{"type":"script","doc_desc":{"producers":[{"name":"Reproducibility WBG","abbr":"DECDI","affiliation":"World Bank - Development Impact Department","role":"Verification and preparation of metadata"}],"prod_date":"2026-08-10","version":"1"},"project_desc":{"authoring_entity":[{"name":"Gaurav Nayyar","affiliation":"World Bank","email":"gnayyar@worldbank.org"},{"name":"Anukriti Siddharth","affiliation":"World Bank","email":"sanukriti@worldbank.org"},{"name":"Sharmista Appaya","affiliation":"World Bank","email":"sappaya@worldbank.org"},{"name":"Elwyn Davies","affiliation":"World Bank","email":"edavies@worldbank.org"},{"name":"Lelys Dinarte-Diaz","affiliation":"World Bank","email":"ldinartediaz@worldbank.org"},{"name":"Sam Fraiberger","affiliation":"World Bank","email":"sfraiberger@worldbank.org"},{"name":"Yi Jie Gwee","affiliation":"World Bank","email":"ygwee@worldbank.org"},{"name":"Yan Liu","affiliation":"World Bank","email":"yanliu@worldbank.org"},{"name":"Jeremy Ng","affiliation":"World Bank","email":"njeremy@worldbank.org"},{"name":"Tiago C. Peixoto","affiliation":"World Bank","email":"tpeixoto@worldbank.org"},{"name":"Manuel Ramos-Maqueda","affiliation":"World Bank","email":"mramosmaqueda@worldbank.org"},{"name":"Anja Sautmann","affiliation":"World Bank","email":"asautmann@worldbank.org"},{"name":"Katherine Stapleton","affiliation":"World Bank","email":"kstapleton@worldbank.org"},{"name":"Shu Yu","affiliation":"World Bank","email":"syu2@worldbank.org"}],"title_statement":{"title":"Reproducibility package for World Development Report 2026: The Promise of Artificial Intelligence","idno":"FR_WLD_2026_706"},"data_statement":"Some data is restricted and has not been included in the reproducibility package. For more details, refer to the README file.","software":[{"name":"R","version":"4.5.1"},{"name":"Stata","version":"19.5 MP"},{"name":"Excel"}],"scripts":[{"title":"Reproducibility package for World Development Report 2026: The Promise of Artificial Intelligence","date":"2026-08","notes":"Computational reproducibility verified by Development Impact (DECDI) Analytics team, World Bank.","instructions":"See README in reproducibility package.","file_name":"FR_WLD_2026_706","zip_package":"FR_WLD_2026_706.zip","dependencies":"R dependencies are listed in the file renv.lock. Stata dependencies are listed in the ado folder. Python dependencies are listed in requirements.txt."}],"repository_uri":[{"name":"Reproducible Research Repository (World Bank)","uri":"https:\/\/reproducibility.worldbank.org"}],"production_date":"2026-08-10","abstract":"World Development Report 2026 will investigate the development implications of AI as a general-purpose technology (GPT) and assess what might be the best policy choices to leverage the benefits of AI while offsetting potential risks. In doing so, it will focus on the institutional and governance arrangements needed to ensure inclusive and responsible deployment of AI. The aim of the Report is to provide a developing-country perspective on a topic for which the academic and policy discussion has focused primarily on high-income countries.","geographic_units":[{"name":"World","code":"WLD"}],"output":[{"type":"Flagships & Reports","title":"World Development Report 2026: The Promise of Artificial Intelligence"}],"language":[{"name":"English","code":"EN"}],"technology_requirements":"Runtime: ~40 minutes","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":"MIT License","uri":"https:\/\/opensource.org\/license\/mit"},{"name":"World Bank IGO Rider","uri":"https:\/\/github.com\/worldbank\/metadata-editor\/blob\/main\/WB-IGO-RIDER.md"}],"contacts":[{"name":"Gaurav Nayyar","affiliation":"World Bank","email":"gnayyar@worldbank.org"},{"name":"Reproducibility WBG","affiliation":"World Bank","email":"reproducibility@worldbank.org"}],"datasets":[{"name":"Generative AI Adoption Data","note":"Source: Semrush web traffic data on generative AI (proprietary, subscription-based), extracted via Semrush's API in June 2025; extensive and intensive Gen AI adoption measures as used in Liu, Huang, and Wang (2025). Used for Figure 1.1, panels a and b, and for Figure 4.12, panel b (country-level ChatGPT extensive\/intensive usage microdata provided directly by the paper's authors). Data contains information from November 2022 to May 2025.\nAccess date: June 2025 (Chapter 1); November 2025 (Chapter 4, provided by the paper's authors).\nFile location: \"Chapter 1\\1 - Data\\Raw\"; \"Chapter 4\\1 - Data\\Raw\".\nFile names: ChatGPT_extensive_intensive_202211_202505.dta.\nLiu, Yan, Jingyun Huang, and He Wang. 2025. \"Who on Earth Is Using Generative AI? Global Trends and Shifts in 2025.\" Policy Research Working Paper 11231. World Bank. http:\/\/hdl.handle.net\/10986\/43859. License: CC BY 3.0 IGO.","access_type":"Data access requires purchase or human approval and is not included in the reproducibility package.","license":"Proprietary (Semrush subscription terms)","license_uri":"https:\/\/www.semrush.com\/projects\/","uri":"https:\/\/www.semrush.com\/projects\/","citation":"Semrush. (2025). \"Generative AI Adoption Data\" [dataset]. https:\/\/www.semrush.com\/projects. Access date: June 2025."},{"name":"World Development Indicators ","note":"Source: World Bank, World Development Indicators. \nIndicators used across chapters: Individuals using the Internet, % of population (IT.NET.USER.ZS; Chapter 1 Figure 1.1, accessed June 11, 2026; Chapter 7 Figure 7.1 panel b, accessed May 2026); Access to electricity, % of population (EG.ELC.ACCS.ZS; Chapter 7 Figure 7.1 panel a, accessed May 2026); GDP, current US$ (NY.GDP.MKTP.CD; Chapter 2 Figure 2.4, accessed July 16, 2026); GDP per capita, PPP, current international $ (NY.GDP.PCAP.PP.CD; Chapter 8 Figure B8.2.1, accessed 2026); Population, total (SP.POP.TOTL; Chapter 2 Figure 2.4, accessed July 16, 2026); GDP per capita, PPP, constant 2021 international $ (NY.GDP.PCAP.PP.KD; Chapter 4 Figure 4.12 panel a diagnostic check, accessed 2025); and API pulls of Employment to population ratio, 15+ (SL.EMP.TOTL.SP.ZS), Population ages 15-64 (SP.POP.1564.TO), and Population, total (SP.POP.TOTL) via the code in Figure_4_11b.do (Chapter 4 Figure 4.12 panel b, accessed June 2026; stored as processed files EMP_SHR.dta, WAP.dta, POP.dta).\nFile location: \"Chapter 1\\1 - Data\\Raw\"; \"Chapter 2\\1 - Data\"; \"Chapter 7\\1 - Data\" (embedded in figure workbooks); \"Chapter 8\\1 - Data\"; \"Chapter 4\\1 - Data\"; \"Chapter 4\\1 - Data\\Processed\".\nFile names: API_IT.NET.USER.ZS_DS2_en_csv_v2_293302_June_11_2026.csv; API_NY.GDP.MKTP.CD_DS2_en_csv_v2_234.csv; API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_2869.csv; Figure 7.1 panel a.xlsx; Figure 7.1 panel b.xlsx; API_NY.GDP.PCAP.PP.KD_DS2_en_csv_v2_33608_July_15_2026.csv; EMP_SHR.dta; WAP.dta; POP.dta.","access_type":"Data is publicly available and included in the reproducibility package.","license":"Creative Commons Attribution 4.0 International License","license_uri":"https:\/\/data.worldbank.org\/summary-terms-of-use","uri":"https:\/\/data.worldbank.org\/indicator","citation":"World Bank. 2026. World Development Indicators [dataset]. World Bank. https:\/\/data.worldbank.org. Accessed May-July 2026."},{"name":"World Bank Country and Lending Groups ","note":"Source: World Bank country classification by income level. Versions used: FY2027 classification (CLASS_2026_07_01.xlsx; Chapter 1, Chapter 6; accessed July 1, 2026), FY2026 classification (CLASS_2025_07_02.xlsx; Chapter 2 Figure 2.4; accessed August 20, 2025), 2024 download based on 2023 GNI (CLASS.xlsx; Chapter 5 Figure 5.4), and historical classifications OGHIST based on 2025 GNI (OGHIST_2026_07_01.xlsx; Chapter 5, Chapter 8, Spotlight 7; accessed 2026); wb_income_fy26.csv (Spotlight 7). Chapter 4 includes two vintages: CLASS_2026_07_01.xlsx (FY2027; Figures 4.3b, 4.8, 4.10a, and 4.12a; accessed July 1, 2026) and CLASS_2025_10_07.xlsx (FY2026 vintage; accessed October 2025; likely the source of the income_full_fy26 variable in wageworker2020_24.dta used for the HIC\/Non-HIC split in Figure 4.7).\nFile location: \"Chapter 1\\1 - Data\\Raw\"; \"Chapter 2\\1 - Data\"; \"Chapter 5\\1 - Data\"; \"Chapter 6\\1 - Data\\Raw\"; \"Chapter 8\\1 - Data\"; \"Spotlight 7\\1 - Data\"; \"Chapter 4\\1 - Data\\Raw\".\nFile names: CLASS_2026_07_01.xlsx; CLASS_2025_07_02.xlsx; CLASS.xlsx; OGHIST_2026_07_01.xlsx; wb_income_fy26.csv; CLASS_2025_10_07.xlsx.","access_type":"Data is publicly available and included in the reproducibility package.","license":"Creative Commons Attribution 4.0 International License","license_uri":"https:\/\/data.worldbank.org\/summary-terms-of-use","uri":"https:\/\/datahelpdesk.worldbank.org\/knowledgebase\/articles\/906519-world-bank-country-and-lending-groups","citation":"World Bank. 2026. World Bank Country and Lending Groups [dataset]. World Bank. https:\/\/datahelpdesk.worldbank.org\/knowledgebase\/articles\/906519. Accessed July 1, 2026."},{"name":"World Population Prospects","note":"Source: Total population by sex (both sexes), World Population Prospects (dashboard), Population Division, Department of Economic and Social Affairs, United Nations. Median values, years 2021-2025. Used for Chapter 1, Figure 1.1.\nAccess date: January 13, 2026.\nFile location: \"Chapter 1\\1 - Data\\Raw\".\nFile names: Total_pop_model_based_est_Jan_13_2026.xlsx.","access_type":"Data is publicly available and included in the reproducibility package.","license":"Creative Commons Attribution 3.0 IGO","license_uri":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/igo\/","uri":"https:\/\/population.un.org\/wpp\/","citation":"United Nations, Department of Economic and Social Affairs, Population Division. 2026. World Population Prospects [dataset]. United Nations. https:\/\/population.un.org\/wpp\/. Accessed January 13, 2026."},{"name":"GenAI Exposure Data","note":"Source: Underlying data provided directly by the authors of Gmyrek, Viollaz, and Winkler (2026), Policy Research Working Paper 11328 and background paper for WDR 2026. Used for Chapter 1 Figure 1.3 (panels a and b; Excel data provided by the authors in April 2026) and Chapter 5 Figure 5.3 (country-level exposure .rds files). The corresponding reproducibility package is at https:\/\/reproducibility.worldbank.org\/catalog\/449. Chapter 4 additionally uses two extracts provided directly by the paper's authors: scatter_WDR_data.xlsx (cross-country GenAI exposure scores; Figure 4.3 panel b; accessed April 2026) and emp_by_4dISCO_v2.xlsx (employment by 4-digit ISCO-08 code, sheets \"HICs\"\/\"nonHICs\"; Figure 4.12 panel a; accessed July 2026). The paper is cited under two related identifiers: World Bank Policy Research Working Paper 11328 and ILO Working Paper 166 (https:\/\/doi.org\/10.54394\/00033147). The underlying individual-level LFS microdata used by Chapter 4 code is restricted and covered in a separate entry.\nAccess date: April 2026 (Chapter 1); 2025 (Chapter 5).\nFile location: \"Chapter 1\\1 - Data\"; \"Chapter 4\\1 - Data\\Raw\".\nFile names: Figure 1.3, panel a.xlsx; Figure 1.3, panel b.xlsx; scatter_WDR_data.xlsx; emp_by_4dISCO_v2.xlsx. ","access_type":"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":"Gmyrek, Pawel; Viollaz, Mariana; Winkler, Hernan. 2026a. \u201cDisruption without Dividend? How the Digital Divide and Task Differences Split GenAI\u2019s Global Impact.\u201d Policy Research Working Paper; 11328. "},{"name":"Expertise and Automation by GenAI Data ","note":"Source: Underlying data provided directly by the authors of Gmyrek, Jimenez, Winkler, and Yu (2026), background paper for World Development Report 2026: Artificial Intelligence. Used for Chapter 1, Figure 1.5.\nAccess date: Provided by the authors in April 2026.\nFile location: \"Chapter 1\\1 - Data\".\nFile names: Figure 1.5.xlsx.","access_type":"Data is included in the reproducibility package.","uri":"https:\/\/www.worldbank.org\/en\/publication\/wdr2026\/brief\/world-development-report-2026-background-papers","citation":"Gmyrek, Pawel, Hector Jimenez, Hernan Jorge Winkler, and Shu Yu. 2026. \"Expertise and Automation by GenAI.\" Background paper for World Development Report 2026: Artificial Intelligence. World Bank. Accessed April 2026.","license_uri":"https:\/\/www.worldbank.org\/ext\/en\/legal\/terms-conditions"},{"name":"Global Giants in the AI Supply Chain","note":"Source: Table taken from Frost, Rishabh, and Shreeti (2026), \"Global Giants in the AI Supply Chain,\" BIS Bulletin 122 (February), Graph 1.A. Only publicly listed AI companies are kept. Used for Chapter 2, Table 2.1.\nAccess date: 2026.\nFile location: \"Chapter 2\\3 - Output\".\nFile names: Table 2.1.xlsx.","access_type":"Data is publicly available and included in the reproducibility package.","license":"BIS Terms and conditions of use","license_uri":"https:\/\/www.bis.org\/terms_conditions.htm","uri":"https:\/\/www.bis.org\/publ\/bisbull122.pdf","citation":"Frost, Jon, Kumar Rishabh, and Vatsala Shreeti. 2026. \"Global Giants in the AI Supply Chain.\" BIS Bulletin 122 (February). Bank for International Settlements. https:\/\/www.bis.org\/publ\/bisbull122.pdf."},{"name":"World Economic Outlook Database","note":"Source: International Monetary Fund (IMF), World Economic Outlook (WEO) Database, nominal GDP (current prices, U.S. dollars), accessed via IMF DataMapper. Hyperscaler capex projections for 2026 added through desktop research (links in workbook). Used for Chapter 2, Figure 2.1.\nAccess date: May 2026.\nFile location: \"Chapter 2\\1 - Data\".\nFile names: Figure 2.1.xlsx.","access_type":"Data is publicly available and included in the reproducibility package.","license":"The Use of IMF Data","license_uri":"https:\/\/www.imf.org\/en\/About\/copyright-and-terms","uri":"https:\/\/www.imf.org\/external\/datamapper\/NGDPD@WEO\/OEMDC\/ADVEC\/WEOWORLD","citation":"International Monetary Fund. 2026. World Economic Outlook Database [dataset]. IMF. Accessed May 2026."},{"name":"Mineral Commodity Summaries 2026","note":"Source: U.S. Geological Survey (USGS), Mineral Commodity Summaries 2026. Chapters used: Gallium (p. 83), Rare Earths (p. 153), Tantalum (p. 187); 2025 production values are USGS estimates. Used for Chapter 2, Figure 2.3, panels a, b, and c.\nAccess date: March 9, 2026.\nFile location: \"Chapter 2\\1 - Data\".\nFile names: Figure 2.3, panel a.xlsx; Figure 2.3, panel b.xlsx; Figure 2.3, panel c.xlsx.","access_type":"Data is publicly available and included in the reproducibility package.","license":"Public domain (U.S. Government work)","license_uri":"https:\/\/www.usgs.gov\/information-policies-and-instructions\/copyrights-and-credits","uri":"https:\/\/pubs.usgs.gov\/periodicals\/mcs2026\/mcs2026.pdf","citation":"U.S. Geological Survey. 2026. Mineral Commodity Summaries 2026. USGS. https:\/\/pubs.usgs.gov\/periodicals\/mcs2026\/mcs2026.pdf. Accessed March 9, 2026."},{"name":"GitHub Innovation Graph","note":"Source: GitHub Innovation Graph, topics dataset (AI-related GitHub pushes by country and quarter, Q1-Q4 2025). Used for Chapter 2, Figure 2.4.\nAccess date: July 16, 2026.\nFile location: \"Chapter 2\\1 - Data\".\nFile names: topics.csv.","access_type":"Data is publicly available and included in the reproducibility package.","license":"Creative Commons CC0 1.0 Universal","license_uri":"https:\/\/github.com\/github\/innovationgraph\/blob\/main\/LICENSE.md","uri":"https:\/\/github.com\/github\/innovationgraph\/blob\/main\/data\/topics.csv","citation":"GitHub. 2026. GitHub Innovation Graph [dataset]. GitHub. https:\/\/github.com\/github\/innovationgraph. Accessed July 16, 2026."},{"name":"The Artificial Intelligence Index Report 2026 ","note":"Source: Data folder for the Artificial Intelligence Index Report 2026 (Stanford HAI), section \"1. Research and Development\". Used for Chapter 2, Figures 2.2 (fig_1.8.6.csv) and 2.5 (fig_1.1.8.csv).\nAccess date: April 2026.\nFile location: \"Chapter 2\\1 - Data\".\nFile names: fig_1.8.6.csv; fig_1.1.8.csv.","access_type":"Data is publicly available and included in the reproducibility package.","license":"Attribution-NoDerivatives 4.0 International","license_uri":"https:\/\/creativecommons.org\/licenses\/by-nd\/4.0\/?ref=chooser-v1","uri":"https:\/\/hai.stanford.edu\/ai-index\/2026-ai-index-report, https:\/\/drive.google.com\/drive\/folders\/1zJTOg0iR0j5SijCwFutwWvDt143lW277","citation":"Stanford Institute for Human-Centered Artificial Intelligence (HAI). 2026. The Artificial Intelligence Index Report 2026 [dataset]. Stanford University. https:\/\/hai.stanford.edu\/ai-index\/2026-ai-index-report. Accessed April 2026."},{"name":"Data on AI Models","note":"Source: Epoch AI, AI Models dataset (public table of AI models with country of developing organization and frontier-model flag). Used for Chapter 3, Figure 3.1, panels a and b.\nAccess date: June 2, 2026.\nFile location: \"Chapter 3\\1 - Data\".\nFile names: AI Models public table.csv.","access_type":"Data is publicly available and included in the reproducibility package.","license":"Creative Commons Attribution 4.0 International License","license_uri":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/","uri":"https:\/\/epoch.ai\/data\/ai-models","citation":"Epoch AI. 2026. \"Data on AI Models\" [dataset]. Epoch AI. https:\/\/epoch.ai\/data\/ai-models. Accessed June 2, 2026."},{"name":"Statistics of Common Crawl Monthly Archives","note":"Source: Common Crawl crawl statistics, distribution of URLs by primary language, crawl CC-MAIN-2026-08 (February 2026). \nUsed for Chapter 3, Figure 3.2, panel a. Original download file: languages.csv, copied into the figure workbook (sheet \"Raw\").\nAccess date: February 2026.\nFile location: \"Chapter 3\\1 - Data\".\nFile names: Figure 3.2 panel a.xlsx.","access_type":"Data is publicly available and included in the reproducibility package.","license":"Common Crawl Terms of Use","license_uri":"https:\/\/commoncrawl.org\/terms-of-use","uri":"https:\/\/commoncrawl.github.io\/cc-crawl-statistics\/plots\/languages.html","citation":"Common Crawl. 2026. Statistics of Common Crawl Monthly Archives - Distribution of Languages [dataset]. Common Crawl. Accessed February 2026."},{"name":"Open Training Datasets by Language and Type","note":"Source: Hugging Face data on open training datasets by language (multilingual and monolingual types), accessed via the OECD.AI Policy Observatory visualization. Original download file: data.csv, copied into the figure workbook (sheet \"Raw\"). Used for Chapter 3, Figure 3.2, panel b.\nAccess date: June 2026.\nFile location: \"Chapter 3\\1 - Data\".\nFile names: Figure 3.2 panel b.xlsx.","access_type":"Data is publicly available and included in the reproducibility package.","license":"OECD Terms & Conditions","license_uri":"https:\/\/www.oecd.org\/en\/about\/terms-conditions.html","uri":"https:\/\/oecd.ai\/en\/data?selectedArea=ai-models-and-datasets&selectedVisualization=open-training-datasets-by-language-and-type","citation":"OECD.AI Policy Observatory. 2026. Open Training Datasets by Language and Type (data from Hugging Face) [dataset]. OECD. Accessed June 2026."},{"name":"Lightcast Job Postings Data","note":"Source: Lightcast job postings data (proprietary, subscription-based). Restricted raw and firm-level pulls used across Chapter 4:\n(1) Raw global multi-country panel (100+ countries), used to build the country-level panels for Figure 4.7 (01_build_country_parquets.py); restricted to authorized users of the World Bank internal data environment. The pooled analysis panel derived from it (pooled_panel_target95_Qge_m10_with_emp20.parquet) likewise cannot be shared publicly.\n(2) Lightcast-derived MPS decile file, used as a task-exposure control in the preferred Figure 4.7 specification. File: mps_deciles_summary.csv.\n(3) SAR\/India firm-level job postings, 2020Q1-2025Q3, used for Figure B4.3.1. File: job_postings_merged.dta. Accessed June 2025.\n(4) Origin and harmonized company names derived from Lightcast for India and other SAR countries, supporting the fuzzy-matching step in Figure B4.3.1. Files: comany_name_harmonized.csv; sar_name_clean.csv.\nAll files are registered in the data hash report.\nFile location: \"Chapter 4\\1 - Data\\Figure_4_7\\Processed\"; \"Chapter 4\\1 - Data\\Data_B4.3.1\\raw\\job_post\"; \"Chapter 4\\1 - Data\\Data_B4.3.1\\raw\\key\".\nFile names: mps_deciles_summary.csv; job_postings_merged.dta; comany_name_harmonized.csv; sar_name_clean.csv.","access_type":"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.","license":"Proprietary (Lightcast)","uri":"https:\/\/lightcast.io\/products\/data\/overview","citation":"Lightcast. \"Job Postings Data\" [dataset]. https:\/\/lightcast.io\/products\/data\/overview. Accessed 2025-2026."},{"name":"Lightcast Job Postings - Author-Aggregated Data","note":"Source: Authors' aggregations of proprietary Lightcast job postings data; only these aggregates are shared, the raw data cannot be redistributed. Two aggregates are used:\n(1) Gen AI vacancies and % of total vacancies by income group, 2021-2024, as aggregated in the World Bank Digital Progress and Trends Report 2025 (Figure 5.3, panel b). Used for Chapter 3, Figure 3.3. File: Figure 3.3.xlsx. Access date: 2025.\n(2) Share of postings requiring AI skills by country income group and distribution of AI-related postings by 4-digit occupation in LMICs. Used for Chapter 4, Figure 4.9, panels a and b. File: Figure 4.9 panels a and b.xlsx. Access\/aggregation date: April 21, 2026.\nFile location: \"Chapter 3\\1 - Data\"; \"Chapter 4\\1 - Data\".\nFile names: Figure 3.3.xlsx; Figure 4.9 panels a and b.xlsx.","access_type":"Data is included in the reproducibility package","license":"The Figure 3.3 aggregates are reproduced from a World Bank publication (CC BY 3.0 IGO); underlying Lightcast data proprietary","uri":"https:\/\/lightcast.io\/products\/data\/overview","citation":"Authors' compilation. 2026. \"AI Job Postings Aggregates by Country Income Group\" [dataset]. Based on Lightcast Job Postings Data (https:\/\/lightcast.io\/products\/data\/overview) and the World Bank Digital Progress and Trends Report 2025. Accessed 2025-2026."},{"name":"AI Procurement Contracts","note":"Source: Authors' compilation. 2026. AI Procurement Contracts [dataset]. World Bank, World Development Report 2026 - Artificial Intelligence. A web scraper collected 6,127,564 procurement contracts from the World Bank, Open Contracting Partnership, and the EU Tenders Electronic Daily (TED) platform; DeepSeek and Gemini were used to classify contracts as AI-related, yielding 17,865 AI contracts with valid income group classification, further classified into tool categories. The cleaned dataset is in the process of being uploaded to the Development Data Hub. Intermediate\/raw classification files (s3_tagged_notices.csv; NewSet.xlsx; per_stage_tag_results.csv; Reclassified_Tools_v4.csv) are WDR 2026 team outputs, not public elsewhere; exact reproduction of the LLM classification is not guaranteed. Used for Chapter 5, Figures 5.2 and 5.7.\nAccess year: 2026.\nFile location: \"Chapter 5\\1 - Data\" and \"Chapter 5\\1 - Data\\Data Organized by Figure\\Procurement (Figures 5.2, 5.7)\".\nFile names: AI Procurement Contracts.xlsx; s3_tagged_notices.csv; NewSet.xlsx; per_stage_tag_results.csv; Reclassified_Tools_v4.csv.","access_type":"Data is forthcoming in the World Bank Development Data Hub.","uri":"Forthcoming https:\/\/datacatalog.worldbank.org","citation":"Authors' compilation. 2026. AI Procurement Contracts [dataset]. World Bank, World Development Report 2026 - Artificial Intelligence. Based on procurement notices from the World Bank, Open Contracting Partnership, and EU Tenders Electronic Daily."},{"name":"Datahub Country List ","note":"Source: Datahub core country-list dataset (country names with ISO 3166-1 alpha-2 codes), used to merge country identifiers when building the AI Procurement Contracts dataset. Used for Chapter 5, Figures 5.2 and 5.7.\nAccess year: 2026.\nFile location: \"Chapter 5\\1 - Data\\Data Organized by Figure\\Procurement (Figures 5.2, 5.7)\\Files to Re-Create AI Procurement Contracts\".\nFile names: filtered_data.csv.","access_type":"Data is publicly available and included in the reproducibility package.","license":"Open Data Commons Public Domain Dedication and License v1.0","license_uri":"https:\/\/opendatacommons.org\/licenses\/pddl\/","uri":"https:\/\/datahub.io\/core\/country-list","citation":"Datahub. 2026. Country List [dataset]. Datahub. https:\/\/datahub.io\/core\/country-list. Accessed 2026."},{"name":"International Survey on Revenue Administration","note":"Source: International Monetary Fund, ISORA latest published data, downloaded from the IMF data portal in late 2025. Used for Chapter 5, Figure 5.4.\nAccess date: November 2025.\nFile location: \"Chapter 5\\1 - Data\" (duplicated in \"Chapter 5\\1 - Data\\Data Organized by Figure\\ISORA (Figure 5.4)\").\nFile names: dataset_2025-08-25T16_24_03.921163402Z_DEFAULT_INTEGRATION_ISORA_ISORA_LATEST_DATA_PUB_4.0.0.csv.","access_type":"Data is publicly available and included in the reproducibility package.","license":"The Use of IMF Data","license_uri":"https:\/\/www.imf.org\/en\/About\/copyright-and-terms","uri":"https:\/\/data.imf.org\/en\/datasets\/ISORA:ISORA_LATEST_DATA_PUB","citation":"International Monetary Fund. 2025. International Survey on Revenue Administration (ISORA) [dataset]. IMF. https:\/\/data.imf.org\/en\/datasets\/ISORA:ISORA_LATEST_DATA_PUB. Accessed November 2025."},{"name":"Medical Imaging and Nuclear Medicine Global Resources Database","note":"Source: International Atomic Energy Agency (IAEA) IMAGINE database, radiology workforce availability across income groups. Used for Chapter 5, Figure 5.5, panels a and b.\nAccess date: March 1, 2026.\nFile location: \"Chapter 5\\1 - Data\" (duplicated in \"Chapter 5\\1 - Data\\Data Organized by Figure\\Radiologists (Figure 5.5)\").\nFile names: Radiologists.xlsx.","access_type":"Data is publicly available and included in the reproducibility package.","license":"IAEA Terms of Use","license_uri":"https:\/\/www.iaea.org\/about\/terms-of-use","uri":"https:\/\/www.iaea.org\/resources\/hhc\/nuclear-medicine\/databases\/imagine\/radiologists","citation":"International Atomic Energy Agency. 2026. IMAGINE - IAEA Medical Imaging and Nuclear Medicine Global Resources Database [dataset]. IAEA. Accessed March 1, 2026."},{"name":"AI and Data for Better Governance Survey","note":"Source: Data for Better Governance Team, World Bank. Anonymized results of the AI Data for Better Governance Survey as of June 17, 2026 (65 responses from 58 unique countries); internal World Bank survey, anonymized version in the process of being uploaded to the World Bank Microdata Library. Used for Overview, Chapter 5 (Figure 5.6), Chapter 8 (Figures 8.2, B8.1.1, 8.4, 8.5), and Chapter 9 (Figures 9.1, 9.5).\nAccess year: 2026.\nFile location: \"0 - Overview\\1 - Data\"; \"Chapter 5\\1 - Data\"; \"Chapter 8\\1 - Data\"; \"Chapter 9\\1 - Data\".\nFile names: anonymized_agency_survey.xlsx.","access_type":"Data is forthcoming in the World Bank Microdata Library.","uri":"Forthcoming at https:\/\/microdata.worldbank.org\/","citation":"World Bank, Data for Better Governance Team. 2026. AI and Data for Better Governance Survey [dataset]. World Bank Microdata Library (forthcoming)."},{"name":"EdTech Meta-Analysis Data ","note":"Source: Meta-analysis of EdTech in education interventions from Burneo, Dinarte-Diaz,  Lopez, and Molina. (2026), background paper for WDR 2026. One row per effect size estimate with standardized effect size, standard error, confidence intervals, intervention description, and study information. Used for Chapter 5, Figure B5.2.1.\nAccess year: 2026.\nFile location: \"Chapter 5\\1 - Data\" (duplicated in \"Chapter 5\\1 - Data\\Data Organized by Figure\\Tutoring Box (Figure B5.2.1)\" and \"Chapter 5\\1 - Data\\Figure_B5.2.1\\data\").\nFile names: meta_results.csv.","access_type":"Data is included in the reproducibility package.","uri":"https:\/\/www.worldbank.org\/en\/publication\/wdr2026\/brief\/world-development-report-2026-background-papers","citation":"Burneo, Dinarte-Diaz,  Lopez, and Molina. World Bank. 2026. EdTech Meta-Analysis Data [dataset]. Accessed 2026."},{"name":"BACI: International Trade Database at the Product-Level","note":"Source: CEPII, BACI international trade database, HS 2022 version, year 2024, with the accompanying country code crosswalk. Used for Chapter 6, Figures 6.1 and 6.2.\nAccess date: March 2026.\nFile location: \"Chapter 6\\1 - Data\\Raw\".\nFile names: BACI_HS22_Y2024_V202601.csv; country_codes_V202601.csv.","access_type":"Data is publicly available and included in the reproducibility package.","license":"Etalab 2.0","license_uri":"https:\/\/www.etalab.gouv.fr\/wp-content\/uploads\/2018\/11\/open-licence.pdf","uri":"https:\/\/www.cepii.fr\/DATA_DOWNLOAD\/baci\/doc\/baci_webpage.html","citation":"CEPII. 2026. BACI: International Trade Database at the Product-Level, HS22 version, 2024 [dataset]. CEPII. Accessed March 2026."},{"name":"Balanced Trade in Services dataset","note":"Source: WTO-OECD Balanced Trade in Services dataset; sectors \"Financial services\" and \"Insurance and pension services\", year 2024. Used for Chapter 6, Figure 6.2.\nAccess date: March 2026.\nFile location: \"Chapter 6\\1 - Data\\Raw\".\nFile names: WtoData_20260317180803.csv.","access_type":"Data is publicly available and included in the reproducibility package.","license":"WTO Terms of Use","license_uri":"https:\/\/www.wto.org\/english\/info_e\/copyrights_permissions_e.htm","uri":"https:\/\/www.wto.org\/english\/res_e\/statis_e\/gstdh_batis_e.htm","citation":"World Trade Organization and OECD. 2026. Balanced Trade in Services (BaTIS) [dataset]. Accessed March 2026."},{"name":"Federal Lobbying by Issue","note":"Source: OpenSecrets, Federal Lobbying by Issue, datasets for all top issues downloaded one by one for each year from 1998 to 2025. Used for Chapter 6, Figure 6.3. \nAccess date: 2026.\nFile location: \"Chapter 6\\1 - Data\\Raw\".\nFile names: lobbying_top_issues_1998.csv through lobbying_top_issues_2025.csv (28 files).","access_type":"Data is publicly available and included in the reproducibility package.","license":"Creative Commons Attribution-Noncommercial-Share Alike 3.0","license_uri":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/3.0\/deed.en","uri":"https:\/\/www.opensecrets.org\/federal-lobbying\/top-issues","citation":"OpenSecrets. 2026. Federal Lobbying by Issue [dataset]. OpenSecrets. https:\/\/www.opensecrets.org\/federal-lobbying\/top-issues. Accessed 2026."},{"name":"Pew Research Center Global Attitudes Survey, Spring 2025","note":"Source: Poushter et al. 2025, Pew Research Center Global Attitudes Survey, Spring 2025. Question 31 on feelings about increased use of AI in daily life. Used for Chapter 6, Figure 6.4.\nAccess date: July 2026.\nFile location: \"Chapter 6\\1 - Data\\Raw\".\nFile names: Pew Research Center Global Attitudes Spring 2025 Dataset CSV.csv. \nThe data can be downloaded directly from the link below after logging in; after that, the user should place the file in the appropriate folder to generate Figure 6.4.","access_type":"Data is publicly available but does not allow redistribution and is not included in the reproducibility package.","license":"Pew Research Center terms of use","license_uri":"https:\/\/www.pewresearch.org\/about\/terms-and-conditions\/","uri":"https:\/\/www.pewresearch.org\/dataset\/spring-2025-survey-data\/","citation":"Pew Research Center. 2025. Global Attitudes Survey, Spring 2025 [dataset]. https:\/\/www.pewresearch.org\/dataset\/spring-2025-survey-data\/. Accessed July 2026."},{"name":"Pew Research Center American Trends Panel Wave 152","note":"Source: Pew Research Center, American Trends Panel Wave 152 (latest AI wave available as of July 2026), used to add U.S. responses to the Spring 2025 Global Attitudes analysis. Used for Chapter 6, Figure 6.4.\nAccess date: July 2026.\nFile location: \"Chapter 6\\1 - Data\\Raw\".\nFile names: ATP W152.csv.\nThe data can be downloaded directly from the link below after logging in; after that, the user should place the file in the appropriate folder to generate Figure 6.4.","access_type":"Data is publicly available but does not allow redistribution and is not included in the reproducibility package.","license":"Pew Research Center terms of use","license_uri":"https:\/\/www.pewresearch.org\/about\/terms-and-conditions\/","uri":"https:\/\/www.pewresearch.org\/dataset\/american-trends-panel-wave-152\/","citation":"Pew Research Center. 2025. American Trends Panel Wave 152 [dataset]. Pew Research Center. Accessed July 2026."},{"name":"Global Findex Database 2024","note":"Source: World Bank Global Findex Database, 2024 values, accessed via Data360: Mobile phone ownership, 15+ (https:\/\/data360.worldbank.org\/en\/int\/indicator\/WB_FINDEX_CON1?SEX=_T&AGE=Y_GE15&URBANISATION=_T&COMP_BREAKDOWN_1=_T&COMP_BREAKDOWN_2=_T&COMP_BREAKDOWN_3=_T&view=datatable&average=incomeGroup) and Main mobile phone is a smartphone (https:\/\/data360.worldbank.org\/en\/int\/indicator\/WB_FINDEX_CON9A?view=datatable&minYear=2024&maxYear=2024&SEX=_T&AGE=Y_GE15&URBANISATION=_T&COMP_BREAKDOWN_1=_T&COMP_BREAKDOWN_2=_T&COMP_BREAKDOWN_3=_T&average=incomeGroup), income group aggregates. Used for Chapter 7, Figure 7.2, panel a.\nAccess date: July 2026.\nFile location: \"Chapter 7\\1 - Data\".\nFile names: Figure 7.2 panel a.xlsx.","access_type":"Data is publicly available and included in the reproducibility package.","license":"Creative Commons Attribution 4.0 International License","license_uri":"https:\/\/data.worldbank.org\/summary-terms-of-use","citation":"World Bank. 2024. Global Findex Database [dataset]. World Bank Data360. https:\/\/data360.worldbank.org\/en\/int\/indicator Accessed July 2026."},{"name":"Global Digital Inclusion 2024 Device Pricing Data","note":"Source: Global Digital Inclusion Partnership (GDIP). 2024. \"The Price of the World in Your Pocket: A Price Only Some Can Afford: 2024 Device Pricing.\" Excel file \"Device pricing data (Excel)\". Used for Chapter 7, Figure 7.2, panel b.\nAccess date: July 2026.\nFile location: \"Chapter 7\\1 - Data\".\nFile names: Figure 7.2 panel b.xlsx.","access_type":"Data is publicly available and included in the reproducibility package.","uri":"https:\/\/globaldigitalinclusion.org\/2024\/09\/10\/the-price-of-the-world-in-your-pocket-a-price-only-some-can-afford-2024\/","citation":"Global Digital Inclusion Partnership. 2024. 2024 Device Pricing [dataset]. GDIP. Accessed July 2026."},{"name":"OECD Survey of Adult Skills 2023","note":"Source: OECD (2024), \"Do Adults Have the Skills They Need to Thrive in a Changing World?: Survey of Adult Skills 2023,\" OECD Skills Studies. Country-level data on percentage of 35-44-year-olds by training sponsor type. Used for Chapter 7, Figure 7.4.\nAccess date: May 27, 2026.\nFile location: \"Chapter 7\\1 - Data\".\nFile names: Figure 7.4.xlsx.","access_type":"Data is publicly available and included in the reproducibility package.","license":"OECD Terms & Conditions","license_uri":"https:\/\/www.oecd.org\/en\/about\/terms-conditions.html","uri":"https:\/\/stat.link\/n4cktb","citation":"OECD. 2024. Do Adults Have the Skills They Need to Thrive in a Changing World?: Survey of Adult Skills 2023 [dataset]. OECD Skills Studies, OECD Publishing. Accessed May 27, 2026."},{"name":"Data Centers by Population","note":"Source: Combined dataset obtained directly from the authors of Straub et al. (2026), Infrastructure Foundations (World Bank, Sustainable Infrastructure Series). https:\/\/doi.org\/10.60572\/PY8T-MB68. Number of data centers from TeleGeography (proprietary, access year 2026); The map was created using the file Map 7.1.csv, which was created by aggregating the number of data centers by country using restricted data from the report and population from World Bank WDI, SP.POP.TOTL (publicly available, access year 2025). Map updated by the cartography team. Used for Chapter 7, Map 7.1. Aggregated data is included in the package. \nFile location: \"Chapter 7\\1 - Data\".\nFile names: Map 7.1.csv. \n","access_type":"Data access was granted directly to the study authors by the data owners\/managers. It was obtained with a custom data license that does not allow for redistribution, and it is not included in the reproducibility package. Aggregated intermediate data is included in the package. ","uri":"https:\/\/www2.telegeography.com\/","citation":"Telegeography. (2026). Data Centers by Population [dataset]. Unpublished data. "},{"name":"Teaching and Learning International Survey","note":"Source: OECD Teaching and Learning International Survey (TALIS) 2024, teacher questionnaire (ttgintt4.dta). Used for Chapter 7, Table 7.1 (items TT4G36, TT4G24G, TT4G21G) and Spotlight 3, Figures S3.1A and S3.1B (items TT4G35G, TT4G35H).\nAccess date: January 12, 2026.\nFile location: \"Chapter 7\\1 - Data\"; \"Spotlight 3\\1 - Data\".\nFile names: ttgintt4.dta.","access_type":"Data is publicly available and included in the reproducibility package.","license":"OECD Terms & Conditions","license_uri":"https:\/\/www.oecd.org\/en\/about\/terms-conditions.html","uri":"https:\/\/www.oecd.org\/en\/data\/datasets\/talis-2024-database.html","citation":"OECD. 2024. Teaching and Learning International Survey (TALIS) 2024 [dataset]. OECD. https:\/\/www.oecd.org\/en\/data\/datasets\/talis-2024-database.html. Accessed January 12, 2026."},{"name":"World Bank GovTech Maturity Index (GTMI)","note":"Source: World Bank GovTech Maturity Index database, December 2025 version. Used for Chapter 8, Figure 8.3.\nAccess year: 2026.\nFile location: \"Chapter 8\\1 - Data\" (duplicated in \"Chapter 8\\1 - Data\\Data Organized by Figure\\GTMI Database (Figure 8.3)\").\nFile names: WBG_GovTech_Dataset_Dec2025.xlsx.","access_type":"Data is publicly available and included in the reproducibility package.","license":"Creative Commons Attribution 4.0 International License","license_uri":"https:\/\/data.worldbank.org\/summary-terms-of-use","uri":"https:\/\/datacatalog.worldbank.org\/search\/dataset\/0037889\/govtech-dataset","citation":"World Bank. 2025. GovTech Dataset (GovTech Maturity Index) [dataset]. World Bank Data Catalog. Accessed 2026."},{"name":"AI-Mediated Access to Government Services Data","note":"Data was obtained from a forthcoming background paper. Jordan, Luke, Manuel Ramos-Maqueda, Tiago C. Peixoto, and Tapan Parikh. \"RADAR: Benchmarking AI-Mediated Access to Government Services.\" Background paper for WDR 2026. Ratings from four major LLMs across services in navigating online public service queries. Used for Chapter 8, Figure B8.2.1.\nAccess year: 2026.\nFile location: \"Chapter 8\\1 - Data\".\nFile names: scores.csv.","access_type":"Data is included in the reproducibility package.","uri":"https:\/\/www.worldbank.org\/en\/publication\/wdr2026\/brief\/world-development-report-2026-background-papers","citation":"Jordan, Luke, Manuel Ramos-Maqueda, Tiago C. Peixoto, and Tapan Parikh. Forthcoming. \"RADAR: Benchmarking AI-Mediated Access to Government Services.\" Background paper for World Development Report 2026: Artificial Intelligence. World Bank."},{"name":"GRIDMAP Data Markets Dashboard","note":"Source: Global Regulations, Institutional Development, and Market Authorities Perspective Toolkit (GRIDMAP): Data Markets Module, World Bank. Figure 9.3 uses the gap to Minimum Package (MP) by income group from the dashboard Overview plus MP values from the accompanying World Bank document; Figure 9.4 uses Survey responses (Pillar 2: Institutional Oversight, Category 6: Resources, Data Protection Authority budget question). Used for Chapter 9, Figures 9.3 and 9.4.\nAccess date: July 1, 2026.\nFile location: \"Chapter 9\\1 - Data\".\nFile names: figure_9.3.xlsx; figure_9.4.xlsx.","access_type":"Data is publicly available and included in the reproducibility package.","license":"Creative Commons Attribution 4.0 International License","license_uri":"https:\/\/data.worldbank.org\/summary-terms-of-use","uri":"https:\/\/www.worldbank.org\/en\/data\/interactive\/2025\/10\/07\/gridmap-data-markets-dashboard","citation":"World Bank. 2025. GRIDMAP Data Markets Dashboard [dataset]. World Bank. https:\/\/www.worldbank.org\/en\/data\/interactive\/2025\/10\/07\/gridmap-data-markets-dashboard. Accessed July 1, 2026."},{"name":"United Nations Governing AI for Humanity Report Data","note":"Source: United Nations. 2024. Governing AI for Humanity: Final Report. Data taken directly from Figure (a) (representation in seven non-UN international AI governance initiatives) and Figure 12 (Top 60 AI countries, 2023 Tortoise Index, in major plurilateral AI governance initiatives). Used for Chapter 9, Figure 9.6.\nAccess date: July 1, 2026.\nFile location: \"Chapter 9\\1 - Data\".\nFile names: figure_9.6.xlsx.","access_type":"Data is publicly available and included in the reproducibility package.","uri":"https:\/\/www.un.org\/sites\/un2.un.org\/files\/governing_ai_for_humanity_final_report_en.pdf","citation":"United Nations. 2024. Governing AI for Humanity: Final Report. United Nations. Accessed July 1, 2026."},{"name":"Energy and AI Dataset","note":"Source: IEA (International Energy Agency). 2025. Energy and AI. Data annex, \"Regional Data\" spreadsheet (Annex A, Table A.4: Data centres electricity consumption by region). Used for Spotlight 4, Figure S4.1.\nAccess date: April 2026.\nFile location: \"Spotlight 4\\1 - Data\" (revised copy with figure in \"Spotlight 4\\3 - Output\").\nFile names: Data_annex_Energy_and_AI.xlsx.\nThe data can be downloaded directly from the link below after logging in; after that, the user should place the file in the appropriate folder to generate Spotlight 4, Figure S4.1. The final numbers used to create the figure are included in the revised copy with the figure, only with the relevant numbers. \n","access_type":"Data is publicly available but does not allow redistribution and is not included in the reproducibility package.","license":"Terms of Use for Non-CC Material","license_uri":"https:\/\/www.iea.org\/terms\/terms-of-use-for-non-cc-material","uri":"https:\/\/www.iea.org\/data-and-statistics\/data-product\/energy-and-ai","citation":"International Energy Agency. 2025. Energy and AI Dataset [dataset]. IEA. https:\/\/www.iea.org\/data-and-statistics\/data-product\/energy-and-ai. Accessed April 2026."},{"name":"ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity","note":"Source: World Bank ASPIRE database, Social Insurance Coverage (% of total population), indicator PER_SI_ALLSI.COV_POP_TOT. Used for Spotlight 5, Figure S5.1.\nAccess date: May 12, 2026.\nFile location: \"Spotlight 5\\1 - Data\"\nFile names: API_PER_SI_ALLSI.COV_POP_TOT_DS2_en_excel_v2_2323.xls.","access_type":"Data is publicly available and included in the reproducibility package.","license":"Creative Commons Attribution 4.0 International License","license_uri":"https:\/\/data.worldbank.org\/summary-terms-of-use","uri":"https:\/\/data.worldbank.org\/indicator\/per_si_allsi.cov_pop_tot","citation":"World Bank. 2026. ASPIRE: The Atlas of Social Protection Indicators of Resilience and Equity [dataset]. World Bank. https:\/\/data.worldbank.org\/indicator\/per_si_allsi.cov_pop_tot. Accessed May 12, 2026."},{"name":"AI Agencies Institutional Frameworks Dataset","note":"Source: Authors' compilation. 2026. AI Agencies Institutional Frameworks Dataset [dataset]. World Bank, World Development Report 2026 - Artificial Intelligence. Based on systematic desktop research across 218 World Bank-recognized countries and territories to identify dedicated AI agencies, using various government agency websites (links documented in the \"Links\" tab of the workbook). Used for Spotlight 7, Figure S7.2.\nAccess year: 2026.\nFile location: \"Spotlight 7\\1 - Data\".\nFile names: Institutional_Framework.xlsx.","access_type":"Data is included in the reproducibility package.","citation":"Authors' compilation. 2026. AI Agencies Institutional Frameworks Dataset [dataset]. World Bank, World Development Report 2026 - Artificial Intelligence. Based on various government agency websites."},{"name":"World Bank Enterprise Surveys - AI Follow-up and Consolidated Data","note":"Source: World Bank Enterprise Surveys (WBES) Data Portal. Comprises (1) the 2026 AI Follow-up surveys for 10 economies (India, Jordan, Kenya, Malaysia, Mexico, Nigeria, Thailand, Turkiye, United States, plus the New Comprehensive file), listed under \"Other surveys\" conducted in 2026, and (2) consolidated WBES datasets from the \"Microdata across Economies\" tab (ES Indicators Database - Global Methodology; Firm Level Factor Ratios). Used across chapters for Figures O.2, 1.2, 1.4, 4.2 (a, b), 4.4, 4.5, 4.13 (a, b), and 7.3.\nAccess date: June 10, 2026.\nFile location: \"WBES AI Replication Package\\source\".\nFile names: India-2026-AI follow-up data.dta; Jordan-2026-AI follow-up data.dta; Kenya-2026-AI follow-up data.dta; Malaysia-2026-AI follow-up data.dta; Mexico-2026-AI follow-up data.dta; Nigeria-2026-AI follow-up data.dta; Thailand-2026-AI follow-up data.dta; Turkiye-2026-AI follow-up data.dta; United States-2026-AI follow-up data.dta; New_Comprehensive_June_4_2026.dta; ES-Indicators-Database-Global-Methodology_June_4_2026.dta; Firm Level Factor Ratios_June_4_2026.dta.","access_type":"Data is publicly available but does not allow redistribution and is not included in the reproducibility package.","license":"World Bank Enterprise Surveys Data Access Protocol","license_uri":"https:\/\/login.enterprisesurveys.org\/en\/terms","uri":"https:\/\/login.enterprisesurveys.org\/en\/signin","citation":"World Bank. 2026. World Bank Enterprise Surveys - AI Follow-up Surveys [dataset]. World Bank Enterprise Surveys Data Portal. https:\/\/www.enterprisesurveys.org. Accessed June 10, 2026."},{"name":"ILOSTAT Employment Indicators","note":"Three indicator pulls: (1) Employment by Sex and Status in Employment (EMP_2EMP_SEX_STE_NB_A), Figure 4.3 panel b, file EMP_2EMP_SEX_STE_NB_A-full-2026-06-06.dta, downloaded via https:\/\/rplumber.ilo.org\/data\/indicator?id=EMP_2EMP_SEX_STE_NB_A, accessed June 2026; (2) general 'Employment Indicators' used in Figure 4.6, accessed 2026, specific indicator code not stated; (3) Employment by Sex and Economic Activity (EMP_TEMP_SEX_EC2_NB_A), Figure 4.8, file EMP_TEMP_SEX_EC2_NB_A.csv, via ILOSTAT bulk download facility, accessed March 2026. File location: \"Chapter 4\\1 - Data\\Raw\".","access_type":"Data is publicly available and included in the reproducibility package.","license":"ILO Open Access Policy","license_uri":"https:\/\/www.ilo.org\/rights-and-permissions","uri":"https:\/\/ilostat.ilo.org\/data\/","citation":"International Labour Organization (ILO). 2026. \"ILOSTAT Database\" [dataset]. https:\/\/ilostat.ilo.org\/data\/. Accessed March-June 2026 (see note)."},{"name":"Anthropic Economic Index","note":"Anthropic Economic Index, September 2025 Release, accessed via Hugging Face. Used in Figure 4.6 panels a and b, combined into AI productivity gain estimates Figure 4.6.xlsx. File location: \"Chapter 4\\1 - Data\".","access_type":"Data is publicly available and included in the reproducibility package.","license_uri":"https:\/\/huggingface.co\/terms-of-service","uri":"https:\/\/huggingface.co\/datasets\/Anthropic\/EconomicIndex","citation":"Anthropic. 2025. \"Anthropic Economic Index\" [dataset]. September 2025 Release. https:\/\/huggingface.co\/datasets\/Anthropic\/EconomicIndex. Accessed 2025."},{"name":"GPTs are GPTs AI Exposure Scores","note":"Eloundou et al. (2024) LLM occupational exposure scores. The paper did not publish a ready-made results file; scores were reconstructed by re-running the authors' published code against their published inputs from GitHub. Used in: (1) Figure 4.6 as eloundou2024_score.dta, accessed 2026; (2) Figure B4.2.1 as the human_beta and gpt4_beta measures, sourced from GPTs_are_GPTs_paper_AI_exposure_data.csv. File location: \"Chapter 4\\1 - Data\".","access_type":"Data is publicly available and included in the reproducibility package.","license":"MIT License","license_uri":"https:\/\/github.com\/openai\/GPTs-are-GPTs\/blob\/main\/LICENSE","uri":"https:\/\/github.com\/openai\/GPTs-are-GPTs","citation":"Eloundou, T., Manning, S., Mishkin, P., and Rock, D. 2024. \"GPTs are GPTs: Labor Market Impact Potential of LLMs.\" Science 384(6702): 1306-1308. https:\/\/doi.org\/10.1126\/science.adj0998. Data reconstructed by authors from https:\/\/github.com\/openai\/GPTs-are-GPTs. Accessed 2026."},{"name":"Exposure to Generative AI","note":"Language Modeling and Image Generation AIOE\/AIIE indices, sourced from the AIOE-Data GitHub repo. Used in Figure B4.2.1 as felten2023_lm and felten2023_img. Files: Language Modeling AIOE and AIIE.xlsx (sheet 'LM AIOE'), Image Generation AIOE and AIIE.xlsx (sheet 'IG AIOE'). File location: \"Chapter 4\\1 - Data\".","access_type":"Data is publicly available and included in the reproducibility package.","uri":"https:\/\/github.com\/AIOE-Data\/AIOE","citation":"Felten, E. W., Raj, M., and Seamans, R. 2023. \"Occupational Heterogeneity in Exposure to Generative AI.\" SSRN Working Paper 4414065. https:\/\/doi.org\/10.2139\/ssrn.4414065. Accessed 2026."},{"name":"Occupational, Industry, and Geographic Exposure to AI","note":"Occupation-level AI exposure index used in Figure B4.3.1 (ai_exposure.dta). File location: \"Chapter 4\\1 - Data\\Data_B4.3.1\\raw\\ai_exp\".","access_type":"Data is publicly available and included in the reproducibility package.","citation":"Felten, E., Raj, M., and Seamans, R. 2021. \"Occupational, Industry, and Geographic Exposure to Artificial Intelligence: A Novel Dataset and Its Potential Uses.\" Strategic Management Journal 42(12): 2195-2217. Accessed November 2025."},{"name":"ILO Generative AI and Jobs","note":"Occupational exposure scores, ILO Working Paper 96. Not obtained directly from ILO; sourced from the reproducibility package of a separate World Bank paper (catalog https:\/\/reproducibility.worldbank.org\/catalog\/168, path RR_LAC_2024_168-v01\/...\/bases\/ILO\/Gmyrek_Berg_Bescond_scores_2023.csv). Used in Figure B4.2.1 as gbb2023_score. File location: \"Chapter 4\\1 - Data\".","access_type":"Data is publicly available and included in the reproducibility package.","license":"Modified BSD3","license_uri":"https:\/\/opensource.org\/license\/bsd-3-clause\/","uri":"https:\/\/reproducibility.worldbank.org\/catalog\/168","citation":"Gmyrek, P., Winkler, H., & Garganta, S. 2024. \"Reproducibility package for Buffer or Bottleneck? Generative AI, employment exposure and the digital divide in Latin America.\" World Bank. https:\/\/doi.org\/10.60572\/106F-PK97. Accessed 2026."},{"name":"ILO Refined Global Index of Occupational Exposure","note":"Used in: (1) Figure B4.2.1 as ilo25, manually extracted from the paper's Annex Table A1 ('ISCO-08 occupations by exposure gradient') since it is a PDF-embedded table with no downloadable file; (2) Figure 4.12 panel a as the basis for scenarios.xlsx tabs 'scenario_2.5%' and 'scenario_10%' (columns A-I), accessed April 2026 - columns J-L in that file are the WDR team's own scenario indicators calculated in Excel, not original ILO data. File location: \"Chapter 4\\1 - Data\".","access_type":"Data is publicly available and included in the reproducibility package.","license":"ILO Open Access Policy","license_uri":"https:\/\/www.ilo.org\/rights-and-permissions","uri":"https:\/\/www.ilo.org\/sites\/default\/files\/2025-05\/WP140_web.pdf","citation":"Gmyrek, P., Berg, J., Kaminski, K., Konopczynski, F., Ladna, A., Nafradi, B., Roslaniec, K., and Troszynski, M. 2025. \"Generative AI and Jobs: A Refined Global Index of Occupational Exposure.\" ILO Working Paper 140. https:\/\/www.ilo.org\/publications\/generative-ai-and-jobs-refined-global-index-occupational-exposure. Accessed April 2026."},{"name":"Labour Force Survey Microdata GenAI Exposure","note":"Individual-level labor force survey microdata provided by Gmyrek et al. (2026a). Two variants: All_countries_individuals.rds (Figure 4.12a, via WDR_emp_estimations_review.R) and All_countries_individuals_w_raw_sector.rds (Figure 4.8, via 01_lfs_data_collapse_country_isic.R, produces exposure_data_country_yr_isic2d.csv). Three further processed files derived from this microdata (exposure_2d.dta, exposure_2d_by_income.dta, exposure_2d_by_lmic.dta) are also excluded from the package per the Figure 4.8 README.","access_type":"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.","license":"Custom License","citation":"Gmyrek, Pawel; Viollaz, Mariana; Winkler, Hernan. 2026. \"Disruption without Dividend?\" [dataset]. Unpublished data. Accessed March 2026."},{"name":"Occupational Classification Crosswalk ","note":"US Bureau of Labor Statistics 2010 SOC-to-ISCO-08 crosswalk (sheet '2010 SOC to ISCO-08'), used in Figure B4.2.1 to align Felten (2023) and Eloundou (2024) measures (natively SOC 2010) to ISCO-08. File: isco_soc_crosswalk.xls. File location: \"Chapter 4\\1 - Data\". (Derived Stata file crosswalk_soc2010_to_isco08.dta is a processed output and does not need its own entry.)","access_type":"Data is publicly available and included in the reproducibility package.","license_uri":"https:\/\/www.bls.gov\/developers\/termsOfService.htm","uri":"https:\/\/www.bls.gov\/soc\/isco_soc_crosswalk.xls","citation":"U.S. Bureau of Labor Statistics. \"2010 SOC to ISCO-08 Crosswalk\" [dataset]. https:\/\/www.bls.gov\/soc\/isco_soc_crosswalk.xls. Accessed 2026.","license":"U.S. Bureau of Labor Statistics Terms of Service"},{"name":"ChatGPT Users per Internet User ","note":"Values on ChatGPT users per internet user by income group, taken directly from the paper's published figures\/tables (public, CC BY 3.0 IGO). Used in Figure 4.3 panel a, file Figure 4.3 panel a.xlsx. File location: \"Chapter 4\\1 - Data\". The underlying raw series was originally pulled by the paper's authors via Semrush's API (proprietary), but only the paper's already-published aggregate values are reused here.","access_type":"Data is publicly available and included in the reproducibility package.","license":"Creative Commons Attribution 3.0 IGO (CC BY 3.0 IGO)","license_uri":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/igo\/","uri":"http:\/\/hdl.handle.net\/10986\/43859","citation":"Liu, Yan; Huang, Jingyun; Wang, He. 2025. \"Who on Earth Is Using Generative AI? Global Trends and Shifts in 2025.\" Policy Research Working Paper 11231. World Bank. http:\/\/hdl.handle.net\/10986\/43859. Accessed March 2026."},{"name":"FactSet Firm-to-Firm International Relationship Data","note":"Firm-to-firm international relationship data for SAR countries, 2020Q1-2025Q3, used to identify GVC-supplier status of firms in the Lightcast job postings data. Files: IND_firm_International_AIExposure.dta and SAR_firm_International_AIExposure.dta. File location: \"Chapter 4\\1 - Data\\Data_B4.3.1\\raw\\factset\". Proprietary; not included.","access_type":"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.","license_uri":"https:\/\/www.factset.com\/legal","uri":"https:\/\/www.factset.com\/download","citation":"FactSet. \"Firm-to-Firm International Relationship Data\" [dataset]. https:\/\/www.factset.com\/download. Accessed November 2025."},{"name":"LLM-Classified Local and Multi-national Company Data","note":"Firm headquarters\/ownership classification (local vs. MNC affiliate) for firms in the Lightcast job postings data, generated via an LLM pipeline (Gemini 2.5 Flash-Lite, queried by firm name) identifying country of incorporation, primary country of operations, listing status, and foreign-ownership indicators. Files: IND_local_FULL_DETAILED_*.csv (14 dated files, Nov-Dec 2025) and SA_local_FULL_DETAILED_2026-01-29.csv. File location: \"Chapter 4\\1 - Data\\Data_B4.3.1\\raw\\mnc\\ind\"; \"Chapter 4\\1 - Data\\Data_B4.3.1\\raw\\mnc\\sar\". Proprietary (built from Lightcast firm names); not included.","access_type":"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.","license":"Custom License","citation":"Authors' compilation. 2025-2026. \"LLM-Classified Local and Multi-national Company Data\" [dataset]. Unpublished data. Based on Lightcast job postings data and Gemini 2.5 Flash-Lite LLM classification. Accessed November 2025-January 2026."},{"name":"Fuzzy-Matched Lightcast-FactSet Linking Data","note":"Fuzzy-matching results linking Lightcast firms to FactSet firms (MinHash candidate identification + string similarity + manual verification), for India and other SAR countries. Files: Fuzzy_merge_v2.xlsx and Fuzzy_merge_SAR.csv. File location: \"Chapter 4\\1 - Data\\Data_B4.3.1\\raw\\key\". Proprietary (built from two proprietary sources); not included.","access_type":"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.","license":"Custom License","citation":"Authors' compilation. 2025-2026. \"Lightcast-FactSet Fuzzy-Matched Firm Linkage Dataset\" [dataset]. Unpublished data. Based on Lightcast job postings data and FactSet firm relationship data."},{"name":"World Bank Digital Business Database","note":"World Bank 2024 Digital Business Database (Zhu et al. 2022), based on Crunchbase, CB Insights, Pitchbook, and Bitter Bridge, covering approximately 3,600 AI-related businesses in LMICs. File: digibiz_description.csv. Used in Figure 4.10 panel b. The classified output ('Category' column only) has separately been uploaded to the World Bank Development Data Hub as 'AI startup classification in developing countries' https:\/\/datacatalog.worldbank.org\/int\/data\/dataset\/0067142\/ai_startup_classification_in_developing_countries","access_type":"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.","license":"Custom License","citation":"Zhu, T. et al. 2022. \"World Bank Digital Business Database.\" Based on Crunchbase, CB Insights, Pitchbook, and Bitter Bridge. Unpublished data. Accessed 2025."},{"name":"Potential Growth Estimates ","note":"Potential growth estimates from Box 1.1 of Global Economic Prospects, June 2026. Values copied into AI productivity gain estimates Figure 4.6.xlsx ('Raw>>potential_growth'). Used in Figure 4.6 panel b. File location: \"Chapter 4\\1 - Data\".","access_type":"Data is publicly available and included in the reproducibility package.","license":"Creative Commons Attribution 4.0 International (CC BY 4.0)","license_uri":"https:\/\/www.worldbank.org\/en\/about\/legal\/terms-of-use-for-datasets","uri":"https:\/\/thedocs.worldbank.org\/en\/doc\/2b672b3b0415d6b66c45b66579db4ef5-0050012026\/related\/GEP-Jun-2026-Box-1-1.pdf","citation":"Kose, M. Ayhan, and Kersten Stamm. 2026. \"Box 1.1: How Much Will AI Affect Global Growth?\" In Global Economic Prospects, June 2026, 8-13. World Bank. https:\/\/thedocs.worldbank.org\/en\/doc\/2b672b3b0415d6b66c45b66579db4ef5-0050012026\/related\/GEP-Jun-2026-Box-1-1.pdf. Accessed 2026."},{"name":"UN Comtrade Database - AI-Related Trade Data","note":"Trade-flow data (exports\/imports, 2024, all reporters\/partners) queried using three sets of AI-related HS Commodity Codes, each identified from a separate published report: (1) wto_2024.csv - HS codes from WTO, World Trade Report 2025 'Making Trade and AI Work Together to the Benefit of All' (AI-enabling products), accessed July 7, 2026; (2) oecd_2024.csv - HS codes from OECD (2025) 'Mapping the Semiconductor Value Chain' Annex D, accessed July 7, 2026; (3) fed_2024.csv - HS codes from FED (2026) 'The Global Trade Effects of the AI Infrastructure Boom' Table 1, accessed July 7, 2026. Query settings held constant: Goods, Annual, HS classification, 2024 period, all reporters\/partners, World as 2nd partner, Exports and Imports flows. Used in Figure 4.10 panel a. UN Comtrade's terms do not permit redistribution of bulk\/raw extracted records, so these files have been removed from the reproducibility package.","access_type":"Data is publicly available but does not allow redistribution, and it is not included in the reproducibility package.","license":"UN Comtrade License Agreement","license_uri":"https:\/\/comtradeplus.un.org\/LicenseAgreement","uri":"https:\/\/comtradeplus.un.org\/","citation":"United Nations. 2026. \"UN Comtrade Database\" [dataset]. https:\/\/comtradeplus.un.org\/. HS codes per World Trade Organization, World Trade Report 2025; OECD, Mapping the Semiconductor Value Chain (2025); Federal Reserve, FEDS Notes (2026). Accessed July 7, 2026."},{"name":"Occupation and Income Classification Data","note":"Authors' estimates combining three shareable auxiliary inputs to Figure 4.7 (confirmed as pre-built inputs, not generated by any script in this package): (1) occupation_scores.dta - occupation-level AI exposure measures, including the AI substitution measure used for the treatment variable; contains fields AIsub, GBB, AIOE, CAIOE, GENOE1yr, GENOE5yr, GENOE10yr, read by 02_build_pooled_panel_for_replication.R lines 16\/64; (2) emp_occ1.csv - occupation-level employment weights by country\/year, read by 02_build_pooled_panel_for_replication.R lines 17\/65; (3) wageworker2020_24.dta - income group classification (variable income_full_fy26) used to split countries into HIC\/Non-HIC, read by 03_make_figure_4_7.R line 54, likely built from CLASS_2025_10_07.xlsx (see World Bank Country Income Classification entry). File location: \"Chapter 4\\1 - Data\\Figure_4_7\\Processed\".","access_type":"Data is included in the reproducibility package","citation":"Authors' estimates. 2026. \"Occupation and Income Classification Data\" [dataset]. Based on multiple occupational AI exposure indices (Gmyrek\/Berg\/Bescond and Felten et al. measures) and World Bank income classifications."}],"reproduction_instructions":"The package is organized as one sub-package per chapter\/spotlight, each with its own README describing data sources, code, and execution steps; a consolidated README at the root summarizes the full package. Most figures start from the raw or intermediary datasets included in each chapter's \"1 - Data\" folder; several figures are produced directly in Excel with no code involved.\n\nA new user will be able to reproduce the findings of the report by doing the following:\n\n**Chapter 1** (runtime: 2 minutes)\n1. Opening the `Rproj` under the Chapter 1 folder.\n2. Updating the project globals and running the Stata `fig 1.2` do file. \n\n**Chapter 2** (runtime: 4 minutes)\n1. Opening the `Rproj` under the Chapter 2 folder and running the R script.\n2. Updating the project globals and running the Stata `ffigures_2_2_2_5` do file. \n\n**Chapter 3** (runtime: 2 minutes)\n1. Opening the `Rproj` under the Chapter 3 folder and running the R scripts (`Figure_3.1_a`, `Figure_3.1_b`).\n\n**Chapter 4**\n1. Figures prepared in Excel (no code involved): Figures 4.3a, 4.6, and 4.9.\n2. Figure 4.3b: Updating the working directory in the do-file `Chapter 4\/2 - Code\/Figure 4.3 panel b`.\n3. Figure B4.2.1: Updating the working directory in the do-file `Figure B4.2.1` and the R script `Figure B4.2.1.Rmd`.\n4. Figure 4.7: Updating the working directory of the R script `Chapter 4\/2 - Code\/Figure_4_7\/03_make_figure_4_7`.\n5. Figure B4.3.1: Updating the working directory in the do-file `Chapter 4\/2 - Code\/Code_B4.3.1\/0master_do_file`.\n6. Figure 4.8: Updating the directory in the code files in `Chapter 4\/2 - Code\/Figure 4.8`.\n7. Figure 4.10a: Updating the working directory in the R script `Figure_4_10_panel_a\/prepare_data`.\n8. Figure 4.12a: Updating the working directory in the do-file `Figure_4_12_panel_a`.\n\n**Chapter 5** (runtime: 5 minutes)\n1. Adjusting the file paths in `Chapter 5 Full R Reproducibility.Rmd` and running the script. \n2. Adjusting the file paths in the `Radiologists Replication` do file to point to the local copy of the package, and running the do file. \n\n**Chapter 6** (runtime: 2 minutes)\n1. Opening the `.Rproj`, opening main.R, and running the code. \n\n**Chapter 7** (runtime: 2 minutes)\n1. Opening the do-file `Table_7_1`, changing the path, and running it.\n2. Reviewing the Excel-based figures.\n\n**Chapter 8** (runtime: 3 minutes)\n1. Opening the R project `Chapter 8`.\n2. Restoring the environment by running `renv::restore()` and following the prompts. \n3. Opening `Chapter 8 Full R Reproducibility.Rmd` and running the code.\n\n**Chapter 9** (runtime: 2 minutes)\n1. Opening the `Chapter 9.Rproj` and running the `main` R Script. \n\n**Spotlight 3** (runtime: 1 minute)\n1. Updating the working directory on line 1 of the `TALIS replication` do file and running it. \n\n**Spotlight 4**\n1. Figures prepared in Excel (no code involved).\n\n**Spotlight 5** (runtime: 1 minute)\n1. Updating the working directory in line 20 of the do-file `Figure S_5_1 replication` and running it. \n\n**Spotlight 7** (runtime: 2 minutes)\n1. Opening the R project `Spotlight 7.Rproj`.\n2. Restoring the environment by running `renv::restore()`, and following the prompts.\n3. Opening `AI_Agency_Income_Analysis.Rmd` and running the code. \n\n**WBES AI Replication Package** (runtime: 5 minutes)\n1. Updating the working directory at the beginning of each do-file and running `aies_replication_run` do.\n\nSince not all the data is included, the package includes the results produced by replicators. These files can be used to review the results presented in the report. ","technology_environment":"Report exhibits were reproduced on computers with the following specifications:\n\n1. **Computer 1: Chapter 4**\n   - OS: Windows 11 Enterprise\n   - Processor: Intel(R) Xeon(R) Platinum 8562Y+ 2.80 GHz (2 processors)\n   - Memory available: 32.0 GB\n   - Software versions: Stata 19.5 MP; R 4.6.1\n\n2. **Computer 2: Chapter 1, Spotlight 7, WBES AI Replication Package**\n   - OS: Windows 11 Enterprise\n   - Processor: Intel(R) Xeon(R) Platinum 8562Y+ 2.80 GHz (2 processors)\n   - Memory available: 32.0 GB\n   - Software versions: Stata 19.5 MP; R 4.5.3\n\n3. **Computer 3: Chapter 3, Chapter 6, Chapter 9**\n   - OS: Windows 11 Enterprise 25H2\n   - Processor: Intel(R) Xeon(R) Gold 6226R CPU @ 2.90 GHz (2 processors)\n   - Memory available: 16.0 GB\n   - Software versions: R 4.6.1 (run in RStudio)\n\n4. **Computer 4: Chapter 8**\n   - OS: Windows 11 Enterprise\n   - Processor: Intel(R) Xeon(R) Platinum 8562Y+ 2.80 GHz (2 processors)\n   - Memory available: 32.0 GB\n   - Software versions: R 4.6.1; Stata 19.5 MP\n\n5. **Computer 5: Overview, Chapter 2, Chapter 5, Chapter 7, Spotlight 3, Spotlight 5**\n   - OS: macOS Tahoe 26.6\n   - Processor: Apple M4 Pro\n   - Memory available: 24.0 GB\n   - Software versions: Stata 19.5 MP; R 4.6.1"},"datacite":{"creators":[{"givenName":"Gaurav","familyName":"Nayyar","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04"}]},{"givenName":"Anukriti","familyName":"Siddharth","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Sharmista","familyName":"Appaya","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Elwyn","familyName":"Davies","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Lelys","familyName":"Dinarte-Diaz","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Sam","familyName":"Fraiberger","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Yi Jie","familyName":"Gwee","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Yan","familyName":"Liu","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Jeremy","familyName":"Ng","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Tiago C.","familyName":"Peixoto","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Manuel","familyName":"Ramos-Maqueda","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Anja","familyName":"Sautmann","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Katherine","familyName":"Stapleton","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]},{"givenName":"Shu","familyName":"Yu","nameType":"Personal","affiliation":[{"name":"World Bank","affiliationIdentifier":"https:\/\/ror.org\/00ae7jd04","affiliationIdentifierScheme":"ROR","schemeUri":"https:\/\/ror.org"}]}],"titles":[{"lang":"en","title":"Reproducibility package for World Development Report 2026: The Promise of Artificial Intelligence"},{"title":"FR_WLD_2026_706","titleType":"Other"}],"publisher":"World Bank","publicationYear":"2026","types":{"resourceType":"Reproducibility package","resourceTypeGeneral":"Other"},"url":"https:\/\/reproducibility.worldbank.org\/index.php\/catalog\/study\/FR_WLD_2026_706","language":"en"},"tags":[{"tag":"DOI"},{"tag":"Open Code"},{"tag":"Restricted Data"}],"schematype":"script"}