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Taming Counterfeit Markets with Consumer Information
Last registered on August 25, 2020


Trial Information
General Information
Taming Counterfeit Markets with Consumer Information
Initial registration date
February 29, 2020
Last updated
August 25, 2020 5:32 PM EDT

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Primary Investigator
University of California, Berkeley
Other Primary Investigator(s)
PI Affiliation
Strathmore University
Additional Trial Information
On going
Start date
End date
Secondary IDs
In the presence of imperfectly observed quality, a market breakdown can occur in which sellers of high-quality products do not find it profitable to remain in the market. Such market dynamics can affect both buyers and sellers, but little research has explored these dynamics empirically, particularly in developing county settings where substandard and counterfeit goods have been reported to be widespread in numerous industries. In this project, we study the market for an important productive input – hybrid maize seeds. We study whether an information campaign to improve Kenyan farmers’ ability to detect seed quality and empower then to obtain recourse can: (1) increase usage of techniques to verify quality, (2) increase adoption of quality-verified products, (3) increase product quality and profitability at firms in the local market, and (4) improve agricultural outcomes. The information campaign is to be randomized across small rural markets in Western Kenya. Findings will provide direct evidence for an intervention relevant for boosting yields of a staple crop crucial for food security. We also aim to provide generalizable lessons on the role of information asymmetry in a market as a barrier to the adoption of productive inputs.
External Link(s)
Registration Citation
Hsu, Eric and Anne Wambugu. 2020. "Taming Counterfeit Markets with Consumer Information." AEA RCT Registry. August 25. https://doi.org/10.1257/rct.5510-1.3.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
(1) usage of techniques to verify quality
(2) adoption of quality-verified products
(3) increase product quality and profitability at firms in the local market
(4) agricultural outcomes including seed germination rate and yield
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We randomize information treatments across market areas in Bungoma, Busia, Kakamega, and Transnzoia Counties in Western Kenya.
Experimental Design Details
Not available
Randomization Method
Randomization done using Stata 13
Randomization Unit
We randomize sublocations (a low-level administrative unit) into a treatment group. Within a sublocation, a market is chosen to be the one to be surveyed and treated. Within a market areas, randomly chosen seed sellers and maize farmers are chosen to be surveyed.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
320 market areas
Sample size: planned number of observations
320 market areas, 2560 maize farmers, 640 seed sellers
Sample size (or number of clusters) by treatment arms
160 market areas assigned to treatment (with 80 assigned to Treatment #1, and 80 assigned to Treatment #1 plus Treatment #2)
160 market areas assigned to control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Strathmore Unviersity
IRB Approval Date
IRB Approval Number
IRB Name
UC Berkeley
IRB Approval Date
IRB Approval Number
Analysis Plan

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