Ex-ante and ex-post measures to mitigate hypothetical bias. Are they alternative or complementary tools to increase the reliability and validity of DCE estimates?

Hypothetical bias remains at the heart of controversy about the reliability and validity of value estimates from discrete choice experiments (DCEs). This especially applies to environmental valuation, where typically no market value exists for the good under study. This has motivated a large body of literature that investigates possible approaches to test for and mitigate this bias. Our study provides further evidence to this debate by testing whether the use of ex-ante or ex-post mitigation strategies is effective in reducing hypothetical bias in DCEs. Specifically, we use individual and multiple ex-ante reminders along with ex-post data analysis to test whether their individual or joint use improves the quality of the willingness to pay (WTP) estimates. The analysis is carried out with the use of the state-of-the-art mixed logit model, as well as using the innovative semi-parametric logit-mixed-logit, which can capture non-standard heterogeneity distributions. The empirical study focuses on preferences for environmental and social impacts of organic olive production. Comparing three experimental treatments to a control treatment, we test whether cheap talk addressing hypothetical bias, a scale reminder or a combination of both affect stated WTP. In addition, we use ex-post data analysis aimed at correcting WTP estimates. Results show that cheap talk scripts and scale reminders, alone or in conjunction, did not significantly influence the results obtained from a sub-sample with standard budget constraint reminders. The ex-post approach outperforms the ex-ante approach and provides a significant reduction on mean WTP estimates.