Preference misperceptions and intertemporal choices

Last registered on February 11, 2021

View Trial History

Pre-Trial

Trial Information

General Information

Title

State-dependent mispredictions and intertemporal choices

RCT ID

AEARCTR-0007153

Initial registration date

February 10, 2021

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

February 11, 2021, 11:58 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country

Switzerland

Region

Zurich

Primary Investigator

Name

Sili Zhang

Affiliation

University of Zurich

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

In development

Start date

2021-02-11

End date

2021-03-25

Keywords

Behavior, Lab

Additional Keywords

JEL code(s)

C91, D11, D12, D91

Secondary IDs

Abstract

This study investigates preference misprediction, especially due to projection bias (Loewenstein et al.,2003) and the lack of experience, in intertemporal choices. The objectives are two-fold: first, it aims to demonstrate the existence of a behavioral tendency to project current preferences into the future from a state-dependent valuation pattern; second, it aims to show how this state-dependent misprediction of preferences confounds the identification of time preferences from state-varying choices. To this end, I run an online experiment with multiple sessions across weeks in an environment with unpleasant effort tasks (e.g. Augenblick and Rabin, 2018), where participants at different stages of mandatory work can forgo payoffs to avoid (or equivalently, require compensation for) additional work at different point of time.

External Link(s)

Registration Citation

Citation

Zhang, Sili. 2021. "State-dependent mispredictions and intertemporal choices." AEA RCT Registry. February 11. https://doi.org/10.1257/rct.7153-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

4 between-subject treatments: Good state w/o experience; Bad state w/o experience; Good state with experience; Bad state with experience

Intervention Start Date

2021-02-11

Intervention End Date

2021-03-25

Primary Outcomes

Primary Outcomes (end points)

Willingness-to-accept for (or equivalently, willingness-to-pay to avoid) one additional task

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Self-reported and hypothetical measures of patience;
Self-reported behaviors that are typically attributed to time preferences (saving, procrastination in coursework, and vaccine take-up)

Secondary Outcomes (explanation)

Experimental Design

The study aims to detect state dependence in an intertemporal setting, where the state is varied by different degrees of aversiveness. Motivated by the paradigm in Augenblick and Rabin (2018), the experimental setting consists of unpleasant effort tasks and valuation choices of these tasks on top. The decision state is varied by the cumulated fatigue/tiredness from the tasks at different stages of mandatory work and the particular task involved is to count the numbers of “0”s in a matrix (e.g. Abeler et al., 2011).

The experiment consists of three sessions that are one week apart. Participants are required to complete a certain number of mandatory tasks in two or three out of three sessions, depending on the treatment. The main choice variable of interest is participants’ willingness-to-accept for additional work at different weeks elicited in different states, either good state (not tired) or bad state (tired). These within-subject choices vary in time horizon as well as decision states, depending on the treatment. State-dependent mispredictions predict that these valuation choices will be smaller in Good state than in Bad state treatment.

Experimental Design Details

Randomization Method

randomization will be done by a computer

Randomization Unit

individual level

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

200 participants (To minimize attrition across weeks, the experiment will be implemented among participants from the subject pool affiliated with the University of Zurich and the Swiss Federal Institute of Technology in Zurich.)

Sample size: planned number of observations

200 participants (To ensure that the intertemporal choice, Choice 2, is incentive-compatible through a BDM mechanism and to be able to collect Choice 3 in Week 3, the experiment will only implement Choice 2 with some probabilities in Week 2. The reason is that those whose Choice 2 was implemented will not make Choice 3 in Week 3 since the additional task in Week 3 has been settled according to Choice 2. Thus, I will only have a full sample when analyzing state dependence from between-subject variations. When analyzing the interaction with time preferences using within-subject variations, I will only have a subsample who have made both Choice 2 in Week 2 and Choice 3 in Week 3.)

Sample size (or number of clusters) by treatment arms

50 participants in Good state w/o experience;
50 participants in Bad state w/o experience;
50 participants in Good state with experience;
50 participants in Bad state with experience.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB