Public Perceptions of AI's Influence on Employment and Implications for Social Policy Preferences: Evidence from Experiments

Last registered on January 06, 2025

Pre-Trial

General Information

Title

RCT ID

AEARCTR-0015098

Initial registration date

December 31, 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

January 06, 2025, 12:21 PM EST

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

Menghan Shen

Affiliation

Sun Yatsen University, China

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Other Primary Investigator(s)

Additional Trial Information

Status

On going

Start date

2024-12-20

End date

2025-02-20

Keywords

Behavior, Labor

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

We explore the impact of information dissemination regarding the percentage of the workforce in China whose job tasks are likely to be affected by the introduction of Large Language Models (LLMs), with exposure rates detailed by specific job positions. Our study investigates how this information influences individuals' beliefs about job replacement and AI's effects on the labor market and personal employment. Additionally, we assess its impact on support for government tax incentives for AI industry growth, perceptions of governmental responsibilities, opinions on Universal Basic Income (UBI) and related policies, views on social insurance and employment support, the skill sets considered valuable in education, and preferences for their children's future careers.

External Link(s)

Registration Citation

Citation

Shen, Menghan. 2025. "Public Perceptions of AI's Influence on Employment and Implications for Social Policy Preferences: Evidence from Experiments ." AEA RCT Registry. January 06. https://doi.org/10.1257/rct.15098-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

we provide information intervention about what percentage of workforce in China would have their work tasks affected by the introduction of LLMs and specify the exposure rate by actual work positions.

Intervention Start Date

2024-12-20

Intervention End Date

2025-02-20

Primary Outcomes

Primary Outcomes (end points)

1. Beliefs about Job Replacement:
o My job may be replaced.
o Recently graduated college students may face job replacement.
o Any position in the labor market is at risk of replacement.
2. Beliefs about AI's Impact on the Labor Market:
o White-collar jobs may be significantly affected.
o AI will soon have a major influence on the labor market.
o AI will contribute to job creation.
o AI will likely exacerbate income inequality.
3. Beliefs about AI's Impact on My Work:
o AI will enhance my productivity at work.
o AI might lead to a reduction in my salary.
o The rapid development of AI makes me concerned.
4. Support for Government Tax Benefits for AI Industry Growth:
o Do you support the government providing tax incentives to promote the AI industry's growth?
5. Agreement with Government Responsibilities:
o The government should ensure basic needs for all unemployed individuals.
o The government should work to reduce income inequality.
o The government should implement a Universal Basic Income (UBI).
6. Opinions on UBI and Related Policies:
o Increase unemployment benefits.
o Replace unemployment benefits with UBI.
o Provide training and skills development.
7. Opinions on Social Insurance and Employment Support:
o Should the level of unemployment insurance benefits be increased?
o Should social insurance benefits and employment support be enhanced?
8. What skill sets do you think it is valuable in school?
9. Job Preference for Children:
o Would you like your children to become government officials or work in state-owned enterprises?
o What is your primary reason for this preference?

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

we conduct the experiment in shopping districts in Guangzhou, China.

Experimental Design Details

Not available

Randomization Method

We conduct individual-level experiments by distributing electronic questionnaires to adults in public areas. When participants scan a QR code with their phones to access the questionnaire, they are randomly assigned to either a treatment or control group by the questionnaire company. We asked the questionnaire company to conduct this randomization automatically every time someone scans our bar code.

Randomization Unit

individual

Was the treatment clustered?