An Empirical Specification of Rational Inattention Multinomial Logit (RI- MNL) Model for Panel Datasets

Abstract

Abstract Classical discrete choice models are based on Random Utility Maximization (RUM), which assumes that decision makers have complete information on all of the attributes of their choices. Nowadays, this assumption is becoming less relevant with the expansion of the internet and social media, which provide a universal platform that grants anyone access to an endless amount of information. As a result, decision-making environments are transforming into settings in which individuals can be overwhelmed with information sources competing for their attention, causing them to become less attentive to the alternatives in their choice set. In such cases, individuals find themselves in scenarios where they wish to maximize utility but are uncertain about the payoffs associated with each action. There have been recent theoretical advancements in the study of the behaviour of inattentive decision makers in discrete choice contexts with the introduction of the Rational Inattention Multinomial Logit (RI-MNL) model. This paper proposes a closed-form empirical specification in the case of panel choice datasets to supplement the theoretical foundation of the (RI-MNL) model. Following this proposal is a sample application of the proposed specification using data from a panel survey on residential location preferences for the Greater Toronto Area in 2020 and 2021. This paper illustrates how the findings of the empirical model can be interpreted in the context of rational inattention theory.