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Gender quotas: Fairness, Meritocracy, and Participation of Women in Politics

Last registered on October 13, 2020


Trial Information

General Information

Gender quotas: Fairness, meritocracy, and participation of women in politics
Initial registration date
October 12, 2020

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
October 13, 2020, 9:12 AM EDT

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


Primary Investigator

University of Copenhagen

Other Primary Investigator(s)

PI Affiliation
University of Dar es Salaam
PI Affiliation
University of Dar es Salaam
PI Affiliation
Copenhagen Business School
PI Affiliation
University of Copenhagen

Additional Trial Information

On going
Start date
End date
Secondary IDs
Gender quotas in politics are often introduced as measures to increase women’s representation in parliaments and cabinets, trigger societal change, and ultimately promote fairer gender norms. We study one of the first systems of gender quotas, implemented in Tanzania in 1985 as a number of reserved seats for women in parliament. We conduct an online survey to measure preferences for numerical and substantive representation of women in the national parliament and its national committees, and conduct four information experiments to study perceptions of fairness, meritocracy and patronage in relation to the gender quota in Tanzania. The analysis contributes to research on the effect of quotas and affirmative action in politics.
External Link(s)

Registration Citation

Likwelile, Servacius et al. 2020. "Gender quotas: Fairness, meritocracy, and participation of women in politics." AEA RCT Registry. October 13.
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Experimental Details


We run our survey both 2 weeks before and 2 weeks after the national election in October 28th to measure changes in perceptions about long term political goals.

In the first part of the survey, respondents estimate the current distribution of wealth and gender in Tanzania and, then, indicate what their ideal distributions look like. Our research adopts Rawls' idea of the original position and applies it to principles of gender equality. When asking about ideal distributions, we aim to elicit respondents' preferences freed of their respective characteristics and specific position in society (Rawls (1971)).

We combine responses on ideal and estimated distributions with the following 4 treatments:

1 - Survey experiment: Civic action

We implement one survey experiment, where respondents are randomly assigned to two treatments in support of participation of women in politics:
1. Some of your fellow students want to send this week a petition letter to the government, requesting action to increase gender equality. Would you sign the petition letter??
2. Would you donate 5,000 TZS for a female candidate’s campaign this week?

2 - Survey experiment: Gender quota

We implement a second survey experiment on the gender quota in politics in Tanzania, where we randomize a question about whether the quota should be kept constant, or increased (with and without peer effects).
1. BASELINE: Do you agree that the number of reserved seats for women should be kept constant at 30% in future parliaments?
2. INCREASE: Do you agree that the number of reserved seats for women should be increased from 30% to 50%?
3. DECREASE: Do you agree that the number of reserved seats for women should be decreased from 30% to 15%?

3 - Survey experiment: Meritocracy

We use a third survey experiment to measure the importance of three components of meritocracy: experience, education and effort. We randomize between male and female candidates.
Treatment: The next parliamentary elections are coming up at the end of this month. What are the most important qualities for a [female:male] candidate to become Member of Parliament?

4 – Survey experiment: Patronage

The last survey experiment measures the influence of patronage on candidate selection. Here, we also randomize between male and female candidate:
Treatment: Imagine that after the election you would have to decide between two [female:male] Members of the Parliament. One of them is to become the Speaker of the National Assembly. Assuming that they have equal experience, education, and motivation, which one would you choose as Speaker of the National Assembly?

J. Rawls. A Theory of Justice. Belknap Press of Harvard University Press, Cambridge, Massachussets, 1edition, 1971. ISBN 0-674-88014-5.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
(A) Differences in estimated vs. ideal distribution in number of seats in parliament and committee

(B) Survey experiments:
1. Support to take action on equal representation between women and men on a 5 point likert scale from ‘very much agree’ to ‘very much disagree’.
2. Agreement with proposed quota policy – scaled from ‘absolutely not’ to ‘absolutely yes’
3. Scale of importance for each element of merit – education, experience, effort – on a scale from 1- very important to 10- not important at all.
4. Choice of candidate as Speaker when the two candidates only differ in whether or not they have family relations to other Members of Parliament.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

1. Survey Experiment Civic Action:
Treatment is to increase the cost of civic action in support of the gender quota:
Probability(Take Action | High Cost) – Probability(Take Action | Low Cost)

2. Survey Experiment Gender Quota:
Questions vary for whether there is support to increase, decrease, or keep the quota constant.

3. Survey Experiment Merit:
Treatment is to indicate importance of merit for either a male or a female candidate:
Importance Effort | Female – Importance Effort | Male
Importance Education | Female – Importance Education | Male
Importance Experience | Female – Importance Experience | Male
Rank Merit Elements | Female – Rank Merit Elements | Male

4. Survey Experiment Patronage:
Treatment is to choose from two male or two female candidates either one with or one without family relations.
Probability (Punish for Family Relations | Female) – Probability (Punish for Family Relations | Male)

by self-identification, demographics, gender norms, support of gender quotas, interest in politics, peer effects.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We set up an online survey and sent out via e-mail.

The survey takes about 15 min to fill in, respondents are compensated both in the first as well as in the second round.

Within the survey we ask for their estimates of the distributions of wealth and political power between men and women, their ideal versions of these distributions, demographics, gender norms, interest in politics, views on gender quotas, and peers' influence. Moreover, we conduct the four survey experiments described above. Finally, we include questions to compute the social desirability index by Crowne and Marlowe (1960).

D. P. Crowne and D. Marlowe. A New Scale of Social Desirability Independent of Psychopathology. Journal of Consulting Psychology, 24(4):349–354, 1960. doi:10.1037/h0047358.
Experimental Design Details
Randomization Method
Randomization to each treatment in office by a computer.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
We don't cluster our treatment.
Sample size: planned number of observations
492 individuals
Sample size (or number of clusters) by treatment arms
In each treatment arm there are 246 students for survey experiments but the gender quota support experiment. There, we have 3 treatment arms, i.e. 164 respondents in each.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
A power analysis was conducted and the sample chosen, using a minimum detectable effect size for the average treatment effect of 0.4 (for survey experiment on gender quota, only survey experiment with three treatment groups).

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials