Gender Preferences of Government Officials and the Impact

Last registered on November 15, 2023


Trial Information

General Information

Gender Preferences of Government Officials and the Impact
Initial registration date
November 05, 2023

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
November 15, 2023, 1:37 PM EST

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


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Primary Investigator

Renmin University of China

Other Primary Investigator(s)

PI Affiliation
Peking University
PI Affiliation
Renmin University of China

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Women continue to be significantly underrepresented in leadership roles, a persistent issue that can have far-reaching consequences. This underrepresentation may be attributed to the prevalence of gender stereotypes, and the existence of misperceived beliefs or perceptions about gender. This raises a crucial question: will individuals change their behavior once they become aware of these stereotypes or their misconceptions? We investigate the issue within the specific context of local government leaders in China. These leaders play an important role in evaluating the performance of their subordinates and determining promotions. The existence of stereotypes and misperceived beliefs may induce them to favor male subordinates in evaluations and promotions. To identify and mitigate the potential impact of stereotypes, we administer an Implicit Association Test (IAT) to these government leaders, followed by the random disclosure of the IAT scores to a portion of them. Besides, we elicit other beliefs or perceptions about gender and randomly provide some of them with feedback. These experiments allow us to explore whether revealing stereotypes and correcting misperceived beliefs serves as a powerful intervention in improving female leadership representation within the government.
External Link(s)

Registration Citation

Lu, Fangwen, Qiong Zhang and Haiyan Zhang. 2023. "Gender Preferences of Government Officials and the Impact." AEA RCT Registry. November 15.
Experimental Details


The intervention specifically targets local government leaders in China. It involves the random provision of feedback regarding their IAT scores and an explanation of these scores, and correcting their misperceived beliefs about gender-related issues (if a prevalent misperception is identified). The main objective of this intervention is to assess whether revealing stereotypes and correcting misperceived beliefs serves as an effective intervention in enhancing the representation of female leadership within the government.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
1) Evaluations towards the performance of their subordinates
2) Promotions of subordinates
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will administer a pre-intervention survey among a group of local government officials, which include an Implicit Association Test aimed at identifying implicit stereotypes against female leaders, beliefs or perceptions about gender-related issues, and an exploration of various individual characteristics.

One intervention is to provide their own scores in the Implicit Association Test (IAT) to a random set of the participants via SMS. If the misperception about gender-related issues prevalently exists, we will randomly select some individuals and correct their wrong beliefs later. Data on how these leaders evaluate and promote their subordinates in the short-term, mid-term and long-term will be collected.
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
No cluster design
Sample size: planned number of observations
Around 1,000 individuals
Sample size (or number of clusters) by treatment arms
Around 500 individuals in each of the treatment and control groups.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
IRB at Renmin University of China
IRB Approval Date
IRB Approval Number