Matching Digital Service Export Jobs In Kenya

Last registered on March 13, 2023


Trial Information

General Information

Matching Digital Service Export Jobs In Kenya
Initial registration date
March 07, 2023

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
March 13, 2023, 3:01 PM EDT

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


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Primary Investigator

Federal Reserve Bank of Minneapolis

Other Primary Investigator(s)

PI Affiliation
Duke University
PI Affiliation
University of Oxford
PI Affiliation
Duke University
PI Affiliation
Strathmore University

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Many wealthier countries face long-term labor shortages while many poorer countries face high levels of unemployment. But migration between countries is politically difficult. Digital service export jobs, in which workers in poorer countries work remotely for firms in wealthier countries, offer a possible route to higher employment in higher-quality jobs. Our study relies on random assignment to causally identify the value of digital service export jobs to workers. We also study how firms can improve hiring for these jobs.
External Link(s)

Registration Citation

Garlick, Robert et al. 2023. "Matching Digital Service Export Jobs In Kenya ." AEA RCT Registry. March 13.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Employment, earnings, retention, performance, skill development
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We work with a recruiting agency to randomize which candidates are included on lists of work-seekers sent to firms, generating random variation in the probability of hiring. We estimate treatment effects on candidates’ employment, earnings, skill development, and job ladders. We also use random variation in the types of workers hired to build a predictive model of worker productivity, incorporating detailed baseline skill assessments, and assessing which hiring rules maximize average worker performance and gender inclusivity.
Experimental Design Details
Not available
Randomization Method
Random number generation using computer software (Stata)
Randomization Unit
Candidate work-seekers
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
The treatment is not clustered
Sample size: planned number of observations
Roughly 2,000 candidates. The exact number of observations depends on ongoing fundraising.
Sample size (or number of clusters) by treatment arms
Roughly 1,000 candidates in treatment and 1,000 candidates in control. The exact number of observations depends on ongoing fundraising.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Duke University
IRB Approval Date
IRB Approval Number
IRB Name
Strathmore University
IRB Approval Date
IRB Approval Number