Amel Awadelkarim, Arjun Seshadri, Itai Ashlagi, Irene Lo, Johan Ugander
arXiv 1 Jun 2023 · Statistics — Applications · 2 citations (OpenAlex)
arXiv:2306.01801 · PDF · DOI · OpenAlex · Extracted main text
School choice mechanism designers use discrete choice models to understand and predict families' preferences. The most widely-used choice model, the multinomial logit (MNL), is linear in school and/or household attributes. While the model is simple and interpretable, it assumes the ranked preference lists arise from a choice process that is uniform throughout the ranking, from top to bottom. In this work, we introduce two strategies for rank-heterogeneous choice modeling tailored for school choice. First, we adapt a context-dependent random utility model (CDM), considering down-rank choices as occurring in the context of earlier up-rank choices. Second, we consider stratifying the choice modeling by rank, regularizing rank-adjacent models towards one another when appropriate. Using data on household preferences from the San Francisco Unified School District (SFUSD) across multiple years, we show that the contextual models considerably improve our out-of-sample evaluation metrics across all rank positions over the non-contextual models in the literature. Meanwhile, stratifying the model by rank can yield more accurate first-choice predictions while down-rank predictions are relatively unimproved. These models provide performance upgrades that school choice researchers can adopt to improve predictions and counterfactual analyses.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Arjun Seshadri, Stephen Ragain, and Johan Ugander (2020) Learning Rich Rankings. In Advances in Neural Information Processing Systems, Vol. 33. 9435–9446 self | 1.000 | 5 | 3 | 100% |
| 2 | Randall G. Chapman and Richard Staelin (1982) Exploiting Rank Ordered Choice Set Data within the Stochastic Utility Model | 0.928 | 4 | 3 | 100% |
| 3 | Jerry A. Hausman and Paul A. Ruud (1987) Specifying and testing econometric models for rank-ordered data | 0.874 | 6 | 4 | 67% |
| 4 | R. Duncan Luce (1959) Individual Choice Behavior: A Theoretical analysis | 0.737 | 3 | 2 | 100% |
| 5 | Jonathan Tuck, Shane Barratt, and Stephen Boyd (2021) A Distributed Method for Fitting Laplacian Regularized Stratified Models | 0.737 | 3 | 2 | 100% |
| 6 | Caterina Calsamiglia, Chao Fu, and Maia Güell (2020) Structural Estimation of a Model of School Choices: The Boston Mechanism versus Its Alternatives | 0.644 | 2 | 2 | 100% |
| 7 | Dennis Fok, Richard Paap, and Bram Van Dijk (2012) A rank-ordered logit model with unobserved heterogeneity in ranking capabilities | 0.644 | 2 | 2 | 100% |
| 8 | Justine S. Hastings, Thomas J. Kane, and Douglas O. Staiger (2008) Heterogeneous Preferences and the Efficacy of Public School Choice | 0.644 | 2 | 2 | 100% |
| 9 | R. Duncan Luce (1977) The choice axiom after twenty years | 0.644 | 2 | 2 | 100% |
| 10 | Daniel McFadden and Kenneth Train (2000) Mixed MNL models for discrete response | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 42 scored citations.