Generative AI, the Future of Work, and Policy Preferences

Last registered on March 21, 2026
View Trial History

Pre-Trial

Trial Information

General Information

Title
Generative AI, the Future of Work, and Policy Preferences
RCT ID
AEARCTR-0017244
Initial registration date
December 02, 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
December 05, 2025, 9:32 AM EST

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

Last updated
March 21, 2026, 11:47 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
Brazil
Region
Country
India
Region
Country
Philippines
Region
Country
Ukraine
Region
Country
United States of America
Region
Country
South Africa
Region

Primary Investigator

Name
Neha Betai
Affiliation
George Washington University
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Maggie Chen
PI Affiliation
George Washington University
Contact Investigator

Additional Trial Information

Status
On going
Start date
2025-12-02
End date
2026-07-31
Keywords
Governance, Labor
Additional Keywords
Technological changes, reskilling decisions
JEL code(s)
O33, J24, F42
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This project examines how individuals in low- and middle-income countries perceive and respond to the rapid diffusion of generative artificial intelligence across economic, social, and governance domains. While generative AI offers substantial potential productivity gains, uncertainty about its implications for employment, privacy, fairness, and global power dynamics may change adoption patterns. The uncertainty and risks associated with its adoption also raises concerns about data governance, unequal global distribution of AI capabilities, and appropriate roles of governments in regulating or promoting AI.
To identify the mechanisms shaping adoption, beliefs, and governance preferences, we conduct a randomized survey experiment with participants across diverse demographic and occupational groups. Respondents are assigned to one of six informational treatments: (1) practical instruction on AI use and prompt engineering; (2) labor-market disruption risks; (3) labor-market opportunities; (4) algorithmic bias; (5) privacy concerns; and (6) geopolitical competition and global inequality in AI capacity. The experiment measures effects on willingness to reskill, willingness to use and invest in AI, peer contact and social effects, and willingness to supply data. It also captures policy preferences, including attitudes toward AI-related redistribution, desired government promotion and regulation of AI, the use of AI in allocation decisions, and preferences over domestic versus international AI development and governance. These outcomes allow us to assess not only behavioral adjustments but also how individuals think governments should respond to the opportunities and risks of AI.
The study aims to provide causal evidence on the belief channels shaping AI adoption, skill adaptation, and support for national and global AI governance. The results inform policy interventions aimed at expanding access to AI’s benefits while addressing emerging socioeconomic, ethical, and geopolitical concerns.
External Link(s)

Registration Citation

Citation
Betai, Neha and Maggie Chen. 2026. "Generative AI, the Future of Work, and Policy Preferences ." AEA RCT Registry. March 21. https://doi.org/10.1257/rct.17244-3.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
The intervention consists of a randomized provision of short informational modules designed to influence participants’ perceptions of generative artificial intelligence. Each participant is randomly assigned to one of six treatment conditions or a control condition. All interventions are delivered within an online survey with no deception being used.
Each informational module highlights a distinct dimension of AI’s potential impact:
1. AI Skills and Usage Tutorial
2. Labor-Market Disruption
3. Labor-Market Opportunities
4. Algorithmic Bias
5. Privacy Risks
6. Geopolitics and Global Inequality in AI Capacity
7. Control Group

After receiving the assigned module, respondents complete a set of questions measuring willingness to use or invest in AI tools, willingness to reskill, peer contact and social effects, willingness to supply data, trust in AI technologies, preferences for domestic AI regulation and promotion, preferences for AI’s role in public decision-making, preferences for national vs. international AI governance.

The intervention is minimal-risk and informational in nature.
Intervention (Hidden)
The intervention consists of a randomized provision of short informational modules designed to influence participants’ perceptions of generative artificial intelligence (AI). Each participant is randomly assigned to one of six treatment conditions or a control condition. All interventions are delivered within an online survey.
Each informational module highlights a distinct dimension of AI’s potential impact:
1. AI Skills and Usage Tutorial
Explains the importance of prompt engineering highlighting studies that show productivity gains when AI use is coupled with prompt engineering. It introduces basic prompt-engineering concepts and explains how workers can integrate AI into everyday tasks.
2. Labor-Market Disruption
Focuses on job market related risks from AI (job displacement, potential reduction in employment/income for certain demographic groups), highlighting findings from relevant studies. After the general information, participants will see substitution exposure rates tailored to the occupation they selected in the pretreatment survey.
3. Labor-Market Opportunities
Describes on positive impacts of AI on the job market such as productivity gains, and opportunities for skill upgrading and complementary human AI collaboration. After the general information, participants will see augmentation exposure rates tailored to the occupation they selected in the pretreatment survey.
4. Algorithmic Bias
Explains how AI systems can promote discrimination, generate biased outcomes, and reinforce social inequities by citing relevant documented cases and studies highlighting the bias.
5. Privacy Risks
Highlights concerns about data collection, surveillance, misuse of personal information, and risks associated with sharing data with AI systems.
6. Geopolitics and Global Inequality in AI Capacity
Presents information about international competition in AI development, disparities in access to AI technologies, and implications for global power dynamics and inequality.
7. Control Group
Receives no informational content before answering outcome questions.
Intervention Start Date
2025-12-02
Intervention End Date
2026-04-30

Primary Outcomes

Primary Outcomes (end points)
The primary outcomes capture individuals’ intended behavioral responses to AI and their preferences regarding AI-related regulation and governance. Outcomes span six broad domains: (i) AI adoption and investment, (ii) reskilling and labor-market adjustment, (iii) social interaction and peer substitution, (iv) Data-sharing behavior, (v) trust and fairness of AI based decision making, and (vi) preferences for AI governance and regulation. All outcome variables are measured after treatment exposure.
Primary Outcomes (explanation)
The primary outcomes will be measured using multiple individual survey items under each outcome domain and will be analyzed separately.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study conducts an online randomized survey experiment to examine how different types of information about artificial intelligence can influence individuals’ beliefs, adoption intentions, and policy preferences. Participants are recruited from two populations, general online workers (e.g., MTurk) and skilled freelancers on a major freelancing platform, with an emphasis on respondents residing in low- and middle-income countries.
After providing baseline demographic information, each participant is randomly assigned to one of six informational treatment conditions that highlight the impact of AI or a control group. Following exposure to the assigned module, all participants complete a standardized set of outcome questions on AI-related adoption intentions, willingness to reskill, attitudes toward data sharing, and preferences for domestic and international AI governance.
Randomization is conducted at the individual level within the survey platform. Total planned sample size is approximately 3,500-4000 respondents.
Experimental Design Details
Randomization Method
Randomization is conducted automatically by the survey platform using a computer-generated random number assignment. Each participant is assigned to one of the treatment arms or the control group with equal probability. The process is fully automated and free of any influence from the researchers.
Randomization Unit
Randomization is conducted is at the individual level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Approximately 3,000-4000 individuals (no higher-level clusters; randomization at the individual level).
Sample size: planned number of observations
Approximately 3,000-4000 individuals.
Sample size (or number of clusters) by treatment arms
Even distribution of participants across all treatment arms and control groups
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
The George Washington University Office of Human Research
IRB Approval Date
2025-11-21
IRB Approval Number
NCR256825
Analysis Plan

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Post-Trial

Post Trial Information

Study Withdrawal

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

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