工人在不同发展阶段接触人工智能的情况（英）

Piotr Lewandowski, Karol Madoń, and Albert F. Park ADB ECONOMICSWORKING PAPER SERIES Workers’ Exposure to Artificial IntelligenceAcross Development Stages Piotr Lewandowski (piotr.lewandowski@ibs.org.pl)is a labor economist and the president of the boardand Karol Madoń (karol.madon@ibs.org.pl) is aneconomist at the Institute for Structural Research(IBS). Albert F. Park (afpark@adb.org) is the chiefeconomist and director general of the EconomicResearch and Development Impact Department,Asian Development Bank. Piotr Lewandowski, Karol Madoń,and Albert F. Park No. 836 | January 2026 TheADB Economics Working Paper Seriespresents research in progress to elicit commentsand encourage debate on development issuesin Asia and the Pacific. The views expressedare those of the authors and do not necessarilyreflect the views and policies of ADB orits Board of Governors or the governmentsthey represent. Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2026 Asian Development Bank6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, PhilippinesTel +63 2 8632 4444; Fax +63 2 8636 2444www.adb.org Some rights reserved. Published in 2026. ISSN 2313-6537 (print), 2313-6545 (PDF)Publication Stock No. WPS260018-2DOI: http://dx.doi.org/10.22617/WPS260018-2 The views expressed in this publication are those of the authors and do not necessarily reflect the views and policiesof the Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for anyconsequence of their use. The mention of specific companies or products of manufacturers does not imply that theyare endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned. By making any designation of or reference to a particular territory or geographic area in this document, ADB does notintend to make any judgments as to the legal or other status of any territory or area. This publication is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO)https://creativecommons.org/licenses/by/3.0/igo/. By using the content of this publication, you agree to be boundby the terms of this license. For attribution, translations, adaptations, and permissions, please read the provisionsand terms of use at https://www.adb.org/terms-use#openaccess. This CC license does not apply to non-ADB copyright materials in this publication. If the material is attributedto another source, please contact the copyright owner or publisher of that source for permission to reproduce it.ADB cannot be held liable for any claims that arise as a result of your use of the material. Please contact pubsmarketing@adb.org if you have questions or comments with respect to content, or if you wishto obtain copyright permission for your intended use that does not fall within these terms, or for permission to usethe ADB logo. Corrigenda to ADB publications may be found at http://www.adb.org/publications/corrigenda. ABSTRACT This paper develops a task-adjusted,country-specificmeasure of workers’ exposure toartificialintelligence (AI) across 108 countries. Building on Felten et al. (2021), we adapt theartificialintelligenceoccupationalexposure (AIOE) index to worker-level data from the Programme for theInternationalAssessment of Adult Competencies(PIAAC)and extend it globally usingcomparablesurveys and regression-based predictions,covering about 89%of globalemployment. Accounting forcountry-specifictask structures reveals substantial cross-countryheterogeneity: workers in low-income countries exhibit AI exposure levels roughly 0.8 UnitedStates (US) standard deviations below those in high-income countries, largely due to differencesinwithin-occupation task content.Regression decompositions attribute most cross-countryvariation toinformation and communications technology intensity and human capital. High-income countries employ the majority of workers in highly AI-exposed occupations, while low-income countries concentrate in less exposed ones. Using two PIAAC cycles, we document risingAI exposure in high-income countries, driven by shifts in within-occupation tasks rather thanemployment structure. Keywords:jobtasks, occupations, AI, technology, skillsJEL codes:J21, J23, J24 1.INTRODUCTION The rapid progress of large language models (LLMs) and generativeartificial intelligence(GenAI) has drawn considerable public attention, largely due to concerns about potentiallabordisplacement. Yet, empirical evidence on GenAI’slabormarket effects remains limited, primarilybecause of scarce systematic data onartificial intelligenceinvestment and application. To addressthis gap, researchers have turned to measuring workers’ exposure to AI, typically combiningpatent or AI application data with occupational task information(Felten et al., 2021, 2018; Gmyreket al., 2023; Hampole et al., 2025; Webb, 2020).Most studies focus o