Skip to main content
SpringerLink
Log in

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

  • Original Research Article
  • Published:
Applied Health Economics and Health Policy Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Hole AR, Kolstad JR. Mixed logit estimation of willingness to pay distributions: a comparison of models in preference and WTP space using data from a health-related choice experiment. Empir Econ. 2012;42(2):445–69.

    Article  Google Scholar 

  2. Vernazza CR, Wildman JR, Steele JG, Whitworth JM, Walls AWG, Perry R, Matthews R, Hahn P, Donaldson C. Factors affecting patient valuations of caries prevention: using and validating the willingness to pay method. J Dent. 2015;43:981–8.

    Article  Google Scholar 

  3. Hanley N, Ryan M, Wright R. Estimating the monetary value of health care: lessons from environmental economics. Health Econ. 2003;12:3–16.

    Article  Google Scholar 

  4. Hynes S, Campbell D, Howley P. A Holistic vs. an attribute-based approach to agri-environmental policy valuation: do welfare estimates differ? J Agric Econ. 2011;62(2):305–29.

    Article  Google Scholar 

  5. Soto JR, Adams DC, Escobedo FJ. Landowner attitudes and willingness to accept compensation from forest carbon offsets: application of best–worst choice modeling in Florida USA. For Policy Econ. 2016;63:35–42.

    Article  Google Scholar 

  6. DeShazo JR, Fermo G. Designing choice sets for stated preference methods: the effects of complexity on choice consistency. J Environ Econ Manag. 2002;44:123–43.

    Article  Google Scholar 

  7. Campbell D, Hutchinson WG, Scarpa R. Lexicographic preferences in discrete choice experiments: consequences on individual-specific willingness to pay estimates. Working Paper Fondazione Eni Enrico Matei 2006; No. 128.2006.

  8. Carlsson F, Kataria M, Lampi E. Dealing with ignored attributes in choice experiments on valuation of Sweden’s environmental quality objectives. Environ Resour Econ. 2010;47:65–89.

    Article  Google Scholar 

  9. Hensher D, Rose J, Greene W. The implications on willingness to pay of respondents ignoring specific attributes. Transportation. 2005;32:203–22.

    Article  Google Scholar 

  10. Hole AR. A discrete choice model with endogenous attribute attendance. Econ Lett. 2011;110(3):203–5.

    Article  Google Scholar 

  11. Hole AR, Kolstad JR, Gyrd-Hansen D. Inferred vs. stated attribute non-attendance in choice experiments: a study of doctors’ prescription behavior. J Econ Behav Organ. 2013;96:21–31.

    Article  Google Scholar 

  12. Krucien N, Watson V, Ryan M. Is best–worst scaling suitable for health state valuation? A comparison with discrete choice experiments. Health Econ. 2017;26(12):e1–16.

    Article  Google Scholar 

  13. Krucien N, Sicsic J, Ryan M. For better or worse? Investigating the validity of best–worst discrete choice experiments in health. Health Econ. 2019;28(4):572–86.

    Article  Google Scholar 

  14. Flynn TN, Peters TJ, Coast J. Quantifying response shift or adaptation effects in quality of life by synthesising best–worst scaling and discrete choice data. J Choice Model. 2013;6:34–43.

    Article  Google Scholar 

  15. Louviere JJ, Flynn TN, Marley AAJ. Best–worst scaling: theory, methods and applications. Cambridge: Cambridge University Press; 2015.

    Book  Google Scholar 

  16. Ryan M, Watson V. Comparing welfare estimates from payment card contingent valuation and discrete choice experiments. Health Econ. 2009;18:389–401.

    Article  Google Scholar 

  17. Mahieu P-A, Riera P, Giergiczny M. Determinants of willingness-to-pay for water pollution abatement: a point and interval data payment card application. J Environ Manag. 2012;108:49–53.

    Article  Google Scholar 

  18. Flynn TN, Louviere JJ, Peters TJ, Coast J. Best–worst scaling: what it can do for health care research and how to do it. J Health Econ. 2007;26:171–89.

    Article  Google Scholar 

  19. Najafzadeh M, Ungar WJ, Hadioonzadeh A, Tsao N, Lynd LD. Comparing different experimental designs for best–worst scaling choice experiments: the case of asthma control. Int J Health Pref Res. 2018;1:3–16.

    Google Scholar 

  20. Victoor A, Delnoij DM, Friele RD, Rademakers JJ. Determinants of patient choice of healthcare providers: a scoping review. BMC Health Serv Res. 2012;12:272.

    Article  Google Scholar 

  21. Kim MJ, Damiano PC, Hand J, Denehy GE, Cobb DS, Qian F. Consumers’ choice of dentists: how and why people choose dental school faculty members as their oral health care providers. J Dent Educ. 2012;76(6):695–704.

    PubMed  Google Scholar 

  22. Kiiskinen U, Suominen-Taipale AL, Cairns J. Think twice before you book? Modelling the choice of public vs private dentist in a choice experiment. Health Econ. 2010;19:670–82.

    Article  Google Scholar 

  23. Erdem S, Campbell D. Preferences for public involvement in health service decisions: a comparison between best–worst scaling and trio-wise stated preference elicitation techniques. Eur J Health Econ. 2017;18(9):1107–23.

    Article  Google Scholar 

  24. Louviere JJ, Islam T. A comparison of importance weights and willingness-to-pay measures derived from choice-based conjoint, constant sum scales and best–worst scaling. J Bus Res. 2008;61:903–11.

    Article  Google Scholar 

  25. Campbell D, Erdem S. Position bias in best–worst scaling surveys: a case study on trust in institutions. Am J Agric Econ. 2015;97(2):526–45.

    Article  Google Scholar 

  26. Flynn TN, Louviere JJ, Peters TJ, Coast J. Estimating preferences for a dermatology consultation using best–worst scaling: comparison of various methods of analysis. BMC Med Res Methodol. 2008;8:76.

    Article  Google Scholar 

  27. Swait J, Louviere J. The role of the scale parameter in the estimation and comparison of multinomial logit models. J Market Res. 1993;30(3):305–14.

    Article  Google Scholar 

  28. Coast J, Flynn TN, Natarajan L, Sproston K, Lewis J, Louviere JJ, Peters TJ. Valuing the ICECAP capability index for older people. Soc Sci Med. 2008;67:874–82.

    Article  Google Scholar 

  29. Kuhfeld WF. Marketing research methods in SAS. Cary: SAS Institute Inc.; 2010.

    Google Scholar 

  30. Sever I, Verbič M, Klaric Sever E. Estimating willingness‐to‐pay for health care: a discrete choice experiment accounting for non‐attendance to the cost attribute. J Eval Clin Pract. (in press).

  31. Lancsar E, Savage E. Deriving welfare measures from discrete choice experiments: inconsistency between current methods and random utility and welfare theory. Health Econ Lett. 2004;13:901–7.

    Article  Google Scholar 

  32. Hanley N, Mourato S, Wright RE. Choice modelling approaches: a superior alternative for environmental valuation? J Econ Surv. 2001;15(3):435–62.

    Article  Google Scholar 

  33. Hole AR, Norman R, Viney R. Response patterns in health state valuation using endogenous attribute attendance and latent class analysis. Health Econ. 2016;25:212–24.

    Article  Google Scholar 

  34. Scarpa R, Thiene M, Hensher DA. Preferences for tap water attributes within couples: an exploration of alternative mixed logit parameterizations. Water Resour Res. 2012;48:W01520.

    Article  Google Scholar 

  35. Ryan M, San Miguel F. Testing for consistency in willingness to pay experiments. J Econ Psychol. 2000;21:305–17.

    Article  Google Scholar 

  36. Potoglou D, Burge P, Flynn T, Netten A, Malley J, Forder J, Brazier JE. Best–worst scaling vs. discrete choice experiments: an empirical comparison using social care data. Soc Sci Med. 2011;72:1717–27.

    Article  Google Scholar 

  37. van Dijk JD, Groothuis-Oudshoorn CGM, Marshall DA, Ijzerman MJ. An empirical comparison of discrete choice experiment and best–worst scaling to estimate stakeholders’ risk tolerance for hip replacement surgery. Value Health. 2016;19(4):316–22.

    Article  Google Scholar 

  38. Whitty J, Ratcliffe J, Chen G, Scuffham PA. Australian public preferences for the funding of new health technologies: a comparison of discrete choice and profile case best–worst scaling methods. Med Decis Mak. 2014;34(5):638–54.

    Article  Google Scholar 

  39. He J, Dupras J, Poder TG. The value of wetlands in Quebec: a comparison between contingent valuation and choice experiment. J Environ Econ Policy. 2016;6(1):51–78.

    Article  Google Scholar 

  40. Poder TG, He J. Willingness to pay and the sensitivity of willingness to pay for interdisciplinary musculoskeletal clinics: a contingent valuation study in Quebec, Canada. Int J Health Econ Manag. 2016;16(4):337–61.

    Article  Google Scholar 

  41. Dyachenko T, Reczek RW, Allenby GM. Models of sequential evaluation in best–worst choice tasks. Market Sci. 2014;33(6):763–884.

    Article  Google Scholar 

  42. Jobstvogt N, Watson V, Kenter JO. Looking below the surface: the cultural ecosystem service values of UK marine protected areas (MPAs). Ecosyst Serv. 2014;10:97–110.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Colgate-Palmolive Co. for providing toothbrushes and toothpastes for study participants. We are also very grateful to Professor Silvana Jukić Krmek for her help in preparing and conducting the survey.

Author information

Authors and Affiliations

  1. Institute for Tourism, Vrhovec 5, 10000, Zagreb, Croatia

    Ivan Sever

  2. School of Economics and Business, University of Ljubljana, Kardeljeva ploščad 17, 1000, Ljubljana, Slovenia

    Miroslav Verbič

  3. Institute for Economic Research, Kardeljeva ploščad 17, 1000, Ljubljana, Slovenia

    Miroslav Verbič

  4. School of Dental Medicine, Department of Endodontics and Restorative Dentistry, University of Zagreb, Gundulićeva 5, 10000, Zagreb, Croatia

    Eva Klaric Sever

Authors
  1. Ivan Sever
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miroslav Verbič
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eva Klaric Sever
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

IS designed the model and performed the analytic calculations; MV supervised the project, providing overall direction and planning; EKS contributed to sample preparation and carried out the surveys. All authors contributed to the final manuscript by providing critical feedback and helping shape the research, analysis and writing of the manuscript.

Corresponding author

Correspondence to Miroslav Verbič.

Ethics declarations

The study was approved by the appropriate institutional research ethics committee and has been performed in accordance with the ethical standards of the Declaration of Helsinki. Informed consent was obtained from all individual participants included in the study.

Funding

This study received no funding.

Conflict of interest

No potential conflict of interest was reported by the authors, Ivan Sever, Miroslav Verbič and Eva Klarić Sever.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sever, I., Verbič, M. & Klaric Sever, E. Estimating Attribute-Specific Willingness-to-Pay Values from a Health Care Contingent Valuation Study: A Best–Worst Choice Approach. Appl Health Econ Health Policy 18, 97–107 (2020). https://doi.org/10.1007/s40258-019-00522-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40258-019-00522-2

Search

Navigation