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Can Appeals Identify and Exploit Donor Heterogeneity?
Last registered on September 29, 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
Last updated
September 29, 2020 11:45 AM EDT
Location(s)

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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. September 29. https://doi.org/10.1257/rct.6301-1.1.
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+.”
Experimental Design Details
Not available
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