Do monetary incentives matter for identifying social preferences? [Registered Report]

Last registered on October 02, 2025

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Pre-Trial

Trial Information

General Information

Title

Do monetary incentives matter for identifying social preferences? [Registered Report]

RCT ID

AEARCTR-0015147

Initial registration date

January 10, 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

January 13, 2025, 1:07 PM EST

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

Last updated

October 02, 2025, 5:03 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country

United States of America

Region

Primary Investigator

Name

Aljosha Henkel

Affiliation

ETH Zurich, KOF Swiss Economic Institute

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Thomas Epper

PI Affiliation

CNRS, IESEG School of Management, University of Lille & iRisk Research Center on Risk and Uncertainty

Contact Investigator

PI Name

Ernst Fehr

PI Affiliation

University of Zurich, Department of Economics

Contact Investigator

PI Name

Julien Senn

PI Affiliation

University of Zurich, Department of Economics

Contact Investigator

Additional Trial Information

Status

In development

Start date

2025-01-10

End date

2025-12-31

Keywords

Behavior

Additional Keywords

Social Preferences, Incentives, Inequality Aversion, Altruism

JEL code(s)

C80, C90, D30, D63

Secondary IDs

Prior work

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

Abstract

This is the preregistration for the registered report entitled “Do monetary incentives matter for identifying social preferences.” Social preferences play an important role in a wide variety of domains. It is therefore important to measure them accurately. In this context, a particularly important question is whether monetary incentives matter for their identification. In this proposal, we provide the details of an experiment with a general population sample aimed at answering the following questions: i) Do incentives affect subjects’ willingness to pay to increase, and their willingness to pay to decrease the payoff of others? ii) Do incentives affect the distribution of social preferences? iii) Do incentives affect the precision of estimated parameters of a model of inequality aversion? We attach to this submission the (accepted) proposal of our registered report, which provides details on the proposed research design and outlines our plan for the data analysis.

External Link(s)

Registration Citation

Citation

Epper, Thomas et al. 2025. "Do monetary incentives matter for identifying social preferences? [Registered Report]." AEA RCT Registry. October 02. https://doi.org/10.1257/rct.15147-1.1

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

We measure social preferences using a money allocation tasks in which participants have to decide how to allocate experimental currency units (ECUs) between themselves and an anonymous other participant of the study. Our key treatment variation manipulates whether subjects’ decisions in the money allocation task are incentivized or not. In the HYPOTHETICAL treatment, we do not incentivize subjects’ choices, i.e., their decisions are hypothetical and do neither affects their own payoff, nor the payoff of the other participant. In contrast, we incentivize subjects’ decision in two treatments (LOW-INCENTIVES and HIGH-INCENTIVES) by paying them (and the recipients) on the basis of their choice in a randomly drawn choice situation. These treatments allow us to cleanly assess whether and how monetary incentives affect the measurement of social preferences. Moreover, the increase in stakes size between the LOW-INCENTIVES and the HIGH-INCENTIVES treatments, which are scaled by a factor of 5, allows us to assess whether stake size matters. See the attached registered report for details.

Intervention (Hidden)

Intervention Start Date

2025-01-10

Intervention End Date

2025-12-31

Primary Outcomes

Primary Outcomes (end points)

(1) Descriptive evidence: Subjects’ modal choice across negatively sloped budget lines and across positively sloped budget lines.

(2) Clustering results: Distribution of subjects’ preference types resulting from the Dirichlet Process (DP) means clustering algorithm (Bayesian nonparametric approach).

(3) Structural estimates: Distributions of structurally estimated parameters of aversion towards disadvantageous inequality (behindness aversion) and advantageous inequality (aheadness aversion).

See the attached registered report for details.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

The end of the survey includes a module with additional survey questions that relate to subjects’ general preferences for redistribution, altruism, trust in government, political preferences, beliefs about causes of success in life, career choices, health behaviors with externalities, religiosity, and childhood experiences.

Secondary Outcomes (explanation)

Experimental Design

Our design is based on Fehr (r) al. (2022, 2023). We elicit social preferences using a task in which participants have to decide how to allocate experimental currency units (ECUs) between themselves and an anonymous other participant of the study. These choice situations systematically vary the cost and the efficiency consequences of redistribution. In addition to the money allocation task, the study will include questions related to subjects’ socio-demographics as well as a few additional survey items.

See the attached registered report for details.

Experimental Design Details

Randomization Method

The randomization is implemented directly on Qualtrics. Qualtrics randomly assigns subjects into either the HYPOTHETICAL treatment, the LOW-INCENTIVES treatment, or the HIGH-INCENTIVES treatment.

Randomization Unit

Individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

Sample size: planned number of observations

3,000 subjects from a population sample that is broadly representative of the US population with respect to age and gender. Data collection will be completed in collaboration with Prolific. While our main analysis will rely on a general population sample, we will also collect responses from an additional 300 students carefully prescreened from Prolific. The study will include control questions for the money allocation task. Participants who fail to pass the control question will be excluded from the final sample. See the attached registered report for details.

Sample size (or number of clusters) by treatment arms

3,000 general population subjects from Prolific: 1000 per treatment.
300 students from Prolific: 100 per treatment.

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

Supporting Documents and Materials

IRB