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Dynamic Learning and Context-Dependence in Sequential, Attribute-Based, Stated-Preference Valuation Questions

机译:基于属性的状态偏好评估价值问题中的动态学习和上下文相关性

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摘要

A hybrid stated-preference model is presented that combines the referendum contingent valuation response format with an experimentally designed set of attributes. A sequence of valuation questions is asked to a random sample in a mail-out mail-back format. Econometric analysis shows greater discrimination between alternatives in the final choice in the sequence, and the vector of preference parameters shifts. Lead and lag choice sets have a structural influence on current choices and unobserved factors induce positive correlation across the responses. These results indicate that people learn about their preferences for attribute-based environmental goods by comparing attribute levels across choice sets.
机译:提出了一个混合陈述-偏好模型,该模型结合了全民投票或有估价响应格式和一组实验设计的属性。以邮寄回邮格式向随机样本询问一系列评估问题。计量经济学分析显示,在序列中最终选择的备选方案之间存在更大的区分度,并且偏好参数的向量也发生了变化。超前和滞后选择集对当前选择具有结构性影响,未观察到的因素会在整个响应中引起正相关。这些结果表明,人们可以通过比较选择集中的属性级别来了解他们对基于属性的环境商品的偏好。

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