Environmental and Resource Economics

, Volume 39, Issue 4, pp 481–495 | Cite as

Cognitive ability and scale bias in the contingent valuation method

An analysis of willingness to pay to reduce mortality risk
Article

Abstract

This study investigates whether or not the scale bias found in contingent valuation (CVM) studies on mortality risk reductions is a result of cognitive constraints among respondents. Scale bias refers to insensitivity and non-near-proportionality of the respondents’ willingness to pay (WTP) to the size of the risk reduction. Two hundred Swedish students participated in an experiment in which their cognitive ability was tested before they took part in a CVM-study asking them about their WTP to reduce bus-mortality risk. The results imply that WTP answers from respondents with a higher cognitive ability are less flawed by scale bias.

Keywords

Cognitive ability Contingent valuation Mortality risk Near-proportionality Scale bias 

JEL Codes

D80 I10 Q51 

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References

  1. Alberini A, Cropper M, Krupnick A and Simon NB (2004). Does the value of a statistical life vary with the age and health status? Evidence from the USA and Canada. J Environ Econ Manage 48(1): 769–792 CrossRefGoogle Scholar
  2. Andersson H, Svensson M (2007) Cognitive ability and scale bias in the contingent valuation method. Working paper 2007:1, VTI, Department of Transport Economics, Stockholm, SwedenGoogle Scholar
  3. Barthomolew DJ (1987). Latent variable models and factor analysis. Oxford University Press, New York, NY, USA Google Scholar
  4. Bateman IJ and Brouwer R (2006). Consistency and construction in stated WTP for health risk reductions: a novel scope-sensitivity test. Resour Energy Econ 28(3): 199–214 CrossRefGoogle Scholar
  5. Bateman IJ, Carson RT, Day B, Hanemann M, Hanley N, Hett T, Jones-Lee M, Loomes G, Mourato S, Özdemiroḡlu E, Pearce DW, Sugden R and Swanson J (2002). Economic valuation with stated preference techniques: a manual. Edward Elgar, Cheltenham, UK Google Scholar
  6. Benjamin DJ and Shapiro JM (2005). Does cognitive ability reduce psychological bias?   . Harvard University, Mimeo Google Scholar
  7. Blumenschein K, Johannesson M, Yokoyama KK and Freeman PR (2001). Hypothetical versus real willingness to pay in the health care sector: results from a field experiment. J Health Econ 20(3): 441–457 CrossRefGoogle Scholar
  8. Carlsson F, Johansson-Stenman O and Martinsson P (2004). Is transport safety more valuable in the air?. J Risk Uncertainty 28(2): 147–163 CrossRefGoogle Scholar
  9. Carson RT and Mitchell RC (1995). Sequencing and nesting in contingent valuation surveys. J Environ Econ Manage 28(2): 155–173 CrossRefGoogle Scholar
  10. Carson RT, Flores NE and Meade NF (2001). Contingent valuation: controversies and evidence. Environ Resour Econ 19(2): 173–210 CrossRefGoogle Scholar
  11. Clark J and Friesen L (2006). The causes of order effects in contingent valuation surveys: an experimental investigation. University of Canterbury, New Zealand, Mimeo Google Scholar
  12. Corso PS, Hammitt JK and Graham JD (2001). Valuing mortality-risk reduction: using visual aids to improve the validity of contingent valuation. J Risk Uncertainty 23(2): 165–184 CrossRefGoogle Scholar
  13. Desvousges WH, Reed JF, Dunford RW, Boyle KJ, Hudson SP, Wilson KN (1993) In: Contingent valuation: a critical assessment. Hausman JA (ed) Measuring Natural resource damage with contingent valuation: a test of validity and reliability. The Netherlands: Norht-Holland, Amsterdam, pp 91–159Google Scholar
  14. Diamond PA and Hausman JA (1994). Contingent valuation: is some number better than no number?   .  J Econ Perspect 8(4): 45–64 Google Scholar
  15. Duckworth AL and Seligman ME (2005). Self-discioline outdoes IQ in predicting academic performance of adolescents. Psychol Sci 16(12): 939–944 CrossRefGoogle Scholar
  16. Frederick S (2005). Cognitive reflection and decision making. J Econ Perspect 19(4): 25–42 CrossRefGoogle Scholar
  17. Frederick S and Fischhoff B (1998). Scope (In)sensitivity in elicited valuations. J Risk Decision Policy 3(2): 109–123 CrossRefGoogle Scholar
  18. Green D, Jacowitz KE, Kahneman D and McFadden D (1998). Referendum contingent valuation anchoring and willingness to pay for public goods. Resour Energy Econ 20(2): 85–116 CrossRefGoogle Scholar
  19. Hammitt JK (2000). Evaluating contingent valuation of environmental health risks: the proportionality test. AERE (Association of Environmental and Resource Economics) Newslett 20(1): 14–19 Google Scholar
  20. Hammitt JK and Graham JD (1999). Willingness to pay for health protection: inadequate sensitivity to probability. J Risk Uncertainty 18(1): 33–62 CrossRefGoogle Scholar
  21. Hammar H and Johansson-Stenman O (2004). The value of risk-free cigarettes – do smokers underestimate the risk?   .  Health Econ 13(1): 59–71 CrossRefGoogle Scholar
  22. Hammitt JK and Zhou Y (2006). The economic value of air-pollution-related health risks in China: a contingent valuation study. Environ Resour Econ 33(3): 399–423 CrossRefGoogle Scholar
  23. Hanley N and Shogren J (2005). Is cost-benefit analysis anomaly proof?. Environ Resour Econ 32(1): 13–34 CrossRefGoogle Scholar
  24. Harrison GW (2006). Experimental evidence on alternative environmental valuation methods. Environ Resour Econ 34(1): 125–186 CrossRefGoogle Scholar
  25. Heberlein TA, Wilson MA, Bishop RC and Schaeffer NC (2005). Rethinking the scope test as a criterion for validity in contingent valuation. J Environ Econ Manage 50(1): 1–22 CrossRefGoogle Scholar
  26. Johnson R (2006). Comment on “Revealing differences in willingness to pay due to the dimensionality of stated choice designs: an initial assessment”. Environ Resour Econ 34(1): 45–50 CrossRefGoogle Scholar
  27. Jones-Lee MW (1974). The value of changes in the probability of death or injury. J Political Econ 82(4): 835–849 CrossRefGoogle Scholar
  28. Kahneman D (2003). Maps of bounded rationality: psychology for behavioral economics. Am Econ Rev 93(5): 1449–1475 CrossRefGoogle Scholar
  29. Kahneman D and Tversky A (1972). Subjective probability: a judgement of representativeness. Cogn Psychol 3(3): 430–454 CrossRefGoogle Scholar
  30. Kahneman D and Tversky A (1983). Can irrationality be intelligently discussed?   .  Behav Brain Sci 6: 509–510 CrossRefGoogle Scholar
  31. Kahneman D, Ritov I and Schkade D (1999). Economic preferences or attitude expressions? An analysis of dollar responses to public issues. J Risk Uncertainty 19(1–3): 203–235 CrossRefGoogle Scholar
  32. Kahneman D, Slovic P and Tversky A (1982). Judgement under uncertainty: heuristics and biases. Cambridge University Press, New York, NY, USA Google Scholar
  33. Lawley DN and Maxwell AE (1963). Factor analysis as a statistical method. Butterworths, London, UK Google Scholar
  34. NOAA (1993) Report of the NOAA panel on contingent valuation. Federal Register 58, Arrow, K, Solow, R, Portney, P, Leamer, E, Radner, R, and Schuman, HGoogle Scholar
  35. Persson U, Norinder A, Hjalte K and Gralén K (2001). The value of a statistical life in transport: findings from a new contingent valuation study in Sweden. J Risk Uncertainty 23(2): 121–134 CrossRefGoogle Scholar
  36. Rabin M (2002). Inference by believers in the law of small numbers. Quart J Econ 117(3): 775–816 CrossRefGoogle Scholar
  37. SIKA (2007) Vägtrafikskador 2002–2005 (Road traffic injuries 2002–2005). Internet, www.sika-institute.se, 4/26/07Google Scholar
  38. Smith VK (1992). Arbitrary values, good causes and premature verdicts. J Environ Econ Manage 22(1): 71–89 CrossRefGoogle Scholar
  39. Sugden R (2005). Anomalies and stated preference techniques: a framework for a discussion of coping strategies. Environ Resour Econ 32(1): 1–12 CrossRefGoogle Scholar
  40. Weinstein MC, Shepard DS and Pliskin JS (1980). The economic value of changing mortality probabilities: a decision-theoretic approach. Quart J Econ 94(2): 373–396 CrossRefGoogle Scholar
  41. Wolfe R and Johnson S (1995). Personality as a predictor of college performance. Educ Psychol Measure 55: 177–185 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.Department of Transport EconomicsSwedish National Road and Transport Research Institute (VTI)StockholmSweden
  2. 2.Department of Business, Economics, Statistics and InformaticsÖrebro UniversityÖreborSweden

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