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Dynamic Quotas with Learning

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  • Karp, Larry
  • Costello, Christopher J

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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Bibliographic 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 qt88x3f17p.

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Date of creation: 18 Oct 2000
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Handle: RePEc:cdl:agrebk:qt88x3f17p

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Keywords: quotas; asymmetric infonnation; searching;

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References

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  1. Yohe, Gary Wynn, 1977. "Comparisons of price and quantity controls: A survey," Journal of Comparative Economics, Elsevier, vol. 1(3), pages 213-233, September.
  2. Michael Hoel & Larry Karp, 1999. "Taxes and Quotas for a Stock Pollutant with Multiplicative Uncertainty," Working Papers 1999.15, Fondazione Eni Enrico Mattei.
  3. Roberts, Marc J. & Spence, Michael, 1976. "Effluent charges and licenses under uncertainty," Journal of Public Economics, Elsevier, vol. 5(3-4), pages 193-208.
  4. Malcomson, James M, 1978. "Prices vs. Quantities: A Critical Note on the Use of Approximations," Review of Economic Studies, Wiley Blackwell, vol. 45(1), pages 203-07, February.
  5. Watson, William D. & Ridker, Ronald G., 1984. "Losses from effluent taxes and quotas under uncertainty," Journal of Environmental Economics and Management, Elsevier, vol. 11(4), pages 310-326, December.
  6. Stavins, Robert N., 1996. "Correlated Uncertainty and Policy Instrument Choice," Journal of Environmental Economics and Management, Elsevier, vol. 30(2), pages 218-232, March.
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Cited by:
  1. Karp, Larry & Zhang, Jiangfeng, 2001. "Bayesian Learning and the Regulation of Greenhouse Gas Emissions," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt2fr0783c, Department of Agricultural & Resource Economics, UC Berkeley.

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