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Modeling Indivisible Demand

Author

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  • Sanghak Lee

    (Tippie College of Business, University of Iowa, Iowa City, Iowa 52242)

  • Greg M. Allenby

    (Fisher College of Business, Ohio State University, Columbus, Ohio 43210)

Abstract

Disaggregate demand in the marketplace exists on a grid determined by the package sizes offered by manufacturers and retailers. Although consumers may want to purchase a continuous-valued amount of a product, realized purchases are constrained by available packages. This constraint might not be problematic for high-volume demand, but it is potentially troubling when demand is small. Despite the prevalence of packaging constraints on choice, economic models of choice have been slow to deal with their effects on parameter estimates and policy implications. In this paper we propose a general framework for dealing with indivisible demand in economic models of choice, and we show how to estimate model parameters using Bayesian methods. Analyses of simulated data and a scanner-panel data set of yogurt purchases indicate that ignoring packaging constraints can bias parameter estimates and measures of model fit, which results in the inaccurate measures of metrics such as price elasticity and compensating value. We also show that a portion of nonpurchase in the data (e.g., 2.27% for Yoplait Original) reflects the restriction of indivisibility, not the lack of preference. The importance of demand indivisibility is also highlighted by the counterfactual study where the removal of the smallest package size (i.e., 4 oz) mainly results in nonpurchase in the yogurt category instead of switching to larger package sizes.

Suggested Citation

  • Sanghak Lee & Greg M. Allenby, 2014. "Modeling Indivisible Demand," Marketing Science, INFORMS, vol. 33(3), pages 364-381, May.
  • Handle: RePEc:inm:ormksc:v:33:y:2014:i:3:p:364-381
    DOI: 10.1287/mksc.2013.0829
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    References listed on IDEAS

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    Cited by:

    1. Bhat, Chandra R. & Mondal, Aupal & Asmussen, Katherine E. & Bhat, Aarti C., 2020. "A multiple discrete extreme value choice model with grouped consumption data and unobserved budgets," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 196-222.
    2. Jean-Pierre H. Dubé, 2018. "Microeconometric Models of Consumer Demand," NBER Working Papers 25215, National Bureau of Economic Research, Inc.
    3. Allenby, Greg M., 2017. "Structural forecasts for marketing data," International Journal of Forecasting, Elsevier, vol. 33(2), pages 433-441.
    4. John R. Howell & Sanghak Lee & Greg M. Allenby, 2016. "Price Promotions in Choice Models," Marketing Science, INFORMS, vol. 35(2), pages 319-334, March.
    5. Kim, Chul & Jun, Duk Bin & Park, Sungho, 2018. "Capturing flexible correlations in multiple-discrete choice outcomes using copulas," International Journal of Research in Marketing, Elsevier, vol. 35(1), pages 34-59.
    6. Ludovic Stourm & Raghuram Iyengar & Eric T. Bradlow, 2020. "A Flexible Demand Model for Complements Using Household Production Theory," Marketing Science, INFORMS, vol. 39(4), pages 763-787, July.
    7. Lee, Sanghak & Thomas, Suman Ann & Allenby, Greg M., 2020. "An economic analysis of demand of the very poor," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 544-556.
    8. Kim, Chul & Smith, Adam N. & Kim, Jaehwan & Allenby, Greg M., 2023. "Outside good utility and substitution patterns in direct utility models," Journal of choice modelling, Elsevier, vol. 49(C).
    9. Sanghak Lee & Sunghoon Kim & Sungho Park, 2022. "A sequential choice model for multiple discrete demand," Quantitative Marketing and Economics (QME), Springer, vol. 20(2), pages 141-178, June.
    10. Bhat, Chandra R., 2022. "A closed-form multiple discrete-count extreme value (MDCNTEV) model," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 65-86.
    11. Mou, Shandong & Robb, David J. & DeHoratius, Nicole, 2018. "Retail store operations: Literature review and research directions," European Journal of Operational Research, Elsevier, vol. 265(2), pages 399-422.
    12. Hung Tran & Tien Mai, 2023. "Network-based Representations and Dynamic Discrete Choice Models for Multiple Discrete Choice Analysis," Papers 2306.04606, arXiv.org.
    13. Lee, Sanghak & Kim, Hyowon & Kim, Jaehwan & Allenby, Greg M., 2018. "A choice model for mixed decision variables," Journal of choice modelling, Elsevier, vol. 28(C), pages 82-96.

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