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The cost-effectiveness of cash versus lottery incentives for a web-based, stated-preference community survey

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  • Aleksandra Gajic
  • David Cameron
  • Jeremiah Hurley

Abstract

We present the results of a randomized experiment to test the effectiveness and cost-effectiveness of response incentives for a stated-preference survey of a general community population. The survey was administered using a mixed-mode approach, in which community members were invited to participate using a traditional mailed letter using contact information for a representative sample of the community; but individuals completed the survey via the web, which exploited the advantages of electronic capture. Individuals were randomized to four incentive groups: (a) no incentive, (b) prepaid cash incentive ($2), (c) a low lottery (10 prizes of $25) and (d) a high lottery (2 prizes of $250). Letters of invitation were mailed to 3,000 individuals. In total, 405 individuals (14.4%) contacted the website and 277 (9.8%) provided complete responses. The prepaid cash incentive generated the highest contact and response rates (23.3 and 17.3%, respectively), and no incentive generated the lowest (9.1 and 5.7%, respectively). The high lottery, however, was the most cost-effective incentive for obtaining completed surveys: compared with no incentive, the incremental cost-effectiveness ratio (ICER) per completed survey for high lottery was $13.89; for prepaid cash, the ICER was $18.29. This finding suggests that the preferred response incentive for community-based, stated-preference surveys is a lottery with a small number of large prizes. Copyright Springer-Verlag 2012

Suggested Citation

  • Aleksandra Gajic & David Cameron & Jeremiah Hurley, 2012. "The cost-effectiveness of cash versus lottery incentives for a web-based, stated-preference community survey," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 13(6), pages 789-799, December.
    • Handle: RePEc:spr:eujhec:v:13:y:2012:i:6:p:789-799
    DOI: 10.1007/s10198-011-0332-0
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    Cited by:

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    2. Menegaki, Angeliki, N. & Olsen, Søren Bøye & Tsagarakis, Konstantinos P., 2016. "Towards a common standard – A reporting checklist for web-based stated preference valuation surveys and a critique for mode surveys," Journal of choice modelling, Elsevier, vol. 18(C), pages 18-50.
    3. Jeremiah Hurley & Emmanouil Mentzakis & Mita Giacomini & Deirdre DeJean & Michel Grignon, 2017. "Non-market resource allocation and the public’s interpretation of need: an empirical investigation in the context of health care," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 49(1), pages 117-143, June.
    4. Voslinsky, Alisa & Azar, Ofer H., 2021. "Incentives in experimental economics," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 93(C).
    5. Cauane Blumenberg & Aluísio J. D. Barros, 2018. "Response rate differences between web and alternative data collection methods for public health research: a systematic review of the literature," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 63(6), pages 765-773, July.
    6. Hurley, Jeremiah & Mentzakis, Emmanouil, 2013. "Health-related externalities: Evidence from a choice experiment," Journal of Health Economics, Elsevier, vol. 32(4), pages 671-681.
    7. Uttam Khanal & Clevo Wilson & Shunsuke Managi & Boon Lee & Viet-Ngu Hoang & Robert Gifford, 2018. "Psychological influence on survey incentives: valuing climate change adaptation benefits in agriculture," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 20(2), pages 305-324, April.
    8. Pierre-Alexandre Mahieu & Romain Crastes & Jordan Louviere & Ewa Zawojska, 2016. "Rewarding truthful-telling in stated preference studies," Working Papers 2016-33, Faculty of Economic Sciences, University of Warsaw.

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    More about this item

    Keywords

    Stated-preference survey; Discrete-choice survey; Response incentives; I10; C83; C90;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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