Scaling Laws for Economic Productivity: Experimental Evidence in White-Collar Professional Tasks

Last registered on May 06, 2025
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Pre-Trial

Trial Information

General Information

Title
Scaling Laws for Economic Productivity: Experimental Evidence in White-Collar Professional Tasks
RCT ID
AEARCTR-0015945
Initial registration date
May 04, 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
May 06, 2025, 5:26 AM EDT

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

Locations

Country
United Kingdom of Great Britain and Northern Ireland
Region

Primary Investigator

Name
Ali merali
Affiliation
Yale University
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2025-05-12
End date
2025-07-31
Keywords
Labor
Additional Keywords
AI
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study investigates the productivity impacts of sixteen generative artificial intelligence (AI) technologies with varying training compute sizes. By analyzing scaling laws, we measure how economic impacts evolve as the size of generative AI models increases. Our research is conducted in a real-world context, employing professional consultants, managers, data analysts, solicitors, and software engineers to perform tasks akin to their routine professional work. Additionally, we explore the relationship between the skill-bias of technological change and the degree of automation. Our findings offer critical insights into the scaling effects of AI on productivity and the dynamics of skill-biased technological advancements in the labor market.
External Link(s)

Registration Citation

Citation
merali, Ali. 2025. "Scaling Laws for Economic Productivity: Experimental Evidence in White-Collar Professional Tasks." AEA RCT Registry. May 06. https://doi.org/10.1257/rct.15945-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
See Analysis Plan for full regression specifications/ definitions:
1. log Productivity by log model compute (Main)
2. Interaction between respondent baseline ability and log model compute (Main 2)
Intervention (Hidden)
Intervention Start Date
2025-05-12
Intervention End Date
2025-07-31

Primary Outcomes

Primary Outcomes (end points)
log productivity (time taken)
earnings per hour
grade
These three interacted with a dummy variable on ability
Primary Outcomes (explanation)
Please see analysis plan for more details

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Recruiting 200 professional across five different professions (managers, consultants, data analysts, solicitors, and software engineers. Each professional will first complete a baseline task of ability. Then they will complete three short follow-up tasks. In each follow-up task individuals will equally be split into one of sixteen groups with different generative AI models. They will also all complete some short questions (eg. on their experience as a professional, familiarity with AI tools, etc.)
Experimental Design Details
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
250 (50 individuals each across 5 different professions)
Sample size: planned number of observations
750= 250 professionals completing three tasks each
Sample size (or number of clusters) by treatment arms
750 observations/ 16 treatments= roughly 47 tasks per treatment arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Yale University Institutional Review Board
IRB Approval Date
2024-07-16
IRB Approval Number
N/A
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials