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arXiv · 2102.02945

Hypothetical bias in stated choice experiments: Part II. Macro-scale analysis of literature and effectiveness of bias mitigation methods

Abstract

This paper reviews methods of hypothetical bias (HB) mitigation in choice experiments (CEs). It presents a bibliometric analysis and summary of empirical evidence of their effectiveness. The paper follows the review of empirical evidence on the existence of HB presented in Part I of this study. While the number of CE studies has rapidly increased since 2010, the critical issue of HB has been studied in only a small fraction of CE studies. The present review includes both ex-ante and ex-post bias mitigation methods. Ex-ante bias mitigation methods include cheap talk, real talk, consequentiality scripts, solemn oath scripts, opt-out reminders, budget reminders, honesty priming, induced truth telling, indirect questioning, time to think and pivot designs. Ex-post methods include follow-up certainty calibration scales, respondent perceived consequentiality scales, and revealed-preference-assisted estimation. It is observed that the use of mitigation methods markedly varies across different sectors of applied economics. The existing empirical evidence points to their overall effectives in reducing HB, although there is some variation. The paper further discusses how each mitigation method can counter a certain subset of HB sources. Considering the prevalence of HB in CEs and the effectiveness of bias mitigation methods, it is recommended that implementation of at least one bias mitigation method (or a suitable combination where possible) becomes standard practice in conducting CEs. Mitigation method(s) suited to the particular application should be implemented to ensure that inferences and subsequent policy decisions are as much as possible free of HB.

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BibTeXRIS

Milad Haghani, Michiel C. J. Bliemer, John M. Rose, Harmen Oppewal, Emily Lancsar. 2021-02-05. Hypothetical bias in stated choice experiments: Part II. Macro-scale analysis of literature and effectiveness of bias mitigation methods. https://doi.org/10.1016/j.jocm.2021.100322

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