Environmental and Resource Economics

, Volume 52, Issue 1, pp 109–131 | Cite as

Accounting for Latent Attitudes in Willingness-to-Pay Studies: The Case of Coastal Water Quality Improvements in Tobago



The study of human behaviour and in particular individual choices is of great interest in the field of environmental economics. Substantial attention has been paid to the way in which preferences vary across individuals, and there is a realisation that such differences are at least in part due to underlying attitudes and convictions. While this has been confirmed in empirical work, the methods typically employed are based on the arguably misguided use of responses to attitudinal questions as direct measures of underlying attitudes. As discussed in other literature, especially in transport research, this potentially leads to measurement error and endogeneity bias, and attitudes should rather be treated as latent variables. In this paper, we illustrate the use of such an Integrated Choice and Latent Variable model in the context of beach visitors’ willingness-to-pay for improvements in water quality. We show how a latent attitudinal variable, which we refer to as a pro-intervention attitude, helps explain both the responses from the stated choice exercise as well as answers to various rating questions related to respondent attitudes. The incorporation of the latent variable leads to important gains in model fit and substantially different willingness-to-pay patterns.


Integrated Choice and Latent Variable (ICLV) model Discrete choice Latent attitude Coastal water Beach recreation Taste heterogeneity 


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  1. Abou-Zeid M, Ben-Akiva M, Bierlaire M, Choudhury C, Hess S (2010) Attitudes and value of time heterogeneity. In: Vande Voorde E, Vanelslander T (eds) Applied transport economics—a management and policy perspective. De Boeck, forthcoming, pp 525–548Google Scholar
  2. Aldrich G, Grimsrud K, Thacher J, Kotchen M (2007) Relating environmental attitudes and contingent values: how robust are methods for identifying preference heterogeneity?. Environ Res Econ 374: 757–775CrossRefGoogle Scholar
  3. Alvarez-Daziano R, Bolduc D (2009) Canadian consumers perceptual and attitudinal responses toward green automobile technologies: an application of hybrid choice models, 2009 EAERE-FEEM-VIU European Summer School in Resources and Environmental Economics: Economics, Transport and Environment Venice International UniversityGoogle Scholar
  4. Ashok K, Dillon WR, Yuan S (2002) Extending discrete choice models to incorporate attitudinal and other latent variables. J Mark Res 39(1): 31–46CrossRefGoogle Scholar
  5. Baker G, Burnham T (2001) Consumer response to genetically modified foods: market segment analysis and implications for producers and policy makers. J Agric Resour Econ 26(2): 387–403Google Scholar
  6. Beck M, Rose J, Hensher D (2010) Identifying response bias in stated preference surveys: attitudinal influences in emissions charging and vehicle selection. Institute of Transport and Logistics Sudies, University of SydneyGoogle Scholar
  7. Beharry N (2008) Valuing benefits of improved coastal water quality for beach recreationists in tobago : a discrete choice experiment application. PhD thesis, University of York, UKGoogle Scholar
  8. Beharry-Borg N, Hensher DA, Scarpa R (2009) An analytical framework for joint versus separate decisions by couples in choice experiments: the case of coastal water quality in tobago. Environ Resour Econ 43: 95–117CrossRefGoogle Scholar
  9. Beharry-Borg N, Scarpa R (2010) Valuing quality changes in Caribbean coastal waters for heterogeneous beach visitors. Ecol Econ 69: 1124–1139CrossRefGoogle Scholar
  10. Ben-Akiva M, McFadden D, Gärling T, Gopinath D, Walker J, Bolduc D, Börsch-Supan A, Delquié P, Larichev O, Morikawa T et al (1999) Extended framework for modeling choice behavior. Mark Lett 10(3): 187–203CrossRefGoogle Scholar
  11. Ben-Akiva M, McFadden D, Train K, Walker J, Bhat C, Bierlaire M, Bolduc D, Boersch-Supan A, Brownstone D, Bunch DS et al (2002) Hybrid choice models: progress and challenges. Mark Lett 13(3): 163–175CrossRefGoogle Scholar
  12. Bolduc D, Ben-Akiva M, Walker J, Michaud A (2005) Hybrid choice models with logit kernel: applicability to large scale models. In: Lee-Gosselin M, Doherty S (eds) Integrated land-use and transportation models: behavioural foundations. Elsevier, Oxford, pp 275–302Google Scholar
  13. Boxall P, Adamowicz W (2002) Understanding heterogeneous preferences in random utility models: a latent class approach. Environ Resour Econ 23: 421–446CrossRefGoogle Scholar
  14. Breffle W, Morey E, Thacher J (2005) Combining attitudinal and choice data to improve estimates of preferences and preference heterogeneity: a FIML, discrete-choice, latent-class model, working paper Department of Economics. University of New Mexico, AlbuquerqueGoogle Scholar
  15. Daly A, Hess S, Train K (in press) Assuring finite moments for willingness to pay estimates from random coefficients models. Transportation.  10.1007/s11116-011-9331-3
  16. Daly AJ, Hess S, Patruni B, Potoglou D, Rohr C (in press) Using ordered attitudinal indicators in a latent variable choice model: a study of the impact of security on rail travel behaviour. Transportation.  10.1007/s11116-011-9351-z
  17. Doornik JA (2001) Ox: an object-oriented matrix language. Timberlake Consultants Press, LondonGoogle Scholar
  18. Eid J, Langeheine R, Diener E (2003) Comparing typological structures across cultures by multigroup latent class analysisa primer. J Cross Cult Psychol 342: 195–210CrossRefGoogle Scholar
  19. Fosgerau M, Bjørner TB (2006) Joint models for noise annoyance and willingness to pay for road noise reduction. Transp Res Part B 40: 164–178CrossRefGoogle Scholar
  20. Green P (1984) Hybrid models for conjoint analysis: an expository review. J Mark Res 21: 155–169CrossRefGoogle Scholar
  21. Greene WH, Hensher DA (2003) A latent class model for discrete choice analysis: contrasts with mixed logit. Transp Res Part B 37(8): 681–698CrossRefGoogle Scholar
  22. Hess S, Rose J (2008) Should reference alternatives in pivot design SC surveys be treated differently?. Environ Resour Econ 42: 297–317CrossRefGoogle Scholar
  23. Johansson MV, Heldt T, Johansson P (2005) Latent variables in a travel mode choice model: attitudinal and behavioural indicator variables, working paper Department of Economics. Uppsala University, SwedenGoogle Scholar
  24. Johansson MV, Heldt T, Johansson P (2006) The effects of attitudes and personality traits on mode choice. Transp Res Part A 40(6): 507–525Google Scholar
  25. Keane M, Harris K (1999) A model of health plan choice: inferring preferences and perceptions from a combination of revealed preference and attitudinal data. J Econom 89: 131–157Google Scholar
  26. Menezes LD, Bartholomew D (1996) New developments in latent structure analysis applied to social attitudes. J R Stat Soc 1592(Series A): 213224Google Scholar
  27. Milon JW, Scrogin D (2006) Latent preferences and valuation of wetland ecosystem restoration. Ecol Econ 56: 162–175CrossRefGoogle Scholar
  28. Morey E (1981) The demand for site-specific recreational activities: A characteristics approach. J Environ Econ Manag 8: 345–371CrossRefGoogle Scholar
  29. Morey E, Thacher J, Breffle W (2006) Using angling characteristics and attitudinal data to identify environmental preference classes: a latent class model. Environ Resour Econ 34: 91–115CrossRefGoogle Scholar
  30. Morey E, Thiene M, Salvo MD, Signorello G (2008) Using attitudinal data to identify latent classes that vary in their preference for landscape preservation. Ecol Econ 68: 536–546CrossRefGoogle Scholar
  31. Pennings J, Leuthold R (2000) The role of farmers behavioral attitudes and heterogeneity in futures contracts usage. J Agric Econ 82(4): 908–919CrossRefGoogle Scholar
  32. Temme D, Paulssen M, Dannewald T (2008) Incorporating latent variables into discrete choice models a simultaneous estimation approach using SEM software. Bus Res 1: 220–237Google Scholar
  33. Thacher J, Morey E, Craighead W (2005) Using patient characteristics and attitudinal data to identify treatment preference groups: a latent-class model. Depress Anxiety 212: 47–54CrossRefGoogle Scholar
  34. Train K (2009) Discrete choice methods with simulation, 2nd edn. Cambridge University Press, CambridgeGoogle Scholar
  35. Yamaguchi K (2000) Multinomial logit latent-class regression models: an analysis of the predictors of gender-role attitudes among japanese women. Am J Sociol 1056: 1702–1740CrossRefGoogle Scholar
  36. Yáñez M, Raveau S, de D. Ortúzar J (2010) Inclusion of latent variables in mixed logit models: modelling and forecasting. Transp Res Part A 44(9): 744–753Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Institute for Transport StudiesUniversity of LeedsLeedsUK
  2. 2.Sustainable Resources InstituteUniversity of LeedsLeedsUK

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