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A flexible multiple discrete–continuous probit (MDCP) model: application to analysis of expenditure patterns of domestic tourists in India

Author

Listed:
  • Shobhit Saxena

    (Indian Institute of Science)

  • Abdul Rawoof Pinjari

    (Indian Institute of Science
    Indian Institute of Science (IISc))

  • Chandra R. Bhat

    (The University of Texas at Austin)

  • Aupal Mondal

    (The University of Texas at Austin)

Abstract

Traditional multiple discrete–continuous (MDC) choice models impose tight linkages between consumers’ discrete choice and the continuous consumption decisions due to the use of a single utility parameter driving both the decision to choose and the extent of choice. Recently, Bhat (Trans Res Part B Methodol 110:261–279, 2018) proposed a flexible MDCEV model that employs a utility function with separate parameters to determine the discrete choice and continuous consumption values. However, the flexible MDCEV model assumes an independent and identically distributed (IID) error structure across the discrete and continuous baseline utilities. In this paper, we formulate a flexible non-IID multiple discrete–continuous probit (MDCP) model that employs a multivariate normal stochastic distribution to allow for a more general variance–covariance structure. In doing so, we revisit Bhat (Trans Res Part B: Methodol 109: 238-256, 2018) flexible utility functional form and highlight that the stochastic conditions he used to derive the likelihood function are not always consistent with utility maximization. We offer an alternate interpretation of the model as representing a two-step decision-making process, where the consumers first decide which goods to choose and then decide the extent of allocation to each good. We demonstrate an application of the proposed flexible MDCP model to analyze households’ expenditure patterns on their domestic tourism trips in India. Our results indicate that, if the analyst is willing to compromise on the strict utility-maximizing aspect of behavior, while also enriching the behavioral dimension through the relaxation of the tie between the discrete and continuous consumption decisions, the preferred model would be the flexible non-IID MDCP model. On the other hand, if the analyst wants the model to be strictly grounded on utility-maximizing behavior (which may also have benefits by way of welfare measure computations), and is willing to assume a very tight tie between the discrete and continuous consumption decision processes, the preferred model would be the non-IID traditional MDCP model.

Suggested Citation

  • Shobhit Saxena & Abdul Rawoof Pinjari & Chandra R. Bhat & Aupal Mondal, 2024. "A flexible multiple discrete–continuous probit (MDCP) model: application to analysis of expenditure patterns of domestic tourists in India," Transportation, Springer, vol. 51(4), pages 1299-1326, August.
  • Handle: RePEc:kap:transp:v:51:y:2024:i:4:d:10.1007_s11116-022-10364-y
    DOI: 10.1007/s11116-022-10364-y
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    References listed on IDEAS

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    1. Khan, Mubassira & Machemehl, Randy, 2017. "Analyzing tour chaining patterns of urban commercial vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 102(C), pages 84-97.
    2. Hanemann, W Michael, 1984. "Discrete-Continuous Models of Consumer Demand," Econometrica, Econometric Society, vol. 52(3), pages 541-561, May.
    3. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
    4. Saxena, Shobhit & Pinjari, Abdul Rawoof & Bhat, Chandra R., 2022. "Multiple discrete-continuous choice models with additively separable utility functions and linear utility on outside good: Model properties and characterization of demand functions," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 526-557.
    5. Igal Hendel, 1999. "Estimating Multiple-Discrete Choice Models: An Application to Computerization Returns," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 66(2), pages 423-446.
    6. Koichi Yonezawa & Timothy J. Richards, 2017. "Consumer Risk‐reduction Behavior and New Product Purchases," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 38(7), pages 1003-1016, October.
    7. Pinjari, Abdul Rawoof, 2011. "Generalized extreme value (GEV)-based error structures for multiple discrete-continuous choice models," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 474-489, March.
    8. Chih-Wen Yang & Cheng-Lung (Richard) Wu & Jin-Long Lu, 2021. "Exploring the interdependency and determinants of tourism participation, expenditure, and duration: An analysis of Taiwanese citizens traveling abroad," Tourism Economics, , vol. 27(4), pages 649-669, June.
    9. von Haefen, Roger H. & Phaneuf, Daniel J., 2003. "Estimating preferences for outdoor recreation:: a comparison of continuous and count data demand system frameworks," Journal of Environmental Economics and Management, Elsevier, vol. 45(3), pages 612-630, May.
    10. Nader Asgary & Gilberto de los Santos & Vern Vincent & Victor Davila, 1997. "The Determinants of Expenditures by Mexican Visitors to the Border Cities of Texas," Tourism Economics, , vol. 3(4), pages 319-328, December.
    11. Pollak, Robert A & Wales, Terence J, 1992. "Price-Augmenting Returns to Scale: An Application to Nonseparable Two-Stage Technologies," The Review of Economics and Statistics, MIT Press, vol. 74(2), pages 213-220, May.
    12. Sobhani, Anae & Eluru, Naveen & Faghih-Imani, Ahmadreza, 2013. "A latent segmentation based multiple discrete continuous extreme value model," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 154-169.
    13. Andrea Pellegrini & Igor Sarman & Rico Maggi, 2021. "Understanding tourists’ expenditure patterns: a stochastic frontier approach within the framework of multiple discrete–continuous choices," Transportation, Springer, vol. 48(2), pages 931-951, April.
    14. Bhat, Chandra R., 2008. "The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 274-303, March.
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