Firms' Expectations of AI and Household Labor-Market Beliefs

Last registered on October 16, 2026
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Pre-Trial

Trial Information

General Information

Title
Firms' Expectations of AI and Household Labor-Market Beliefs
RCT ID
AEARCTR-0019919
Initial registration date
October 09, 2026

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
October 16, 2026, 2:24 PM EDT

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
Shihan Xie
Affiliation
University of Illinois at Urbana-Champaign
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Other Primary Investigator(s)

PI Name
Ding Dong
PI Affiliation
Hong Kong Baptist University
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PI Name
Pengfei Wang
PI Affiliation
Peking University HSBC Business School
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Additional Trial Information

Status
In development
Start date
2026-10-10
End date
2026-11-30
Keywords
Labor
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Artificial intelligence may change how many workers firms need in different occupations. This study asks how households form beliefs about the labor-market effects of AI adoption, and whether these beliefs matter for their economic decisions. We ask whether learning what firms themselves expect about AI-driven changes in employment for an occupation changes workers' beliefs about their own job security, earnings, and outside options, and their expectations of national unemployment. We then ask whether these belief changes affect household spending and saving, and demand for information about jobs and AI skills.
External Link(s)

Registration Citation

Citation
Dong, Ding, Pengfei Wang and Shihan Xie. 2026. "Firms' Expectations of AI and Household Labor-Market Beliefs." AEA RCT Registry. October 16. https://doi.org/10.1257/rct.19919-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Respondents are randomly assigned to one of two groups:
- Control: no information.
- Treatment: respondents read information from a September 2026 survey of 255 Chinese firms on the share of firms that expect AI adoption to reduce, or to increase, headcount in each of five occupation categories over the next year. Respondents whose occupation is known are shown the figures for their own occupation (current occupation for workers, target occupation for job seekers and students).
Intervention Start Date
2026-10-10
Intervention End Date
2026-10-30

Primary Outcomes

Primary Outcomes (end points)
- Share of a hypothetical one-off 10,000 yuan windfall that the respondent would spend on consumption, saving, debt repayment, risky assets, training within 12 months.
- Planned monthly saving over the next 12 months relative to the past 12 months
- Workers: probability that the employer cuts headcount, and that it cuts average pay, for the respondent's type of job because of AI; probability of involuntary job loss because of AI
- Workers: expected percent change in own earnings in 12 months; reservation wage as a percent of current monthly earnings
- Probability that the urban unemployment rate rises in the next 12 months, ages 16-24 and ages 25-59; workers' probability of finding a same-type job within 3 months after job loss
- Choice of post-survey resource (AI-tools guide for own occupation, hiring summary for other occupations, or neither); preferred type of additional job information
- Job seekers and students: probabilities that firms cut hiring or pay in the target occupation because of AI and of switching to another type of job; students' probability of applying for civil-service or public-institution jobs; expected monthly earnings; job-finding probability
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We embed an information experiment in the China Household Survey of Expectations, a monthly online household survey. Respondents are randomly assigned with equal probability to a control group or a treatment group which receives information about the share of firms that expect AI adoption to increase or reduce headcount in each of five occupation categories in the next year. Information about firm expectations comes from a September 2026 survey of 255 firms by the Shanghai National Accounting Institute. We measure the allocation of a hypothetical windfall, saving plans, beliefs about job and pay cuts at the respondent's own employer, expected income, reservation wages, beliefs about national unemployment, and demand for job-related information.
Experimental Design Details
Not available
Randomization Method
Randomization by computer within the survey platform, with equal probability across the two arms; stratified by survey path and occupation category where the platform supports it.
Randomization Unit
Individual respondent
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
3,000 individuals
Sample size: planned number of observations
3,000 individuals
Sample size (or number of clusters) by treatment arms
About 1,500 per arm (control, treatment)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Peking University
IRB Approval Date
2026-10-10
IRB Approval Number
N/A
Analysis Plan