{"type":"script","doc_desc":{"producers":[{"name":"Reproducibility WBG","abbr":"DIME","affiliation":"World Bank - Development Impact Department","role":"Verification and preparation of metadata"}],"prod_date":"2025-03-11","version":"1"},"project_desc":{"authoring_entity":[{"name":"Pierre Mandon","affiliation":"World Bank","email":"pmandon@worldbank.org"}],"title_statement":{"title":"Reproducibility package for Beyond The AI Divide: A Simple Approach To Identifying Global And Local Overperformers In AI Preparedness","idno":"RR_WLD_2025_284"},"data_statement":"All data sources are publicly available but not all are included in the reproducibility package. (Accessible Data)","software":[{"name":"R","version":"4.4.0"},{"name":"Stata","version":"Version 18 MP"}],"scripts":[{"title":"Reproducibility package for Beyond The AI Divide: A Simple Approach To Identifying Global And Local Overperformers In AI Preparedness","date":"2025-03","notes":"Computational reproducibility verified by Development Impact (DIME) Analytics team, World Bank.","instructions":"See README in reproducibility package.","notes.1":"Computational reproducibility verified by Development Impact (DIME) Analytics team, World Bank.","file_name":"RR_WLD_2025_284","zip_package":"RR_WLD_2025_284.zip","dependencies":"Stata dependencies are listed in the ado folder. R dependencies are saved in the renv folder."}],"repository_uri":[{"name":"Reproducible Research Repository (World Bank)","uri":"https:\/\/reproducibility.worldbank.org"}],"production_date":"2025-03-11","abstract":"This paper examines global disparities in artificial intelligence preparedness, using the 2023 Artificial Intelligence Preparedness Index developed by the International Monetary Fund alongside the multidimensional Economic Complexity Index. The proposed methodology identifies both global and local overperformers by comparing actual artificial intelligence readiness scores to predictions based on economic complexity, offering a comprehensive assessment of national artificial intelligence capabilities. The findings highlight the varying significance of regulation and ethics frameworks, digital infrastructure, as well as human capital and labor market development in driving artificial intelligence overperformance across different income levels. Through case studies, including Singapore, Northern Europe, Malaysia, Kazakhstan, Ghana, Rwanda, and emerging demographic giants like China and India, the analysis illustrates how even resource-constrained nations can achieve substantial artificial intelligence advancements through strategic investments and coherent policies. The study underscores the need for offering actionable insights to foster peer learning and knowledge-sharing among countries. It concludes with recommendations for improving artificial intelligence preparedness metrics and calls for future research to incorporate cognitive and cultural dimensions into readiness frameworks.","geographic_units":[{"name":"World","code":"WLD"}],"keywords":[{"name":"Ai Preparedness"},{"name":"Economic Complexity"},{"name":"Peer Learning"},{"name":"Policy Overperformance"}],"topics":[{"id":"F63   ","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","name":"Economic Development","parent_id":"F6"},{"id":"H11","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","parent_id":"H1","name":"Structure, Scope, and Performance of Government"},{"id":"O33","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","parent_id":"O3","name":"Technological Change: Choices and Consequences \u2022 Diffusion Processes"},{"id":"O38","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","vocabulary":"Journal of Economic Literature (JEL)","parent_id":"O3","name":"Government Policy"}],"output":[{"type":"Working Paper","description":"Policy Research Working Papers (PRWP) WPS11073","title":"Beyond The AI Divide: A Simple Approach To Identifying Global And Local Overperformers In AI Preparedness","uri":"http:\/\/documents.worldbank.org\/curated\/en\/099517502242572646","doi":"https:\/\/doi.org\/10.1596\/1813-9450-11073"}],"language":[{"name":"English","code":"EN"}],"technology_requirements":"The code takes approximately 15 minutes to run.","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":"Pierre Mandon","affiliation":"World Bank","email":"pmandon@worldbank.org"},{"name":"Reproducibility WBG","affiliation":"World Bank","email":"reproducibility@worldbank.org"}],"datasets":[{"name":"Artificial Intelligence Preparedness Index (AIPI) database ","note":"Source: IMF AI Preparedness Index (AIPI). Date Accessed: November 2024 . Datasets: imf-dm-export-*.xls (5) ","uri":"https:\/\/www.imf.org\/external\/datamapper\/datasets\/AIPI#:~:text=AI%20Preparedness%20Index%20(AIPI)%20assesses,integration%2C%20and%20regulation%20and%20ethics ","license_uri":"https:\/\/www.imf.org\/en\/About\/copyright-and-terms#data","access_type":"Data is publicly available and included in the reproducibility package."},{"name":"Economic Complexity Indexes, for Trade, Technology and Research ","uri":"https:\/\/oec.world\/en\/rankings\/eci\/hs6\/hs96","note":"Source:  The Observatory of Economic Complexity. Date Accessed: December 2024. Datasets: Data-ECI-*.csv (3). Instructions: Download and place in the rawData folder ","license_uri":"https:\/\/oec.world\/en\/resources\/terms","access_type":"Data is publicly available but does not allow redistribution."},{"name":"Database from Sala-i-Martin et al. (2004) \"Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach\" ","uri":"https:\/\/www.openicpsr.org\/openicpsr\/project\/116024\/version\/V1\/view","note":"Source: Sala-I-Martin, Xavier, Doppelhofer, Gernot, and Miller, Ronald I. Replication data for: Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach. Nashville, TN: American Economic Association [publisher], 2004. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-12-06. https:\/\/doi.org\/10.3886\/E116024V1. Date Accessed: December 2024. Datasets: BACE_data.xls ","license_uri":"https:\/\/www.openicpsr.org\/openicpsr\/project\/116024\/version\/V1\/view?path=\/openicpsr\/116024\/fcr:versions\/V1\/LICENSE.txt&type=file","access_type":"Data is publicly available and included in the reproducibility package."},{"name":"World Bank Official Boundaries ","uri":"https:\/\/datacatalog.worldbank.org\/search\/dataset\/0038272\/World-Bank-Official-Boundaries","note":"Source: World Bank Development Data Hub. Date Accessed: December 2024. Datasets: wb_countries_admin0_10m.shp, wb_countries_admin0_10m.dbf.","access_type":"Data is publicly available and included in the reproducibility package.","license_uri":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/","license":"Creative Commons Attribution 4.0"},{"name":"World Development Indicators","note":"Data Accessed: December 2024. Indicator: Total population. Instructions: Downloadable directly from Stata through the command \"wbopendata, indicator(SP.POP.TOTL) clear\" included in the Do_AiPI_PCA_ECI.do script.","access_type":"Data is publicly available and included in the reproducibility package."}],"reproduction_instructions":"To run the package:\n* Open the do-file \"Do_AiPI_PCA_ECI\" file, change directory paths and run the script\n* Open the do-file \"Do\\_AIPI_&_BMA\" file, change the directory paths, and run the script\n* Open the code.Rproj file\n* Open the script.R file and run the script","technology_environment":"\u2013 OS: Windows 10 Enterprise\n\u2013 Processor: Intel(R) Xeon(R) Gold 6132 CPU @ 2.60GHz (2 processors)\n\u2013 Memory available: 32 GB\n\u2013 Software version: Stata 18.0 MP, R 4.4."},"tags":[{"tag":"Accessible Data"},{"tag":"DOI"},{"tag":"Open Code"}],"schematype":"script"}