人工智能时代的劳动力需求：来自美国招聘数据的早期证据

Policy Research Working Paper Labor Demand in the Age of Generative AI Early Evidence from the U.S. Job Posting Data Yan Liu Policy Research Working Paper11263 Abstract This paper examines the causal impact of generative artificialintelligence on U.S. labor demand using online job postingdata. Exploiting ChatGPT’s release in November 2022 as anexogenous shock, the paper applies difference-in-differencesand event study designs to estimate the job displacementeffects of generative artificial intelligence. The identificationstrategy compares labor demand for occupations with highversus low artificial intelligence substitution vulnerabilityfollowing ChatGPT’s launch, conditioning on similar for occupations with above-median artificial intelligencesubstitution scores fell by an average of 12 percent relativeto those with below-median scores. The effect increasedfrom 6 percent in the first year after the launch to 18 per-cent by the third year. Losses were particularly acute forentry-level positions that require neither advanced degrees(18 percent) nor extensive experience (20 percent), as wellas those in administrative support (40 percent) and profes- This paper is a product of the Digital Transformation and the Office of the Chief Economist, Prosperity Vertical. It is partof a larger effort by the World Bank to provide open access to its research and make a contribution to development policydiscussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at yanliu@worldbank.org. A verified reproducibility package for this paper is available The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about developmentissues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry thenames of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those LaborDemandintheAgeofGenerativeAI: EarlyEvidencefromtheU.S.JobPostingData YanLiu†1,HeWang‡11WorldBank JELcodes:O33,J23,J21 Keywords:GenerativeArtificialIntelligence,TechnologyAdoption,LaborDemand,Online JobPostings WewouldliketothankAartKraay,DanielLederman,FranziskaLieselotteOhnsorge,JonahMatthewRexer,andparticipantsatvariousseminarsforhelpfulcommentsandsuggestions.Thefindings,interpretations,andconclusionsexpressedinthispaperareentirelythoseoftheauthors.TheydonotnecessarilyrepresenttheviewsoftheWorldBankanditsaffiliatedorganizations,orthoseoftheExecutiveDirectorsoftheWorldBankorthegovernmentsthey Corresponding Author. Email: yanliu@worldbank.orgEmail: hwang21@worldbank.orgEmail: syu2@worldbank.org 1Introduction Has Generative AI (GenAI) started displacing workers?And what kind of jobs face greater dis-placement risks? Since ChatGPT’s debut in late 2022, the adoption of GenAI has spread rapidlyacross countries, sectors, and occupations (Liu and Wang 2024; Bick, Blandin, and Deming 2024; Bonney et al. 2024; Liu, Huang, and Wang 2025). The expanding capabilities of GenAI, coupled with its accelerating adoption, have reignited longstanding concerns about technology-driven job This paper investigates the impact of GenAI on labor demand in the U.S. using the near-universeof online job posting data spanning 2018Q1 to 2025Q2.3We exploit the public release of ChatGPT in November 2022 as an exogenous shock and employ difference-in-differences (DiD) and event-study methodologies to identify GenAI’s labor-displacement effects. Our analysis tracks how theseeffects have evolved over time as GenAI capabilities have improved and adoption has deepened. It Our empirical framework combines two complementary dimensions that jointly determine howGenAI affects labor demand across occupations: GenAI exposure, which measures the theoreticaltechnical applicability of GenAI, and AI-substitution vulnerability, which captures the practicallikelihood that employers replace workers with AI. GenAI exposure reflects the extent to which AIis useful and applicable to specific tasks and occupations, but by itself it does not predict realizedlabor demand effects since it conflates automation and augmentation. To address this limitation, Our results demonstrate a large, statistically significant, and intensifying negative impact ofGenAI on job postings for more substitutable occupations. In the U.S., we estimate that by mid-2025, postings in occupations with above-median AI substitution scores declined by an averageof 12% relative to those with below-median scores, conditional on comparable levels of GenAIexposure.Event study analysis further confirms no significant difference in job posting trends This paper makes several contributions to the emerging literature on GenAI and labor demand. First, to our knowledge, this is the first study to identify the causal impact of GenAI