Can Inclusive Education Programs Reduce Gender and Racial Discrimination in the Labor Market?

Last registered on May 18, 2020
View Trial History

Pre-Trial

Trial Information

General Information

Title
Can Inclusive Education Programs Reduce Gender and Racial Discrimination in the Labor Market?
RCT ID
AEARCTR-0003842
Initial registration date
December 17, 2019

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
December 18, 2019, 10:52 AM EST

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

Last updated
May 18, 2020, 5:38 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
Peru
Region
Lima

Primary Investigator

Name
Jorge Aguero
Affiliation
University of Connecticut
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Jorge Agüero
PI Affiliation
University of Connecticut
Contact Investigator
PI Name
Francisco Galarza
PI Affiliation
Universidad del Pacífico
Contact Investigator

Additional Trial Information

Status
On going
Start date
2019-03-15
End date
2020-05-31
Keywords
Education, Gender, Labor
Additional Keywords
market discrimination, race, correspondence study
JEL code(s)
J7
Secondary IDs
Abstract
This correspondence study will evaluate the nature of labor market discrimination of the indigenous population of a Latin American country. Previous correspondence studies in the same country found that the callback rate for white men and women applying to jobs was significantly higher than that of indigenous men and women, with the largest gap existing between white men and indigenous women (Galarza and Yamada, 2014). To further assess the level of statistical discrimination compared to taste-based discrimination present in this country's labor market, the study will test a nationwide education initiative's effectiveness as a signal of high aptitude and competency when participation in said program is placed on a CV. We will do this by sending four fictitious CVs to each vacancy job postings in urban areas of the country. We will randomly assign the signaling of aptitude and competency via education program participation to candidates.
External Link(s)

Registration Citation

Citation
Aguero, Jorge, Jorge Agüero and Francisco Galarza. 2020. "Can Inclusive Education Programs Reduce Gender and Racial Discrimination in the Labor Market?." AEA RCT Registry. May 18. https://doi.org/10.1257/rct.3842-1.2000000000000002
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Randomly assigned two applicants (out of four) that applies to a job with participating in education inclusion program.
Intervention Start Date
2019-06-03
Intervention End Date
2020-05-31

Primary Outcomes

Primary Outcomes (end points)
Callback rates (employers calling the fictitious job candidates because they are interested in hiring them/interviewing them)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This study follows a correspondence study design approach. Fictitious CVs will be sent to employers who listed job openings in the job markets we are observing. Four CVs will be sent to each employer reflecting the combination of paternal and maternal last names (mixed-races and indigenous). Half of the candidates receive the treatment, which is the participation in an merit-base scholarship, will be randomly selected. Callback rates for the CVs will be observed and recorded.
Experimental Design Details
This study follows a correspondence study design approach. Fictitious CVs will be sent to employers in Lima, Peru who listed job openings in the job markets we are observing. Four CVs will be sent to each employerreflecting the combination of paternal and maternal last names (mixed-races and indigenous). Half of the candidates receive the treatment, which is being a recipient of Beca18, will be randomly selected using Resume-Randomizer. Callback rates for the CVs will be observed and recorded.
Randomization Method
Done in office by a computer using randomized response software.
Randomization Unit
Individual Job Applicant
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
1,000 job postings
Sample size: planned number of observations
4,000 CVs
Sample size (or number of clusters) by treatment arms
2,000 CVs with treatment, 2,000 CVs without treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
UConn Institutional Review Board
IRB Approval Date
2019-01-07
IRB Approval Number
H18-256
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials