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dc.contributor.advisorBrown, Alexander L.
dc.contributor.advisorShaw, W. Douglass
dc.creatorChen, Sihong
dc.date.accessioned2012-10-19T15:29:20Z
dc.date.accessioned2012-10-22T18:05:43Z
dc.date.available2012-10-19T15:29:20Z
dc.date.available2012-10-22T18:05:43Z
dc.date.created2011-08
dc.date.issued2012-10-19
dc.date.submittedAugust 2011
dc.identifier.urihttps://hdl.handle.net/1969.1/ETD-TAMU-2011-08-10054
dc.description.abstractThis thesis is concerned with (i) how to estimate the perceived mortality risk, (ii) how to calculate the welfare change of mortality risk reduction and (iii) whether ambiguity aversion influences subjects' treatment decision. This study is an important topic in environmental and resource economics, and the attempt to introduce ambiguity preference into the models might shed light on future research in nonmarket valuation. In this study, I estimate the economic value of reducing mortality risk relating to arsenic in drinking water employing contingent valuation in U.S. arsenic hot spots. Re-cent studies have shown that perceived risk is a more reliable variable than scientific assessments of risk when applied to interpret and predict individual's averting behavior. I am also interested in the confidence level of perceived risk, which was elicited and treated as the degree of risk ambiguity in this paper. I develop a formal parametric model to calculate the mean willingness to pay (WTP) for mortality risk reduction, and find weak evidence of ambiguity aversion.en
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.subjectSubjective Risken
dc.subjectWillingness to Payen
dc.subjectAmbiguityen
dc.titleRisk Perception and Willingness to Pay for Removing Arsenic in Drinking Wateren
dc.typeThesisen
thesis.degree.departmentAgricultural Economicsen
thesis.degree.disciplineAgricultural Economicsen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelMastersen
dc.contributor.committeeMemberBessler, David A.
dc.type.genrethesisen
dc.type.materialtexten


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