University of California, Berkeley and Giannini Foundation)
Christopher Costello (University of California, Santa Barbara)

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Abstract

We study the optimal quota sequence, in a stationary environment, where a regulator and a non-strategic firm have asymmetric information, The regulator is able to learn about the unknown cost parameter by using a quota that is slack with positive probability, It is never optimal for the regulator to learn gradually, In the first period, he either ignores the possibility of learning, or he tries to improve his information, Regardless of the outcome in the first period, he never experiments in subsequent periods.

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Publisher Info
Paper provided by Department of Agricultural & Resource Economics, UC Berkeley in its series Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series with number 914.

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Date of creation: 18 Oct 2000
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Handle: RePEc:cdl:agrebk:914

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Related research
Keywords: quotas; asymmetric infonnation; searching;

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