Online Digital Skills Training and Labor Market Outcomes: Experimental Evidence from Peru

Last registered on November 15, 2024
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Pre-Trial

Trial Information

General Information

Title
Online Digital Skills Training and Labor Market Outcomes: Experimental Evidence from Peru
RCT ID
AEARCTR-0014782
Initial registration date
November 11, 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
November 15, 2024, 1:45 PM EST

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

Locations

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Primary Investigator

Name
Richard Freund
Affiliation
University of Cape Town
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Other Primary Investigator(s)

PI Name
Rafael Novella
PI Affiliation
University College London
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PI Name
David Rosas-Shady
PI Affiliation
Inter-American Development Bank
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PI Name
Eduardo Haro
PI Affiliation
MIDE Development
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Additional Trial Information

Status
On going
Start date
2021-05-01
End date
2025-01-31
Keywords
Labor
Additional Keywords
job training, advanced digital skills, online training, technology
JEL code(s)
J24, M53, J48
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Unemployment and a lack of formal jobs continue to be to be major challenges in developing countries. At the same time, digital transformation is creating a need for more individuals with advanced digital skills. In this context, online training programs emerge as a potential solution, offering scalable short-term training in technical and socio-emotional skills. This paper aims to investigate the causal impacts of the Empleabilidad Digital program, which provided free scholarships for online technology-related courses to unemployed individuals and jobseekers in Peru. To do so, we utilize an individual-level randomized controlled trial, which was embedded during the applications to the program.

To assess the impacts of the program, we will use government administrative records merged with our experimental sample. Specifically, we will use the Planilla Electrónica (PE), the Peruvian matched employer-employee dataset. The PE is a document that all formal firms in Peru with more than two workers are required to submit. Access to information from the PE is restricted and has been granted with express authorization from the Ministry of Labor and Promotion of Employment to identify the effects of the Empleabilidad Digital program on formal labor market indicators.
External Link(s)

Registration Citation

Citation
Freund, Richard et al. 2024. "Online Digital Skills Training and Labor Market Outcomes: Experimental Evidence from Peru." AEA RCT Registry. November 15. https://doi.org/10.1257/rct.14782-1.0
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Experimental Details

Interventions

Intervention(s)
As part of the Empleabilidad Digital program, selected participants underwent approximately 70 hours of free online digital skills training, offered by the Cisco Networking Academy. The program offered scholarships for courses in network and communications administration, Python programming, and IT technical support. All learning was fully online on the virtual learning platform, and course completion combined both synchronous and asynchronous components. Participants also received training in employability skills, as well as job search tools.
Intervention Start Date
2021-06-01
Intervention End Date
2021-09-01

Primary Outcomes

Primary Outcomes (end points)
We are primarily interested in the effects of the program on individuals' formal labor market outcomes. Formal labor market outcomes are measured using government administrative records merged with our sample. Our main outcome variables from the dataset are i) working in a formal job, ii) formal labor income, iii) working in a high-skilled occupation, and iv) working in an entry-level technology job.
Primary Outcomes (explanation)
Formal job: this takes the value of one in each month if the participant appears in the admin dataset. Participants who are not present in the dataset in any given month are assigned a formal employment value of zero.

Formal labor income: this captures the formal income reported for a participant in the admin dataset in each month. Participants who are not present in the dataset in any given month are assigned a formal income value of zero.

High-skill occupation: this uses information on a participant’s reported occupation code. The major occupational groups (at one digit) in the PE dataset are equivalent to the ones of the International Standard Classification of Occupations 2008. This variable takes the value of one in each month if a participant is working in the first, second, or third major occupational group, which includes managers, professionals, and technicians and associative professionals. Participants who are not present in the dataset in any given month are assigned a value of zero.

Entry-level technology occupation: this uses information on a participant’s reported occupation code and takes the value of one if a participant is working in a high skilled, entry level technology job related to the training. Specifically, this variable takes the value of one in each month if a participant appears in the admin dataset working as a i) computer analyst, ii) programmer, iii) Technician, systems/except computer science; iv) Technician, computer analysis; v) Technician, computer programming, and vi) Technician, computer services for users. Participants who are not present in the dataset in any given month are assigned a value of zero.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experimental design followed an oversubscription model, whereby the program sought to attract a pool of eligible applicants that was greater than the capacity of the approximately 2,400 vacancies reserved for evaluation. Applicants who met the minimum eligibility criteria were invited to take a competency test. Among those who took and passed the competency test, applicants were then randomly assigned to receive the training or not. Randomization took place within each training course, and was stratified by gender.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
4,793 individuals
Sample size: planned number of observations
4,793 individuals
Sample size (or number of clusters) by treatment arms
2,455 individuals control, 2,348 individuals 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
IRB Approval Date
IRB Approval Number
Analysis Plan