Improving matching of clients and workers on an online platform

Last registered on July 14, 2025
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Pre-Trial

Trial Information

General Information

Title
Improving matching of clients and workers on an online platform
RCT ID
AEARCTR-0016380
Initial registration date
July 11, 2025

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
July 14, 2025, 6:54 AM EDT

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

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Primary Investigator

Name
David McKenzie
Affiliation
World Bank
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Other Primary Investigator(s)

PI Name
Caio Piza
PI Affiliation
World Bank
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Additional Trial Information

Status
On going
Start date
2025-07-11
End date
2026-06-30
Keywords
Gender, Labor
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We work with an online jobs platform in Mozambique that aims to match casual/informal workers with clients. The experiment tests whether administering a short survey of preferences, asking clients for their preferences on gender of workers, and using AI recommender systems improve matching compared to the status quo algorithm.
External Link(s)

Registration Citation

Citation
McKenzie, David and Caio Piza. 2025. "Improving matching of clients and workers on an online platform." AEA RCT Registry. July 14. https://doi.org/10.1257/rct.16380-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Clients coming to the platform to look for workers for odd-jobs are randomly assigned to be matched to workers under different sets of matching conditions.
Intervention Start Date
2025-07-11
Intervention End Date
2025-11-30

Primary Outcomes

Primary Outcomes (end points)
We use platform data to measure four key outcomes:
1) Share of searches that result in client requesting worker information
2) Share of searches that result in client contacting worker
3) Share of searches that result in a work contract occurring
4) Share of work contracts going to women
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Clients are assigned to one of the following five groups:
1) Status quo matching
2) The legacy algorithm, incorporating survey preferences
3) The legacy algorithm, incorporating survey preferences + gender preferences
4) AI matching
5) AI matching + gender preferences
Experimental Design Details
Not available
Randomization Method
Random assignment by computer at the client level
Randomization Unit
Client
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
Sample size uncertain - clients will be randomized as they search for a client, and then all subsequent searches by the client will be under the same matching approach - so search results will be clustered at the client level.
We anticipate randomizing clients on a rolling basis for 3-4 months, with approximately 1000-2000 clients per month.
Sample size: planned number of observations
Uncertain, but several thousand clients
Sample size (or number of clusters) by treatment arms
Uncertain, since budget limits restrict the proportion the partner can allocate to AI.
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