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Mixed Logit Models for Multiparty Elections

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  • Glasgow, Garrett

Abstract

Mixed logit (MXL) is a general discrete choice model thus far unexamined in the study of multicandidate and multiparty elections. Mixed logit assumes that the unobserved portions of utility are a mixture of an IID extreme value term and another multivariate distribution selected by the researcher. This general specification allows MXL to avoid imposing the independence of irrelevant alternatives (IIA) property on the choice probabilities. Further, MXL is a flexible tool for examining heterogeneity in voter behavior through random-coefficients specifications. MXL is a more general discrete choice model than multinomial probit (MNP) in several respects, and can be applied to a wider variety of questions about voting behavior than MNP. An empirical example using data from the 1987 British General Election demonstrates the utility of MXL in the study of multicandidate and multiparty elections.

Suggested Citation

  • Glasgow, Garrett, 2001. "Mixed Logit Models for Multiparty Elections," Political Analysis, Cambridge University Press, vol. 9(2), pages 116-136, January.
    • Handle: RePEc:cup:polals:v:9:y:2001:i:02:p:116-136_00
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    Cited by:

    1. M. Lefebvre & C. Biguzzi & E. Ginon & S. Gomez-y-Paloma & S. R. H. Langrell & S. Marette & G. Mateu & A. Sutan, 2017. "Mandatory integrated pest management in the European Union: experimental insights on consumers’ reactions," Review of Agricultural, Food and Environmental Studies, Springer, vol. 98(1), pages 25-54, July.
    2. Joan L. Walker & Moshe Ben-Akiva & Denis Bolduc, 2007. "Identification of parameters in normal error component logit-mixture (NECLM) models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(6), pages 1095-1125.
    3. Schillaci Carmela Elita & Romano Marco & Nicotra Melita, 2013. "Territory’s Absorptive Capacity," Entrepreneurship Research Journal, De Gruyter, vol. 3(1), pages 109-126, January.
    4. Schmidt, Tobias, 2007. "Motives for Innovation Co-operation? Evidence from the Canadian Survey of Innovation," ZEW Discussion Papers 07-018, ZEW - Leibniz Centre for European Economic Research.
    5. Kiran Tomlinson & Austin R. Benson, 2022. "Graph-Based Methods for Discrete Choice," Papers 2205.11365, arXiv.org, revised Nov 2023.
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    7. Tobias Schmidt, 2010. "Absorptive capacity-one size fits all? A firm-level analysis of absorptive capacity for different kinds of knowledge," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 31(1), pages 1-18.
    8. Wagner Antonio Kamakura, 2016. "Using Voter-choice Modeling to Plan Final Campaigns in Runoff Elections," RAC - Revista de Administração Contemporânea (Journal of Contemporary Administration), ANPAD - Associação Nacional de Pós-Graduação e Pesquisa em Administração, vol. 20(6), pages 753-776.
    9. Shaoming Cheng, 2007. "Structure of Firm Location Choices: An Examination of Japanese Greenfield Investment in China," Asian Economic Journal, East Asian Economic Association, vol. 21(1), pages 47-73, March.
    10. Jie Yu & Peter Goos & Martina Vandebroek, 2009. "Efficient Conjoint Choice Designs in the Presence of Respondent Heterogeneity," Marketing Science, INFORMS, vol. 28(1), pages 122-135, 01-02.
    11. Carmela Elita Schillaci & Marco Romano & Melita Nicotra, 2012. "Science Parks and Entrepreneurship: Enhancing Territorial Absorptive Capacity in a Hostile Region," DSI Essays Series, DSI - Dipartimento di Studi sull'Impresa, vol. 28.

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