TY - JOUR
T1 - Ready-made oTree apps for time preference elicitation methods
AU - Rose, JE (Julia)
AU - Rose, Michael
PY - 2019/9
Y1 - 2019/9
N2 - In the realm of time preference elicitation, several different methods to elicit preferences from individual choices and subsequently estimate discounting and utility function parameters have evolved. We provide ready-made oTree (Chen et al., 2016) applications for three of the mostly used elicitation tools in experiments. The first method (CTB — Convex Time Budgets) elicits discounting and utility function curvature jointly from choices over time (Andreoni and Sprenger, 2012; Andreoni et al., 2015). The second method elicits time preferences and utility function curvature using two different tasks and jointly estimating parameters (DMPL — Double Multiple Price List). It is fully nested in our first method and can be complemented via a risk elicitation task by Holzmeister (2017). The third method elicits discounting without eliciting utility (Attema et al., 2016) by measuring discounting without using explicit functional forms for utility. We provide the general theoretical background of the elicitation approaches, experimental designs, as well as a detailed description of our programs. Both programs can be easily and flexibly adapted to the user’s needs, even without any prior programming knowledge in oTree.
AB - In the realm of time preference elicitation, several different methods to elicit preferences from individual choices and subsequently estimate discounting and utility function parameters have evolved. We provide ready-made oTree (Chen et al., 2016) applications for three of the mostly used elicitation tools in experiments. The first method (CTB — Convex Time Budgets) elicits discounting and utility function curvature jointly from choices over time (Andreoni and Sprenger, 2012; Andreoni et al., 2015). The second method elicits time preferences and utility function curvature using two different tasks and jointly estimating parameters (DMPL — Double Multiple Price List). It is fully nested in our first method and can be complemented via a risk elicitation task by Holzmeister (2017). The third method elicits discounting without eliciting utility (Attema et al., 2016) by measuring discounting without using explicit functional forms for utility. We provide the general theoretical background of the elicitation approaches, experimental designs, as well as a detailed description of our programs. Both programs can be easily and flexibly adapted to the user’s needs, even without any prior programming knowledge in oTree.
U2 - 10.1016/j.jbef.2019.04.011
DO - 10.1016/j.jbef.2019.04.011
M3 - Article
SN - 2214-6350
VL - 23
SP - 23
EP - 28
JO - Journal of Behavioral and Experimental Finance
JF - Journal of Behavioral and Experimental Finance
ER -