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Discrete Choice Models for Commuting Interactions

Author

Listed:
  • Jan (J.) Rouwendal

    (Vrije Universiteit Amsterdam; Tinbergen Institute, The Netherlands)

  • Or Levkovich

    (Vrije Universiteit Amsterdam)

  • Ismir Mulalic

    (DTU, KRAKS)

Abstract

An emerging quantitative spatial economics literature models commuting interactions by a gravity equation that is mathematically equivalent to a multinomial logit model. This model is widely viewed as restrictive because of the independence of irrelevant alternatives (IIA) property that links substitution behavior in response to changes in the attractiveness of choice alternatives to choice probabilities in a mechanistic way. This is relevant for counterfactual analysis. In this paper we examine the appropriateness of the commuting model from a theoretical as well as an empirical point of view. We show that conventional specification tests of the multinomial logit model are of limited use when alternative specific constants are used, as is common in the recent literature, and offer no information with respect to the validity of IIA. In particular, we show that maximum likelihood estimation of relevant nested logit model is impossible because the crucial parameters are not identified. We discuss cross-nested and mixed logit as alternatives. We argue that a comparison between predicted and actual changes in commuting flows in response to a change in the attractiveness of choice alternatives provides a more informative test for the validity of the multinomial logit model for commuting interaction and report the results of such a test – as well as others – for data referring to Copenhagen.

Suggested Citation

  • Jan (J.) Rouwendal & Or Levkovich & Ismir Mulalic, 2017. "Discrete Choice Models for Commuting Interactions," Tinbergen Institute Discussion Papers 17-067/VIII, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20170067
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    File URL: https://papers.tinbergen.nl/17067.pdf
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    References listed on IDEAS

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    1. Stephen J. Redding & Esteban Rossi-Hansberg, 2017. "Quantitative Spatial Economics," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 21-58, September.
    2. Small, Kenneth A & Song, Shunfeng, 1992. ""Wasteful" Commuting: A Resolution," Journal of Political Economy, University of Chicago Press, vol. 100(4), pages 888-898, August.
    3. Ferdinando Monte & Stephen J. Redding & Esteban Rossi-Hansberg, 2018. "Commuting, Migration, and Local Employment Elasticities," American Economic Review, American Economic Association, vol. 108(12), pages 3855-3890, December.
    4. Rouwendal, Jan, 1998. "Search Theory, Spatial Labor Markets, and Commuting," Journal of Urban Economics, Elsevier, vol. 43(1), pages 1-22, January.
    5. Small, Kenneth A & Hsiao, Cheng, 1985. "Multinomial Logit Specification Tests," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 26(3), pages 619-627, October.
    6. Costas Arkolakis, 2016. "Optimal City Structure," 2016 Meeting Papers 301, Society for Economic Dynamics.
    7. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    8. Jonathan Eaton & Samuel Kortum, 2002. "Technology, Geography, and Trade," Econometrica, Econometric Society, vol. 70(5), pages 1741-1779, September.
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    Cited by:

    1. James Lennox, 2020. "More working from home will change the shape and size of cities," Centre of Policy Studies/IMPACT Centre Working Papers g-306, Victoria University, Centre of Policy Studies/IMPACT Centre.

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    More about this item

    Keywords

    quantitative spatial economics; multinomial logit; mixed logit; independence of irrelevant alternatives;
    All these keywords.

    JEL classification:

    • R1 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics
    • R2 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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