The importance of efficient designs in discrete choice experiments for environmental valuation

Abstract

Discrete Choice Experiments (DCEs) are considered to be an appealing approach that is nowadays frequently used among researchers in many fields. No exception is their application in the field of environmental valuation, where we usually deal with relatively small sample sizes. This Master thesis analyzes the efficiency of different experimental designs used in DCEs. Moreover, it tries to examine the effect of prior parameter values in generation of efficient designs for a DCE. All the results are applied on a real case study devoted to climate change adaptations of winemakers in the Rioja region, Spain. The results are obtained through simulation exercises, which are emphasized to be crucial for checking our statistical design before conducting a DCE. The conclusions highlight the use of a specific method to set the prior parameter values necessary for the generation of an efficient design. Finally, the simulation exercises also draw conclusions regarding the trustworthiness of results obtained in the real case study.