Can Appeals Identify and Exploit Donor Heterogeneity?

Last registered on December 20, 2020

Pre-Trial

Trial Information

General Information

Title
Can Appeals Identify and Exploit Donor Heterogeneity?
RCT ID
AEARCTR-0006301
Initial registration date
August 22, 2020

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
August 24, 2020, 9:52 AM EDT

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

Last updated
December 20, 2020, 4:55 PM EST

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

Locations

Primary Investigator

Affiliation
U. of Illinois at Urbana-Champaign

Other Primary Investigator(s)

PI Affiliation
University of Birmingham

Additional Trial Information

Status
In development
Start date
2020-08-07
End date
2021-01-15
Secondary IDs
Abstract
Charitable fundraisers frequently announce giving by others, and research shows that this can increase donations. However, this mechanism may not put information about peers to the most efficient use if it is costly to inform individuals who are indifferent to peer actions or if it causes some individuals to give less. We investigate heterogeneity in charitable responses to peer decisions, testing whether a fundraiser can identify donor types. To do so, we run a pair of online surveys in which a panel of subjects can donate some of their earnings. The baseline solicitation contains a simple elicitation of beliefs and contingent preferences. We use these responses to describe heterogeneity in the effects of information-about-peers treatments that we randomly assign in the second solicitation.
External Link(s)

Registration Citation

Citation
Drouvelis, Michalis and Ben Marx. 2020. "Can Appeals Identify and Exploit Donor Heterogeneity?." AEA RCT Registry. December 20. https://doi.org/10.1257/rct.6301
Experimental Details

Interventions

Intervention(s)
Our interventions provide information about past donations. Our objective is to test for heterogeneity in treatment effects on current donations.
Intervention Start Date
2020-08-30
Intervention End Date
2021-01-14

Primary Outcomes

Primary Outcomes (end points)
Our primary outcome is the change in individual i's donation between the baseline solicitation and the experiment.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
As secondary outcomes that speak to mechanisms, we also estimate changes in beliefs.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We are conducting our study using Prolific (www.prolific.co), a web-based platform for recruiting participants. It consists of two stages: a baseline solicitation and an experiment. The baseline solicitation, now completed, provided the information necessary to design the experiment pre-registered here. In both, subjects completed an unrelated survey for another study, then had the opportunity to donate some of a $3 bonus to one of a list of charities, and then were asked questions eliciting beliefs about donations. We asked subjects about the average donation of others (“belief1_i”), the share making a non-zero donation (“belief2_i”), and the share donating $1 or more (“belief3_i”). We also asked subjects how much they would donate if they knew that others had donated an average of $0.50 and if others had donated $1.

We designed the experiment to answer the following research questions:
R1. Do reported hypothetical conditional donations predict the effect of information about the average donation of others?
R2. Do reported beliefs about the donations of others predict the effect of information about:
R2a) the share of individuals donating?
R2b) the share of individuals donating $1 or more?
R2c) the average donation?

Our design randomizes information between subjects. In the control group (denoted by CG) subjects will make their donation decisions without being provided any information about giving behavior in the baseline solicitation. In our treatments, we accompany the solicitation with information about past donations from the first wave. The “Average Donation” treatment (T1) states that “Participants in our last Prolific survey donated an average of $.64.” The “Share Donating” treatment (T2) states that “43% of participants in our last Prolific survey made a donation.” The “Share Donating $1+” treatment (T3) states that “35% of participants in our last Prolific survey donated $1 or more.”

Our analysis will test whether effects of each treatment are heterogeneous in the relevant subject beliefs. Research question R1 relates to heterogeneity in reported hypothetical conditional donations. We use the elicited hypotheticals to define D-hypo_i as the predicted treatment effect of learning the average of others' donations, which we calculate using a linear combination of the hypothetical donations conditional on others’ donations of $0.50 and $1. Only 87 subjects believe that their donation would change, i.e. that D-hypo_i ≠ 0 . We therefore split all of these subjects between C and T1, with the remaining subjects distributed evenly across all four arms according to stratified randomization described next.

To promote balance across treatment arms and enhance precision for our heterogeneity analyses, we utilized stratification in our randomization. We stratified based on the sign of four variables: D-hypo_i, belief1_i, belief2_i , and belief3_i . Because only 13 subjects have D-hypo_i <0 , we did not further subdivide them using the other variables. Because subjects with D-hypo_i >0 are assigned to either C or T1, we did not subdivide them based on belief2_i or belief3_i. We created a separate category for subjects with belief1_i =0 , many of whom report the logically inconsistent belief that belief2_i >0 , i.e. that some subjects donate but that the average donation is zero. Then, to the extent that we could do so without reducing the count within strata to single digits, we further subdivided strata into “high” and “low” groups based on whether their belief1_i (which was the most accurate belief on average) was above or below the median for their stratum. We assigned subjects in each stratum as evenly as possible across treatment arms and then as needed assigned any additional subject to arms in the following order: Control, then “Average Donation,” and then “Share Donating $1+.”

December 20, 2020 update: Because information about moments of the donation distribution had limited effects on subjects’ beliefs about these moments, we decided to run a small follow-up to test whether increasing the salience of the information would have more effect. In this follow-up experiment, the single treatment group sees the “Share Donating” treatment (T2) stating that “43% of participants in our last Prolific survey made a donation,” while the control group does not see this sentence. In this version, the treatment information appears on the second line of text, rather than following the table of donation options, and it appears in bold. We focus on subjects who did not complete the main experiment or were in the control group to avoid multiple treatments. We focus on subjects whose baseline belief about the share making a non-zero donation (“belief2_i”) was not too close to the true value of 43% so that the expected direction of response in beliefs would be clear.
Experimental Design Details
Randomization Method
randomization by computer prior to invitation to experiment
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
We will invite all 382 subjects that participated in the baseline survey. We expect attrition of roughly 10 percent.
Sample size (or number of clusters) by treatment arms
124 individuals control, 117 average-donation treatment, 69 share-donated treatment, 72 share-donate-1+ treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Illinois at Urbana-Champaign
IRB Approval Date
2020-07-09
IRB Approval Number
20967

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials