Accounting for implicit and explicit payment vehicles in a discrete choice experiment

ABSTRACT

This study estimates the benefits of beach quality improvements, using travel costs as an implicit and entrance fee as an explicit payment vehicle in two otherwise identical labelled discrete choice site selection models. Including entrance fee as an explicit payment vehicle in addition to implicit travel costs is expected to affect beach visitors’ preferences and willingness to pay (WTP) since travel costs only are not expected to measure maximum WTP. Convergent validity of preference parameters and WTP derived from the two identical discrete choice experiments (DCEs) is tested using a split-sample approach and specifying a mixed logit choice model. Both preferences and scale parameters are significantly different between the two samples. As expected, mean WTP values are higher when an explicit entrance fee is included in the DCE. Our results suggest that implicit payment vehicles in choice experiments underestimate welfare changes. Beach visitors’ positive WTP holds promise for the introduction of economic instruments such as entrance fees to support the financial sustainability of improved beach management.

KEYWORDS:

• Discrete choice experiment
• beach site selection choice
• travel costs
• payment vehicle
• willingness to pay

Acknowledgments

We are grateful to the University of Sindh, Jamshoro, and the Higher Education Commission (HEC), Islamabad, Pakistan, for their financial support. The authors are also thankful to Dr. Heman Das Lohano from the Institute of Business Administration (IBA), Karachi, Pakistan for his helpful discussions and comments, especially with regards to the effects coding used in our models. As always, the usual disclaimer applies.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. In view of the fact that our DCE design includes the beach locations as labelled alternatives, including a travel cost increase as a monetary attribute with varying levels, as Oviedo et al. (2016) do in their study, is not possible due to the fixed distance from a respondent's home to the locations of alternative beach sites in our choice set.

2. In 2014 when the survey was conducted, 1 Euro was equal to approximately 132.32 Pakistani Rupee (PKR).

3. Because the sum of the effects codes is equal to 0, the sum of the attribute with three levels is also equal to zero, that is β₀ + β₁ + β₂ = 0, which can also be expressed as β₀ = - β₁ - β₂. For calculating WTP for the medium and the higher site quality improvements, we applied the following formulas using the above definition of effects coding: WTP _medium = (β₁ - β₀ )/β_{payment vehicle} = (β₁ - ( - β₁ - β₂))/ β_{payment vehicle} = (2*β₁ + β₂))/β_{payment vehicle} and WTP _high = (β₂ - β₀ )/ β_{payment vehicle} = (β₂ - ( - β₁ - β₂))/ β_{payment vehicle} = (2*β₂ + β₁))/ β_{payment vehicle}.

4. The ML model results using alternative distributional assumptions are available from the author upon request.

Additional information

Funding

This work was supported by Higher Education Commission (HEC), Islamabad, Pakistan [grant number NO/SU/PLAN/F.SCH/105], [grant number NO/SU/PLAN/F.SCH/232].