scholarly journals Scope Elasticity of Willingness to pay in Discrete Choice Experiments

2021 ◽  
Author(s):  
Anders Dugstad ◽  
Kristine M. Grimsrud ◽  
Gorm Kipperberg ◽  
Henrik Lindhjem ◽  
Ståle Navrud
Keyword(s):  
Willingness To Pay ◽  
Discrete Choice ◽  
Stated Preference ◽  
Choice Experiments ◽  
Discrete Choice Experiments ◽  
Model Specification ◽  
Economic Significance ◽  
Scope Sensitivity ◽  
Order Of Magnitude ◽  
Unit Of Measurement

AbstractSensitivity to scope in nonmarket valuation refers to the property that people are willing to pay more for a higher quality or quantity of a nonmarket public good. Establishing significant scope sensitivity has been an important check of validity and a point of contention for decades in stated preference research, primarily in contingent valuation. Recently, researchers have begun to differentiate between statistical and economic significance. This paper contributes to this line of research by studying the significance of scope effects in discrete choice experiments (DCEs) using the scope elasticity of willingness to pay concept. We first formalize scope elasticity in a DCE context and relate it to economic significance. Next, we review a selection of DCE studies from the environmental valuation literature and derive their implied scope elasticity estimates. We find that scope sensitivity analysis as validity diagnostics is uncommon in the DCE literature and many studies assume unitary elastic scope sensitivity by employing a restrictive functional form in estimation. When more flexible specifications are employed, the tendency is towards inelastic scope sensitivity. Then, we apply the scope elasticity concept to primary DCE data on people’s preferences for expanding the production of renewable energy in Norway. We find that the estimated scope elasticities vary between 0.13 and 0.58, depending on the attribute analyzed, model specification, geographic subsample, and the unit of measurement for a key attribute. While there is no strict and universally applicable benchmark for determining whether scope effects are economically significant, we deem these estimates to be of an adequate and plausible order of magnitude. Implications of the results for future DCE research are provided.

scholarly journals The Impact of Environmental Sustainability Labels on Willingness-to-Pay for Foods: A Systematic Review and Meta-Analysis of Discrete Choice Experiments

Nutrients ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 2677
Author(s):  
Anastasios Bastounis ◽  
John Buckell ◽  
Jamie Hartmann-Boyce ◽  
Brian Cook ◽  
Sarah King ◽  
...  
Keyword(s):  
Willingness To Pay ◽  
Discrete Choice ◽  
Environmental Sustainability ◽  
Purchasing Power Parity ◽  
Meta Analysis ◽  
Choice Experiments ◽  
Discrete Choice Experiments ◽  
Environmental Damage ◽  
Environmentally Sustainable ◽  
The Impact

Food production is a major contributor to environmental damage. More environmentally sustainable foods could incur higher costs for consumers. In this review, we explore whether consumers are willing to pay (WTP) more for foods with environmental sustainability labels (‘ecolabels’). Six electronic databases were searched for experiments on consumers’ willingness to pay for ecolabelled food. Monetary values were converted to Purchasing Power Parity dollars and adjusted for country-specific inflation. Studies were meta-analysed and effect sizes with confidence intervals were calculated for the whole sample and for pre-specified subgroups defined as meat-dairy, seafood, and fruits-vegetables-nuts. Meta-regressions tested the role of label attributes and demographic characteristics on participants’ WTP. Forty-three discrete choice experiments (DCEs) with 41,777 participants were eligible for inclusion. Thirty-five DCEs (n = 35,725) had usable data for the meta-analysis. Participants were willing to pay a premium of 3.79 PPP$/kg (95%CI 2.7, 4.89, p ≤ 0.001) for ecolabelled foods. WTP was higher for organic labels compared to other labels. Women and people with lower levels of education expressed higher WTP. Ecolabels may increase consumers’ willingness to pay more for environmentally sustainable products and could be part of a strategy to encourage a transition to more sustainable diets.

State of the Art of Contingent Valuation

2020 ◽  
Author(s):  
Tim Haab ◽  
Lynne Lewis ◽  
John Whitehead
Keyword(s):  
Contingent Valuation ◽  
Discrete Choice ◽  
Stated Preference ◽  
Revealed Preference ◽  
Choice Experiments ◽  
Discrete Choice Experiments ◽  
Actual Behavior ◽  
Stated Preference Methods ◽  
Valuation Methods ◽  
Hypothetical Scenario

The contingent valuation method (CVM) is a stated preference approach to the valuation of non-market goods. It has a 50+-year history beginning with a clever suggestion to simply ask people for their consumer surplus. The first study was conducted in the 1960s and over 10,000 studies have been conducted to date. The CVM is used to estimate the use and non-use values of changes in the environment. It is one of the more flexible valuation methods, having been applied in a large number of contexts and policies. The CVM requires construction of a hypothetical scenario that makes clear what will be received in exchange for payment. The scenario must be realistic and consequential. Economists prefer revealed preference methods for environmental valuation due to their reliance on actual behavior data. In unguarded moments, economists are quick to condemn stated preference methods due to their reliance on hypothetical behavior data. Stated preference methods should be seen as approaches to providing estimates of the value of certain changes in the allocation of environmental and natural resources for which no other method can be used. The CVM has a tortured history, having suffered slings and arrows from industry-funded critics following the Exxon Valdez and British Petroleum (BP)–Deepwater Horizon oil spills. The critics have harped on studies that fail certain tests of hypothetical bias and scope, among others. Nonetheless, CVM proponents have found that it produces similar value estimates to those estimated from revealed preference methods such as the travel cost and hedonic methods. The CVM has produced willingness to pay (WTP) estimates that exhibit internal validity. CVM research teams must have a range of capabilities. A CVM study involves survey design so that the elicited WTP estimates have face validity. Questionnaire development and data collection are skills that must be mastered. Welfare economic theory is used to guide empirical tests of theory such as the scope test. Limited dependent variable econometric methods are often used with panel data to test value models and develop estimates of WTP. The popularity of the CVM is on the wane; indeed, another name for this article could be “the rise and fall of CVM,” not because the CVM is any less useful than other valuation methods. It is because the best practice in the CVM is merging with discrete choice experiments, and researchers seem to prefer to call their approach discrete choice experiments. Nevertheless, the problems that plague discrete choice experiments are the same as those that plague contingent valuation. Discrete choice experiment–contingent valuation–stated preference researchers should continue down the same familiar path of methods development.

scholarly journals Users are willing to pay for sanitation, but not as much as they say: empirical results and methodological comparisons of willingness to pay for peri-urban sanitation in Lusaka, Zambia using contingent valuation, discrete choice experiments, and hedonic pricing

2020 ◽  
Vol 10 (4) ◽  
pp. 756-767 ◽  
Author(s):  
James B. Tidwell
Keyword(s):  
Willingness To Pay ◽  
Contingent Valuation ◽  
Discrete Choice ◽  
Controlled Trial ◽  
Choice Experiments ◽  
Hedonic Pricing ◽  
Discrete Choice Experiments ◽  
Randomized Controlled ◽  
Urban Sanitation ◽  
Demand Assessment

Abstract Significant investment is needed to improve peri-urban sanitation. Consumer willingness to pay may bridge some of this gap. While contingent valuation has been frequently used to assess this demand, there are few comparative studies to validate this method for water and sanitation. We use contingent valuation to estimate demand for flushing toilets, solid doors, and inside and outside locks on doors and compare this with results from hedonic pricing and discrete choice experiments. We collected data for a randomized, controlled trial in peri-urban Lusaka, Zambia in 2017. Tenants were randomly allocated to discrete choice experiments (n = 432) or contingent valuation (n = 458). Estimates using contingent valuation were lower than discrete choice experiments for solid doors (US$2.6 vs. US$3.4), higher for flushing toilets ($3.4 vs. $2.2), and were of the opposite sign for inside and outside locks ($1.6 vs. $ − 1.1). Hedonic pricing aligned more closely to discrete choice experiments for flushing toilets ($1.7) and locks (−$0.9), suggesting significant and inconsistent bias in contingent valuation estimates. While these results provide strong evidence of consumer willingness to pay for sanitation, researchers and policymakers should carefully consider demand assessment methods due to the inconsistent, but often inflated bias of contingent valuation.

Design of Discrete Choice Experiments

2019 ◽  
Author(s):  
Deborah J. Street ◽  
Rosalie Viney
Keyword(s):  
Discrete Choice ◽  
Choice Experiment ◽  
Stated Preference ◽  
Choice Experiments ◽  
Individual Characteristics ◽  
Discrete Choice Experiments ◽  
Random Component ◽  
Choice Sets ◽  
Attribute Levels ◽  
Choice Set

Discrete choice experiments are a popular stated preference tool in health economics and have been used to address policy questions, establish consumer preferences for health and healthcare, and value health states, among other applications. They are particularly useful when revealed preference data are not available. Most commonly in choice experiments respondents are presented with a situation in which a choice must be made and with a a set of possible options. The options are described by a number of attributes, each of which takes a particular level for each option. The set of possible options is called a “choice set,” and a set of choice sets comprises the choice experiment. The attributes and levels are chosen by the analyst to allow modeling of the underlying preferences of respondents. Respondents are assumed to make utility-maximizing decisions, and the goal of the choice experiment is to estimate how the attribute levels affect the utility of the individual. Utility is assumed to have a systematic component (related to the attributes and levels) and a random component (which may relate to unobserved determinants of utility, individual characteristics or random variation in choices), and an assumption must be made about the distribution of the random component. The structure of the set of choice sets, from the universe of possible choice sets represented by the attributes and levels, that is shown to respondents determines which models can be fitted to the observed choice data and how accurately the effect of the attribute levels can be estimated. Important structural issues include the number of options in each choice set and whether or not options in the same choice set have common attribute levels. Two broad approaches to constructing the set of choice sets that make up a DCE exist—theoretical and algorithmic—and no consensus exists about which approach consistently delivers better designs, although simulation studies and in-field comparisons of designs constructed by both approaches exist.

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