Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion Consequentiality and the Willingness-To-Pay for Renewables: Evidence from Germany Mark Andor 1 Manuel Frondel 1 , 2 Marco Horvath 1 , 2 , 3 1 RWI Essen 2 Ruhr University Bochum 3 RGS Econ 15th IAEE European Conference, Vienna September 6th, 2017 Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 1
Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion Overview Introduction 1 Data and Experimental Design 2 Descriptive Results 3 Methodology 4 Results and Policy Implications 5 Summary and Conclusion 6 Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 2
Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion Motivation Non-market goods (e.g. reductions in pollution) are valued on basis of stated preferences Contingent Valuation Methods: Single Binary Choice 1 Open-Ended Method 2 Stated preference studies may suffer from hypothetical bias To reduce this bias: Ex ante: Consequential Script Ex post: Question for political consequentiality We investigate the discrepancy in WTP bids across Single Binary Choice and Open-Ended valuation formats while simultaneously controlling for political consequentiality Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 3
Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion Survey Elicitation of WTP for renewable energy using a large-scale survey (among more than 7,000 German households) Renewable energy is financed by a surcharge on the electricity bill (EEG Levy) All survey participants get a brief introduction, indicating: The share of renewable energy in electricity production in 2015: 28% Germany’s target by 2020: 35% The 2015 EEG Levy: 6.17 cents/kwh Information on the cost of the EEG Levy for an average household Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 4
Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion Contingent Valuation Formats Single Binary Choice Would you be willing to pay an additional X cents on the per kilowatt hour surcharge in order to reach the target of 35% renewable energy in the electricity mix by 2020? ( X is randomly replaced with either 1, 2, or 4) Advantage of Single Binary Choice Format: No incentive to strategically over- or understate WTP Open-Ended Format In order to reach the target of 35% renewable energy in the electricity mix in Germany, what would the maximum increase of the per kilowatt hour surcharge in cents be that you would be willing to pay? Advantage of Open-Ended Format: Provides information on the whole range of respondents’ WTP Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 5
Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion Consequential Script Consequential Script We would like to point out that this survey is part of a research project on behalf of the German Federal Ministry of Education and Research (BMBF). The results of this survey will be made available to policy makers and serve as a basis for future decisions, especially with respect to the future level of the surcharge for the promotion of renewable energy technologies (EEG Levy). To reach meaningful conclusions, it is therefore important that you provide exactly the willingness-to-pay you would actually would be willing to pay at most. Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 6
Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion Split-Sample Survey Design Table: Experimental Design: Shares and Number of Observations in Treatment Groups Consequential Script No Yes Total Shares 1 Cent 552 534 1,086 33.8% 2 Cents 525 537 1,062 33.1% Single Binary Choice 4 Cents 528 536 1,064 33.1% Total 1,605 1,607 3,212 52.7% Open-Ended 1,401 1,479 2,880 47.3% Total 3,006 3,086 6,092 100.0% Shares 49.3% 50.7% 100.0% – Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 7
Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion Political Consequentiality Question about perceived political consequentiality How likely do you believe that results of surveys like the present one influence policy decisions on the amount of the surcharge for the promotion of renewable energy technologies (EEG Levy)? Respondents who answer “Very unlikely” are allocated to the inconsequential group (about 40% of all respondents) the rest is allocated to the consequential group (following Vossler and Watson, 2013) Economic theory suggests consequentiality is needed for incentive compatibility (Carson and Groves, 2007; Vossler et al., 2012) Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 8
Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion Descriptives Means Open- Single Binary Variable Variable Definition Ended Choice Age Age of respondent 55.2 55.4 Female Dummy: 1 if respondent is female 0.352 0.329 Children Dummy: 1 if respondent has children 0.704 0.703 College Degree Dummy: 1 if household head has a college degree 0.321 0.312 Script Dummy: 1 if household received a consequential script 0.500 0.500 Consequentiality Dummy: 1 if respondent believes that surveys influence the political decision making 0.591 0.608 Low income Dummy: 1 if net monthly household income is lower than e 1,200 0.073 0.072 Medium income Dummy: 1 if net monthly household income is between e 1,200 and e 2,700 0.361 0.381 High income Dummy: 1 if net monthly household income is between e 2,700 and e 4,200 0.293 0.275 Very high income Dummy: 1 if net monthly household income exceeds e 4,200 0.148 0.151 Missing income Dummy: 1 if respondent did not disclose her income 0.125 0.121 1 Person Dummy: 1 if # household members equals 1 0.269 0.275 2 Persons Dummy: 1 if # household members equals 2 0.489 0.472 3 Persons Dummy: 1 if # household members equals 3 0.132 0.130 > 3 Persons Dummy: 1 if # household members > 3 0.109 0.123 Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 9
Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion Open-Ended and Single Binary Choice Values We convert open-ended bids to discrete values for the comparison Open-ended responses are randomly allocated to 3 different groups (1, 2, and 4 cents) The respective bids are then converted into a binary variable assuming that respondents would have accepted a randomly given increase if their WTP bid were to be at least as large as the respective increase (Balistreri et al, 2001) Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 10
Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion Descriptive Comparison Table: Acceptance Rates of a Rise in the Promotion Cost of Renewable Technologies across Elicitation Formats Single Binary Choice Open-Ended Number of Share of Yes Number of Share of Yes Observations Responses Observations Responses t-Stat 1 Cent 1,086 53.6% 951 70.5% -7.93*** 2 Cents 1,062 46.3% 978 57.4% -5.01*** 4 Cents 1,064 33.7% 951 33.7% 0.03 Total 3,212 44.6% 2,880 53.9% -7.26*** Note: ∗ denotes significance at the 5 %-level, ∗∗ at the 1 %-level, and ∗∗∗ at the 0.1 %-level, respectively. Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 11
Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion Regression Model Yes i = β 0 + β 1 SingleBinaryChoice i + β 2 2 Cents i + β 3 4 Cents i + β 4 Script i + β 5 Consequentiality i + β 6 ( Consequentiality i ∗ SingleBinaryChoice i ) + δ T x i + ǫ i , Yes : Dummy: 1 if individual i accepts a given increase in the EEG Levy SingleBinaryChoice : Dummy: 1 if i received the Single Binary Choice question, rather than the Open-Ended question 2 Cents and 4 Cents : Dummies: 1 if increase was 2 or 4 cents, rather than 1 cent Script : Dummy: 1 if i received Consequential Script Consequentiality : Dummy: 1 if i believes that surveys influence the political decision making x : Socio-economic characteristics Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 12
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