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Does Economics Make You Sexist
Last registered on April 22, 2020


Trial Information
General Information
Does Economics Make You Sexist
Initial registration date
April 19, 2020
Last updated
April 22, 2020 10:14 AM EDT
Primary Investigator
Boston University
Other Primary Investigator(s)
PI Affiliation
University of Chile
PI Affiliation
University of Chile
Additional Trial Information
On going
Start date
End date
Secondary IDs
Recent research has highlighted unequal treatment for women in academic economics along several different dimensions: promotion, hiring, credit for co-authorship, and standards for publication in professional journals. Can the source of these differences lie in biases against women that are pervasive in the discipline, even among students in the earliest stages of their training? In this trial, we investigate the importance of explicit and implicit biases against women among students in economics relative to other fields. We conduct a large scale survey among undergraduate students in Chilean universities, among both entering first-year students and students in years 2 and above. The survey elicits measures of implicit bias, explicit bias, and gender attitudes. The goal of the study is: a) to see whether economics students are more biased relative to students in other disciplines; b) to study whether any such differences are already present upon entry (a selection effect) or develop as students are exposed to economics training (a treatment effect). We also plan to investigate the role of political preferences, religiosity and exposure to female peers and faculty to explain the differences between economics students and students in other fields.

External Link(s)
Registration Citation
Paredes, Valentina, M. Daniele Paserman and Francisco Pino. 2020. "Does Economics Make You Sexist." AEA RCT Registry. April 22. https://doi.org/10.1257/rct.5715-1.1.
Experimental Details
We administered a survey among entering first-year students and students in years 2 and above in Chilean universities. The goal of the survey was to measure various aspects of gender bias: implicit biases, explicit biases, gender attitudes, beliefs about gender differences in ability, and beliefs about the sources of the gender wage gap.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Measures of gender bias:
1) Implicit bias as measured by a gender-career Implicit Association Test (IAT)
2) Implicit bias as measured by a gender-science Implicit Association Test
3) Ambivalent Sexism Inventory: Hostile Sexism
4) Ambivalent Sexism Inventory: Benevolent Sexism
5) Gender attitudes (traditional)
6) Gender attitudes (proactive)
7) Beliefs about gender differences in math
8) Belief that the gender gap is due to discrimination
9) Belief that the gender gap is due to differences in skills and preferences
10) Aggregate "gender bias" score equal to first principal component of previous 9 measures.
Primary Outcomes (explanation)
All of the 9 indexes that we measure are taken as simple averages of responses to several survey questions (see below for details). The aggregate "gender bias score" is calculated as the principal component of the 9 individual measures.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
All respondents were administered the survey. Half of the respondents were administered the gender-career IAT, the other half were administered the gender-science IAT.

The survey was conducted in several waves. Part of the analysis will be based on a between-subject design: compare outcomes between economics and non-economics students, in year 1 (before exposure to any training) versus years 2 and above. Students in years 2 and above represent a different cohort of students.

A subset of the sample was interviewed twice: at the beginning of year 1, and during year 2. For this longitudinal sample we can also conduct a within-subject analysis. Compare the outcomes of economics and non-economics students in year 1 and in year 2. Compare the change in outcomes between year 1 and year 2 among economics students versus non-economics students.
Experimental Design Details
Not available
Randomization Method
Randomization was done by computer.
Randomization Unit
Randomization was done at the level of the individual respondent.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
We ended up with a sample of 3,723 respondents who completed all parts of the survey, over multiple survey rounds.
Sample size: planned number of observations
The survey was sent out to all students in 7 different universities. We estimate that the sample frame included about 25000 students.
Sample size (or number of clusters) by treatment arms
Approximately half the students were in the Gender-Science group, half were in the Gender-Career group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB Name
Comité de Etica, Facultad de Economía y Negocios, Universidad de Chile
IRB Approval Date
IRB Approval Number
Analysis Plan

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