Heterogeneity in General Multinomial Choice Models

TL;DR

This paper introduces a flexible heterogeneous multinomial logit model that captures unobserved heterogeneity in choice behavior, allowing for better analysis of nominal decision data across various fields.

Contribution

It develops a general model that accounts for heterogeneity beyond classical models and enables testing which variables cause this heterogeneity.

Findings

The model effectively captures heterogeneity in voting behavior.

It demonstrates improved analysis of spatial voting data.

The approach integrates theoretical expectations into heterogeneity analysis.

Abstract

Different voters behave differently, different governments make different decisions, or different organizations are ruled differently. Many research questions important to political scientists concern choice behavior, which involves dealing with nominal-scale dependent variables. Drawing on the principle of maximum random utility, we propose a flexible and general heterogeneous multinomial logit model for studying differences in choice behavior. The model systematically accounts for heterogeneity that is not captured by classical models, indicates the strength of heterogeneity, and permits examining which explanatory variables cause heterogeneity. As the proposed approach allows incorporating theoretical expectations about heterogeneity into the analysis of nominal dependent variables, it can be applied to a wide range of research problems. Our empirical example uses data on multiparty elections to demonstrate the benefits of the model in the study of heterogeneity in spatial voting.