Multiple-Ranked Choice Identification in Exploded Logit Systems

In Simple Words

If a person picks a distant restaurant once, it might be a random fluke. But if that person is asked to rank their top three choices and repeatedly picks distant restaurants featuring high ratings, it proves a consistent preference for quality over travel distance.

Research question

Single-choice discrete demand models cannot distinguish whether an unexpected selection stems from unobserved individual preference weights or random idiosyncratic error terms. Without observing sequential preference orderings, researchers cannot reliably estimate the population distribution of taste parameters.

Participants

N = 36,887 human students in Charlotte-Mecklenburg Schools, North Carolina, submitting up to three ranked choices.

Study Design

Exploded multinomial mixed logit estimation where each submitted rank sequentially removes the chosen alternative from the decision set, generating within-individual choice set variation.

Results

Between 27.4% and 50.9% of parents who listed their guaranteed home-school first or second submitted subsequent choices beyond the default, providing within-person substitution patterns that isolated preference parameters from random unobserved utility shocks.

Takeaway

Elicit ranked preferences in choice surveys to dynamically modify individual choice sets and separate systematic utility weights from extreme-value error terms.

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