当前位置: X-MOL 学术J. Agric. Econ. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Investigating cost non-attendance as a driver of inflated welfare estimates in mixed-logit models
Journal of Agricultural Economics ( IF 3.4 ) Pub Date : 2023-06-08 , DOI: 10.1111/1477-9552.12558
Curtis Rollins 1
Affiliation  

Choice models are used by applied economists for many purposes, such as non-market valuation or estimating willingness to pay for novel food and product attributes. Mixed-logit models allow researchers to account for preference heterogeneity and complex decision-making processes when modelling choices. In mixed-logit models, parameters of monetary attributes such as prices typically are assumed to follow a negative lognormal random distribution to ensure that the marginal utility of a price increase is strictly negative. However, this practice can cause means and standard deviations of welfare estimates to ‘explode’ to unfeasibly large levels, as the model assumes there are some marginal utilities of cost approaching zero. This paper examines whether cost non-attendance, which occurs when respondents ignore costs in stated-preference studies, could be a cause of inflated welfare estimates when a lognormal cost parameter is used. A two-class equality-constrained latent-class model is proposed, in which the cost parameter is fixed at zero for a cost non-attender class and is specified as a random lognormal parameter for cost attenders. This proposed model produces mean welfare estimates that are 17 times lower than a mixed-logit model with a lognormal cost parameter, and 10% lower than a model with a non-random cost parameter. These results suggest that cost non-attendance can result in inflated welfare estimates when employing a lognormal cost parameter, and that accounting for cost non-attendance could be a simple, parsimonious solution to this problem.

中文翻译:

调查缺勤成本是混合 Logit 模型中福利估计夸大的驱动因素

应用经济学家将选择模型用于多种目的,例如非市场估值或估计对新食品和产品属性的支付意愿。混合对数模型使研究人员能够在对选择进行建模时考虑偏好异质性和复杂的决策过程。在混合对数模型中,货币属性(例如价格)的参数通常被假设遵循负对数正态随机分布,以确保价格上涨的边际效用严格为负。然而,这种做法可能会导致福利估计的均值和标准差“爆炸”到不可行的大水平,因为该模型假设存在一些接近零的成本边际效用。本文探讨了当受访者在陈述偏好研究中忽略成本时,是否会出现缺勤成本,当使用对数正态成本参数时,可能会导致福利估计过高。提出了一种两类等式约束的潜在类模型,其中对于成本非参与者类,成本参数固定为零,而对于成本参与者,则将成本参数指定为随机对数正态参数。该模型产生的平均福利估计值比具有对数正态成本参数的混合 Logit 模型低 17 倍,比具有非随机成本参数的模型低 10%。这些结果表明,当采用对数正态成本参数时,成本缺勤可能会导致福利估计过高,而考虑成本缺勤可能是解决此问题的简单、简约的方法。提出了一种两类等式约束的潜在类模型,其中对于成本非参与者类,成本参数固定为零,而对于成本参与者,则将成本参数指定为随机对数正态参数。该模型产生的平均福利估计值比具有对数正态成本参数的混合 Logit 模型低 17 倍,比具有非随机成本参数的模型低 10%。这些结果表明,当采用对数正态成本参数时,成本缺勤可能会导致福利估计过高,而考虑成本缺勤可能是解决此问题的简单、简约的方法。提出了一种两类等式约束的潜在类模型,其中对于成本非参与者类,成本参数固定为零,而对于成本参与者,则将成本参数指定为随机对数正态参数。该模型产生的平均福利估计值比具有对数正态成本参数的混合 Logit 模型低 17 倍,比具有非随机成本参数的模型低 10%。这些结果表明,当采用对数正态成本参数时,成本缺勤可能会导致福利估计过高,而考虑成本缺勤可能是解决此问题的简单、简约的方法。该模型产生的平均福利估计值比具有对数正态成本参数的混合 Logit 模型低 17 倍,比具有非随机成本参数的模型低 10%。这些结果表明,当采用对数正态成本参数时,成本缺勤可能会导致福利估计过高,而考虑成本缺勤可能是解决此问题的简单、简约的方法。该模型产生的平均福利估计值比具有对数正态成本参数的混合 Logit 模型低 17 倍,比具有非随机成本参数的模型低 10%。这些结果表明,当采用对数正态成本参数时,成本缺勤可能会导致福利估计过高,而考虑成本缺勤可能是解决此问题的简单、简约的方法。
更新日期:2023-06-08
down
wechat
bug