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
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.
Information on the panel is available at http://www.forsa.com/.
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.
All three terms are assumed to have a trivariate normal distribution, with mean vector zero and covariance matrix
$$\begin{aligned} \Sigma = \left[ \begin{array}{ccc} \sigma _1^2&{}\sigma _{10}&{}\sigma _{1u} \\ \cdot &{}\sigma _0^2&{}\sigma _{0u} \\ \cdot &{}\cdot &{}1 \end{array}\right] . \end{aligned}$$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.
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.
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).
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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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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
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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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DOI: https://doi.org/10.1007/s10640-016-0047-x