Learning and Using Competitor Information in Mwanza, Tanzania

Last registered on May 30, 2024


Trial Information

General Information

Learning and Using Competitor Information in Mwanza, Tanzania
Initial registration date
May 28, 2024

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
May 30, 2024, 3:56 AM EDT

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


Primary Investigator

Cornell University

Other Primary Investigator(s)

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Firms are typically modeled as profit-maximizing agents who know and use information. In low-income countries, information frictions are more salient than typically assumed and many firms are run by a single person. In this project I study whether small urban firms value, learn, and use information about their competitors. First, I measure how well these firms know the prices at other firms. Next, I elicit their willingness to pay for this price information. Contingent on this valuation, I randomly share price information with some firms. I measure whether and how firms change their prices upon learning the prices at their competition.
External Link(s)

Registration Citation

Norton, Ben. 2024. "Learning and Using Competitor Information in Mwanza, Tanzania." AEA RCT Registry. May 30. https://doi.org/10.1257/rct.13703-1.0
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Prices of common staple foods at small urban firms in Mwanza, Tanzania.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
I collect prices of staple foods at small firms in Mwanza, Tanzania. At treatment firms I elicit their beliefs about these prices at nearby firms and their willingness to pay for this price information. Contingent on their willingness to pay, some treatment firms are randomly given the price information. I again collect prices and construct the within-firm change in prices to use as my main outcome. I see whether receiving information causes firms to change prices and how this varies by their relative place in their local price distribution.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
700 firms
Sample size: planned number of observations
700 firms
Sample size (or number of clusters) by treatment arms
200 firms control
200 firms treatment and do not receive information
300 firms treatment and receive information
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number