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Stated preference modelling of intra-household decisions: Can you more easily approximate the preference space?

  • Matthew J. Beck
  • John M. Rose
Article
  • 221 Downloads

Abstract

While the study of choices focuses primarily on the individual decision maker, there is growing interest in the examination of the choices made by groups. Much of the research into the choices of multiple decision makers has revealed that they differ significantly to those of individuals. In this study of household vehicle choice we similarly compare individual choices to group choices and support this finding. Consequently any research into choices that involve groups should acquire data from those groups. In this paper we show how this may be done via an interactive agency choice experiment which makes the individual preferences endogenous to the choice of the group. This method and many like it, however, involve significant time, incentive and administrative costs that often make such studies prohibitive. In this paper we also compare another class of model, minimum information group inference, which is designed to provide an overview of the likely group choice and while not having the specificity of detail as other methods, has the advantage of being much easier and cheaper to implement and is perhaps the only methodology that can be employed when it is not feasible for respondents to interact. We find that this method is a good approximation of group choice, mapping the preference space over which group choice is likely to be located.

Keywords

Household choice Vehicle purchasing Stated preference Group choice 

Notes

Acknowledgements

The authors would like to acknowledge the comments and suggestions from three anonymous referees whose work has helped shape this paper.

Author contribution

Matthew Beck, in conjunction with John Rose, developed the idea for this paper, designed the experiment, collected the data, modelled the data, did the literature review, and wrote the paper.

References

  1. Arentze, T.A.: Individuals’ social preferences in joint activity location choice: a negotiation model and empirical evidence. J. Transp. Geogr. 48, 76–84 (2015)CrossRefGoogle Scholar
  2. Aribarg, A., Arora, N., Bodur, H.O.: Understanding the role of preference revision and concession in group decisions. J. Mark. Res. 39(3), 336–349 (2002)CrossRefGoogle Scholar
  3. Arora, N., Allenby, G.M.: Measuring the influence of individual preference structures in group decision making. J. Mark. Res. 36(4), 476–487 (1999)CrossRefGoogle Scholar
  4. Bateman, I.J., Munro, A.: Household versus individual valuation: what’s the difference. Environ. Resour. Econ. 43(1), 119–135 (2009)CrossRefGoogle Scholar
  5. Beharry-Borg, N., Hensher, D.A., Scarpa, R.: An analytical framework for joint vs separate decisions by couples in choice experiments: the case of coastal water quality in Tobago. Environ. Resour. Econ. 43(1), 95–117 (2009)CrossRefGoogle Scholar
  6. Bradley, M., Vovsha, P.: A model for joint choice of daily activity pattern types of household members. Transportation 32(5), 545–571 (2005)CrossRefGoogle Scholar
  7. Brewer, A.M., Hensher, D.A.: Distributed work and travel behaviour: the dynamics of interactive agency choices between employers and employees. Transportation 27, 117–148 (2000)CrossRefGoogle Scholar
  8. Corfman, K., Lehmann, D.: Models of cooperative group decision making and relative influence: an experimental investigation of family purchase decisions. J. Consum. Res. 14(1), 1–13 (1987)CrossRefGoogle Scholar
  9. Corfman, K., Lehmann, D.: The importance of others’ welfare in evaluating bargaining outcomes. J. Consum. Res. 20(1), 124–137 (1993)CrossRefGoogle Scholar
  10. Coulson, J.S.: Buying decisions within the family and the consumption-brand relationship. In: Newman, J.W. (ed.) On Knowing the Consumer. Wiley, New York (1966)Google Scholar
  11. Dellaert, B.G.C., Prodigalidad, M., Louviere, J.J.: Family members’ projections of each other’s preference and influence: a two-stage conjoint approach. Mark. Lett. 9(2), 135–145 (1998)CrossRefGoogle Scholar
  12. Dosman, D., Adamowicz, W.: Combining stated and revealed preference data to construct an empirical examination of intrahousehold bargaining. Rev. Econ. Househ. 4(1), 15–34 (2006)CrossRefGoogle Scholar
  13. Greene, W., Hensher, D.A.: A latent class model for discrete choice analysis: contrasts with mixed logit. Transp. Res. Part B 37(8), 681–698 (2003)CrossRefGoogle Scholar
  14. Gupta, S., Vovsha, P.: A model for work activity schedules with synchronization for multiple-worker households. Transportation 40(4), 827–845 (2013)CrossRefGoogle Scholar
  15. Haab, T.C., McConnell, K.E.: Valuing environmental and natural resources: the econometrics of non-market valuation. Edward Elgar, Cheltenham (2002)CrossRefGoogle Scholar
  16. Hensher, D.A.: Hypothetical bias, stated choice experiments and willingness to pay. Transp. Res. Part B 44(6), 735–752 (2010)CrossRefGoogle Scholar
  17. Hensher, D.A., Beck, M.J., Rose, J.M.: Accounting for preference and scale heterogeneity in establishing whether is matters who is interviewed to reveal household automobile purchase preferences? Environ. Resour. Econ. 49(1), 1–22 (2011)CrossRefGoogle Scholar
  18. Hensher, D.A., Puckett, S.M.: Power, concession and agreement in freight distribution chains subject to distance-based user charges. Int. J. Logist. Res. Appl. 11(2), 81–100 (2008)CrossRefGoogle Scholar
  19. Hensher, D.A., Rose, J.M., Black, I.: Interactive agency choice in automobile purchase decisions: the role of negotiation in determining equilibrium choice outcomes. J. Transp. Econ. Policy 42(2), 269–296 (2008)Google Scholar
  20. Hess, S., Ben-Akiva, M., Gopinath, D., Walker J.: Advantages of Latent Class Over Continuous Mixture of Logit Models. Working Paper. http://www.stephanehess.me.uk/papers/Hess_Ben-Akiva_Gopinath_Walker_May_2011.pdf (2011). Accessed 20 Apr 2015
  21. Inoa, I.A., Picard, N., de Palma, A.: Intra-household Decision Models of Residential and Job Location. THEMA Working Papers 2014-05, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise (2014)Google Scholar
  22. Krinsky, I., Robb, R.: On approximating the statistical properties of elasticities. Rev. Econ. Stat. 68(4), 715–719 (1986)CrossRefGoogle Scholar
  23. Krishnamurthi, L.: Conjoint models of family decision making. Int. J. Res. Mark. 5(3), 185–198 (1988)CrossRefGoogle Scholar
  24. Louviere, J.J., Hensher, D.A., Swait, J.: Stated choice methods: analysis and application. Cambridge University Press, Cambridge (2000)CrossRefGoogle Scholar
  25. Manski, C.F.: Economic analysis of social interactions. J. Econ. Perspect. 14(3), 115–136 (2000)CrossRefGoogle Scholar
  26. Marcucci, E., Stathopoulos, A., Danielis, R., Rotaris, L.: Comparing single and joint preferences: a choice experiment on residential location in three-member households. Environ. Plan. Part A 43(5), 1209–1225 (2012)CrossRefGoogle Scholar
  27. McFadden, D.: Economic choices. Am. Econ. Rev. 91(3), 351–378 (2001)CrossRefGoogle Scholar
  28. McFadden, D.: Overview of the Invitational Choice Symposium. Asilomar Conference Center, California (2001b)Google Scholar
  29. Molin, E.J.E., Oppewal, H., Timmermans, H.J.P.: Conjoint modeling of residential group preferences: a comparison of hierarchical information integration approaches. J. Geogr. Syst. 4(4), 343–358 (2003)CrossRefGoogle Scholar
  30. Myers, D.G., Lamm, H.: The group polarization phenomenon. Psychol. Bull. 83(4), 602–627 (1976)CrossRefGoogle Scholar
  31. O’Neill, V., Hess, S.: Heterogeneity assumptions in the specification of bargaining models: a study of household level trade-offs between commuting time and salary. Transportation 41(4), 745–763 (2014)CrossRefGoogle Scholar
  32. Puckett, S.M. (2006). Economic behaviour of interdependent road freight stakeholders under variable road user charges: advanced stated choice analysis. Ph.D. thesis, Institute of Transport and Logistics Studies, The University of SydneyGoogle Scholar
  33. Rao, V., Steckel, J.: A polarization model for describing group preferences. J. Consum. Res. 18(1), 108–118 (1991)CrossRefGoogle Scholar
  34. Rose, J.M., Bliemer, M.J.C.: Stated preference experimental design strategies. In: Hensher, D.A., Button, K.J. (eds.) Handbook of Transport Modelling. Elsevier, Oxford (2008)Google Scholar
  35. Rose, J.M., Bliemer, M.J.C., Hensher, D.A., Collins, A.: Designing efficient stated choice experiments in the presence of reference alternatives. Transp. Res. Part B 42(4), 395–406 (2008)CrossRefGoogle Scholar
  36. Rose, J.M., Hensher, D.A.: Modelling agent interdependency in group decision making: methodological approaches to interactive agent choice experiments. Transp. Res. Part E 40(1), 63–79 (2004)CrossRefGoogle Scholar
  37. Rungie, C., Scarpa, R., Thiene, M.: The influence of individuals in forming collective household preferences for water quality. J. Environ. Econ. Manag. 68(1), 161–174 (2014)CrossRefGoogle Scholar
  38. Scott, D.M., Kanaroglou, P.S.: An activity-episode generation model that captures interactions between household heads: development and empirical analysis. Transp. Res. Part B Methodol. 36(10), 875–896 (2002)CrossRefGoogle Scholar
  39. Srinivasan, S., Bhat, C.R.: Modeling household interactions in daily in-home and out-of-home maintenance activity participation. Transportation 32(5), 523–544 (2005)CrossRefGoogle Scholar
  40. Wen, C.H., Koppelman, F.: A conceptual and methodological framework for the generation of activity-travel patterns. Transportation 27(1), 5–23 (2000)CrossRefGoogle Scholar
  41. Wind, Y.: Preference of relevant others and individual choice models. J. Consum. Res. 3(1), 50–57 (1976)CrossRefGoogle Scholar
  42. Zhang, J., Fujiwara, A.: Intrahousehold interaction in transit-oriented residential choice behavior represented in stated preference approach. Transp. Res. Rec. 2134, 73–81 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Institute of Transport and Logistics StudiesThe University of SydneyDarlingtonAustralia
  2. 2.Business Intelligence and Data Analytics (BIDA)University of Technology, SydneyUltimoAustralia

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