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Field
Trial Status
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Before
in_development
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After
on_going
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Abstract
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Before
Gender segregation in labor markets is a prevalent issue in poor countries, leading to significant wage gaps and talent misallocation. Efforts to address this segregation by promoting female labor supply depend on the level of discrimination women face upon entering the labor market. Our study tests for gender discrimination in Uganda, characterized by severe asymmetric information problems. Partnering with a vocational training center, we conduct an experiment with employers to examine the effect of gender on hiring decisions for trainees.
Our key intuition is that, in contexts characterized by severe asymmetric information, trustworthiness is a key determinant of hiring decisions. Given the evidence that women are generally perceived as more prosocial/trustworthy, we investigate whether this perception induces a comparative advantage for women in the labor market. We conduct two primary analyses: first, we assess gender differences in hiring outcomes; second, we examine how these gender differences evolve as the relevance of asymmetric information (moral hazard) in hiring decreases. We also examine the interplay with ability, gender preferences, and the drivers of women/men perceived differences in trustworthiness. We compare managers' beliefs about gender differences in behavior with data on actual behavior of job seekers.
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After
Gender segregation in labor markets is a prevalent issue in poor countries, leading to significant wage gaps and talent misallocation. Efforts to address this segregation by promoting female labor supply depend on the level of discrimination women face upon entering the labor market. Our study tests for gender discrimination in Uganda, characterized by severe asymmetric information problems. Partnering with a vocational training center, we conduct an experiment with employers to examine the effect of gender on hiring decisions for trainees.
Our key intuition is that, in contexts characterized by severe asymmetric information, trustworthiness is a key determinant of hiring decisions. Given the evidence that women are generally perceived as more prosocial/trustworthy, we investigate whether this perception induces a comparative advantage for women in the labor market. We conduct two primary analyses: first, we assess gender differences in hiring outcomes; second, we examine how these gender differences evolve as the relevance of asymmetric information in hiring decreases. We also examine the interplay with ability, gender preferences, and the drivers of women/men perceived differences in trustworthiness. We compare managers' beliefs about gender differences in behavior with data on actual behavior of job seekers.
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Trial Start Date
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Before
June 10, 2024
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After
June 07, 2024
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Trial End Date
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Before
July 31, 2024
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After
August 31, 2025
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Last Published
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Before
June 25, 2024 10:45 AM
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After
April 01, 2025 06:34 PM
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Intervention (Public)
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Before
Our study investigates gender discrimination in hiring within male-dominated sectors in Uganda, focusing on the roles of trustworthiness and ability. We partner with vocational training centers to conduct an experiment with hiring managers. Hiring managers are presented with example CVs of vocationally trained workers. These CVs are designed to represent potential candidates they might consider hiring, and are randomly assigned to be either male or female workers. This randomization allows us to examine gender-based differences in hiring preferences. Managers indicate their hiring preferences based on the provided CVs, knowing that we will refer workers with characteristics they favor. To test the effect of asymmetric information (moral hazard) on gender differences in hiring, we randomize the provision of monitoring support to the managers. We have a pure control arm with no monitoring support, one treatment arm where monitoring support focuses on stealing and other common misbehavior practices, and one active control arm where the monitoring support focuses on workers' safety and wellbeing.
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After
Our study investigates gender discrimination in hiring within male-dominated sectors in Uganda, focusing on the roles of trustworthiness. We partner with vocational training centers to conduct an experiment with hiring managers. Hiring managers are presented with example CVs of vocationally trained workers. These CVs represent trainee candidates and are randomly assigned to be either male or female workers. This randomization allows us to test for gender-based differences in hiring preferences. Following the IRR paradigm, managers indicate their hiring preferences based on the provided CVs, knowing that we will refer them to workers with characteristics they favor. To test the effect of asymmetric information on gender differences in hiring, we randomize the provision of monitoring support to the managers via randomized audit visits. We have a pure control arm with no monitoring support, one treatment arm where monitoring support focuses on monitoring the trainees against stealing and misbehavior (monitoring trainees) and other common misbehavior practices, and one active control arm where the monitoring support focuses on workers' safety and wellbeing (monitoring firm).
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Intervention Start Date
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Before
June 10, 2024
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After
June 07, 2024
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Intervention End Date
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Before
July 31, 2024
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After
August 31, 2025
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Primary Outcomes (End Points)
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Before
OFFER: Beliefs about hiring worker after the probation period (0-10)
MEET: choice of receiving the referral of a person like the respondent (yes/no).
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After
MEET: choice of receiving the referral of a trainee like the respondent (yes/no).
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Primary Outcomes (Explanation)
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Before
- Offer: main outcome measure of hiring;
- Meet: measure of interest in the candidate -- which may include the interest in meeting profiles which are rare/uncommon (e.g., women mechanics).
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After
- Meet: binary choice of meeting with a trainee with characteristics similar to the candidate (measure of interest in the candidate).
Pre-Registration Update: Following initial data collection in the mechanics sector, we expand to additional male-dominated sectors due to observed heterogeneity in workplace gender diversity preferences, aiming to enhance external validity and statistical power as detailed in the attached document. Our main treatment and between-subject randomization remain unchanged, with the addition of a within-subject variation to further increase precision. We note that in our first round of data collection, due to technical difficulties, we were unable to record our second main outcome, OFFER. We will therefore exclude this outcome moving forward.
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Experimental Design (Public)
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Before
Each CV is randomly assigned to a combination of gender (female/male) and ability (GPA 5 out 5 / GPA 3 out of 5).
Monitoring support randomization at the manager level:
T0) Pure Control Arm: Receives no monitoring support.
T1) No Harassment Monitoring: Focuses on monitoring workers' safety and wellbeing.
T2) Moral Hazard Monitoring: Focuses on monitoring moral hazard, specifically misbehavior (stealing, disrespect etc).
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After
Each CV is randomly assigned to a combination of gender (female/male) and ability (GPA 5 out 5 / GPA 3 out of 5).
Monitoring support randomization at the manager level:
T0) Pure Control Arm: Receives no monitoring support.
T1) Monitoring Firm: Receives monitoring support in the form of audit visits focused on monitoring workers' safety and wellbeing.
T2) Monitoring Trainee: Receives monitoring support in the form of audit visits focused on monitoring trainees misbehavior (stealing, disrespect etc).
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Randomization Unit
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Before
CV level randomization for gender; Manager level randomization for monitoring.
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After
Profile gender: CV-ability level randomization; Monitoring support: manager level randomization in between-subject; cv-block level randomization for monitoring in within-subject (first 24 CVs + 12 additional CVs).
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Planned Number of Clusters
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Before
300 hiring managers from sector "mechanics". We will expand to other male-dominated sectors if we do not reach the target because of lack of firms within the Kampala metropolitan area.
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After
We plan to collect data from 300 hiring managers in a sector. Should we be unable to reach our target due to insufficient firms within the Kampala metropolitan area, we will expand to additional sectors. [UPDATE]: The first sector was mechanics. Although we reached 300 firms, we identified power and external validity issues as detailed in the attached document. Consequently, we have updated our plan to expand to welding, carpentry, and gas stations. The final number of clusters will depend on the number of firms we can reach in each sector. Based on previous literature, 300 is considered an upper bound to the total number of firms within each sector.
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Planned Number of Observations
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Before
7200 CVs
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After
The total number of observations equals at most the total number of respondents, times the number of CVs. In the between-subject randomization: 24 CV per manager. Within-subject randomization: 36 CV per sector in the within-subject specification.
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Sample size (or number of clusters) by treatment arms
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Before
Gender treatment: 3600 male CV and 3600 female CV.
Monitoring treatment: 100 managers (2400 CVs) in each arm.
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After
Gender treatment: 50% male CV and 50% female CV.
Monitoring treatment: 300 managers (12 to 24 CVs in each arm) per sector.
[Pre-Registration Update]: Following initial data collection in the mechanics sector, we expand to additional male-dominated sectors due to observed heterogeneity in workplace gender diversity preferences, aiming to enhance external validity and statistical power as detailed in the attached document. Our main treatment and between-subject randomization remain unchanged, with the addition of a within-subject variation to further increase precision. The expansion of our data collection to new sectors increases our sample size by up to 300 observations (managers) per sector. In addition, the within-subject variation adds 12 observations (CVs) per manager.
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Power calculation: Minimum Detectable Effect Size for Main Outcomes
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Before
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After
Pre-Registration Update: After our initial data collection, we updated the preregistration to include three additional male-dominated sectors beyond the initial sector (mechanics). This expansion aims to increase statistical power and enhance external validity, following the discovery of significant and unexpected heterogeneity in gender bias within our initial sector of interest. This heterogeneity substantially reduced our power to detect the effect of our "Monitoring Trainee" treatment. Our treatment and randomization procedures remain unchanged. We now report updated power calculations.
Gender treatment effect: at the initial sample size (24 CVs x 300 managers), we are highly powered to detect the effect of gender on hiring choices (Meet:0-1).
Monitoring Trainee treatment effect: At our initial sample size (N=300), statistical power to detect the effect of monitoring on gender bias was only 33%. Increasing the sample size to our target of 1,200 nearly doubles our power, raising it to 56.6%. While we would prefer an even larger sample, we are limited by the number of available businesses within each sector. Given the disproportionate influence of the minority of managers who explicitly lack gender diversity preferences and for which we would not expect treatment effects, a more cost-effective approach to enhancing power is to focus exclusively on respondents who express at least some preference for workplace diversity. Increasing our sample size to 300 firms across 4 sectors, while also focusing on managers with any preference for diversity greater than zero when testing for the effect of Monitoring Trainee, our power raises to 82.8%. At the current sample size, we are powered to detect an effect of Monitoring Trainee leveraging within-subject variation.
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Intervention (Hidden)
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Before
Hiring managers are presented with example CVs of vocationally trained workers. The CV content is randomized from workers' registration files and GPA data from from the partner vocational training centers. Each CV has both a male and female version, and a low and high GPA version, resulting in 24 unique CVs with 96 combinations. Profiles are presented using the Incentivized Resume Rating Paradigm (Kessler et al. 2019).
Each manager evaluates 24 randomly selected profiles, which are stratified by gender, and ability. We display the CVs in random order and in blocks of 8 with breaks in-between. For each profile, the manager decides whether they want to be matched with a similar worker to start a probation period at the firm. Firms are incentivized to hire the workers with an amount of money that covers transport, food and other worker-related costs. Moreover, firms are randomized to receive or not monitoring support from the experimenters.
Audit visits performed by local team members. Both managers and workers are informed of the visits, but they are not announced.
There are two types of visits that we provide, and the type of visit is randomized at the firm level. One third of the sample is randomized to monitoring support that aims at reducing workers' stealing and other common misbehaviors via monitoring. One third of the sample is randomized to monitoring support that aims at preventing harassment and promoting safety of the workers. The last third of the sample is assigned to a pure control, and receives no visits.
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After
Pre-Registration Update: Following initial data collection in the mechanics sector, we expand to additional male-dominated sectors due to observed heterogeneity in workplace gender diversity preferences, aiming to enhance external validity and statistical power as detailed in the attached document. Our main treatment and between-subject randomization remain unchanged, with the addition of a within-subject variation to further increase precision.
In the between-subject monitoring variation, each manager is randomly assigned to one of three groups: monitoring trainees, monitoring firm, or control. One-third of the sample receives monitoring support aimed at reducing stealing and other common misbehaviors among new workers (monitoring trainees). Another third receives monitoring support designed to prevent harassment and promote workplace safety (monitoring firm). The final third serves as a pure control group, receiving no monitoring or visits. Audit visits are performed by local team members. Both managers and workers are informed of the visits, but they are not pre-announced. [UPDATE] In the within-subject version of the intervention (that is, from the second sector onwards) managers make decisions under two of the three treatments prior to knowing their definitive treatment assignment.
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Secondary Outcomes (End Points)
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Before
- Quality: beliefs about workers worker's skills on a 0-10 scale
- Behavior: beliefs about workers behavior (trustworthiness, honesty, skills) on a 0-10 scale
- Effort: beliefs about workers effort (hardworking, not lazy, focused, punctual) on a 0-10 scale
- Earnings: beliefs about workers' earnings a year from the experiment (UGX)
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After
- Quality: beliefs about workers worker's skills on a 0-10 scale;
- Behavior: beliefs about workers behavior (trustworthiness, honesty, skills) on a 0-10 scale;
- Effort: beliefs about workers effort (hardworking, not lazy, focused, punctual) on a 0-10 scale;
- Earnings: beliefs about workers' earnings a year from the experiment (UGX).
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Secondary Outcomes (Explanation)
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Before
- Quality: measure of workers' skills
- Behavior: measure of workers' moral hazard (misbehavior)
- Effort: measure of workers' moral hazard (productivity)
- Earnings: measure of workers' outside options
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After
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