Using Equivalent Offsets to Test Reference Dependence: Evidence from Three Experimental Paradigms

Last registered on June 04, 2025

View Trial History

Pre-Trial

Trial Information

General Information

Title

Using Equivalent Offsets to Test Reference Dependence: Evidence from Three Experimental Paradigms

RCT ID

AEARCTR-0015748

Initial registration date

May 25, 2025

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

June 04, 2025, 9:49 AM EDT

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

Locations

Country

United States of America

Region

Primary Investigator

Name

Ao Wang

Affiliation

National University of Singapore

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

In development

Start date

2025-06-02

End date

2025-07-31

Keywords

Behavior

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

This paper tests reference dependence by examining whether the effect of a payoff increase on choice behavior is offset by an equivalent increase in the reference point, following the framework of Rees-Jones and Wang (2022). We apply this approach across three experimental paradigms: an effort task, an investment game, and a binary lottery choice experiment.

External Link(s)

Registration Citation

Citation

Wang, Ao. 2025. "Using Equivalent Offsets to Test Reference Dependence: Evidence from Three Experimental Paradigms." AEA RCT Registry. June 04. https://doi.org/10.1257/rct.15748-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention (Hidden)

Intervention Start Date

2025-06-02

Intervention End Date

2025-07-31

Primary Outcomes

Primary Outcomes (end points)

How choices—such as selecting a risky investment, supplying more labor, or opting for a tri-outcome lottery—respond to experimental variation in payoff levels and hypothesized reference points.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

Below we summarize the main features of the planned experiments. For detailed experimental design, screenshots, data-generating processes, theoretical analysis, and proposed statistical methods, please refer to the analysis plan.

There are three experimental settings: an investment game, an effort task, and a binary lottery choice experiment.

In the Investment Game, subjects make repeated choices between safe and risky options to accumulate earnings. Across-subject variation comes from random assignment to either [Baseline], where earnings depend solely on the subject’s choices, or [AutoInvest], where earnings also reflect automatic exposure to an index fund. Within-subject variation arises from random variation in payoff magnitudes across rounds.

In the Effort Task, subjects first complete transcription tasks and then answer 50 questions involving a choice between a low-paying lottery requiring no further effort and a high-paying lottery requiring additional effort. Variation is entirely within-subject, as both payments and effort requirements vary randomly across questions.

In the Binary Lottery Choice experiment, subjects make 50 choices between two lotteries. Across-subject variation comes from assignment to either [Plain], where lotteries are presented in a standard format, or [Contingent], where the same lotteries are structured to isolate the contingency of the common consequence. Within-subject variation is introduced through random variation in lottery payoffs.

Experimental Design Details

Randomization Method

oTree, Prolific

Randomization Unit

Individual Level and Question Level

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

Sample size: planned number of observations

1,400 subjects

Sample size (or number of clusters) by treatment arms

Investment Game: 500 subjects
- 250 for "Baseline"
- 250 for "AutoInvest"

Effort Task: 500 subjects

Binary Lottery Choice: 400 subjects
- 200 for "Plain"
- 200 for "Contingent"

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

Supporting Documents and Materials

IRB