The Impact of Beliefs on Treatment Efficacy

Last registered on February 28, 2020


Trial Information

General Information

The Impact of Beliefs on Treatment Efficacy
Initial registration date
February 15, 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
February 18, 2020, 2:14 PM EST

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

Last updated
February 28, 2020, 10:03 AM EST

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


Primary Investigator

University of Warwick

Other Primary Investigator(s)

Additional Trial Information

In development
Start date
End date
Secondary IDs
There exists ample evidence documenting the role of a "placebo" (often a sugar pill) in improving objective outcomes in medical trials, including a smaller literature identifying beliefs as the channel through which this effect operates (Malani, 2006; Kamenica et al, 2013; Benedetti et al, 2005). In this paper I plan to test whether a congruent effect exists in the social sciences. That is, do recipients of government interventions or research treatments experience better outcomes if they have more positive beliefs about the efficacy of the treatment they are receiving? To identify the role of beliefs in determining treatment efficacy, I am partnering with a non-profit organization, Wholesome Wave, to run an experiment in which I measure the impact of exogenous changes in beliefs. Wholesome Wave provides fruit and vegetable gift cards to low-income individuals in the United States to encourage healthy eating. I plan to introduce an intervention in a new Wholesome Wave program in Corpus Christi, TX in which participants will be asked to set a goal for the amount they want to spend on fruits and vegetables. I will randomly assign individuals to receive either optimistic or no information about the efficacy of goal setting. My primary outcome of interest is the impact of this exogenous variation in beliefs on subsequent goal achievement, measured through card expenditures. In the event that beliefs do indeed affect treatment efficacy, such a finding would allow researchers to better understand the generalizability of observed treatment effects and also provide a tool for policymakers looking for a cost-effective means of improving intervention outcomes.
External Link(s)

Registration Citation

Toma, Mattie. 2020. "The Impact of Beliefs on Treatment Efficacy." AEA RCT Registry. February 28.
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Experimental Details


I will ask all participants to set a goal for fruit and vegetable consumption. Half of these participants will be randomly assigned to the treatment group and will receive information indicating that research has shown that goal-setting is effective in helping people stick to healthy habits like eating fruits and vegetables. To further shape beliefs regarding the efficacy of goal setting, treatment participants will also answer a comprehension question about the information they just received. Control participants, meanwhile, will not receive information about the efficacy of goal setting. All participants will also receive follow-up text reminders regarding the goal they set; reminders sent to treatment participants will reiterate that goal-setting has been shown to be effective.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
I will use purchase-level card expenditure data to determine whether those who receive the optimistic information about the efficacy of goal setting get closer to achieving their goal over the subsequent month.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
See the Intervention section above.
Experimental Design Details
Randomization Method
Treatments are assigned within the survey platform Qualtrics using their random number generator.
Randomization Unit
Treatments are assigned at the individual level.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
700 individuals
Sample size: planned number of observations
700 individuals.
Sample size (or number of clusters) by treatment arms
350 individuals in each beliefs treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Harvard University IRB
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

Analysis Plan

MD5: e5f9c45f04aec2c7c2345d4b355e1145

SHA1: 586eaa437a1a4722e596b018173a93f7817cbd2e

Uploaded At: February 15, 2020


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials