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BackgroundDiscrete choice experiments (DCEs) are increasingly used for health state valuations. However, the values derived from initial DCE studies vary widely. We hypothesize that these findings indicate the presence of unknown sources of bias that must be recognized and minimized. Against this background, we studied whether values derived from a DCE are sensitive to how well the DCE design spans the severity range.MethodsWe constructed an experiment involving three variants of DCE tasks for health state valuation: standard DCE, DCE-death, and DCE-duration. For each type of DCE, an experimental design was generated under two different conditions, enabling a comparison of health state values derived from current best practice Bayesian efficient DCE designs with values derived from ‘severity-stratified’ designs that control for coverage of the severity range in health state selection. About 3000 respondents participated in the study and were randomly assigned to one of the six study arms.ResultsImposing the severity-stratified restriction had a large effect on health states sampled for the DCE-duration approach. The unstratified efficient design returned a skewed distribution of selected health states, and this introduced bias. The choice probability of bad health states was underestimated, and time trade-offs to avoid bad states were overestimated, resulting in too low values. Imposing the same restriction had limited effect in the DCE-death approach and standard DCE.ConclusionVariation in DCE-derived values can be partially explained by differences in how well selected health states spanned the severity range. Imposing a ‘severity stratification’ on DCE-duration designs is a validity requirement.Electronic supplementary materialThe online version of this article (10.1007/s40273-018-0694-6) contains supplementary material, which is available to authorized users.

Disclosing the energy performance information for buildings has been expected to become an important policy for controlling energy demand and reducing CO2 emissions, but its effectiveness remains controversial. This study investigates the effect of energy performance information on consumer residential choice by using a discrete choice experiment in South Korea. The estimation results confirmed that the energy efficiency level of the given housing has a significant effect on consumer residential choice when the related information is actually delivered. Combined with evidence from the simulation study, we suggest that obligating the owners to provide energy performance information to potential buyers/tenants would be necessary for enhancing the use of the information during the consumer decision-making process. Additionally, the simulation result implies that the effectiveness of the policy can be underestimated by the price premium related to energy efficiency. Therefore, we suggest that the government should control the price premium for high-efficiency buildings at the early stage so that the policy related to disclosing the energy performance can be on track.

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In a big data environment, there are growing concerns about the violation of consumer rights regarding information privacy. To induce rational regulations for protecting personal information, it is necessary to separately estimate consumers' values related to different types of personal information. In this article, discrete choice experiments using hypothetical information leakage situations given certain compensation amounts and discrete choice models were used to quantitatively analyze the value of personal information. The results indicate that consumers generally place high value on information that could cause immediate and actual damage from the leakage after identification, such as basic personal information and purchase list and payment information. Consumers value location information and personal medical information differently based on their perceived importance of privacy and their prior experience with personal information leakage. We suggest that the level of regulation should differ according to the type of personal information based on the consumers' valuation. This article contributes to a better understanding of a quantitative approach to pricing personal information.