What is the best way to elicit forecasts if the goal is to measure the skill of forecasters? For continuous outcomes, there are two common approaches: elicit probabilities at fixed quantiles of the outcome distribution (e.g., what is the probability gas will cost less than $4.00 per gallon in 1 month?), or elicit quantiles at fixed probabilities (e.g., what is the price such that there is a 25% chance a gallon of gas will cost less than it in 1 month?). We compare these methods using a simulation study and a longitudinal survey in which 1,194 participants provided five waves of forecasts in both formats on a total of 36 real-world questions. Although probability elicitation is more ubiquitous in the literature, we show in our simulation and survey data that quantile elicitation allows us to estimate forecaster skill more efficiently with less measurement error. While quantile elicitation is time-consuming and associated with comprehension difficulty indicated by incoherent forecasts (violations of monotonicity), there is a clear reduction in incoherence across survey waves, suggesting comprehension of the quantile elicitation format improves with practice and is trainable.

The paper performs an in-depth comparison of four incentivised risk elicitation tasks. We show by means of a simulation exercise that part of the often observed heterogeneity of estimates across tasks is due to task-specific measurement error induced by the mere mechanics of the tasks. We run a replication experiment in a homogeneous subject pool using a between subjects one-shot design. Results shows that the task estimates vary over and above what can be explained by the simulations. We investigate the possibility the tasks elicit different types of preferences, rather than simply provide a different measure of the same preferences. In particular, the availability of a riskless alternative plays a prominent role helping to explain part of the differences in the estimated preferences.

This paper investigates risk preferences using an artefactual field experiment conducted with a non-standard subject pool of farmers in Ghana. I introduce an alternative methodology for studying preferences following replication of a seminal risk elicitation procedure by Binswanger (Am J Agric Econ 62(3):395407, 1980). An important feature of both approaches is that they are easy to understand and, hence, are particularly suitable for eliciting preferences among subjects with low levels of formal education. I successfully replicate Binswanger's study, documenting how his original result of the moderate level of risk aversion for an average farmer can be generalized to a different country. However, using my alternative approach, whereby lotteries are presented in the loss domain, I find that half of my experimental subjects violated expected utility theory. This approach is of relevance to the current literature on studying risk preferences among subjects with poor literacy skills.