{"type":"script","doc_desc":{"producers":[{"name":"Reproducibility WBG","abbr":"DIME","affiliation":"World Bank - Development Impact Department","role":"Verification and preparation of metadata"}],"prod_date":"2024-07-16","version":"1"},"project_desc":{"authoring_entity":[{"name":"Pawel Gmyrek","email":"gmyrek@ilo.org","affiliation":"ILO"},{"name":"Hernan Winkler","email":"hwinkler@worldbank.org","affiliation":"World Bank"},{"name":"Santiago Garganta","affiliation":"CEDLAS-UNLP","email":"santiago.garganta@gmail.com"}],"title_statement":{"title":"Reproducibility package for Buffer or Bottleneck? Generative AI, employment exposure and the digital divide in Latin America","idno":"RR_LAC_2024_168"},"data_statement":"Some data is confidential and has not been included in the reproducibility package. For more details, please refer to the README file.","datasets":[{"name":"ILO\/RESEARCH data portal - AI and Jobs","note":"Source: International Labour Organization. \nFilename: Gmyrek_Berg_Bescond_scores_2023.csv\nLocated at: bases\/ILO\/\nThis file includes all data extracted from the paper: AI exposure scores by 4-digit ISCO08 occupations from \u201cGmyrek, P., Berg, J., Bescond, D., 2023. Generative AI and Jobs: A global analysis of potential effects on job quantity and quality (Working paper). ILO.\u201d \nThe scores can be dynamically visualised and downloaded at the link below.","access_type":"Published with the package","uri":"https:\/\/pgmyrek.shinyapps.io\/AI_Data_Portal_Research\/ "},{"name":"ILOSTAT","note":"Source: International Labour Organization.\nLocated at: bases\/ILO\/Microdata_ILO.\nFilenames: EMP_SEX_ISCO08_2D_Augmentation.csv, EMP_SEX_ISCO08_2D_Automation.csv, EMP_SEX_ISCO08_2D_Bigunknown.csv \n\nThis data cannot be made public according to the ILO micro data policy and the bilateral agreements with countries providing the micro data to the ILO. Users can contact Pawel Gmyrek (gmyrek@ilo.org) from ILO to discuss access to the data.","access_type":"Data is restricted. Users must contact ILO to discuss access to the data."},{"name":"SEDLAC harmonization project","note":"Source: World Bank and CEDLAS. Located at: bases\/sedlac.\nThis subfolder contains all the restricted-use databases generated in the project and sourced from the SEDLAC harmonization project (World Bank and CEDLAS). The original data source is the World Bank Stata API datalibweb, for internal use only. The do files required to access these data are included in the reproducibility package. Please note that access is facilitated through datalibweb, which is exclusively available to World Bank Group employees. If you are an employee, follow the instructions provided in the README file to proceed. ","access_type":"Confidential and not included in the package"}],"software":[{"name":"Stata","version":"18 MP"},{"name":"R","version":"4.3.2"}],"scripts":[{"file_name":"RR_LAC_2024_168","zip_package":"RR_LAC_2024_168.zip","title":"Reproducibility package (code) for Buffer or Bottleneck? Employment Exposure to Generative AI and the Digital Divide in Latin America","dependencies":"All dependencies are stored in the ado folder.","instructions":"See README in the reproducibility package.","notes":"Computational reproducibility verified by Development Impact (DIME) Analytics team, World Bank"}],"repository_uri":[{"name":"Reproducible Research Repository (World Bank)","uri":"https:\/\/reproducibility.worldbank.org"}],"technology_environment":"Paper exhibits were reproduced in a computer with the following specifications:\n\u2022 OS: Windows 11 Enterprise, version 21H2\n\u2022 Processor: Intel(R) Xeon(R) Gold 6226R CPU @ 2.90GHz, 16 Core(s)\n\u2022 Memory available: 15.7 GB\n\u2022 Software version: Stata 18, R 4.4.0\n","technology_requirements":"~2 hours runtime","reproduction_instructions":"Most data used is restricted and not included in the reproducibility package.  Once users have access to all the data,  run the do files and R scripts in the order described in the README file, after changing the file paths. \nThe README contains information on accessing all datasets.","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":"Hernan Winkler","affiliation":"World Bank","email":"hwinkler@worldbank.org"},{"name":"Reproducibility WBG","affiliation":"World Bank","email":"reproducibility@worldbank.org"}],"output":[{"type":"Working Paper","title":"Buffer or Bottleneck?  Employment Exposure to Generative AI and the Digital Divide in Latin America","description":"Policy Research Working Paper (PRWP) 10863","authors":"Pawe\u0142 Gmyrek, Hernan Winkler, Santiago Garganta","uri":"http:\/\/documents.worldbank.org\/curated\/en\/099826507262419608\/IDU197096bf316be814a251b452145b5f0fd5aca","doi":"https:\/\/doi.org\/10.1596\/1813-9450-10863"}],"production_date":"2024-07","abstract":"Empirical evidence on the potential impacts of generative AI (GenAI) is mostly focused on high-income countries. In contrast, little is known about the role of this technology on the future economic pathways of developing economies. This article contributes to fill this gap by estimating the exposure of the Latin American labor market to GenAI. It provides detailed statistics of GenAI exposure between and within countries by leveraging a rich set of harmonized household and labor force surveys. To account for the slower pace of technology adoption in developing economies, it adjusts the measures of exposure to GenAI by using the likelihood of accessing digital technologies at work. This is then used to assess the extent to which the digital divide across and within countries will be a barrier to maximize the productivity gains among occupations that could otherwise be augmented by GenAI tools. The findings show that certain characteristics consistently correlate with higher exposure. Specifically, urban-based jobs that require higher education, are situated in the formal sector, and are held by individuals with higher incomes are more likely to come into interaction with this technology. Moreover, there is a pronounced tilt towards younger workers facing greater exposure, including the risk of job automation, particularly in the finance, insurance, and public administration sectors. When adjusting for access to digital technologies, we find that the digital divide is a major barrier to realizing the positive effects of GenAI on jobs in the region. In particular, nearly half of the positions that could potentially benefit from augmentation are hampered by lack of use of digital technologies. This negative effect of the digital divide is more pronounced in poorer countries. ","geographic_units":[{"name":"Latin America and the Caribbean","code":"LAC","type":"Region"}],"language":[{"name":"English","code":"EN"}],"identifiers":[{"type":"doi","identifier":"https:\/\/doi.org\/10.60572\/106f-pk97"}]},"tags":[{"tag":"DOI"},{"tag":"Limited-access data"},{"tag":"Open code"}],"schematype":"script"}