Mitigating Hypothetical Bias: Evidence on the Effects of Correctives from a Large Field Study

Abstract

The overestimation of willingness-to-pay (WTP) in hypothetical responses is a well-known finding in the literature. Various techniques have been proposed to remove or, at least, reduce this bias. Using about 30,000 responses on WTP for a variety of power mixes from a panel of 6500 German households and the fixed-effects estimator to control for unobserved heterogeneity, this article simultaneously explores the effects of two common ex-ante approaches—cheap talk and consequential script—and the ex-post certainty approach to calibrating hypothetical WTP responses. Based on a switching regression model that accounts for the potential endogeneity of respondent certainty, we find evidence for a lower WTP among those respondents who classify themselves as definitely certain about their answers. Although neither cheap talk nor the consequential-script corrective reduce WTP estimates, receiving either of these scripts increases the probability that respondents indicate definite certainty about their WTP bids.

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Notes

  1. 1.

    In Germany, for instance, this contradiction exists because for such contracts cheap green electricity, e. g. produced on the basis of competitive water power, is frequently imported from abroad.

  2. 2.

    Information on the panel is available at http://www.forsa.com/.

  3. 3.

    A summary of the descriptive results, as well as the questionnaire, both in German, can be retrieved from the project page: http://www.rwi-essen.de/eval-map.

  4. 4.

    While there may be biases from ordering effects (see e. g. Carlsson et al. 2012), randomizing the draws of the alternatives should minimize such biases (Bateman and Langford 1997; Clark and Friesen 2008).

  5. 5.

    All three terms are assumed to have a trivariate normal distribution, with mean vector zero and covariance matrix

    $$\begin{aligned} \Sigma = \left[ \begin{array}{ccc} \sigma _1^2&{}\sigma _{10}&{}\sigma _{1u} \\ \cdot &{}\sigma _0^2&{}\sigma _{0u} \\ \cdot &{}\cdot &{}1 \end{array}\right] . \end{aligned}$$
  6. 6.

    The comparison undertaken in Table 3 is in nominal terms, but the conclusions hold if we account for the moderate inflation that prevailed since the survey analyzed by Grösche and Schröder (2011). Specifically, when comparing real values, the difference in WTP for the 100 % renewable mix is not statistically significant. By contrast, we find significantly lower WTP for mixes comprising 75 % nuclear.

  7. 7.

    There is one exception: Among those who are less certain on their WTP bids, the mean WTP of respondents who received a cheap-talk script is lower at a 1 % significance level than for the control group.

  8. 8.

    About 85 % of our respondents agree with the statement that the electricity production from renewable energy technologies should be supported (Andor et al. 2014: 1).

  9. 9.

    Interestingly, among those who are definitely sure, the WTP for females and males are statistically indistinguishable (Table 5), whereas the probability of being definitely sure is statistically significantly lower for females (Table 6).

References

  1. Andor MA, Frondel M, Vance C (2014) Hypothetische Zahlungsbereitschaft für grünen Strom: Bekundete Präferenzen privater Haushalte für das Jahr 2013. Perspektiven der Wirtschaftspolitik 15(4):1–12

    Article  Google Scholar 

  2. Bateman IJ, Langford IH (1997) Budget constraint, temporal and ordering effects in contingent valuation studies. Environ Plan A 29:1215–1228

    Article  Google Scholar 

  3. Bateman IJ, Burgess D, Matthews DI (2008) Contrasting NOAA guidelines with learning design contingent valuation (LDCV): preference learning versus coherent arbitrariness. J Environ Econ Manage 55:127–141

    Article  Google Scholar 

  4. Bishop RC, Heberlein TA (1979) Measuring values of extramarket goods: Are indirect measures biased? Am J Agric Econ 61(5):926–930

    Article  Google Scholar 

  5. Blumenschein K, Blomquist GC, Johannesson M, Horn N, Freeman PR (2008) Eliciting willingness to pay in the without bias: evidence from a field experiment. Econ J 118(525):114–137

    Article  Google Scholar 

  6. Blumenschein K, Johannesson M, Blomquist GC, Liljas B, O’Connor RM (1998) Experimental results on expressed certainty an hypothetical bias in contingent valuation. South Econ J 65(1):169–177

    Article  Google Scholar 

  7. Bulte E, Gerking S, List LA, de Zeeuw A (2005) The effect of varying the causes of environmental problems on stated WTP values: evidence from a field study. J Environ Econ Manage 49(2):330–342

    Article  Google Scholar 

  8. Carlsson F, Frykblom P, Lagerkvist CJ (2005) Using cheap talk as a test of validity in choice experiments. Econ Lett 89(2):147–152

    Article  Google Scholar 

  9. Carlsson F, Mørkbak MR, Olsen SB (2012) The first time is the hardest: a test of ordering effects in choice experiments. J Choice Model 5(2):19–37

    Article  Google Scholar 

  10. Clark J, Friesen L (2008) The causes of order effects in contingent valuation surveys: an experimental investigation. J Environ Econ Manage 56(2):195–206

    Article  Google Scholar 

  11. Cummings RG, Elliot S, Harrison GW, Murphy J (1997) Are hypothetical referenda incentive-compatible? J Polit Econ 105(3):609–621

    Article  Google Scholar 

  12. Cummings RG, Harrison GW, Rutström EE (1995) Homegrown values and hypothetical surveys: Is the dichotomous choice approach incentive-compatible? Am Econ Rev 85(1):260–266

    Google Scholar 

  13. Cummings RG, Taylor LO (1999) Unbiased value estimates for environmental goods: a cheap talk design for the contingent valuation method. Am Econ Rev 89(3):649–665

    Article  Google Scholar 

  14. Frew EJ, Whynes DK, Wolstenholme JL (2003) Eliciting willingness to pay: comparing closed-ended with open-ended and payment scale formats. Med Decis Mak 23(2):150–159

    Article  Google Scholar 

  15. Frondel M, Vance C (2013) Heterogeneity in the effect of home energy audits: theory and evidence. Environ Res Econ 55(3):407–418

    Article  Google Scholar 

  16. Frondel M, Vance C (2010) Fixed, random, or something in between? A variant of Hausman’s specification test for panel data estimators. Econ Lett 107:327–329

    Article  Google Scholar 

  17. Grösche P, Schröder C (2011) Eliciting public support for greening the electricity mix using random parameter techniques. Energy Econ 33(2):363–370

    Article  Google Scholar 

  18. Halstead J, Luloff A, Stevens TH (1992) Protest bidders in contingent valuation. Northeast J Agric Res Econ 21(2):160–169

    Google Scholar 

  19. Harrison GW (2006) Experimental evidence on alternative environmental valuation methods. Environ Res Econ 34(1):125–162

    Article  Google Scholar 

  20. Harrison GW, Harstad R, Rutström EE (2004) Experimental methods and elicitation of values. Exp Econ 7(2):123–140

    Article  Google Scholar 

  21. Harrison GW, Rutström EE (2008) Experimental evidence on the existence of hypothetical bias in value elicitation methods. In: Plott C, Smith VL (eds) Handbook of experimental economics results, vol 1, 1st edn. Elsevier Science, New York, pp 752–767

  22. Johannesson M, Liljas B, Johansson P-O (1998) An experimental comparison of dichotomous choice contingent valuation questions and real purchase decisions. Appl Econ 30(5):643–647

    Article  Google Scholar 

  23. Lancaster KJ (1966) A new approach to consumer theory. J Polit Econ 74:132–157

    Article  Google Scholar 

  24. Landry CE, List JA (2007) Using ex-ante approaches to obtain credible signals for value in contingent markets: evidence from the field. Am J Agric Econ 89(2):420–429

    Article  Google Scholar 

  25. List JA (2001) Do explicit warnings eliminate the hypothetical bias in elicitation procedures? evidence from field auctions for sportscards. Am Econ Rev 91(5):1498–1507

    Article  Google Scholar 

  26. List JA, Gallet CA (2001) What experimental protocol influence disparities between actual and hypothetical stated values? Environ Res Econ 20(3):241–254

    Article  Google Scholar 

  27. Lusk JL (2003) Effects of cheap talk on consumer willingness-to-pay for golden rice. Am J Agric Econ 85(4):840–856

    Article  Google Scholar 

  28. Maddala GS (1983) Limited-dependent and qualitative variables in econometrics, reprint 1999. Cambridge University Press, Cambridge

    Google Scholar 

  29. Menges R, Schröder C, Traub S (2005) Altruism, warm glow and the willingness-to-donate for green electricity: an artefactual field experiment. Environ Res Econ 31(4):431–458

    Article  Google Scholar 

  30. Murphy JJ, Stevens T, Weatherhead D (2005) Is cheap talk effective at eliminating hypothetical bias in a provision point mechanism? Environ Res Econ 30(3):327–343

    Article  Google Scholar 

  31. Watson V, Ryan M (2007) Exploring preference anomalies in double bounded contingent valuation. J Health Econ 26(3):463–482

    Article  Google Scholar 

  32. Whitehead JC, Cherry TL (2007) Willingness to pay for a green energy program: a comparison of ex-ante and ex-post hypothetical bias mitigation approaches. Res Energy Econ 29(4):247–261

    Article  Google Scholar 

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Acknowledgments

We are grateful for invaluable comments and suggestions by Peter Grösche, Christoph M. Schmidt and participants of the World Congress of Environmental and Resource Economists, Istanbul, Turkey (2014), as well as two anonymous reviewers. This work has been partly supported by the Collaborative Research Center “Statistical Modeling of Nonlinear Dynamic Processes” (SFB 823) of the German Research Foundation (DFG), within the framework of Project A3, “Dynamic Technology Modeling”. We also gratefully acknowledge financial support by the German Federal Ministery of Education and Research (BMBF) under Grant 01LA1113A.

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Correspondence to Manuel Frondel.

Appendices

Appendix 1: Extract of Questionnaire

The elicitation of the WTP for specific electricity mixes began with a brief introduction on the diversity of production technologies, followed by a short description of the survey design, including several practical examples. Upon displaying the introductory text, both the cheap-talk and consequential scripts were presented to the respective treatment groups before posing the question on WTP, yet not to the control group. The translations of these texts and scripts into English is reported below:

“Electricity can be produced with different energy sources and technologies. Among these are coal- or natural gas fired power plants, nuclear power, or renewable energy technologies such as photovoltaics, hydropower, and wind turbines. A household might obtain electricity that is produced from a single source such as a fossil fuel, or it might alternatively obtain electricity that is produced from some mix of different sources such as fossil fuels, nuclear power, and renewable energies.

We will now present you with different electricity offers that are distinguished solely by the proportions of fossil fuels, nuclear energy, and renewable energy with which the electricity is produced. For each of these offers, we request that you report the maximum amount that you, personally, would be willing to pay. As a basis for comparison, please consider an energy mix comprised exclusively of the fossil sources coal, natural gas, and oil, which has a price of €100 per month.

Example

The price for the comparison offer is €100. If the price you would be willing to pay for the alternative offer were €70, please record the amount €70. If the price you would be willing to pay for the alternative offer were instead €180, please record the amount €180. Of course, any other values may also be recorded.

Now we would like to ask you about how much you would be willing to pay for different energy sources and energy technologies. In what follows, we will refer to this as your ‘willingness to pay’.”

Appendix 2: Table

See Table 7

Table 7 Fixed-effects results without switching regression correction

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Andor, M.A., Frondel, M. & Vance, C. Mitigating Hypothetical Bias: Evidence on the Effects of Correctives from a Large Field Study. Environ Resource Econ 68, 777–796 (2017). https://doi.org/10.1007/s10640-016-0047-x

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Keywords

  • Willingness-to-pay
  • Cheap talk
  • Certainty approach

JEL Classification

  • D12
  • Q21
  • Q41