A calibrated choice experiment method
提出一种新的偏好诱导方法：校准选择实验，通过向受访者提供基于先前实验的支付意愿最佳估计并允许其调整，来缓解起始点偏差。基于2017年和2020年对美国消费者对本地和有机番茄偏好的调查数据，发现校准后的支付意愿受随机起始点影响更小。
Abstract 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.