The Impact of Heterogeneity on Social Learning's Efficacy

Last registered on September 20, 2022

Pre-Trial

Trial Information

General Information

Title
The Impact of Heterogeneity on Social Learning's Efficacy
RCT ID
AEARCTR-0009537
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.

Locations

Region

Primary Investigator

Affiliation

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2022-08-08
End date
2022-09-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
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

Citation
Alidaee, Hossein. 2022. "The Impact of Heterogeneity on Social Learning's Efficacy." AEA RCT Registry. September 20. https://doi.org/10.1257/rct.9537-1.1
Experimental Details

Interventions

Intervention(s)
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
2022-08-29
Intervention End Date
2022-09-30

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?
No

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)
IRB

Institutional Review Boards (IRBs)

IRB Name
IFMR Human Subjects Committee
IRB Approval Date
2022-07-28
IRB Approval Number
N/A
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials