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Gender composition of microentrepreneur teams and business performance in Rwanda
Last registered on August 13, 2018


Trial Information
General Information
Gender composition of microentrepreneur teams and business performance in Rwanda
Initial registration date
August 12, 2018
Last updated
August 13, 2018 2:16 AM EDT
Primary Investigator
Universidad del Pacifico
Other Primary Investigator(s)
PI Affiliation
University of Cape Town
PI Affiliation
University of Cape Town
Additional Trial Information
On going
Start date
End date
Secondary IDs
We attempt to answer two key questions. First, How does gender composition of micro-entrepreneur teams affect business performance? Second, how do prosocial expenditures, gender attitudes and time use are affected by female participation in microenterprises. In answering these questions we exploit two margins of comparison: We compare outcomes for female and mixed gender teams with (i) all-male teams, and (ii) prospective micro-entrepreneur teams (all female and mixed gender, respectively) in control villages.

The main outcome variable is business performance, measured by administrative and self-reported data on revenues and recharge frequency. The secondary outcome variables are gender attitudes, aspirations, prosocial expenditures, and time allocation. To answer these questions we implemented a clustered RCT in two districts in Rwanda. The intervention entailed recruiting teams of micro-entrepreneurs experimentally varying their gender composition: all-male, all-female, and mixed-gender (2 males, 2 females).
External Link(s)
Registration Citation
Barron, Manuel, Rowan Clarke and Martine Visser. 2018. "Gender composition of microentrepreneur teams and business performance in Rwanda." AEA RCT Registry. August 13. https://doi.org/10.1257/rct.3230-1.0.
Former Citation
Barron, Manuel et al. 2018. "Gender composition of microentrepreneur teams and business performance in Rwanda." AEA RCT Registry. August 13. http://www.socialscienceregistry.org/trials/3230/history/32980.
Sponsors & Partners

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Experimental Details
We set up 136 micro-enterprises in Rwanda, experimentally varying gender composition of the micro-entrepreneur teams in three groups: all males, all females, and mixed gender.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Business performance: number of recharges, revenues; both at team and individual level
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
The secondary outcomes are gender attitudes, prosocial expenditure, and child aspirations. These are our secondary outcomes because they are consequences of business performance (increased household income, strengthening of the female figure in the household).
Secondary Outcomes (explanation)
Gender attitudes and child aspirations will be measured by indexes generated through principal component analysis. Prosocial expenditure is expenditure in children's health and education.
Experimental Design
Experimental Design
In order to perform an accurate comparison of business performance of male versus female teams, we experimentally varied gender composition of the micro-enterprises, in three types of teams: all-male, all-female, and mixed teams (two males and two females).

We formed teams of prospective micro entrepreneurs randomly varying gender composition. After teams came forward and provided a deposit on the equipment they were to receive, we randomly selected half the teams for the intervention, and kept the other half in the control group. The deposit was returned to the control group teams right after randomization. There are six mutually exclusive treatment arms:

T1: All-males, treatment group (N=45 villages)
C1: All-males, control group (N=45 villages)
T2: All-females, treatment group (N=45 villages)
C2: All-females, control group (N=45 villages)
T3: Mixed-gender, treatment group (N=46 villages)
C3: Mixed gender, control group (N=46 villages)
Experimental Design Details
The intervention was implemented following the same plan of action as our implementing partner, thus allowing for scale up. Their staff (with support from IPA field teams) approached the village leaders to probe for their interest in setting up a recharge station, which implied putting together a four-person micro-entrepreneur team to operate it. Unlike our partner’s standard procedure, we randomly sorted interested villages in three groups (all-male, all-female, mixed) and requested a specific gender composition of the micro-entrepreneur teams in each village. We followed our partner’s standard procedure of requesting 40,000 Rwandan francs (around 50 US dollars) as co-investment (“commitment fee”) from the prospective micro-entrepreneur teams. This fee works as a deposit for the recharging station. In order to find the best counterfactual for the micro-entrepreneur teams, the commitment fee was raised from the prospective teams in all 272 villages prior to treatment assignment. Potential micro-entrepreneur teams were told their village had a 50% chance of being selected for expansion of our implanting partner’s activities. If the village was selected in the control group, the money was returned to the teams. If the village was selected in the treatment group, we randomly allocated 100 lights (one per household) for free and recharge kiosks were set up.
Randomization Method
Randomization was done in office by a computer
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
272 villages
Sample size: planned number of observations
1,088 individuals
Sample size (or number of clusters) by treatment arms
45 villages in control 1 (all-male), 45 villages in control 2 (all-female), 46 villages in control 3 (mixed gender), 45 villages in treatment 1 (all male), 45 villages in treatment 2 (all-female), 46 villages in treatment 3 (mixed gender).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The expected sample size is 1088 households across 272 villages. There are 136 teams (544 micro-entrepreneurs) in treatment villages and 136 teams (544 prospective micro-entrepreneurs) in control villages. Since there are 45 clusters in each treatment arm, we calculate power with 90 clusters. The other assumptions are 80% power, 5% significance, 4 individuals per cluster, and an intraclass correlation of 0.02 (calculated from baseline data). The resulting minimum detectable effect (MDE) is 0.3 Standard Deviations. At baseline, one standard deviation of household head income is 39,640 Rwandan francs, so we would be able to detect changes of 11,892 Rwandan francs (13.8 US dollars/month).
IRB Name
University of Cape Town
IRB Approval Date
IRB Approval Number
IRB Name
Rwanda National Ethics Committee
IRB Approval Date
IRB Approval Number
IRB 00001497
Analysis Plan

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Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)