Reducing racial gaps in referrals and hiring: two experiments with the Colombian Public Employment Services

Last registered on June 24, 2024


Trial Information

General Information

Reducing racial gaps in referrals and hiring: two experiments with the Colombian Public Employment Services
Initial registration date
June 12, 2024

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
June 24, 2024, 1:40 PM EDT

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



Primary Investigator

Navarra Center for International Development, University of Navarra

Other Primary Investigator(s)

PI Affiliation
PI Affiliation

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
In this project, we aim to investigate the size of the race gap within the Colombian Public Employment Services (PES) focusing on the role of firms and their relationship with job centers. We mix administrative sources and direct surveys to measure firms' and job counselors' de facto, explicit, and implicit bias within the employment center against afro-descendant job seekers. In addition, we will test how information about firms’ unconscious bias toward afro-descendant job seekers could affect firms hiring behavior. We will also test how information about firms’ beliefs and expectations about afro-descendants could change job counselors’ referral behavior.
External Link(s)

Registration Citation

Duryea, Suzanne , Jaime Millan-Quijano and Yanira Oviedo. 2024. "Reducing racial gaps in referrals and hiring: two experiments with the Colombian Public Employment Services ." AEA RCT Registry. June 24.
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Experimental Details


This project has two interventions.

First, after collecting information about the implicit bias of firms’ human resource personnel we will provide feedback on the result of their own implicit bias. To estimate the causal effect of this information we will allocate firms randomly into two groups. Group 1 will receive an email with the IAT feedback a few weeks after taking the IAT. Group 2 will receive the same feedback approximately 6 months after taking the IAT.

The second intervention uses information about firms’ behavior to change job counselors' referral of Afro-descendants. In the interview with job counselors, we will elicit their beliefs about the probability that a CV from an afro-descendant will be interviewed and hired after being referred. We then can contrast this belief with the same observed probability at the PES and to the information from the interview to firms’ HR. If there is a mismatch, the belief is lower than the actual probability from the SPE, we will send this information to a random group of job counselors expecting them to update their belief and increase the referral rates of Afro-descendants. If the job counselors' beliefs are above the actual rate observed in the PES data, we could use the declared likelihood by firms in our interview. We expect this likelihood to be an upper bound of the actual probability of hiring due in part to social desirability bias by the firms. A possible complication in this case is that job counselors consider in their calculations the fact that firms may overestimate their willingness to hire afro-descendants and our information campaign may not be believable. We will discuss this possibility with CCFs and ASOCAJAS to tailor the intervention. In the case that counselors' beliefs are accurate, we will move to an intervention that uses information about the efforts by governments and institutions in the region to reduce the racial employment gap. In that instance, we will contrast their beliefs against the possible change in the social norm that could be driven by policies or private efforts.

Regardless of the information we provide, once again we divide job counselors into two groups. Group 1 will receive this information a few weeks after we finish data collection. Group 2 will receive the information approximately 4 months later.

Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The main outcome of the first intervention is the probability of hiring an afro-descendant job seeker. For the second intervention, the focus is the probability of referring an afro-descendant job seeker to a job post.
Primary Outcomes (explanation)
This information comes from the data sets of all job openings and all job seekers who interact through the SPE. We will use one year of information including 6 months before data collection, and 4 months after the interventions. For each job post, we observe its characteristics and the job seekers that are referred or apply to the post using the SPE. We also observe the CVs that are hired for each job post. For each CV, we observe demographic characteristics including belonging to an ethnic minority, which includes afro-descendants.

Secondary Outcomes

Secondary Outcomes (end points)
The first part of this project is a description of different indicators of bias against afro-descendant job seekers. We will measure de facto (differential probability of referring or hiring an afro-descendant compared with the probability of a non-afro-descendant), explicit (using direct questions and a list experiment), and implicit (using an implicit association test) biases of firms’ HR personnel and job counselors. In addition, we will document the correlations among these indicators. This analysis will use information from the interviews with firms and job counselors, in combination with SEP applications before the interventions.

These estimations use pre-intervention data.
We aim to use data on de facto, implicit, and explicit bias to evaluate possible heterogeneity in the impacts of our intervention.
We will also use ML tools to analyze the heterogeneity of our results with respect to firm, vacant, job center, job counselor, and job seeker characteristics.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will randomize by intervention.

Intervention 1 - information to firms' personnel about their own IAT results.
Randomization is at the firm level in two groups. We will stratify randomization by firm size, region, and sector.
Treated: They will receive the IAT results a few weeks after completing baseline data collection.
Control: They will receive the IAT results about four (4) months after completing baseline data collection.

Intervention 2 - Information to job counselors about firms' beliefs/attitudes about afro-descendant job seekers.
Randomization is at the job center level in two groups. We will stratify by region and job center size.
Treated: They will receive information about firms a few weeks after completing baseline data collection.
Control: They will receive information about firms about four (4) months after completing baseline data collection.
Experimental Design Details
Not available
Randomization Method
By computer
Randomization Unit
The first intervention will be randomized at the firm level. The second intervention will be randomized the employment center level.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
First intervention: 1500 firms
Second intervention: 350 job counselors in 80 job centers
Sample size: planned number of observations
First intervention: Personnel for at least 1500 firms Second intervention: 350 job counselors in 80 job centers
Sample size (or number of clusters) by treatment arms
Intervention 1: We expect 750 firms by arm.
Intervention 2: We expect 40 job centers (125 job counselors) by arm.

We will check that job centers in Intervention 2 are balanced within the Intervention 1 group to examine heterogeneity in the impact of Intervention 1 with respect to the information given in Intervention 2.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
First intervention: Our goal is to reach 1,500 firms who posted a job opening before our intervention and will be likely to post at least one job offer after our intervention. According to our calculations, the minimum detectable effect from this analysis will be 0.12 standard deviations. Second intervention: With 350 counselors working in 80 job centers we aim to estimate effects above 0.28 standard deviations.

Institutional Review Boards (IRBs)

IRB Name
Econometría S.A.
IRB Approval Date
IRB Approval Number