{"type":"script","doc_desc":{"producers":[{"name":"Reproducibility WBG","abbr":"DIME","affiliation":"World Bank - Development Impact Department","role":"Verification and preparation of metadata"}],"prod_date":"2025-03-15","version":"1"},"project_desc":{"authoring_entity":[{"name":"J\u00f6rg Langbein","affiliation":"World Bank","email":"jlangbein@worldbank.org"},{"name":"Gabriel Demombynes","email":"gdemombynes@worldbank.org","affiliation":"World Bank"},{"name":"Michael Weber","affiliation":"World Bank and IZA","email":"mweber1@worldbank.org"}],"title_statement":{"title":"Reproducibility package for The Exposure Of Workers To Artificial Intelligence In Low- And Middle-Income Countries","idno":"RR_WLD_2025_276"},"data_statement":"Some data is restricted and has not been included in the reproducibility package. For more details, please refer to the README file.","software":[{"name":"Stata","version":"18.0 MP"}],"scripts":[{"title":"Reproducibility package for The Exposure Of Workers To Artificial Intelligence In Low- And Middle-Income Countries","date":"2025-03","notes":"Computational reproducibility verified by Development Impact (DIME) Analytics team, World Bank.","instructions":"See README in reproducibility package.","file_name":"RR_WLD_2025_276","zip_package":"RR_WLD_2025_276.zip","dependencies":"Stata dependencies are listed in the ado folder."}],"repository_uri":[{"name":"Reproducible Research Repository (World Bank)","uri":"https:\/\/reproducibility.worldbank.org"}],"production_date":"2025-03-15","abstract":"Research on the labor market implications of artificial intelligence has focused principally on high-income countries. This paper analyzes this issue using microdata from a large set of low- and middle-income countries, applying a measure of potential artificial intelligence occupational exposure to a harmonized set of labor force surveys for 25 countries, covering a population of 3.5 billion people. The approach advances work by using harmonized microdata at the level of individual workers, which allows for a multivariate analysis of factors associated with exposure. Additionally, unlike earlier papers, the paper uses highly detailed (4 digit) occupation codes, which provide a more reliable mapping of artificial intelligence exposure to occupation. Results within countries, show that artificial intelligence exposure is higher for women, urban workers, and those with higher education. Exposure decreases by country income level, with high exposure for just 12 percent of workers in low-income countries and 15 percent of workers in lower-middle-income countries. Furthermore, lack of access to electricity limits effective exposure in low-income countries. These results suggest that for developing countries, and in particular low-income countries, the labor market impacts of artificial intelligence will be more limited than in high-income countries. While greater exposure to artificial intelligence indicates larger potential for future changes in certain occupations, it does not equate to job loss, as it could result in augmentation of worker productivity, automation of some tasks, or both.","geographic_units":[{"name":"World","code":"WLD"}],"keywords":[{"name":"Jobs And Development"},{"name":" Human Capital And Growth"},{"name":" Digital Economy Strategy"}],"topics":[{"id":"J24","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","name":"Human Capital \u2022 Skills \u2022 Occupational Choice \u2022 Labor Productivity","parent_id":"J2"},{"id":" J21","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","name":"Labor Force and Employment, Size, and Structure","parent_id":"J2"},{"id":" O33","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","name":"Technological Change: Choices and Consequences \u2022 Diffusion Processes","parent_id":"O3"}],"output":[{"type":"PRWP Working Paper","description":"Policy Research Working Papers (PRWP) 11057","title":"The Exposure Of Workers To Artificial Intelligence In Low- And Middle-Income Countries","uri":"http:\/\/documents.worldbank.org\/curated\/en\/099629202052521198"}],"language":[{"name":"English","code":"EN"}],"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.","license":[{"name":"Modified BSD3","uri":"https:\/\/opensource.org\/license\/bsd-3-clause\/"}],"contacts":[{"name":"J\u00f6rg Langbein","affiliation":"World Bank","email":"jlangbein@worldbank.org"},{"name":"Reproducibility WBG","affiliation":"World Bank","email":"reproducibility@worldbank.org"}],"datasets":[{"name":"Global Labor Database (GLD)","note":"Source: World Bank. The Global Labor Database (GLD) includes various harmonized household surveys, accessible by World Bank staff, except for data for Egypt. The harmonization codes and survey documentation for all GLD surveys, both restricted and unrestricted access, are available online in this GitHub repository: https:\/\/github.com\/worldbank\/gld . All data files used from this dataset are listed on page 2 of the README. Data was accessed in February 2024.","uri":"https:\/\/worldbank.github.io\/gld\/README.html","access_type":"Data is not included in the reproducibility package. Access for WB staff and consultants is described in the data URL. There is no documented way to access the data for external users."},{"name":"International Income Distribution Database (I2D2)","note":"Source: World Bank. The I2D2 is a collection of harmonized household surveys managed by different units within the World Bank. All data files used from this dataset are listed on page 2 of the README. Data was accessed internally (through the World Bank). Data was accessed in September 2024.","access_type":"Data is not included in the reproducibility package. Data is internally available for WB staff and consultants using the tool datalibweb. There is no documented way to access the data for external users."},{"name":"International Standard Classification of Occupations (ISCO) and Standard Classification of Occupations (SOC) Crosswalk","note":"Source: U.S. Bureau of Labor Statistics. Data file: \"Excel\/ISCO_SOC_Crosswalk.xls\". Data was accessed in February 2024.","uri":"https:\/\/www.bls.gov\/soc\/isco_soc_crosswalk.xls","license_uri":"https:\/\/www.bls.gov\/bls\/linksite.htm","access_type":"Data is publicly available and included in the reproducibility package."},{"name":"AI Occupational Exposure by Occupation from Felten et al. (2021)","note":"Source: \"Occupational, Industry, and Geographic Exposure to Artificial Intelligence: A Novel Dataset and Its Potential Uses\" by Felten, Raj, and Seamans (2021). The dataset is the data appendix A of this paper, manually imported into DTA file and saved with the name \"SOCAIOE.dta\". The paper can be found here: https:\/\/doi.org\/10.1002\/smj.3286. Data was accessed in March 2024.","uri":"https:\/\/github.com\/AIOE-Data\/AIOE","access_type":"Data is publicly available and included in the reproducibility package."}],"technology_requirements":"Runtime: 30 minutes.","technology_environment":"Paper exhibits were reproduced on a computer with the following specifications:\n\u2022 OS: Windows 11 Enterprise, version 23H2\n\u2022 Processor: 11th Gen Intel(R) Core(TM) i5-1145G7 @ 2.60GHz 1.50 GHz\n\u2022 Memory available: 15.7 GB\n\u2022 Software version: Stata 18.0 MP","reproduction_instructions":"1. **Secure access to data:** Two datasets are only accessible to WB staff and consultants. External users cannot obtain the data necessary to run the code, and no portion of the code can run without the restricted datasets. See the README and the Datasets section for details.\n2. **Download and place data:** Once the data is obtained, users should place it in the appropriate folder.\n3. **Run the code:** After placing the data in the folder:\n    - Open the do-file \"Master\"\n    - Update the global `path` in line 10 to your folder's location\n    - Run the do-file\n\nSince not all the data is included and accessible, the package includes the results produced by replicators in the Results folder. These files can be used to review the results presented in the paper."},"tags":[{"tag":"DOI"},{"tag":"Open Code"},{"tag":"Restricted Data"}],"schematype":"script"}