Can Public Employment Programmes Reduce Long-term Unemployment?

Last registered on August 24, 2026
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Pre-Trial

Trial Information

General Information

Title
Can Public Employment Programmes Reduce Long-term Unemployment?
RCT ID
AEARCTR-0019300
Initial registration date
August 11, 2026

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
August 24, 2026, 9:07 AM EDT

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
Jack Calland
Affiliation
J-PAL Africa
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Other Primary Investigator(s)

PI Name
Simon Franklin
PI Affiliation
Queen Mary University of London
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PI Name
Rob Garlick
PI Affiliation
Emory University
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PI Name
Kate Orkin
PI Affiliation
University of Oxford
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Additional Trial Information

Status
On going
Start date
2026-05-01
End date
2027-06-30
Keywords
Crime, Violence, & Conflict, Health, Labor
Additional Keywords
Social protection
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Public works programmes (PWPs) are one of the most popular forms of anti-poverty policy around the world. There is growing recognition of their short-run / contemporaneous benefits in rural settings, but evidence of longer-term / post-programme effects in urban settings is scarce. We study the City of Cape Town's Expanded Public Works Programme (EPWP), which randomly selects beneficiaries for short-term work opportunities. Surveying beneficiaries with programme experience up to over ten years ago, we assess whether the programme improves long-term outcomes, including employment, earnings, skills and mental health, and how these vary over job characteristics and over recipient characteristics, including gender.
External Link(s)

Registration Citation

Citation
Calland, Jack et al. 2026. "Can Public Employment Programmes Reduce Long-term Unemployment?." AEA RCT Registry. August 24. https://doi.org/10.1257/rct.19300-1.0
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Experimental Details

Interventions

Intervention(s)
See the "Experimental design" section.
Intervention Start Date
2026-05-01
Intervention End Date
2027-06-30

Primary Outcomes

Primary Outcomes (end points)
Our primary outcomes are current employment and earnings.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Our other outcomes are all mechanisms that link the treatment to the primary outcomes and/or welfare measures. These include other job attributes besides earnings (contract type, occupation, employer size, commute time, cost and mode, job satisfaction), employment history both in EPWP and outside EPWP, job search, job market expectations, mental health, risky behavior, consumption, and related topics.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We exploit the randomized allocation of public works jobs to unemployed jobseekers to study the causal effects of the program on employment and mental health outcomes. Since 2013, the City of Cape Town has allocated EPWP opportunities using a centralized algorithm that randomly selects workers from a database of unemployed jobseekers. Project managers at the City set up projects and hire workers from the database. For each work opportunity on a project, the manager can define basic eligibility criteria, such as address, minimum educational qualification, or disability status. Jobseekers are randomly selected from the (usually larger) pool of jobseekers satisfying these criteria, who are then returned on a randomly-ranked waitlist. Managers then call workers on the list to offer them work on the project along with relevant details (pay, duration, location, etc.) The randomized waitlist structure means that different individuals have different probabilities of being called with work offers, which offer causal analysis of the treatment effects of the program.
Experimental Design Details
Not available
Randomization Method
The City of Cape Town uses a centralized algorithm to conduct randomization. The algorithm is a SQL stored procedure that randomly selects and ranks jobseekers from the relevant tables of the database.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Not applicable.
Sample size: planned number of observations
We select 19,316 individuals waitlisted for 1,969 opportunities out of a possible 630,818 individuals and 37,220 opportunities on the database. The database contains a very large number of participants but not all are suitable for analysis due to missing data, timing of work, etc. The number we can contact depends on the cost and time needed to track people, which we will only learn during the surveys. We expect to successfully reach a subset of the 19,316 sampled individuals. If funds and timeline allow, we may sample more individuals from the database.
Sample size (or number of clusters) by treatment arms
9,658 received EPWP work; 9,658 did not receive EPWP work
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Cape Town Faculty of Commerce Research Ethics Committee
IRB Approval Date
2024-08-14
IRB Approval Number
COM/01134/2024
Analysis Plan