Abstract
The discrete choice literature has evolved from the analysis of a choice of a single item from a fixed choice set to the incorporation of a vast array of more complex representations of preferences and choice set formation processes into choice models. Modern discrete choice models include rich specifications of heterogeneity, multi-stage processing for choice set determination, dynamics, and other elements. However, discrete choice models still largely represent socially isolated choice processes —individuals are not affected by the preferences of choices of other individuals. There is a developing literature on the impact of social networks on preferences or the utility function in a random utility model but little examination of such processes for choice set formation. There is also emerging evidence in the marketplace of the influence of friends on choice sets and choices. In this paper we develop discrete choice models that incorporate formal social network structures into the choice set formation process in a two-stage random utility framework. We assess models where peers may affect not only the alternatives that individuals consider or include in their choice sets, but also consumption choices. We explore the properties of our models and evaluate the extent of “errors” in assessment of preferences, economic welfare measures and market shares if network effects are present, but are not accounted for in the econometric model. Our results shed light on the importance of the evaluation of peer or network effects on inclusion/exclusion of alternatives in a random utility choice framework.
Highlights
The task of modeling individuals’ choices is definitely an ambitious one
This paper focuses on the role of social networks in the choice set formation stage
Through a series of Monte Carlo experiments, we investigate the consequences of estimating two-stage independent availability logit (IAL) models that ignore the effects of social networks on choice set formation (CSF) when the underlying data generating process (DGP) is influenced by social connections
Summary
The task of modeling individuals’ choices is definitely an ambitious one. Decisions are influenced by numerous factors that range from the number of alternatives and attributes like price and quality, to a more complex set of drivers like social norms, social pressure, and levels of scrutiny and anonymity. Consumers making automobile choices appear to rely heavily on reviews or consumer reports—which can be viewed as a grouping of trusted individuals This group may influence choice set formation, or attribute perception, or both. Through a series of Monte Carlo experiments, we investigate the consequences of estimating two-stage IAL models that ignore the effects of social networks on CSF when the underlying data generating process (DGP) is influenced by social connections. While network models perform very well, we find that IAL estimates of the welfare impacts and market share gains can significantly underestimate the true impacts of the project This downward bias increases with the degrees of social interactions and, when network effects are strong and operate through both CSF and choice channels, the IAL underestimates welfare and market share gains by approximately 190% and 77%, respectively.
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