Abstract
In addition to choice questions (revealed and stated choices), preference surveys typically include other questions that provide information about preferences. Preference-statement data include questions on the importance of different attributes of a good or the extent of agreement with a particular statement. The intent of this paper is to model and jointly estimate preference heterogeneity using stated-preference choice data and preference-statement data. The starting point for this analysis is the belief that the individual has preferences, and both his/her choices and preference statements are manifestations of those preferences. Our modeling contribution is linking the choice data and preference-statement data in a latent-class framework. Estimation is straightforward using the E-M algorithm, even though our model has hundreds of preference parameters. Our estimates demonstrate that: (1) within a preference class, the importance anglers associate with different Green Bay site characteristics is in accordance with their responses to the preference statements; (2) estimated across-class utility parameters for fishing Green Bay are affected by the preference-statement data; (3) estimated across-class preference-statement response probabilities are affected by the inclusion of the choice data; and (4) both data sets influence the number of classes and the probability of belonging to a class as a function of the individual’s type.
This is a preview of subscription content, access via your institution.
We’re sorry, something doesn't seem to be working properly.
Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.
Abbreviations
- E-M algorithm:
-
Expectation-maximization algorithm
- WTP:
-
Willingness-to-pay
- FCA:
-
Fish consumption advisory
- MWTP:
-
Marginal willingness-to-pay
- SP:
-
Stated preference
- RP:
-
Revealed preference
References
Adamowicz W, Louviere J, Williams M (1994) Combining revealed and stated preference methods for valuing environmental amenities. J Environ Econ Manag 26: 271–271
Akaike H (1974) A new look at the statistical model identification. IEEE Trans Automat Contr 19: 716–723
Aldrich G, Grimsrud K, Thacher J, Kotchen M (2006) Relating environmental attitudes and contingent values: how robust are methods for identifying heterogeneous groups? Environ Resour Econ doi:10.1007/s10640-006-9054-7
Bartholomew D, Leung S (2002) A goodness of fit test for sparse 2p contingency tables. Br J Math Stat Psychol 55(1): 1–15
Ben-Akiva M, Morikawa T (1990) Estimation of travel demand models from multiple data sources. In: Koshi M (eds) Transportation and traffic theory. Elsevier, New York
Ben-Akiva M, Walker M, Bernardino A, Gopinath D, Morikawa T, Polydoropoulos A (2002) Integration of choice and latent variable models. In: Mahmassani H (eds) Perpetual motion: travel behavior research opportunities and application challenges. Pergamon, Oxford
Boxall P, Adamowicz W (2002) Understanding heterogeneous preferences in random utility models: a latent class approach. Environ Resour Econ 23(4): 421–446
Bozdogan H (1987) Model selection and Akaike’s information criterion (AIC): the general theory and its analytical extensions. Psychometrika 52: 345–370
Breffle W, Morey E, Rowe R, Waldman D, Wytinck S (1999) Recreational fishing damages from fish consumption advisories in the waters of Green Bay. Technical report, Prepared by Stratus Consulting for US Fish and Wildlife Service
Breffle W, Rowe R (2002) Comparing choice question formats for evaluating natural resource tradeoffs. Land Econ 78(2): 65–82
Cameron T (1992) Combining contingent valuation and travel cost data for the valuation of nonmarket goods. Land Econ 68(3): 302–317
Choi A, Papandrea F, Bennett J (2007) Assessing cultural values: developing an attitudinal scale. J Cult Econ 31(4): 311–335
Colombo S, Hanley N (2007) Modelling preference heterogeneity in stated choice data for environmental goods: a comparison of random parameter, covariance heterogeneity and latent class logit models. EAERE Annual Conference, Thessalonica, Greece, pp 27–30
Cunha-e-Sá M, Madureira L, Nunes L, and Otrachshenko V (2010) Protesting or justifying? A latent-class model for contingent valuation with attitudinal data. Working paper
Dempster A, Laird N, Rubin D (1977) Maximum likelihood from incomplete observations. J R Stat Soc Ser B 39: 1–38
Dupont D (2004) Do children matter? An examination of gender difference in environmental valuation. Ecol Econ 49: 273–286
Eid M, Langeheine R, Diener E (2003) Comparing typological structures across cultures by multigroup latent class analysis - a primer. J Cross Cult Psychol 34(2): 195–210
Formann A (2003) Latent class model diagnostics—a review and some proposals. Comput Stat Data Anal 41: 549–559
GAUSS: (2000) Manual. Aptech Systems Inc, Maple Valley, WA
Greene W, Hensher D (2003) A latent class model for discrete choice analysis: contrasts with mixed logit. Transp Res B Methodol 37(8): 681–698
Hensher D, Bradley M (1993) Using stated response choice data to enrich revealed preference discrete choice models. Market Lett 4(2): 139–151
Hurvich M, Tsai C (1989) Regression and time series model selection in small samples. Biometrika 76(2): 297–307
Johnson R, Swallow S, Bauer D, Anderson C (2003) Resource economics review. Agric Resour Econ Rev 32(1): 65–82
Kemperman A, Timmermans H (2006) Preferences, benefits, and park visits: a latent class segmentation analysis. Tour Anal 11(4): 221–230
Kritzberg D, Morey E (2008) It’s not where you do it, but who you do it with? A companion and their relative ability as characteristics in site-specific recreational demand models. Working Paper
Lynne G and Rola L (1988) Improving attitude-behavior prediction models with economic variables: farmer actions toward soil conservation. J Soc Psychol 24(1)
Maydeu-Olivares A, Joe H (2005) Limited-and full-information estimation and goodness-of-fit testing in [2. Sup. N] contingency tables: a unified framework. J Am Stat Assoc 100(471): 1009–1021
McCutcheon A (1987) Sexual morality, pro-life values, and attitudes toward abortion - a simultaneous latent structure analysis for 1978-1983. Sociol Methods Res 16(2): 256–275
McFadden D (1986) The choice theory approach to market research. Marketing Science 5(4): 275–297
Morey E, Thacher J, Breffle W (2006) Using angler characteristics and attitudinal data to identify environmental preference classes: a latent-class model. Environ Resour Econ 34(1): 91–115
Morey E, Thiene M, De Salvo M, Signorello G (2008) Using attitudinal data to identify latent classes that vary in their preference for landscape preservation. Ecol Econ 68(1–2): 536–546
Morikawa T, Ben-Akiva M, McFadden D (2002) Discrete choice models incorporating revealed preference and psychometric data. In: PH F, Montgomery A (eds) Economic models in marketing, vol 16. Elsevier Science, Oxford
Owen A, Videras J (2007) Culture and public goods: the case of religion and the voluntary provision of environmental quality. J Environ Econ Manag 54(2): 162–180
Patunru A, Braden J, and Chattopadhyay S (2007) Who cares about environmental stigmas and does it matter? A latent segmentation analysis of stated preferences for real estate. Am J Agric Econ 1–15. doi:10.1111/j.1467-8276.2007.00988
Provencher B, Baerenklau K, Bishop R (2002) A finite mixture logit model of recreational angling with serially correlated random utility. Am J Agric Econ 84(4): 1066–1075
R Development Core Team (2005) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org
Reiser M and Lin Y (1999) A goodness of fit test for the latent class model when expected frequencies are small. Sociol Methodol 81–111
Scarpa R, Thiene M (2005) Destination choice models for rock-climbing in the North-East Alps: a latent-class approach investigating intensity of preferences. Land Econ 81(3): 426–444
Scarpa R, Willis K, Acutt M (2005) Individual-specific welfare measures for public goods: a latent class approach to residential customers of Yorkshire water. In: Koundouri (ed) Econometrics informing natural resource management. Edward Elgar Publisher, Aldershot
Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6: 461–464
Smith V (2009) Personal communication on combining data types. personal email
Swait J, Sweeney J (2000) Perceived value and its impact on choice behavior in a retail setting. J Retail Consum Services 7(2): 77–88
Thacher J, Morey E, Craighead E (2005) Using patient characteristics and attitudinal data to identify treatment preference groups: a latent-class model. Depress Anxiety 21(2): 47–54
Timmins C, Murdock J (2007) A revealed preference approach to the measurement of congestion in travel cost models. J Environ Econ Manag 53: 230–249
Train K (2003) Discrete choice methods with simulation, 1st edn. Cambridge University Press, Cambridge
Vermunt J, Magidson J (2003) Latent GOLD choice. Statistical Innovations, Belmont
Vermunt J, Magidson J (2005) Latent GOLD. Statistical Innovations, Belmont
Ward K, Stedman R, Luloff A, Shortle J, Finley J (2008) Categorizing deer hunters by typologies useful to game managers: A latent-class model. Soc Nat Resour 21(3): 215–229
Wedel M, Kamakura W (2000) Market segmentation: conceptual and methodological foundations, second edition. Kluwer, Boston
Yang CC, Yang CC (2007) Separating latent classes by information criteria. J Classif 24: 183–203
Author information
Authors and Affiliations
Corresponding author
Additional information
William S. Breffle, Edward R. Morey, Jennifer A. Thacher have equally contributed.
Rights and permissions
About this article
Cite this article
Breffle, W.S., Morey, E.R. & Thacher, J.A. A Joint Latent-Class Model: Combining Likert-Scale Preference Statements With Choice Data to Harvest Preference Heterogeneity. Environ Resource Econ 50, 83–110 (2011). https://doi.org/10.1007/s10640-011-9463-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10640-011-9463-0
Keywords
- Latent class
- E-M algorithm
- Choice data
- Preference statements
- Likert-scale
- Preferences
- Heterogeneity