Abstract

Although choice experiments 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 choice experiment, and we explore the ability of the new method to alleviate starting point bias. The new approach utilizes the distribution of preferences from a prior choice experiment to provide real-time feedback to respondents about our best guess of their willingness-to-pay for food attributes, and allows respondents to adjust and calibrate their values. The analysis utilizes data collected in 2017 in two U.S. 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-conduct the survey in May 2020 and implement the calibrated choice experiment. Conventional analysis of the 2020 choice experiment data shows willingness-to-pay is strongly influenced by a starting point: the higher the initial price a respondent encountered, the higher the absolute value of their willingness-to-pay. Despite this bias, we show that when respondents have the opportunity to update their willingness-to-pay when presented with the best-guess, the resulting calibrated willingness-to-pay is much less influenced by the random starting point.