Impact of the introduction of AI technology on the employment environment: International survey

Last registered on July 17, 2024
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Pre-Trial

Trial Information

General Information

Title
Impact of the introduction of AI technology on the employment environment: International survey
RCT ID
AEARCTR-0013927
Initial registration date
July 14, 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 17, 2024, 1:49 PM EDT

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

Locations

Country
Japan
Region
Country
United States of America
Region

Primary Investigator

Name
koji takahashi
Affiliation
Bank for International Settlements
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Other Primary Investigator(s)

PI Name
Yusuke Aoki
PI Affiliation
Indeed
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PI Name
Yuya Takada
PI Affiliation
Re Data Science Co., Ltd.
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Additional Trial Information

Status
Completed
Start date
2024-05-20
End date
2024-06-03
Keywords
Behavior, Finance & Microfinance, Labor
Additional Keywords
JEL code(s)
E24, E31, O30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We investigate how perceptions of generative AI's impact on the labor market affect macroeconomic expectations and individual behavioral intentions. Participants are shown different predictions about AI job replacement and are then asked about their expectations for CPI inflation, GDP growth, and their willingness to engage with AI in their work and studies.
External Link(s)

Registration Citation

Citation
Aoki, Yusuke , Yuya Takada and koji takahashi. 2024. "Impact of the introduction of AI technology on the employment environment: International survey." AEA RCT Registry. July 17. https://doi.org/10.1257/rct.13927-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We investigate how perceptions of the impact of generative AI on the labor market influence macroeconomic expectations and wage change formation. First, participants are asked about the extent to which generative AI will replace workers in the future. Then, participants are randomly assigned to one of three groups. Group A is shown a prediction by an expert stating that AI will replace 14% of all jobs. Group B is shown a prediction that AI will replace a higher percentage, 47% of all jobs. Group C, serving as a placebo, is shown a future prediction about an unrelated astronomical phenomenon (Halley's Comet). For these three groups, we will examine changes in macroeconomic expectations by asking participants to predict CPI inflation and GDP growth rates before and after the treatment. Additionally, we will ask respondents about their willingness to study AI in the future and the extent to which they want to actively use AI in their work, to observe how individual behavioral intentions toward AI are affected.
Intervention (Hidden)
Intervention Start Date
2024-05-20
Intervention End Date
2024-06-03

Primary Outcomes

Primary Outcomes (end points)
expectations of real GDP growth, inflation rate, private investment growth, wages, intention to learn AI, intention to use AI
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The surveys were conducted via the internet, targeting large-scale survey monitors.
Respondents were individuals who had previously registered with the survey company and could win cash or gift vouchers for completing the survey. The sample was equally distributed by sex, age, and job category (16 groups). Sex was divided into two groups: female and male (2 groups). Age was divided into two groups: 20 to 39 years old and 40 to 59 years old (2 groups). Job categories included Sales and Administrative, Engineering, Planning and Specialist, and Creative (4 groups). Only full-time company employees were invited.
We randomly assigned respondents into three groups to evaluate the impact of generative AI on job replacement. Group A, labeled as the "Small impact of AIs" group, received the following treatment: “A well-known study on AI estimates that 14% of current jobs could be replaced by generative AI in the future.” Group B, labeled as the "Large impact of AIs" group, received this treatment: “A well-known study on AI estimates that 47% of current jobs could be replaced by generative AI in the future.” Group C served as the placebo group and was informed with unrelated information as a control: “According to astronomical studies, the next time Halley's Comet will approach the Earth will be in July 2061.”

Respondents were then asked the questions about AI and macroeconomy both before and after receiving the treatment. This allowed us to measure any changes in their responses due to the information provided. This presentation focuses on the findings from Groups A and B.
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
No cluster
Sample size: planned number of observations
8000
Sample size (or number of clusters) by treatment arms
1400 treatment with high replacement ratio, 1400 treatment with low replacement ratio, 1400 placebo with being provided with unrelated information
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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