Consequentiality and the Willingness-To-Pay for Renewables: Evidence - - PowerPoint PPT Presentation

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Consequentiality and the Willingness-To-Pay for Renewables: Evidence - - PowerPoint PPT Presentation

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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 ,

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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 Andor1 Manuel Frondel1,2 Marco Horvath1,2,3

1RWI Essen 2Ruhr University Bochum 3RGS Econ

15th IAEE European Conference, Vienna September 6th, 2017

Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 1

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Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion

Overview

1

Introduction

2

Data and Experimental Design

3

Descriptive Results

4

Methodology

5

Results and Policy Implications

6

Summary and Conclusion

Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 2

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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:

1

Single Binary Choice

2

Open-Ended Method

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

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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

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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

  • ver- 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

  • f respondents’ WTP

Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 5

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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

  • f the German Federal Ministry of Education and Research (BMBF). The results
  • f 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

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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 Single Binary Choice        1 Cent 552 534 1,086 33.8% 2 Cents 525 537 1,062 33.1% 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

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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

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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 e1,200 0.073 0.072 Medium income Dummy: 1 if net monthly household income is between e1,200 and e2,700 0.361 0.381 High income Dummy: 1 if net monthly household income is between e2,700 and e4,200 0.293 0.275 Very high income Dummy: 1 if net monthly household income exceeds e4,200 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

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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

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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

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Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion

Regression Model

Yesi = β0 + β1SingleBinaryChoicei + β2 2 Centsi + β3 4 Centsi + β4 Scripti +β5Consequentialityi + β6(Consequentialityi ∗ SingleBinaryChoicei) +δTxi + ǫi,

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

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Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion

Endogenous Switching Regression Model – First Stage

Switching Regression Model copes with potential endogeneity of consequentiality First Stage divides respondents into two regimes: Consequentialityi = 1 if γT · zi ≥ ui, Consequentialityi = 0

  • therwise,

where z includes factors that may affect whether a respondent believes in consequentiality or not γ of first stage can be estimated by standard probit methods

Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 13

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Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion

Endogenous Switching Regression Model – First Stage

First Stage identification usually requires an exclusion restriction We use two exclusion restrictions:

1

Dummy that indicates whether a respondent took longer than the median duration to finish the survey

2

Locus of Control

Those believing that life’s outcomes are due to their own efforts have an internal locus of control, while those believing that outcomes are due to external factors have an external locus of control (Gatz and Karel, 1993) Index is constructed following Cobb-Clark and Schurer (2013)

Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 14

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Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion

Endogenous Switching Regression Model – Second Stage

Depending on consequentiality status, the second-stage equations are given by: WTP1i = βT

1 · x1i − σ1u · IVM1i + ε1i, if Consequentialityi = 1,

WTP0i = βT

0 · x0i + σ0u · IVM0i + ε0i, if Consequentialityi = 0,

where IVM are variants of the inverse Mills ratios: IVM1i := φ(γT · zi) Φ(γT · zi), IVM0i := φ(γT · zi) 1 − Φ(γT · zi) For the second stage we use the predicted values IVM1i and IVM0i using the probit estimates γ of the first stage

Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 15

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Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion

Linear Probability Model and Probit Estimation Results

Linear Probit Probability Model Marginal Effects SingleBinaryChoice

  • 0.190*** (0.019)
  • 0.202*** (0.020)

2 Cents

  • 0.103*** (0.016)
  • 0.100*** (0.015)

4 Cents

  • 0.263*** (0.015)
  • 0.258*** (0.014)

Script

  • 0.005

(0.013)

  • 0.005

(0.013) Consequentiality 0.208*** (0.019) 0.194*** (0.018) Consequentiality * SingleBinaryChoice 0.124*** (0.026) 0.138*** (0.026) Female 0.079*** (0.014) 0.079*** (0.014) Children

  • 0.052** (0.017)
  • 0.053** (0.017)

Age 0.002** (0.001) 0.002** (0.001) College Degree 0.063*** (0.014) 0.062*** (0.014) High income 0.008 (0.020) 0.006 (0.020) Medium income

  • 0.024

(0.021)

  • 0.027

(0.021) Low income

  • 0.036

(0.033)

  • 0.039

(0.033) Missing income

  • 0.058*

(0.026)

  • 0.059*

(0.026) 1 Person 0.005 (0.027) 0.004 (0.027) 2 Persons

  • 0.045*

(0.023)

  • 0.045*

(0.023) 3 Persons

  • 0.025

(0.025)

  • 0.025

(0.025) Constant 0.461*** (0.038) – – Number of Observations: 5,249 5,249

Note: Standard errors are in parentheses, ∗ 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 16

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Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion

Endogenous Switching Regression Estimation Results

First Stage Second Stage Consequentiality = 0 Consequentiality = 1 SingleBinaryChoice 0.048 (0.036)

  • 0.193***

(0.020)

  • 0.064***

(0.017) 2 Cents

  • 0.006

(0.044)

  • 0.087***

(0.026)

  • 0.113***

(0.020) 4 Cents

  • 0.084

(0.044)

  • 0.221***

(0.025)

  • 0.273***

(0.022) Script 0.091* (0.036)

  • 0.031

(0.021)

  • 0.016

(0.018) Female 0.118** (0.040) 0.090*** (0.025) 0.046* (0.021) Children

  • 0.083

(0.050)

  • 0.033

(0.028)

  • 0.041

(0.024) Age 0.0001 (0.002) 0.001 (0.001) 0.002** (0.001) College Degree 0.298*** (0.041) 0.001 (0.033) 0.021 (0.029) High income 0.075 (0.057)

  • 0.028

(0.033) 0.005 (0.026) Medium income

  • 0.027

(0.059)

  • 0.049

(0.034) 0.008 (0.028) Low income

  • 0.162

(0.094) 0.007 (0.055)

  • 0.019

(0.049) Missing income

  • 0.219** (0.073)

0.017 (0.046)

  • 0.061

(0.042) 1 Person 0.138 (0.077)

  • 0.033

(0.042)

  • 0.013

(0.039) 2 Persons 0.058 (0.065)

  • 0.047

(0.035)

  • 0.065*

(0.032) 3 Persons 0.205** (0.071)

  • 0.039

(0.042)

  • 0.070

(0.038) More time 0.166*** (0.038) – – – – Locus of Control

  • 0.012*** (0.003)

– – – – IVM0 – – 0.191 (0.118) – – IVM1 – – – –

  • 0.322*

(0.139) Constant 0.248* (0.113) 0.345** (0.114) 0.899*** (0.118) Number of Observations: 5,104 1,999 3,105

Note: Standard errors are in parentheses, ∗ 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 17

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Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion

WTP for Renewable Energy in Germany

.2 .4 .6 .8 1 Policy Support 2 4 6 8 10 Levy-increase in ct/kwh Open Ended Single Binary Choice

Willingness-to-pay

Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 18

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Introduction Data and Experimental Design Descriptive Results Methodology Results and Policy Implications Summary and Conclusion

Summary and Conclusion

We find further evidence on the discrepancy between the outcomes of Single Binary Choice and Open-Ended valuation methods Contrasting with the literature we find higher WTP values for the Open-Ended method We find a positive relationship between consequentiality and WTP Consequentiality furthermore seems to reduce the discrepancy between Single Binary Choice and Open-Ended contingent valuation

Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 19

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Table: Balancing in Explanatory Variables Across Treatment Groups

Open-Ended Single Binary Choice Format All 1 Cent 2 Cents 4 Cents Age 55.21 55.37 55.39 55.57 55.16 Female 0.352 0.329 0.319 0.343 0.326 Children 0.704 0.703 0.704 0.714 0.690 College Degree 0.321 0.312 0.317 0.307 0.312 Script 0.500 0.500 0.499 0.500 0.500 Consequentialiy 0.591 0.608 0.620 0.625 0.579 1 Cent 0.331 0.333 1 2 Cents 0.340 0.333 1 4 Cents 0.330 0.333 1 Low income 0.083 0.082 0.080 0.088 0.078 Medium income 0.412 0.433 0.437 0.428 0.434 High income 0.334 0.313 0.304 0.303 0.331 Very high income 0.169 0.172 0.179 0.181 0.157 1 Person 0.269 0.275 0.273 0.269 0.282 2 Persons 0.489 0.472 0.486 0.478 0.453 3 Persons 0.132 0.130 0.123 0.125 0.143 >3 Persons 0.109 0.123 0.118 0.128 0.122 More time 0.510 0.494 0.503 0.508 0.470 # of Observations 3,517 3,524 1,174 1,175 1,175

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.2 .4 .6 .8 1 Policy Support 2 4 6 8 10 Levy-increase in ct/kwh Open Ended Single Binary Choice

Willingness-to-pay if Respondents do not believe that the Survey is consequential

.2 .4 .6 .8 1 Policy Support 2 4 6 8 10 Levy-increase in ct/kwh Open Ended Single Binary Choice

Willingness-to-pay if Respondents believe that the Survey is consequential

Marco Horvath Consequentiality and the Willingness-To-Pay for Renewables September 6th, 2017 21

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Table: Acceptance Rates of a Rise in the Promotion Cost of Renewable Technologies when Elicitation Formats are Crossed with the Consequential Script

Single Binary Choice Open-Ended Consequential Script No Yes No Yes # of Share of # of Share of t Statis- # of Share of # of Share of t Statis- Obs. Yes Obs. Yes tics Obs. Yes Obs. Yes tics 1 Cent 552 53.8% 534 53.4% 0.14 465 70.1% 487 70.8%

  • 0.25

2 Cents 525 47.1% 537 45.6% 0.46 479 57.6% 499 57.1% 0.16 4 Cents 528 34.1% 536 33.4% 0.24 457 31.3% 493 35.9%

  • 1.50

Total 1,605 45.1% 1,607 44.1% 0.56 1,401 53.2% 1,479 54.6%

  • 0.75

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 22

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Table: Acceptance Rates of a Rise in the Promotion Cost of Renewable Technologies when Elicitation Formats are Crossed with Consequentiality

Single Binary Choice Open-Ended Consequentiality No Yes No Yes # of Share of # of Share of t Statis- # of Share of # of Share of t Statis- Obs. Yes Obs. Yes tics Obs. Yes Obs. Yes tics 1 Cent 406 32.0% 666 66.5% 11.65*** 380 53.2% 561 81.8% 9.91*** 2 Cents 398 21.6% 651 61.4% 13.61*** 380 42.9% 592 66.6% 7.48*** 4 Cents 446 13.0% 603 49.3% 13.24*** 391 23.0% 552 41.1% 5.90*** Total 1,250 21.9% 1,920 59.4% 22.29*** 1,151 39.5% 1,705 63.3% 12.87*** 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 23