When aspects of the survey design and the analysis of the responses are ambiguous, the preferred method is the one that underestimates the WTP. [...] The Most Common Techniques Used in CVM In the contingent valuation method, the challenge is to design the survey to get the closest WTP to the actual value. [...] What Makes the Results of a CV Study Unreliable? • High rate of nonresponses to the entire survey or the key valuation questions • Not understanding the task by the respondents • Lack of belief in the proposed program outlined in the scenario • Yes or no question to a referendum that is not followed by an explanation of the cost or value of the program iii . [...] Willingness-to-pay Estimation Methods for Cost-Benefit Analysis 20 The final step involves aggregating the costs and benefits estimated in the former step to find the total benefits and costs using the total number of households and individuals in the population frame of the defined market. [...] Depending on the assumption of each method in the original study and the context of the study, one can take one of these approachesiv: 1- The Equal Effect Model: assumes that the same parameter values underlie all the available studies, i.e., the statistical conditions are the same, or the differences do not affect the estimated values.
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Table of Contents
- Acknowledgments 3
- Executive Summary 4
- Abbreviations 7
- Glossary 8
- Section 1: Introduction 12
- 1.1. Background and Context 12
- 1.2. Purpose and Objectives 13
- 1.3. How to Use This Document 14
- Section 2: Estimating WTP Using Contingent Valuation Method (CVM) 15
- 2.1. Contingent Valuation Method (CVM) 15
- 2.2. Survey Guideline 16
- 2.3. Elicitation Guideline 17
- 2.4. The Most Common Techniques Used in CVM 17
- 2.5. Statistical Analysis 18
- 2.5.1. Statistical Software/Packages for Implementing CVM 18
- 2.6. What Makes the Results of a CV Study Unreliable? 18
- 2.7. Case Study: Estimating WTP for Reusable Food Containers 9 19
- 2.7.1. Case Study Objectives 19
- 2.7.2. Case Study Framework 19
- 2.7.3. Case Study Results and Conclusions 19
- 2.8. Challenges and Disadvantages of CVM 19
- Section 3: Estimating WTP Using Benefit Transfer Method 21
- 3.1. What is Benefit Transfer? 21
- 3.1.1. Unit Value 21
- 3.1.2. Value Function 21
- 3.2. Steps In a Transfer Study 21
- 3.2.1. Defining the Policy and Identifying the Linkages 22
- 3.2.2. Selecting Candidate Studies 23
- 3.2.3. Summarizing Existing Information 24
- 3.2.3.1. Statistical Software/Packages for Meta-Analysis 26
- 3.3. Sources of Uncertainty 26
- 3.3.1. Measurement Uncertainty 26
- 3.3.2. How to Account for Measurement Uncertainty? 27
- 3.3.3. Other Types of Uncertainty 27
- 3.3.3.1. Model Uncertainty 27
- 3.3.3.2. Parameter Uncertainty 27
- 3.3.3.3. Judgement Uncertainty 28
- 3.3.4. How to Deal with Other Forms of Uncertainty? 28
- 3.4. Transferring Existing Information 28
- 3.5. How to Choose Between Unit and Function Values? 13 29
- 3.6. Sensitivity Analysis 31
- 3.7. Case Study 1: Applying Benefit Transfer in Food Safety Context 14 31
- 3.7.1. Case Study Objectives 31
- 3.7.2 Case Study Framework 31
- 3.7.3 Case Study Results 32
- 3.7.4 Case Study Conclusions 33
- 3.8. Case Study 2: Valuing Mortality Risk in the United States Benefit Transfer Literature 5 34
- 3.8.1 Case Study Objectives 34
- 3.8.2 Case Study Framework- Research Synthesis and Potential Issues 34
- 3.8.3 Case Study Results and Conclusions 34
- 3.9. Case Study 3: Benefit Transfer for Avoided Morbidity Using A Preference Calibration Approach 15, 16 36
- 3.9.1 Case Study Objectives 36
- 3.9.2 Case Study Framework 36
- 3.9.3 Case Study Results and Conclusions 36
- 3.10. Benefit Transfer Challenges 37
- Section 4: Other Methods for Estimating WTP 39
- 4.1. Cost of Illness Method 18 39
- 4.1.1. Case Study: Annual Cost of Illness and QALY Losses in the United States Due to Foodborne Pathogens 19 39
- 4.1.1.1 Case Study Objectives 39
- 4.1.1.2 Case Study Framework 39
- 4.1.1.3 Case Study Results and Conclusion 39
- 4.2. Averting Behavior Method 18 40
- 4.2.1. Case Study: Combining Averting Behavior and Contingent Valuation Data in Drinking Water Treatment 20 40
- 4.2.1.1 Case Study Objectives 40
- 4.2.1.2 Case Study Framework 40
- 4.2.1.3 Case Study Results and Conclusion 40
- 4.3. Hedonic Price Models 21 41
- 4.3.1. Case Study: Valuation of Observed Attributes of Beef Products in China 22 41
- 4.3.1.1 Case Study Objectives 41
- 4.3.1.2 Case Study Framework and Results 41
- 4.4. Discrete Choice Experiment (DCE) Method 23 42
- 4.4.1. Case Study: Using Discrete Choice Experiment in the Context of Nutritious Foods 42
- 4.4.1.1 Case Study Objectives 42
- 4.4.1.2 Case Study Framework and Results 42
- 4.5. Travel Cost Method 42
- 4.5.1. Case Study: Estimating Arrival Numbers for Informal Recreation-A Geographical Approach 27 43
- 4.5.1.1 Case Study Objectives 43
- 4.5.1.2 Case Study Framework and Conclusions 43
- Section 5: Recommendations for Application 44
- References 47
