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Gender quotas: Fairness, Meritocracy, and Participation of Women in Politics
Last registered on November 25, 2020


Trial Information
General Information
Gender quotas: Fairness, Meritocracy, and Participation of Women in Politics
Initial registration date
October 12, 2020
Last updated
November 25, 2020 11:16 AM EST

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Primary Investigator
University of Copenhagen
Other Primary Investigator(s)
PI Affiliation
University of Copenhagen
PI Affiliation
University of Copenhagen/Copenhagen Business School
PI Affiliation
University of Dar es Salaam
PI Affiliation
University of Dar es Salaam
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 preferences for inequality and redistributive policies (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. November 25. https://doi.org/10.1257/rct.6584-1.3.
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Experimental Details
We conduct two rounds of an online survey, before and after the national election on October 28, 2020, 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 (A, B) that describe the extent to which one is actively willing to take action to support equal representation between women and men.

This week, some of your fellow students are going to request action from the government to increase gender equality:
1. (A) To show your support, would you sign their petition letter?
2. (B) To show your support, would you donate them 5,000 TZS?

2 - Survey experiment: Gender quota

We implement a second survey experiment on the gender quota in politics, where we randomly ask respondents about their support on one of three different policy options (A, B, C). [This experiment is only included in the first round.]

1. (A) BASELINE: Do you agree that the number of reserved seats for women should be kept constant at 30% in future parliaments?
2. (B) INCREASE: Do you agree that the number of reserved seats for women should be increased from 30% to 50% in future parliaments?
3. (C) DECREASE: Do you agree that the number of reserved seats for women should be decreased from 30% to 15% in future parliaments?

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. How important are the following qualities for a [(A)female; (B)male] candidate to become Member of Parliament? 1
1. Candidate has a university degree.
2. Candidate has previous experience as Member of Parliament.
3. Candidate put a lot of effort into campaigning for the election.

4 – Survey experiment: Patronage

The last survey experiment measures the influence of patronage (family relations) on candidate selection. Here, we randomize between two male and two female (A, B) candidates.

Treatment: Imagine that after the election you would have to decide between two [(A)female; (B)male] Members of the Parliament. One of them will 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 number of seats for women in parliament and committee
(B) Differences in actual (objective) vs. estimated/ideal (subjective) number of seats for women in parliament and committee

(C) Survey experiments:
1. Civic action: Support to take action on a 5-Point Likert Scale from ‘very much agree’ to ‘very much disagree’.
2. [First round only] Gender quota: Agreement with proposed quota policy on a 5-Point Likert Scale from ‘very much agree’ to ‘very much disagree’
3. Meritocracy: Scale of importance for each element of merit – education, experience, effort – on a scale from 1- Not at all important to 10- Very important.
4. Patronage: Choice of candidate as Speaker when the two candidates only differ in whether or not they have family relations to other Members of Parliament. (1- The candidate with no family relations; 2- The candidate with family relations)
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)

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

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 conduct two online surveys (sent out via email) with students at the Faculty of Social Sciences in Dar es Salaam. Students are compensated for their participation and each survey takes about 15 minutes to complete.

The survey covers topics on students' estimated and ideal distributions of wealth and political power between men and women, gender norms, political behavior, views on gender quotas, and peers' influence. Moreover, we conduct four survey experiments described above to uncover possible mechanisms for observed differences between actual, estimated, and ideal distributions of political power between men and women.

The survey contains further a social desirability index, relying on previous research by Crowne and Marlowe (1960), who developed a social desirability scale for surveys. We use the short form of their module developed by Reynolds (1982), which includes unrealistic personality traits, that respondents have to 'agree' or 'disagree' with.

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.

W.M. Reynolds, Development of Reliable and Valid Short Forms of the Marlowe‐Crowne Social Desirability Scale. Journal of Clinical Psychology, 38: 119-125, 1982. https://doi.org/10.1002/1097-4679(198201)38:1<119::AID-JCLP2270380118>3.0.CO;2-I.
Experimental Design Details
Not available
Randomization Method
Randomization to each treatment group 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
ca. 500 individuals
Sample size (or number of clusters) by treatment arms
For each treatment arm, we expect ca. 250 students (2 treatment groups), except of for the gender quota experiment [first round only], where we have 3 treatment arms, i.e. ca. 166 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 the survey experiment on gender quotas, which is the only survey experiment with three treatment groups).
IRB Name
IRB Approval Date
IRB Approval Number