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"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets

Author

Listed:
  • Jin Liu

    (University of Science and Technology of China)

  • Xingchen Xu

    (University of Washington)

  • Yongjun Li

    (University of Science and Technology of China)

  • Yong Tan

    (University of Washington)

Abstract

With the advent of general-purpose Generative AI, the interest in discerning its impact on the labor market escalates. In an attempt to bridge the extant empirical void, we interpret the launch of ChatGPT as an exogenous shock, and implement a Difference-in-Differences (DID) approach to quantify its influence on text-related jobs and freelancers within an online labor marketplace. Our results reveal a significant decrease in transaction volume for gigs and freelancers directly exposed to ChatGPT. Additionally, this decline is particularly marked in units of relatively higher past transaction volume or lower quality standards. Yet, the negative effect is not universally experienced among service providers. Subsequent analyses illustrate that freelancers proficiently adapting to novel advancements and offering services that augment AI technologies can yield substantial benefits amidst this transformative period. Consequently, even though the advent of ChatGPT could conceivably substitute existing occupations, it also unfolds immense opportunities and carries the potential to reconfigure the future of work. This research contributes to the limited empirical repository exploring the profound influence of LLM-based generative AI on the labor market, furnishing invaluable insights for workers, job intermediaries, and regulatory bodies navigating this evolving landscape.

Suggested Citation

  • Jin Liu & Xingchen Xu & Yongjun Li & Yong Tan, 2023. ""Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets," Papers 2308.05201, arXiv.org.
    • Handle: RePEc:arx:papers:2308.05201
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    Cited by:

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    2. Mourelatos, Evangelos & Zervas, Panagiotis & Lagios, Dimitris & Tzimas, Giannis, 2024. "Can AI Bridge the Gender Gap in Competitiveness?," GLO Discussion Paper Series 1404, Global Labor Organization (GLO).

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