Model choice and framing effects: Do discrete choice modeling decisions affect loss aversion estimates?

IF 2.8 3区 经济学 Q1 ECONOMICS Journal of Choice Modelling Pub Date : 2024-11-25 DOI:10.1016/j.jocm.2024.100524
Ruth Quainoo , Gregory Howard , Vasundhara Gaur , Corey Lang
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Abstract

This paper examines whether the presence and magnitude of estimated loss aversion (LA) in a discrete choice experiment is a function of modeling choice. The experiment examined preferences for utility-scale solar energy siting based on a series of installation attributes and changes in household electric bill (the payment vehicle, which can increase or decrease relative to the status-quo). We employ multiple discrete choice modeling approaches and show that, across all models, the implications of accounting for loss aversion are qualitatively similar and match theoretical predictions. Despite this similarity, when comparing results across models we find that model choice has substantial impacts on estimated loss aversion. Specifically, different models estimate loss/gain ratios below two and in excess of six for the same data set. Thus, the consequences of framing decisions, which are an important aspect of nonmarket valuation, are not just the provenance of survey and choice experiment design but may also be heavily influenced by empirical model choice.
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模型选择和框架效应:离散选择建模决策会影响损失规避估计值吗?
本文研究了离散选择实验中估计损失厌恶(LA)的存在和程度是否与建模选择有关。该实验根据一系列安装属性和家庭电费(支付工具,相对于现状可增加或减少)的变化,考察了人们对公用事业规模太阳能选址的偏好。我们采用了多种离散选择建模方法,结果表明,在所有模型中,考虑损失规避的影响在本质上是相似的,并且符合理论预测。尽管存在这种相似性,但在比较不同模型的结果时,我们发现模型选择对估计的损失规避有很大影响。具体地说,对于同一数据集,不同模型估计的损失/收益比率有的低于 2,有的超过 6。因此,作为非市场估价的一个重要方面,框架决策的后果不仅仅是调查和选择实验设计的结果,还可能在很大程度上受到经验模型选择的影响。
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来源期刊
CiteScore
4.10
自引率
12.50%
发文量
31
期刊最新文献
Editorial Board Latent class choice models with an error structure: Investigating potential unobserved associations between latent segmentation and behavior generation Model choice and framing effects: Do discrete choice modeling decisions affect loss aversion estimates? A consistent moment equations for binary probit models with endogenous variables using instrumental variables Transformation-based flexible error structures for choice modeling
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