Demand for Commitment in Volunteering

Last registered on November 13, 2024
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Pre-Trial

Trial Information

General Information

Title
Demand for Commitment in Volunteering
RCT ID
AEARCTR-0007315
Initial registration date
March 12, 2021

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 15, 2021, 10:50 AM EDT

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

Last updated
November 13, 2024, 9:52 AM EST

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

Locations

Country
United States of America
Region
Massachusetts

Primary Investigator

Name
Ryan Westphal
Affiliation
Brandeis University
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
Completed
Start date
2021-03-22
End date
2021-05-31
Keywords
Behavior
Additional Keywords
commitment, naïveté, beliefs, prosocial behavior
JEL code(s)
D9, D83, D64, C91
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
One of the key determinants of commitment demand is perceived present bias. The direction and magnitude of the effect of informing individuals of their present bias are ambiguous and remain an empirical question. The effect of correcting beliefs on present bias on commitment demand is an important determinant of whether commitment devices are an appropriate policy tool, and whether their success depends on improving self awareness of present bias. I will study the effect of updating beliefs about present bias on commitment demand in the setting of decisions to volunteer. Volunteering has been shown to be strongly associated with improved happiness and subjective well-being; however, the "warm glow" of prosocial behavior often comes after costly effort, making the decision to volunteer susceptible to present bias. This study will therefore also provide insight into how to improve both volunteerism rates and subjective well-being of individuals.
External Link(s)

Registration Citation

Citation
Westphal, Ryan. 2024. "Demand for Commitment in Volunteering." AEA RCT Registry. November 13. https://doi.org/10.1257/rct.7315-3.0
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Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
I will measure the impact of beliefs about one's present bias on demand for commitment for volunteering. To do so, I will present a treatment group with information about their present bias and likelihood of volunteering based on responses to survey questions. Demand for commitment is measured using a Becker, Degroot, Marschak mechanism between a commitment device requiring the subject to upload evidence of them volunteering versus a task in which they must upload a generic photo. This will give me a measure of willingness to pay for commitment. I will also measure the effect of commitment on volunteering rates by giving a treatment group their selection from the BDM mechanism and a control group unconditional payment.
Intervention Start Date
2021-03-22
Intervention End Date
2021-05-31

Primary Outcomes

Primary Outcomes (end points)
Willingness to pay for commitment
Volunteering rates
Primary Outcomes (explanation)
Willingness to pay for commitment is measured by switching points in the BDM mechanism
Volunteering rates are based on photo evidence when incentivized, and survey responses when unincentivized.

Secondary Outcomes

Secondary Outcomes (end points)
Beliefs about present bias and likelihood of volunteering
Secondary Outcomes (explanation)
Subjects are asked the probability that they volunteer in the next month
They are also asked how much they procrastinate relative to other students

Experimental Design

Experimental Design
To measure willingness to pay for a commitment device (defined as receiving payment conditional on following through and volunteering in the next month rather than unconditional payment), I will use the Becker, Degroot, Marschak (BDM) Mechanism (1964). Subjects will be asked if they would rather have X dollars unconditionally or 25 dollars conditional on volunteering with a non-profit organization at least once in the next month. Then one of these decisions is chosen at random and implemented.

To measure the effect of information on this willingness to pay, I will have a treatment group who is given information about their present bias as measured by the first stage of the study. Subjects will be told whether our model predicts that they are present biased, and if so, whether our model predicts that they will struggle with procrastination in volunteering. We will then measure the differences in willingness to pay for commitment between the control and treatment groups. To measure the effect of commitment devices on volunteering decisions, a treatment group will receive their choice of an unconditional payment and the commitment device from the BDM mechanism. The control group will receive an unconditional payment no matter their decision. We will then follow up with all subjects during the next month about volunteering decisions with an incentivized survey. All subjects who received the unconditional payment will be asked to self report whether or not
they volunteered.
Experimental Design Details
Randomization Method
Random number generator in Qualtrics
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
240 students
Sample size: planned number of observations
240 students.
Sample size (or number of clusters) by treatment arms
Individuals have a 50% chance of being selected for both the information treatment and the incentive treatment. So both treatments have roughly 120 students in the control group and 120 students in the treatment group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The minimum detectable effect size is approximately $0.5 change in willingness to pay for commitment. This is calculated using a standard deviation of willingness to pay of $1.40 which is approximated from a pilot survey.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Boston College Institutional Review Board
IRB Approval Date
2021-01-19
IRB Approval Number
21.147.01
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
Yes
Intervention Completion Date
May 15, 2021, 12:00 +00:00
Data Collection Complete
Yes
Data Collection Completion Date
May 15, 2021, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
249
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
249
Final Sample Size (or Number of Clusters) by Treatment Arms
Information treatment: 124 treatment, 125 control Incentives treatment: 139 treatment, 110 control
Data Publication

Data Publication

Is public data available?
No

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Program Files

Program Files
No
Reports, Papers & Other Materials

Relevant Paper(s)

Abstract
Low demand for pure commitment in real markets presents a puzzle. A possible explanation is that individuals are unaware of their present bias and their need for commitment. I test the relationship between perceived intertemporal inconsistency and demand for commitment in a novel context, volunteering. I run an experiment that successfully corrects subjects’ beliefs about their present bias and find that this increased awareness does not increase demand for commitment. This low demand for commitment is not driven by a perceived lack of present bias, but rather subjects’ accurate belief that they may fail to follow through, even with the offered level of commitment.
Citation
Westphal, Ryan. "People do not demand commitment devices because they might not work." Journal of Economic Behavior & Organization 228 (2024): 106756.
URL
https://doi.org/10.1016/j.jebo.2024.106756

Reports & Other Materials