Although choice experiments (CEs) have emerged as the most popular stated preference method in applied economics, the method is not free from biases related to order and presentation effects. This paper introduces a new preference elicitation method referred to as a calibrated CE (CCE), and we explore the ability of the new method to alleviate starting-point bias. The new approach utilises the distribution of preferences from a prior CE to provide real-time feedback to respondents about our best guess of their willingness-to-pay (WTP) for food attributes and allows respondents to adjust and calibrate their values. The analysis utilises data collected in 2017 in two US cities, Phoenix and Detroit, on consumer preferences for local and organic tomatoes sold through supermarkets, urban farms and farmers’ markets to establish a prior preference distribution. We re-conducted the survey in May 2020 and implemented the CCE. Conventional analysis of the 2020 CE data shows that WTP is strongly influenced by a starting point: the higher the initial price respondents encountered, the higher the absolute value of their WTP. Despite this bias, we show that when respondents have the opportunity to update their WTP when presented with the best guess, the resulting calibrated WTP is much less influenced by the random starting point.

中文翻译：

---

一种校准选择实验方法

尽管选择实验 (CEs) 已成为应用经济学中最流行的陈述偏好方法，但该方法并非没有与顺序和呈现效应相关的偏差。本文介绍了一种新的偏好获取方法，称为校准 CE (CCE)，我们探讨了新方法减轻起点偏差的能力。新方法利用来自先前 CE 的偏好分布向受访者提供实时反馈，以了解我们对他们对食品属性的支付意愿 (WTP) 的最佳猜测，并允许受访者调整和校准他们的价值观。该分析利用了 2017 年在凤凰城和底特律这两个美国城市收集的关于消费者对通过超市销售的本地和有机西红柿的偏好的数据，城市农场和农贸市场建立优先优惠分配。我们于 2020 年 5 月重新进行了调查并实施了 CCE。对 2020 年 CE 数据的常规分析表明，WTP 受一个起点的强烈影响：受访者遇到的初始价格越高，其 WTP 的绝对值就越高。尽管存在这种偏见，但我们表明，当受访者有机会在给出最佳猜测时更新他们的 WTP 时，得到的校准 WTP 受随机起点的影响要小得多。