The International Handbook on Non-Market Environmental Valuation
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The International Handbook on Non-Market Environmental Valuation

Edited by Jeff Bennett

Non-market environmental valuation (NMEV) is undergoing a period of increased growth in both application and development as a result of increasing recognition of the role of economics in environmental policy issues. Against this backdrop, The International Handbook on Non-Market Environmental Valuation brings together world leaders in the field to advance the development and application of NMEV as a tool for policy-making.
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Chapter 14: Experimental Design Strategies for Stated Preference Studies Dealing with Non-market Goods

John M. Rose, Stuart Bain and Michiel C.J. Bliemer


John M. Rose, Stuart Bain and Michiel C.J. Bliemer INTRODUCTION Given that the vast majority of environmental goods are public goods not traded in real markets, the collection of high-quality data capable of eliciting reliable preference functions or estimating coherent welfare measures from surveyed respondents represents an extremely difficult task to accomplish in practice. Unlike other areas of economic study that deal primarily with private goods typically traded in real markets, the specific area of valuation related to the study of environmental and resource economics has, more than any other discipline, had to rely on stated intentions type techniques such as contingent valuation (CV) and stated choice (SC) methods in order to accomplish its goals. In this chapter, we are interested solely in SC surveys. In particular, this chapter deals specifically with the generation of experimental designs for environmental SC surveys. In reality, an experimental design is nothing more than a matrix of numbers that researchers use to assign values to the attributes of the alternatives present within the hypothetical choice tasks of SC surveys. By using experimental design theory, the assignment of these values occurs in some systematic (that is, non-random) manner. This is done so that analysts can control as many factors as possible influencing observed choices. In creating the experimental design in a very specific and precise manner, the analyst seeks to ensure, as much as is possible, the ability to capture reliable estimates that correspond to the attributes of interest and which are not confounded (again,...

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