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Modelling heterogeneity in scale directly: implications for estimates of influence in freight decision-making groups

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  • Puckett, Sean M.
  • Rose, John M.
  • Bain, Stuart

Abstract

The state of practice in the modelling of heterogeneous preferences does not separate the effects of scale from estimated mean and standard deviation preference measures. This restriction could lead to divergent behavioural implications relative to a flexible modelling structure that accounts for scale effects independently of estimated distributions of preference measures. The generalised multinomial logit (GMNL) model is such an econometric tool, enabling the analyst to identify the role that scale plays in impacting estimated sample mean and standard deviation preference measures, including confirming whether the appropriate model form approaches standard cases such as mixed logit. The GMNL model is applied in this paper to compare the behavioural implications of the minimum information group inference (MIGI) model within a study of interdependent road freight stakeholders in Sydney, Australia. MIGI estimates within GMNL models are compared with extant mixed logit measures (see Hensher and Puckett, 2008) to confirm whether the implications of the restrictive (with respect to scale) mixed logit model are consistent to those from the more flexible GMNL model. The results confirm the overall implication that transporters appear to hold relative power over supply chain responses to variable road-user charges. However, the GMNL model identifies a broader range of potential group decision-making outcomes and a restricted set of attributes over which heterogeneity in group influence is found than the mixed logit model. Hence, this analysis offers evidence that failing to account for scale heterogeneity may result in inaccurate representations of the bargaining set, and the nature of preference heterogeneity, in general.

Suggested Citation

  • Puckett, Sean M. & Rose, John M. & Bain, Stuart, 2012. "Modelling heterogeneity in scale directly: implications for estimates of influence in freight decision-making groups," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 50, pages 1-2.
    • Handle: RePEc:sot:journl:y:2012:i:50:n:2
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    References listed on IDEAS

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    1. Ann Brewer & David Hensher, 2000. "Distributed work and travel behaviour: The dynamics of interactive agency choices between employers and employees," Transportation, Springer, vol. 27(1), pages 117-148, February.
    2. Hensher, David A. & Puckett, Sean M. & Rose, John M., 2007. "Agency decision making in freight distribution chains: Establishing a parsimonious empirical framework from alternative behavioural structures," Transportation Research Part B: Methodological, Elsevier, vol. 41(9), pages 924-949, November.
    3. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
    4. William Greene & David Hensher, 2010. "Does scale heterogeneity across individuals matter? An empirical assessment of alternative logit models," Transportation, Springer, vol. 37(3), pages 413-428, May.
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    Cited by:

    1. Kragt, Marit Ellen, 2013. "Comparing models of unobserved heterogeneity in environmental choice experiments," Working Papers 144447, University of Western Australia, School of Agricultural and Resource Economics.

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