Estimating Attribute-Specific Willingness-to-Pay Values from a Health Care Contingent Valuation Study: A Best-Worst Choice Approach

Appl Health Econ Health Policy. 2020 Feb;18(1):97-107. doi: 10.1007/s40258-019-00522-2.

Abstract

Background: Willingness-to-pay (WTP) studies frequently use a contingent valuation (CV) method to determine the economic value of a good or service. However, a typical CV study is able to estimate the WTP for a good as a whole, but provides no information about the marginal WTP for different attributes of a good.

Objective: The aim was to estimate marginal WTP for different attributes of a CV scenario.

Methods: By using the data from an additional best-worst choice (BWC) experiment, we disaggregated the holistic WTP values for dental care, estimated using the CV method, into attribute-specific WTP values. The study was conducted at the School of Dental Medicine, University of Zagreb, Croatia. Dental school patients were surveyed from March 2016 to January 2017, and their WTP for dental care was estimated using either a CV survey (n = 242), which also included a BWC task, or a discrete choice experiment (DCE) survey (n = 275).

Results: The largest marginal welfare estimate (€13.5) was obtained for the improvement in treatment explanation, followed by the improvements in staff behavior (€8.1) and waiting time in the office (€7.2), and by the changes in dental care provider (€3.4). These estimates were generally highly similar to the traditional marginal WTP estimates obtained with a traditional multi-profile DCE, after adjusting DCE estimates for non-attendance to the cost attribute.

Conclusion: Our BWC-CV framework may serve as a valuable alternative for estimating marginal WTP values for health care attributes when the choice behavior of respondents raises concerns for the validity of DCE estimates.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Choice Behavior*
  • Croatia
  • Dental Health Services / economics*
  • Dental Health Services / statistics & numerical data*
  • Female
  • Health Expenditures / statistics & numerical data*
  • Humans
  • Insurance, Health / economics*
  • Insurance, Health / statistics & numerical data*
  • Male
  • Middle Aged
  • Surveys and Questionnaires