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Estimating the Causal Effect of Matching on Fundraising Velocity
Last registered on October 22, 2019

Pre-Trial

Trial Information
General Information
Title
Estimating the Causal Effect of Matching on Fundraising Velocity
RCT ID
AEARCTR-0004885
Initial registration date
October 21, 2019
Last updated
October 22, 2019 11:21 AM EDT
Location(s)

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Primary Investigator
Affiliation
Other Primary Investigator(s)
PI Affiliation
Stanford University
PI Affiliation
Wesleyan University
Additional Trial Information
Status
In development
Start date
2019-10-22
End date
2021-01-27
Secondary IDs
Abstract
Matching of charitable contributions is a commonly used tactic in non-profits. However, estimating the causal effect of these matches is difficult if the matching is not randomly assigned. We propose an experiment where matching is (partially) randomly assigned for a subset of loans on a microfinance website, allowing for a direct estimate of the effect of the match on loan fundraising velocity. Then, we propose using non-choice data to estimate the same effect. These estimates will be compared to the experimentally derived average causal effect.
External Link(s)
Registration Citation
Citation
Bernheim, B. Douglas, Daniel Bjorkegren and Jeffrey Naecker. 2019. "Estimating the Causal Effect of Matching on Fundraising Velocity." AEA RCT Registry. October 22. https://doi.org/10.1257/rct.4885-1.0.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2019-10-22
Intervention End Date
2020-02-10
Primary Outcomes
Primary Outcomes (end points)
The key object of prediction is the rate at which loans are funded during an initial period after posting. Our primary measure of this rate will be the inverse hyperbolic sine of the funding velocity over the first day.
We will assess how well we measure treatment effects using a variety of metrics (such as bias, mean-squared prediction error, and calibration, as in the working paper (Bernheim, Bjorkegren, Naecker, & Rangel, 2013)).
Primary Outcomes (explanation)
The period considers for each loan will be the first 24 hours or entire fundraising period (whichever is shorter). Fundraising velocity is the number of (non-matching organization) dollars raised per day during that period.
Secondary Outcomes
Secondary Outcomes (end points)
We may also assess performance across these measures:
Inverse hyperbolic sine of funding velocity, first three days
Square root of funding velocity, first day
Square root of funding velocity, first three days
Funding velocity, first day
Funding velocity, first three days
Fraction of all funds raised on the site received by this loan, during the first day
Fraction of all funds raised on the site received by this loan, during the first three days
Secondary Outcomes (explanation)
These metrics represent different approaches to lowering the noise in the measure of funding velocity.
Experimental Design
Experimental Design
N/A
Experimental Design Details
Not available
Randomization Method
Randomization of loans done using the ID numbers of each loan: loans with an identifier ending in zero (e.g., 10, 20, 30) will be assigned to be matched; loans with other identifiers will be assigned to be unmatched.
Participants on Mechanical Turk will be shown a random set of loans selected by computer from the set of loans in previously run experiment.
Randomization Unit
A loan posted on our partner microfinance website.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
1
Sample size: planned number of observations
Depends on depletion of matching funds and amount of other matching activity. We are aiming for 2000 loans total, and 500 in the complier subset. 500 Mechanical Turk Participants
Sample size (or number of clusters) by treatment arms
Depends on depletion of matching funds and amount of other matching activity. We are aiming for 250 loans in matched and unmatched treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Power calculations using historical lending data show that with the observations afforded for the intended funding amount, the MDE is 7% of the mean at conventional significance and power levels.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Stanford University
IRB Approval Date
2017-10-17
IRB Approval Number
IRB-42264
Analysis Plan

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