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Reproducibility package for Labor Demand in the Age of Generative AI: Early Evidence from the U.S. Job Posting Data

2025
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Reference ID
RR_USA_2025_436
Author(s)
Yan Liu, Shu Yu, He Wang
Collections
World Bank Policy Research Working Papers
Metadata
JSON
Created on
Nov 18, 2025
Last modified
Dec 23, 2025
  • Overview
  • Reproducibility Package
  • Description
  • Scope and coverage
  • Disclaimer
  • Access and rights
  • Contacts
  • Information on metadata

    • Overview

    Abstract

    This paper examines the causal impact of generative artificial intelligence on U.S. labor demand using online job posting data. Exploiting ChatGPT’s release in November 2022 as an exogenous shock, the paper applies difference-in-differences and event study designs to estimate the job displacement effects of generative artificial intelligence. The identification strategy compares labor demand for occupations with high versus low artificial intelligence substitution vulnerability following ChatGPT’s launch, conditioning on similar generative artificial intelligence exposure levels to isolate
    substitution effects from complementary uses. The analysis uses 285 million job postings collected by Lightcast from the first quarter of 2018 to the second quarter of 2025Q2. The findings show that the number of postings for occupations with above-median artificial intelligence substitution scores fell by an average of 12 percent relative to those with below-median scores. The effect increased
    from 6 percent in the first year after the launch to 18 percent by the third year. Losses were particularly acute for entry-level positions that require neither advanced degrees (18 percent) nor extensive experience (20 percent), as well as those in administrative support (40 percent) and professional services (30 percent). Although generative artificial intelligence generates new occupations and enhances productivity, which may increase labor demand, early evidence suggests that some occupations may be less likely to be complemented by generative artificial intelligence than others.

    Reproducibility Package

    Scripts
    Get Reproducibility Package
    File name
    RR_USA_2025_436
    Zip package
    RR_USA_2025_436.zip
    Title
    Reproducibility package for Labor Demand In The Shadow Of Generative Ai: Evidence From The U.s. Job Market
    Date
    2025-11
    Dependencies
    R: arrow, haven, dplyr, stringr, readxl, conflicted, ggplot2, lubridate, scales, tidyr, fixest, modelsummary, tibble, broom, colrspacem, gridExtra. Python: pandas, pyreadstat, pyspark. No dependencies are used for Stata.
    Instructions
    See README in reproducibility package.
    Notes
    Computational reproducibility verified by Development Impact (DECDI) Analytics team, World Bank.
    Source code repository
    Repository name URI
    Reproducible Research Repository (World Bank) https://reproducibility.worldbank.org
    Software
    Name
    R
    Version
    4.4.0
    Name
    Python
    Version
    3.12.3
    Name
    Stata
    Version
    18.5 MP

    Reproducibility

    Technology environment

    Paper exhibits were reproduced on two systems with the following specifications:
    • Computer 1 - Stata:
    – OS: Windows 11 Enterprise, version 24H2
    – Processor: Intel(R) Core(TM) Ultra 7 165U (2.10 GHz)
    – Memory available: 32 GB
    • Databricks - R and Python:
    – Runtime: 16.4, Spark 3.5.2, Scala 2.12
    – Driver: Standard_D16ds_v4, 64GB, 16 Cores
    – Worker: Standard_D16ds_v4, 64GB, 16Cores
    Technology requirements

    Runtime: 24 hours.
    Reproduction instructions

    1. Access the data: Three datasets used by the reproducibility package are not included in it. Users need to gain access to all data to be able to run the code. See the README and Datasets description for details.
    2. Run the Stata code: Modify the file path in the do-file "OccupationLevelData" and run it.
    3. Run the Python and R notebooks on Databricks: The notebooks in the folder "noteboom/" run on Databricks. Once they are placed in a Databricks workspace, they need to run in the following order: "1 LightCast Data Aggregation.ipynb", "2 Prepare Data for Analysis.ipynb", and "3 Results.ipynb".

    Since not all the data are included, the package includes the results produced in the reproducibility verification. These files can be used to review the results presented in the paper.

    Data

    Datasets
    Name
    GenAI Exposure Scores by ISCO-08 classification - Supplementary data for: Generative AI and Jobs: A global analysis of potential effects on job quantity and quality
    Note
    Source: Gmyrek, Berg, and Bescond (2023). Data was accessed on October 16, 2024. Data file: "rawdata/GBB-scores-2024-10-16.csv".
    Access policy
    Data is publicly available and included in the reproducibility package.
    Data URL
    https://pgmyrek.shinyapps.io/AI_Data_Portal_Research/
    Citation
    Gmyrek, P., Berg, J., Bescond, D. 2023. "GenAI Exposure Scores - Supplementary data for: Generative AI and Jobs: A global analysis of potential effects on job quantity and quality [Dataset]". Downloaded from https://pgmyrek.shinyapps.io/AI_Data_Portal_Research/. Accessed October 16, 2024.
    Name
    Global Index of Occupational Exposure to Generative AI - Supplementary data for: Generative AI and Jobs - A Refined Global Index of Occupational Exposure
    Note
    Source: Gmyrek, P., Berg, J., Kamiński, K., Konopczyński, F., Ładna, A., Nafradi, B., Rosłaniec, K.,Troszyński, M. (2025). Data was accessed on June 15, 2025. Data file: "rawdata/ILO2025_Final_Scores_ISCO08_Gmyrek_et_al_2025.xlsx".
    Access policy
    Data is publicly available and included in the reproducibility package.
    Data URL
    https://github.com/pgmyrek/2025_GenAI_scores_ISCO08/blob/main/Final_Scores_ISCO08_Gmyrek_et_al_2025.xlsx
    Citation
    Gmyrek, P., Berg, J., Kamiński, K., Konopczyński, F., Ładna, A., Nafradi, B., Rosłaniec, K.,Troszyński, M. 2025. "Global Index of Occupational Exposure to Generative AI - Supplementary data for: Generative AI and Jobs - A Refined Global Index of Occupational Exposure [Dataset]". Downloaded from "https://github.com/pgmyrek/2025_GenAI_scores_ISCO08/blob/main/Final_Scores_ISCO08_Gmyrek_et_al_2025.xlsx". Accessed June 15, 2025.
    Name
    Job Openings and Labor Turnover Survey (JOLTS)
    Note
    Source: Bureau of Labor Statistics (BLS). Data was accessed on July 5, 2025. Data file: "rawdata/JOLTS.xlsx".
    Access policy
    Data is publicly available and included in the reproducibility package.
    License URL
    https://www.bls.gov/bls/linksite.htm
    Data URL
    https://download.bls.gov/pub/time.series/jt/jt.data.2.JobOpenings (series: JTS000000000000000JOL)
    Citation
    Bureau of Labor Statistics. n.d. "Job Openings and Labor Turnover Survey (JOLTS) [Dataset]". Accessed from https://download.bls.gov/pub/time.series/jt/jt.data.2.JobOpenings. Accessed July 5, 2025.
    Name
    AI Complementarity - Supplementary data for: Labor Market Exposure to AI: Cross-country Differences and Distributional Implications
    Note
    Source: Pizzinelli, C., A. Panton, M. M. Tavares, M. Cazzaniga, and L. Li. Data was shared by the data owners following an email request to the authors of "Labor Market Exposure to AI: Cross-country Differences and Distributional Implications" (2023). Data was accessed on November 15, 2024. Data file: "rawdata/AIOE_CAIOE_theta_for_sharing.xlsx".
    Access policy
    Data access was granted directly to the study authors by the data owners. It was obtained with a custom data license that does not allow for redistribution and it is not included in the reproducibility package.
    Citation
    Pizzinelli, C., Panton, A., Tavares, M. M., Cazzaniga, M., Li, L. n.d. "AI Complementarity - Supplementary data for: Labor Market Exposure to AI: Cross-country Differences and Distributional Implications [Dataset]." Unpublished data. Accessed November 15, 2024.
    Name
    Occupation description and skills - Supplementary data for Click, Code, Earn: The Returns to Digital Skills
    Note
    Source: Martins-Neto, A., Liu, Y., Khurana, S., Porraz Lopez, J. M. Data was shared by the data owners following an email request to the authors of "Click, Code, Earn: The Returns to Digital Skills" (2025). Data was accessed on December 9, 2024. Data file: "rawdata/onet_occ.xlsx".
    Access policy
    Data access was granted directly to the study authors by the data owners. It was obtained with a custom data license that does not allow for redistribution and it is not included in the reproducibility package.
    Citation
    Martins-Neto, A., Liu, Y., Khurana, S., Porraz Lopez, J. M. n.d. "Occupation description and skills - Supplementary data for Click, Code, Earn: The Returns to Digital Skills [Dataset]." Unpublished data. Accessed December 9, 2024.
    Name
    Raw Job Postings
    Note
    Source: Lightcast. Data accessed was purchased from the source and made available with a database connection enabled for the authors' Databricks workspace. Hence, there is no raw data file for this dataset. Data was extracted and aggregated from the database using the Databricks notebook "1 LightCast Data Aggregation.ipynb" and was saved into the file "rawdata/LC_USA_MonthlyPostings.csv". Data was accessed on July 5, 2025.
    Access policy
    Data access requires purchase and is not included in the reproducibility package.
    Citation
    Lightcast. n.d. "Raw Job Postings [Dataset]". Distributed via Lightcast Data. Accessed July 5, 2025.
    Data statement

    Some data is restricted and has not been included in the reproducibility package. For more details, please refer to the README file.

    Description

    Output
    Type
    Working Paper
    Title
    Labor Demand in the Age of Generative AI: Early Evidence from the U.S. Job Posting Data
    Description
    Policy Research Working Papers (PRWP) 11263
    URL
    http://documents.worldbank.org/curated/en/099827011182513988
    DOI
    https://doi.org/10.1596/1813-9450-11263
    Authors
    Author Affiliation Email
    Yan Liu World Bank yanliu@worldbank.org
    Shu Yu World Bank syu2@worldbank.org
    He Wang World Bank hwang21@worldbank.org
    Date of production

    2025-11-18

    Scope and coverage

    Geographic locations
    Location Code
    United States of America USA
    Keywords
    Generative Artificial Intelligence Technology Adoption Labor Demand Online Job Postings
    Topics
    ID Topic Parent topic ID Vocabulary Vocabulary URI
    O33 Technological Change: Choices and Consequences • Diffusion Processes O3 Journal of Economic Literature (JEL)
    J23 Labor Demand J2 Journal of Economic Literature (JEL)
    J21 Labor Force and Employment, Size, and Structure J2 Journal of Economic Literature (JEL)

    Disclaimer

    Disclaimer

    The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development/The World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.

    Access and rights

    License
    Name URI
    Modified BSD3 https://opensource.org/license/bsd-3-clause/

    Contacts

    Contacts
    Name Affiliation Email
    Yan Liu World Bank yanliu@worldbank.org
    Reproducibility WBG World Bank reproducibility@worldbank.org

    Information on metadata

    Producers
    Name Abbreviation Affiliation Role
    Reproducibility WBG DECDI World Bank - Development Impact Department Verification and preparation of metadata
    Date of Production

    2025-11-18
    Document version

    1
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