An Empirical Model of Limited Consideration: Robust Inference for Risk Preferences

社会科学中的许多实证工作致力于通过观察个人从有限的备选方案集合(通常被称为“可行集合”)中选择一种产品来学习个人的偏好。然而，有大量的理论和应用文献横跨微观经济学、行为经济学、市场营销学和心理学，表明通常个人在做出选择之前并不真正考虑可行集合中的每一种选择。也有大量文献记载，即使在一群具有相似特征的个人中，个人的偏好--他们对不同产品的品味--也表现出很大的异质性。研究人员提出了两大类离散选择的经验模型，这两类模型既允许在个体考虑的备选方案集合中存在未被观察到的异质性，即“考虑集”，也允许在每个个体持有的偏好中。在一类模型中，偏好的异质性和考虑集的异质性被允许相互依赖。这项研究开发了一种方法来估计偏好和/或考虑集的分布，并对估计的分布进行推断。它还开发了一种方法来估计(和推断)政策干预的福利效应，例如让消费者更多地意识到特定产品或产品属性的干预，或者改变市场上的产品集合等。本研究的主要成果是一组实现该方法的便携式计算机程序的集合，这些程序将公开且免费地与社区共享。本研究提出了考虑集内未观察到的异质性的离散选择的新的经验模型。在本研究中考虑的模型中，决策者在他们所考虑的产品和他们的偏好上都是异质的。第一类模型对考虑集的形成过程没有限制，特别是允许决策者的随机考虑集与她的偏好和现有备选方案的可观察特征的无限制的依赖形式。由于其灵活性，该模型是部分但不是点识别的。第二类模型假定随机考虑集的特定分布(已知到参数)，建立在关于有限考虑/关注的微观经济理论文献的最新理论进展的基础上。然后，它的目的是提供弱条件，以实现对偏好中未观察到的异质性的非参数点识别，以及对考虑集分布的识别。这项研究的目的是为点或集识别分布提出具体的估计器/推断方法。这项研究进一步开发了经验研究人员可以用来实施这些方法的计算机程序包，并将与社区公开和免费分享。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Much empirical work in the social sciences is devoted to learning individuals' preferences from observing their choice of a product from a finite collection of alternatives (often referred to as "feasible set"). Yet, there is a large body of theoretical and applied literature spanning microeconomics, behavioral economics, marketing, and psychology, suggesting that often individuals do not actually consider every alternative in the feasible set before making their choice. There is also a wide literature documenting that individuals' preferences -- their tastes over different products -- exhibit large heterogeneity even within a group of individuals with similar characteristics. The investigators put forward two broad classes of empirical models of discrete choice that allow both for unobserved heterogeneity in the collection of alternatives that the individual considers, i.e. "consideration set", and in the preferences that each individual holds. In one class of models, heterogeneity in preferences and heterogeneity in consideration sets are allowed to depend on each other. This research develops a method to estimate the distribution of preferences and/or consideration sets, and conduct inference on the estimated distributions. It also develops a method to estimate (and conduct inference on) the welfare effect of policy interventions, e.g. ones that make consumers more aware of specific products or product attributes, or those that change the set of products in the market, etc. A primary output of this research is a collection of portable computer programs implementing the methodology, that will be shared with the community openly and free of charges or restrictions.This research puts forward new empirical models of discrete choice with unobserved heterogeneity in consideration sets. In the models considered in this research, decision makers are heterogeneous both in the products they consider and in their preferences. The first class of models places no restriction on the consideration set formation process and, in particular, allows for unrestricted forms of dependence of the decision maker's random consideration set with her preferences and with the observable characteristics of the available alternatives. Due to its flexibility, this model is partially but not point identified. The second class of models assumes specific distributions (known up to parameters) for the random consideration sets, building on recent theoretical advances in the microeconomic theory literature on limited consideration/attention. It then aims at providing weak conditions to achieve non-parametric point identification of unobserved heterogeneity in preferences, as well as identification of the distribution of consideration sets. This research aims at suggesting specific estimators/inference methods for the point or set identified distributions. This research further develops computer packages that empirical researcher can use to implement the methods, and that will be shared with the community openly and free of charges or restrictions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.