The Effects of AIGC Adoption on Competition and Gender Inequality in the Labor Market: A Behavioral Experiment Based on ChatGPT

Last registered on October 19, 2024

Pre-Trial

Trial Information

General Information

Title
The Effects of AIGC Adoption on Competition and Gender Inequality in the Labor Market: A Behavioral Experiment Based on ChatGPT
RCT ID
AEARCTR-0014578
Initial registration date
October 15, 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
October 19, 2024, 9:39 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

Affiliation
The Hong Kong University of Science and Technology (Guangzhou)

Other Primary Investigator(s)

PI Affiliation
The Hong Kong University of Science and Technology (Guangzhou)

Additional Trial Information

Status
In development
Start date
2024-11-01
End date
2026-01-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The rapid spread of Artificial Intelligence-Generated Content (AIGC) tools, such as ChatGPT, is expected to significantly enhance labor productivity. However, their impact on different populations in the labor market remains unclear. For instance, previous research has shown that there is a significant difference in competitive preferences between men and women, which is closely related to the gender wage gap in the labor market. AIGC tools, serve as a disruptive technology, possibly serve as an additional resource that change the existing gender wage gap. On the one hand, AIGC tools may reduce gender differences in productivity in certain industries with simple tasks, but on the other hand, they may exacerbate gender inequality due to inherent differences in confidence and technology acceptance between the genders. Thus, the impact of AIGC tools on labor market competition and gender inequality is still uncertain. Taking ChatGPT as an example, this study conducts a behavioural experiment to investigate whether the availability and suitability of ChatGPT significantly affects competitive preferences and task performance, and whether these effects differ by gender. In detail, participants will experience the stages following the classic competition willingness experiment by Niederle & Vesterlund (2007), and their competition willingness will be measured by the choice-based scheme on whether to compete under different payment schemes (piece rate or tournament). We have two dimensions of treatment, the availability of ChatGPT and the suitability of tasks, that constitutes four conditions and it aims to explain the overall effects of ChatGPT on the labor market. This is because different levels of availability and suitability will result in different usage effects or confidence incentives, which will have distinct impacts on willingness to compete and performance. In addition, by comparing the competitive preferences and task performance across groups and genders, we can analyze the impact of ChatGPT usage on gender differences. The results of this study aim to provide substantial recommendations and guidance for future technological development and labor market policies, ensuring that the potential of AIGC tools is fully realised and that technological progress benefits all demographic groups equitably.
External Link(s)

Registration Citation

Citation
QIN, Lisha and Xu ZHANG. 2024. "The Effects of AIGC Adoption on Competition and Gender Inequality in the Labor Market: A Behavioral Experiment Based on ChatGPT." AEA RCT Registry. October 19. https://doi.org/10.1257/rct.14578-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2024-11-01
Intervention End Date
2025-06-01

Primary Outcomes

Primary Outcomes (end points)
Competition willingness, performances
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In the experimental procedure of this study, we have two types of tasks with different levels of ChatGPT suitability (high suitability and low suitability) and two types of ChatGPT availability (available and unavailable), resulting in a total of four conditions. Participants will be randomly assigned to one of these conditions to follow the steps of the classic competitive preference experiment by Niederle and Vesterlund (2007). Specifically, after completing the first two experimental stages (piece rate and tournament), participants will choose whether to engage in a competitive task in the next stage. The payment scheme will be based either on their future performance (requiring actual work) or their past performance (without requiring actual work). This is designed to measure their competitive preferences while controlling for potential confounding factors.
Experimental Design Details
Not available
Randomization Method
Randomization by a computer
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
0
Sample size: planned number of observations
192 students
Sample size (or number of clusters) by treatment arms
48 students under each condition
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Office of Science and Technology Development and Construction, The Hong Kong University of Science and Technology (Guangzhou)
IRB Approval Date
2024-06-25
IRB Approval Number
HKUST(GZ)-HSP-2024-0050