In-Group Competition for Incentives

Last registered on July 18, 2022

Pre-Trial

Trial Information

General Information

Title
In-Group Competition for Incentives
RCT ID
AEARCTR-0009654
Initial registration date
June 24, 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
June 26, 2022, 5:28 AM EDT

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

Last updated
July 18, 2022, 4:21 AM EDT

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

Locations

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Primary Investigator

Affiliation

Other Primary Investigator(s)

PI Affiliation
Michigan State University
PI Affiliation
Michigan State University
PI Affiliation
University of Ilorin
PI Affiliation
Bayero University Kano

Additional Trial Information

Status
In development
Start date
2022-07-18
End date
2022-08-22
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Governments in developing countries spend billions every year to promote technological adoption. A neglected but important question is how to effectively motivate the agents hired for such outreach projects, especially as digital extension services become more mainstream. We will conduct an experiment that puts hired agents for a mobile phone-based service into two treatment groups: agents working towards individual goals, and others competing for a share of the group-goal in their locality. The goals are based on the number of users that agents can register for KasuwaGo, an app to help farmers, sellers and resellers to trade with one another. We will compare performance means and dispersion for the two groups by measuring (a) the number of agents that achieve the goals. (b) the time taken to reach the goal, and (c) the number of registered users per agent.. The results could help to improve agricultural extension and other piecework-based programs in developing countries.
External Link(s)

Registration Citation

Citation
Ajeigbe, Hakeem et al. 2022. "In-Group Competition for Incentives." AEA RCT Registry. July 18. https://doi.org/10.1257/rct.9654
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2022-07-18
Intervention End Date
2022-08-22

Primary Outcomes

Primary Outcomes (end points)
(a) the number of agents that achieve their set goal.
(b) the time taken to reach the goal, and
(c) the number of registered users per agent.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
To study the goal structures that are more effective, we will conduct an experiment trial allots hired extension agents for a mobile-phone based service to two treatment groups: some agents are given individual goals, while others compete for a share of the goal for the group in their locality. The goals are based on the number of users that agents can register for KasuwaGo an app to help farmers, sellers and resellers to trade with one another. We will compare performance means and dispersion for the two groups.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer.
Randomization Unit
The randomization and sampling strategy is based on practical considerations following the design of the already planned recruitment, training, and outreach of the KasuwaGo app. Given the locality-based recruitment of agents and outreach planned by the project, we plan to use localities as the units of randomization. Each locality from where the youth agents will be recruited from will be stratified into large, medium, and small market size. In each training session, 50% of localities represented will be randomly assigned to T1 and 50% to T2, each stratified by the market size.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
70 localities/towns
Sample size: planned number of observations
220 hired registration agents
Sample size (or number of clusters) by treatment arms
35 localities treatment, 35 localities control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
20% difference in the number of registered users
IRB

Institutional Review Boards (IRBs)

IRB Name
Michigan State University IRB
IRB Approval Date
2022-03-18
IRB Approval Number
STUDY00003942