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U.S. Consumer Demand for Plant-Based Milk Alternative Beverages: Hedonic Metric Augmented Barten’s Synthetic Model

机译：美国消费者对植物牛奶替代饮料的需求：烟台公制增强巴滕的合成模型

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摘要

Consumers in the U.S. increasingly prefer plant-based milk alternative beverages (abbreviated “plant milk”) to conventional milk. This study is motivated by the need to take into consideration varied nutritional and qualitative attributes in plant milk to examine consumers’ purchasing behavior and estimate demand elasticities which are achieved by a new approach combing hedonic pricing model with Barten’s synthetic demand system. The method of estimation is enlightened from the common practice of companies differentiating their products in multidimensions in terms of attributes. A research dataset was uniquely created by associating the products’ purchase data from Nielsen Homescan dataset with exclusive first-hand nutritional data. Estimations began with creating a multidimensional hedonic attribute space based on the qualitative information of different types of plant milk and conventional milk available to consumers and then calculating the hedonic distances by Euclidean distance measurement to reparametrize Barten’s synthetic demand system. Estimation results showed that the highest own-price elasticity pertained to soy milk which was −0.25. Three plant milk types had inelastic demand. Soy milk exerted substituting effects on all types of conventional milk products and vice versa. Soy milk, rice milk and almond milk entertained complementary relationships between each other and four types of conventional milk were strong substitutes within the group.
机译：美国消费者在美国越来越多地更喜欢植物的牛奶替代饮料（缩写“植物牛奶”）到常规牛奶。本研究的推动是需要考虑植物牛奶中不同的营养和定性属性，以检查消费者采购行为和估算需求弹性，这些方法是通过梳理啤酒馆的合成需求系统的新方法实现的。估计方法是从公司的常见实践中开明的，这些公司在属性方面将其产品区分开在多百分比中。通过使用独家第一手营养数据将产品的购买数据与独家第一手营养数据相关联，通过将产品的购买数据与独家第一手营养数据联系起来的研究数据集。估计开始基于不同类型的植物牛奶和常规牛奶的定性信息来创建多维eDonic属性空间，然后通过欧几里德距离测量计算储存蜂窝距离，以便Reparamize调整的合成需求系统。估算结果表明，豆浆的最高自身的弹性为-0.25。三种植物牛奶类型具有无弹性需求。大豆牛奶施加对所有类型的常规牛奶产品的替代效果，反之亦然。豆浆，米牛奶和杏仁牛奶互补的互补关系，四种类型的常规牛奶是本组内的强替代品。

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