Personalization with Self-Selection: Experimental Evidence on Social Information and Charitable Giving (Wave 1)

Last registered on March 16, 2026

Pre-Trial

Trial Information

General Information

Title
Personalization with Self-Selection: Experimental Evidence on Social Information and Charitable Giving (Wave 1)
RCT ID
AEARCTR-0018087
Initial registration date
March 13, 2026

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
March 16, 2026, 7:00 AM EDT

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

Locations

Region

Primary Investigator

Affiliation
The University of Osaka

Other Primary Investigator(s)

PI Affiliation
The University of Osaka
PI Affiliation
Kyoto University

Additional Trial Information

Status
On going
Start date
2026-03-13
End date
2029-03-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Policy targeting with machine learning can personalize interventions at the individual level. Yet when individuals are allowed to accept or decline the assigned treatment, noncompliance arises. The welfare properties of targeting policies under such noncompliance are not well understood. This study empirically evaluates how such self-selection affects social welfare in the context of policy targeting. We apply the framework for targeting under noncompliance proposed by Athey et al. (2025) to a charitable giving setting and evaluate the welfare implications through a two-wave experimental design.

This pre-registration covers Wave 1 of the two-wave experiment. Wave 1 collects data for constructing the machine learning models used for targeting in Wave 2. We conduct a randomized controlled trial (RCT) in a donation experiment to measure how presenting social comparison information and offering participants the option to view it affect charitable giving behavior. Using the Wave 1 data, we will also estimate heterogeneous treatment effects and conduct simulation-based welfare evaluation of policy targeting.

*Athey, S., K. Inoue, and Y. Tsugawa. 2025. "Targeted Treatment Assignment Using Data from Randomized Experiments with Noncompliance." AEA Papers and Proceedings 115: 209–14.
External Link(s)

Registration Citation

Citation
Inoue, Kosuke, Shodai Kitano and Shusaku Sasaki. 2026. "Personalization with Self-Selection: Experimental Evidence on Social Information and Charitable Giving (Wave 1)." AEA RCT Registry. March 16. https://doi.org/10.1257/rct.18087-1.0
Experimental Details

Interventions

Intervention(s)
Participants are randomly assigned to one of three groups. The initial endowment is 1,000 JPY for all groups.

1. Control Group:No information about others' donation behavior (hereafter, social comparison information) is presented.
2. Forced Disclosure Group:Social comparison information is presented prior to the donation decision. The information consists of the donation rate and the average donation amount, obtained from a pilot study.
3. Self-Selection Group:Participants are given the option to view social comparison information and choose whether to view it before making their donation decision. If they choose to view the information, the same content as in the Forced Disclosure Group is presented. If they choose not to view it, the same donation decision screen as in the Control Group is displayed. The default option is set to “not view.”
Intervention Start Date
2026-03-19
Intervention End Date
2026-03-27

Primary Outcomes

Primary Outcomes (end points)
- Donation amount (JPY)
- Whether the participant made a donation or not
Primary Outcomes (explanation)
Each participant is informed that they have a chance of receiving an additional payment of 1,000 JPY through a lottery (with a winning probability of 1 in 10). Conditional on winning, participants indicate, in increments of 1 JPY, how much of the 1,000 JPY they would donate to the Japan Committee for UNICEF. The remaining amount is received by the participant as an additional payment. For participants selected through the lottery, donations are executed in accordance with their stated amounts.

Secondary Outcomes

Secondary Outcomes (end points)
- Donation amount conditional on donors
- Whether the participant chose to view the social comparison information or not (Self-Selection Group only)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In Wave 1, we collect data for constructing the machine learning models used for targeting in Wave 2 and estimate heterogeneity in the effects of presenting social comparison information and granting the right to view it. We administer two online surveys to panelists of an online survey company.

1. Screening Survey (scheduled to begin on March 13, 2026)
The screening survey measures covariates used for constructing the targeting models and estimating treatment effect heterogeneity. The covariates include demographic variables (age, gender, prefecture of residence, education, marital status, household members, occupation, and income), individual characteristics (psychological traits such as reciprocity and social comparison orientation, Big Five, self-image, risk preferences, and loss aversion), donation-related questions (hypothetical donation behavior, beliefs about others' donation amounts, interest in charitable giving, total donations in 2025, and organizations donated to in 2025), and questions on usage of generative AI services. We include two large language model (LLM) bot detection questions to address the concern that respondents may use generative AI to answer surveys.

2. Main Experiment (scheduled to begin on March 19, 2026)
The main experiment implements a randomized controlled trial on charitable giving. Participants are first informed that they have a chance of receiving an additional payment of 1,000 JPY through a lottery (1 in 10 odds). They then report what they would spend the 1,000 JPY on if they were to use it for themselves. After that, they receive an explanation of the donation experiment and complete a comprehension check to confirm their understanding of the incentive mechanism. They then read a description of the donation recipient, the Japan Committee for UNICEF. Participants are randomly assigned to one of the three groups described in the Intervention section. Under their respective treatment conditions, they decide, in increments of 1 JPY, how much to donate if they receive the 1,000 JPY. At the end of the experiment, participants are asked whether they used generative AI to answer the survey. They are assured that their response will not affect their payment.
Experimental Design Details
Not available
Randomization Method
Stratified randomization by age, gender, residence in a major city, and intended donation amount measured in the screening survey
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
9,000 individuals
Sample size (or number of clusters) by treatment arms
3,000 individuals per each group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
A pilot study (N = 600) yielded the following effect sizes (Cohen's d) for mean donation amounts: 0.10 (Control Group vs. Forced Disclosure Group) and 0.07 (Control Group vs. Self-Selection Group). At a significance level of 5% and a power of 80%, detecting the ATE requires 1,578 participants per group for the Control Group vs. the Forced Disclosure Group and the ITT effect requires 3,026 for the Control Group vs. the Self-Selection Group. We therefore set each group at 3,000 participants, sufficient to detect the ITT effect of the Self-Selection Group.
IRB

Institutional Review Boards (IRBs)

IRB Name
Center for Infectious Disease Education and Research, The University of Osaka IRB
IRB Approval Date
2026-02-27
IRB Approval Number
2025CRER0227
Analysis Plan

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