Generative AI and Productivity in the Global South

Last registered on July 13, 2026
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Pre-Trial

Trial Information

General Information

Title
Generative AI and Productivity in the Global South
RCT ID
AEARCTR-0018778
Initial registration date
July 07, 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
July 13, 2026, 7:38 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
Silvia Castro
Affiliation
Ludwig-Maximilians-Universität München
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Other Primary Investigator(s)

PI Name
Maren Mickeler
PI Affiliation
ESSEC
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PI Name
Hoa Ho
PI Affiliation
LMU Munich
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PI Name
Svenja Friess
PI Affiliation
LMU Munich
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PI Name
Evelina Griniute
PI Affiliation
LMU Munich
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Additional Trial Information

Status
In development
Start date
2026-07-01
End date
2027-12-31
Keywords
Behavior, Finance & Microfinance, Firms & Productivity
Additional Keywords
Field Experiment, GenAI, Global South, Productivity
JEL code(s)
G21, J24, O33, D22
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Generative AI (GenAI) tools are rapidly diffusing into professional workplaces, but causal evidence on their productivity effects outside high-income settings remains scarce. We study the introduction of a GenAI-enabled decision-support tool in a large financial-sector organization in Uganda, using a cluster-randomized field experiment in which knowledge workers are assigned to tool access or business-as-usual practice. Combining administrative records, tool-usage logs, and primary data, we estimate intention-to-treat effects on productivity, decision-making processes, and examine heterogeneity in tool adoption and usage to understand the mechanisms. The study provides novel experimental evidence on whether GenAI tools can extend productivity gains to lower-income labor markets, a central question for AI diffusion policy in the Global South.

External Link(s)

Registration Citation

Citation
Castro, Silvia et al. 2026. "Generative AI and Productivity in the Global South." AEA RCT Registry. July 13. https://doi.org/10.1257/rct.18778-1.0
Sponsors & Partners

Sponsors

Experimental Details

Interventions

Intervention(s)
We introduce a customized GenAI tool that supports loan officers in their credit analysis of loan applications. The tool will be trained on the firm’s internal information base (policies, product databases, procedural manuals, previous product to client matches) and employees’ knowledge. Officers receiving the tool are also given training on how to use it, and the tool is available for routine use throughout the intervention period.
Intervention Start Date
2026-09-01
Intervention End Date
2027-12-31

Primary Outcomes

Primary Outcomes (end points)
Employee productivity, loan portfolio composition and performance
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We conduct a branch-level randomized controlled trial at a commercial bank. Branches are matched into 40 pairs on pre-intervention characteristics, and within each pair one branch is randomly assigned to receive the intervention and one to serve as control (80 branches total, 50% treatment and 50% control). Randomization is conducted by computer at the branch level. Control branches continue under business as usual.
Experimental Design Details
Not available
Randomization Method
Pairwise matched randomization by computer
Randomization Unit
Branch
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
80 branches (40 pairs)
Sample size: planned number of observations
All loan officers employed in study branches at the time of randomization (except salary bankers); all active loans in the administrative records (except salary loans)
Sample size (or number of clusters) by treatment arms
40 control and 40 treatment branches
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
College of Business and Management Sciences Research Ethics Committee
IRB Approval Date
2026-04-24
IRB Approval Number
CoBAMS-REC-2026-686
IRB Name
ESSEC Research Ethics Committee
IRB Approval Date
2026-03-10
IRB Approval Number
2026-017
IRB Name
Ethics Committee LMU Munich
IRB Approval Date
2026-04-27
IRB Approval Number
2026-05
Analysis Plan