Environmental and Resource Economics

, Volume 54, Issue 2, pp 201–221 | Cite as

The Effects of Changing Cost Vectors on Choices and Scale Heterogeneity

Article

Abstract

Choice Experiments (CE) are widely used to estimate the values of changes in non-market goods and services. A cost attribute is typically included in a CE questionnaire to enable the estimation of monetary values for changes in the non-market attributes presented. Notwithstanding the central importance of the cost attribute, relatively little research has been undertaken on the impacts of varying cost attribute levels on value estimates, or on individual heterogeneity. In this paper, I present results from mixed logit and generalised mixed logit models that account for unobserved idiosyncratic preference and scale heterogeneity. Respondents are found to anchor their choices on the relative cost levels presented in the survey with results suggesting that people are more sensitive to relative rather than absolute cost vectors. However, the higher cost levels do not lead to significantly higher value estimates, partly because of observed preference heterogeneity towards the environmental attributes. An important observation is that scale heterogeneity is important: accounting for scale— as well as preference—heterogeneity in the generalised mixed logit model leads to significantly improved model fit. The results indicate significant unobserved error variance across respondents, unrelated to whether a high or low cost vector is used.

Keywords

Anchoring effects Choice modelling Environmental valuation Generalised mixed logit models Non-market valuation Preference heterogeneity Reference dependency Scale effects 

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References

  1. ABS (2006) 2006 Census. Australian Bureau of Statistics, CanberraGoogle Scholar
  2. ABS (2007) Statistics—Tasmania, 2007. Australian Bureau of Statistics. Australian Bureau of Statistics, CanberraGoogle Scholar
  3. Ariely D, Loewenstein G, Prelec D (2003) Coherent arbitrariness: stable demand curves without stable preferences. Q J Econ 118(1): 73–105CrossRefGoogle Scholar
  4. Ariely D, Loewenstein G, Prelec D (2006) Tom Sawyer and the construction of value. J Econ Behav Organ 60(1): 1–10CrossRefGoogle Scholar
  5. Banzhaf SH (2010) Consumer surplus with apology: a historical perspective on nonmarket valuation and recreation demand. Annu Rev Resour Econ 2(1): 183–207CrossRefGoogle Scholar
  6. Bateman I, Langford I, Rabash J (1999) Willingness to pay question format effects in contingent valuation. In: Bateman IJ, Willis KG (eds) Valuing environmental preferences: theory and practice of the contingent valuation method in the US, EU and Developing countries. Oxford University Press, OxfordGoogle Scholar
  7. Bateman IJ, Burgess D, Hutchinson WG, Matthews DI (2008a) Learning design contingent valuation (LDCV): NOAA guidelines, preference learning and coherent arbitrariness. J Environ Econ Manag 55: 127–141CrossRefGoogle Scholar
  8. Bateman IJ, Carson RT, Day B, Dupont D, Louviere JJ, Morimoto S, Scarpa R, Wang P (2008b) Choice set awareness and ordering effects in discrete choice experiments. CSERGE Working Paper EDM 08-01. IIED, LondonGoogle Scholar
  9. Bateman IJ, Day BH, Dupont D, Georgiou S, Louviere JJ, Morimoto S, Wang P (2004) Preference formation in choice experiments (CE): task awareness and learning in cognitive process. In: Paper presented at the 2004 EAERE conference, Budapest, 25–28 JuneGoogle Scholar
  10. Braga J, Starmer C (2005) Preference anomalies, preference elicitation and the discovered preference hypothesis. Environ Resour Econ 32(1): 55–89CrossRefGoogle Scholar
  11. Break O’Day Council (2007) Georges catchment and estuary project overview. Break O’Day Council, St. HelensGoogle Scholar
  12. Campbell D, Hutchinson WG, Scarpa R (2008) Incorporating discontinuous preferences into the analysis of discrete choice experiments. Environ Resour Econ 41(3): 401–417CrossRefGoogle Scholar
  13. Carlsson F, Martinsson P (2001) Do hypothetical and actual marginal willingness to pay differ in choice experiments? Application to the valuation of the environment. J Environ Econ Manag 41(2): 179– 192CrossRefGoogle Scholar
  14. Carlsson F, Martinsson P (2008) How much is too Much? An investigation of the effect of the number of choice sets, context dependence and the choice of bid vectors in choice experiments. Environ Resour Econ 40(2): 165–176CrossRefGoogle Scholar
  15. Carson RT, Hanemann MW (2005) Contingent valuation. In: Mäler K-G, Vincent JR (eds) Handbook of environmental economics. Elsevier, AmsterdamGoogle Scholar
  16. Daly A, Hess S, Train K (2011) Assuring finite moments for willingness to pay in random coefficient models. Transportation Published online, 01 April 2011Google Scholar
  17. Davies PE, Long J, Brown M, Dunn H, Heffner D, Knight R (2005) The Tasmanian conservation of freshwater ecosystem values (CFEV) framework: developing a conservation and management system for rivers. In: The freshwater protected areas conference 2004. IRN and WWF-Australia, Sydney, pp 45–50Google Scholar
  18. DPIW (2007) Annual waterways monitoring reports 2006: George catchment. Department of Primary Industries and WaterGoogle Scholar
  19. DPIWE (2005) Environmental management goals for Tasmanian surface waters. Dorset & Break O’Day municipal areas. Department of Primary Industries, Water and Environment, HobartGoogle Scholar
  20. Econometric Software (2010) Nlogit5 (beta-version). Econometric Software Inc, Castle HillGoogle Scholar
  21. Fiebig DG, Keane MP, Louviere J, Wasi N (2009) The generalized multinomial logit model: accounting for scale and coefficient heterogeneity. Mark Sci 29(3): 393–421CrossRefGoogle Scholar
  22. Flachaire E, Hollard G (2007) Starting point bias and respondent uncertainty in dichotomous choice contingent valuation surveys. Resour Energy Econ 29(3): 183–194CrossRefGoogle Scholar
  23. Frykblom P, Shogren JF (2000) An experimental testing of anchoring effects in discrete choice questions. Environ Resour Econ 16(3): 329–341CrossRefGoogle Scholar
  24. Garrod G, Willis KG (1999) Economic valuation of the environment: methods and case studies. Edward Elgar, CheltenhamGoogle Scholar
  25. Green D, Jacowitz KE, Kahneman D, McFadden D (1998) Referendum contingent valuation, anchoring, and willingness to pay for public goods. Resour Energy Econ 20(2): 85–116CrossRefGoogle Scholar
  26. Greene WH, Hensher DA (2007) Heteroscedastic control for random coefficients and error components in mixed logit. Transp Res Part E Logist Trans Rev 43(5): 610–623CrossRefGoogle Scholar
  27. Greene WH, Hensher DA (2010) Does scale heterogeneity across individuals matter? An empirical assessment of alternative logit models. Transportation 37(3): 413–428CrossRefGoogle Scholar
  28. Greene WH, Hensher DA, Rose J (2006) Accounting for heterogeneity in the variance of unobserved effects in mixed logit models. Trans Res Part B Methodol 40(1): 75–92CrossRefGoogle Scholar
  29. Hanley N, Adamowicz W, Wright RE (2005) Price vector effects in choice experiments: an empirical test. Resour Energy Econ 27(3): 227–234CrossRefGoogle Scholar
  30. Hanley N, Wright RE, Alvarez-Farizo B (2006) Estimating the economic value of improvements in river ecology using choice experiments: an application to the water framework directive. J Environ Manag 78(2): 183–193CrossRefGoogle Scholar
  31. Hensher DA (2004) Identifying the influence of stated choice dimensionality on willingness to pay for travel time savings. J Transp Econ Pol 38(3): 425–446Google Scholar
  32. Hensher DA (2006) How do respondents process stated choice experiments? Attribute consideration under varying information load. J Appl Econ 21(6): 861–878CrossRefGoogle Scholar
  33. Hensher DA (2011) Accounting for scale heterogeneity within and between pooled data sources. Institute of Transport and Logistic Studies, SydneyGoogle Scholar
  34. Hensher DA, Greene WH (2003) The mixed logit model: the state of practice. Transportation 30: 133–176CrossRefGoogle Scholar
  35. Hensher DA, Rose JM (2007) Development of commuter and non-commuter mode choice models for the assessment of new public transport infrastructure projects: a case study. Transp Res Part A Policy Pract 41(5): 428–443CrossRefGoogle Scholar
  36. Hensher DA, Rose JM, Greene WH (2005) Applied choice analysis: a primer. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  37. Hensher DA, Rose JM, Li Z (2011) Does the choice model method and/or the data matter? ITLS Working Paper, ITLS-WP-11-14. Institute of Transport and Logistic Studies, SydneyGoogle Scholar
  38. Herriges JA, Shogren JF (1996) Starting point bias in dichotomous choice valuation with follow-up questioning. J Environ Econ Manag 30(1): 112–131CrossRefGoogle Scholar
  39. Kragt ME, Bennett J (2011) Using choice experiments to value catchment and estuary health in tasmania with individual preference heterogeneity. Aust J Agric Res Econ 55(2): 159–179CrossRefGoogle Scholar
  40. Ladenburg J, Olsen SB (2008) Gender-specific starting point bias in choice experiments: evidence from an empirical study. J Environ Econ Manag 56: 275–285CrossRefGoogle Scholar
  41. Lichtenstein S, Slovic P (2006) The construction of preference. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  42. Lliff G (2002) George river catchment: plan for rivercare works for the upper catchment, North George and South George Rivers. George River Catchment Coordinator, St HelensGoogle Scholar
  43. Louviere J (2006) What you don’t know might hurt you: some unresolved issues in the design and analysis of discrete choice experiments. Environ Resour Econ 34(1): 173–188CrossRefGoogle Scholar
  44. Louviere J, Street D, Carson R, Ainslie A, Deshazo JR, Cameron T, Hensher D, Kohn R, Marley T (2002) Dissecting the random component of utility. Mark Lett 13(3): 177–193CrossRefGoogle Scholar
  45. Louviere JJ, Eagle TC (2006) Confound it! That pesky little scale constant messes up our convenient assumptions! CenSoC Working Paper No. 06-002. Centre for the Study of Choice, University of Technology, SydneyGoogle Scholar
  46. Louviere JJ, Hensher DA, Swait JD (2000) Stated choice methods: analysis and applications. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  47. Luisetti T, Bateman IJ, Turner RK (2011) Testing the fundamental assumption of choice experiments: are values absolute or relative?. Land Econ 87(2): 284–296Google Scholar
  48. Mitchell RC, Carson RT (1989) Using surveys to value public goods: the contingent valuation method. Resources for the Future, WashingtonGoogle Scholar
  49. Mørkbak M, Christensen T, Gyrd-Hansen D (2010) Choke price bias in choice experiments. Environ Resour Econ 45(4): 537–551CrossRefGoogle Scholar
  50. NRM North (2008) State of the region: water quality and stream condition in Northern Tasmania 2006. North Water Monitoring Team, LauncestonGoogle Scholar
  51. Payne JW, Bettman JR, Schkade DA (1999) Measuring constructed preferences: towards a building code. J Risk Uncertain 19(1): 243–270CrossRefGoogle Scholar
  52. Poe GL, Giraud KL, Loomis JB (2005) Computational methods for measuring the difference of empirical distributions. Am J Agric Econ 87(2): 353–365CrossRefGoogle Scholar
  53. Revelt D, Train K (1998) Mixed logit with repeated choices: households’ choices of appliance efficiency level. Rev Econ Stat 80(4): 647–657CrossRefGoogle Scholar
  54. Ryan M, Wordsworth S (2000) Sensitivity of willingness to pay estimates to the level of attributes in discrete choice experiments. Scot J Polit Econ 47(5): 504–524CrossRefGoogle Scholar
  55. Sándor Z, Wedel M (2001) Designing conjoint choice experiments using managers’ prior beliefs. J Mark Res 38(4): 430–444CrossRefGoogle Scholar
  56. Scarpa R, Rose JM (2008) Designs efficiency for nonmarket valuation with choice modelling: how to measure it, what to report and why. Aust J Agric Resour Econ 52(3): 253–282CrossRefGoogle Scholar
  57. Silverman J, Klock M (1989) The behavior of respondents in contingent valuation: evidence on starting bids. J Behav Econ 18(1): 51–60CrossRefGoogle Scholar
  58. Slovic P (1995) The construction of preference. Am. Psychol 50(5): 364–371CrossRefGoogle Scholar
  59. Swait J, Louviere J (1993) The role of the scale parameter in the estimation and comparison of multinational logit models. J Mark Res 30(3): 305–314CrossRefGoogle Scholar
  60. Train K (2000) Halton sequences for mixed logit. Paper E00-278. University of California. Institute of Business and Economics, BerkeleyGoogle Scholar
  61. Tversky A, Simonson I (1993) Context-dependent preferences. Manag Sci 39(10): 1179CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Centre of Environmental Economics and Policy, School of Agricultural and Resource EconomicsUniversity of Western Australia, PerthCrawleyAustralia

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