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Environmental risk and welfare valuation under imperfect information

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  • Konishi, Yoshifumi
  • Coggins, Jay S.

Abstract

Consumers are often uninformed, or unsure, about the ambient level of environmental risk. An optimal policy must jointly determine efficient levels of self-protection, information provision, and public risk mitigation efforts. Unfortunately, conventional welfare measures are not amenable to welfare analysis in the presence of imperfect information. We develop a theoretical welfare measure, called quasi-compensating variation, that is a natural extension of compensating variation (CV). We show that this welfare measure offers not only a money metric of the "value of information," but also a means to appropriately evaluate the welfare effects of various policies when consumers are imperfectly informed about ambient risk. This welfare measure allows us to obtain a number of results that the traditional CV measure fails to offer. In particular, we show that the consumer's willingness to pay for a (small) environmental risk reduction is higher for those who underestimate ambient risk than for those who overestimate or are perfectly informed if the marginal return to self-protection increases with ambient risk.

Suggested Citation

  • Konishi, Yoshifumi & Coggins, Jay S., 2008. "Environmental risk and welfare valuation under imperfect information," Resource and Energy Economics, Elsevier, vol. 30(2), pages 150-169, May.
  • Handle: RePEc:eee:resene:v:30:y:2008:i:2:p:150-169
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    References listed on IDEAS

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    Cited by:

    1. Muamba, Francis & Kraybill, David S., 2010. "Risk Belief, Producer Demand, and Valuation of Improved Irrigations: Results from Field Experiments in Mt. Kilimanjaro," 2010 Annual Meeting, July 25-27, 2010, Denver, Colorado 61653, Agricultural and Applied Economics Association.
    2. Konishi, Yoshifumi & Adachi, Kenji, 2011. "A framework for estimating willingness-to-pay to avoid endogenous environmental risks," Resource and Energy Economics, Elsevier, vol. 33(1), pages 130-154, January.

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