The Impact of Heterogeneity on Social Learning's Efficacy

Last registered on September 20, 2022


Trial Information

General Information

The Impact of Heterogeneity on Social Learning's Efficacy
Initial registration date
August 16, 2022

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 25, 2022, 2:08 PM EDT

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

Last updated
September 20, 2022, 7:52 AM EDT

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



Primary Investigator


Other Primary Investigator(s)

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Information acquisition is central to technology adoption decisions. Two common sources of information about returns include (i) central sources, such as government information campaigns, and (ii) social learning from peers. Central sources often have greater data on returns—yet, we lack empirical evidence that social learning is less persuasive. Understanding social learning's efficacy is particularly important for technologies where returns are highly heterogeneous and information acquisition is a major barrier to adoption. I propose one potential mechanism, which I refer to as context uncertainty. I will test this mechanism via a lab-in-the-field experiment with a sample of smallholder farmers. If valid, this mechanism provides a framework to improve informational interventions from central sources.
External Link(s)

Registration Citation

Alidaee, Hossein. 2022. "The Impact of Heterogeneity on Social Learning's Efficacy." AEA RCT Registry. September 20.
Experimental Details


Participants play a mobile game based vignette experiment. The game has multiple rounds. Within each, the participant must choose how intensively to adopt a new agricultural technology. The information provided to them is recommendations from characters in the game who previously tested the technology. Each round of the game features a distinct environment with respect to the distribution of recommendations and what is known about the characters. The participant's payoff for the experiment is based on their average yield in the game based on their adoption decisions.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Chosen adoption intensity (0-10) per round of the game.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment randomizes the order of the information environments, the village names, and the technology names being used. However, all participants experience all members of each set.
Experimental Design Details
Randomization Method
I am doing a crossover design. Each participant is exposed to every round of the game. However, round order is randomized, with a total of 64 possible orderings. To assign orderings, I compute a covariate index for each participant based on listing data. Following the approach outlined in McKenzie (2022), I compute the Mahalanobis distance between all participants, find each participant's matched pairing, and subsequently create a matched quartet from the means of each pair. This generates my first round order decision randomization. There are 6 total order randomizations to create the 2^6 = 64 arms. For the remaining 5 order randomizations, I take the assignments from the previous randomization and create a cell from each matched grouping (i.e. matched quartet). I compute the index mean for this cell and find the closest matching cell. I then alternatively assign the round order accordingly.
Randomization Unit
Randomization is done at the individual farmer level.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
No clustering is planned.
Sample size: planned number of observations
1600 smallholder farmers.
Sample size (or number of clusters) by treatment arms
Each of the farmers will be allocated uniformly across the 64 possible round orderings.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
IFMR Human Subjects Committee
IRB Approval Date
IRB Approval Number
Analysis Plan

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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