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Scaling Laws for Economic Impacts

Last registered on July 16, 2024

Pre-Trial

Trial Information

General Information

Title
Scaling Laws for Economic Impacts
RCT ID
AEARCTR-0013743
Initial registration date
July 09, 2024

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 16, 2024, 2:31 PM EDT

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

Locations

Region

Primary Investigator

Affiliation
Yale University

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2024-07-22
End date
2024-08-09
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study investigates the productivity impacts of thirteen 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 online translators 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. 2024. "Scaling Laws for Economic Impacts." AEA RCT Registry. July 16. https://doi.org/10.1257/rct.13743-1.0
Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
Intervention Start Date
2024-07-22
Intervention End Date
2024-08-09

Primary Outcomes

Primary Outcomes (end points)
See Analysis Plan for full regression specifications/ definitions:
1. Productivity by log model compute (Main)
2. Interaction between respondent baseline ability and log model compute (Main 2)
3. Heterogeneity of productivity impacts by task complexity (interaction between log model compute and language in a logistic regression)
Primary Outcomes (explanation)
See Analysis Plan

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Recruiting 405 professional translators on online freelancer websites (eg. Freelancer.com, fiverr.com) across three languages (Spanish, Arabic, and Hindi). Each translator will first complete a baseline task of language ability. Then they will complete five short follow-up tasks. In each follow-up task half will receive no AI assistance (control), and the other half will be randomly assigned one of thirteen different generative AI models. They will also all complete some short questions (eg. on their experience as a professional translator, 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
390 (130 individuals for each of the three languages)
Sample size: planned number of observations
390 individuals
Sample size (or number of clusters) by treatment arms
75 tasks completed * 13 arms = 975 tasks
975 control tasks

Total= 1950 tasks/ 5 tasks per individual= 390 individuals
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
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