How Military Expenditure Conditions the AI Readiness-Sustainable Development Nexus in Africa - Dataset

This dataset is a cleaned cross-country panel covering African countries over 2020 to 2024, with the 2025 SDG score retained only for the forward-looking robustness test. It links artificial intelligence readiness, military expenditure and sustainable development performance, while also tracking macroeconomic controls that capture industrial structure, income level and trade openness. The dataset supports country-level comparison of how African economies prepare for AI adoption and how this readiness relates to SDG outcomes under different military-expenditure conditions. The .csv file can be used for the replication in Stata, while the .xlsx file contain all details below: Country and time identifiers: • cid: Unique numeric country identifier for sorting and panel estimation. • countryname: Official country name. • year: Observation year. The main estimation period is 2020 to 2024, while 2025 is retained only for the two-year-ahead SDG robustness check. AI readiness indicators: • ai: Overall AI Readiness Score, capturing national preparedness to adopt and deploy AI through government capacity, technology-sector maturity, and data/infrastructure readiness. • govai: Government AI readiness pillar, measuring public-sector preparedness for AI adoption through strategy, governance and ethics, digital capacity, and adaptability. • techai: Technology-sector AI readiness pillar, capturing the strength of the domestic technology ecosystem, including sector maturity, innovation capacity and human capital. • dataai: Data and infrastructure AI readiness pillar, capturing the enabling foundations for AI deployment through infrastructure, data availability and data representativeness. Security, institutional and economic indicators • milex: Military expenditure as a percentage of GDP, capturing defence-related expenditure relative to national economic output. • lnmilex: Natural logarithm of military expenditure, used in the main empirical models and interaction terms. • ind: Industry value added as a percentage of GDP, capturing the contribution of industry, including manufacturing, mining, construction, electricity, water and gas, to national output. • sdg: SDG Index Score, ranging from 0 to 100, capturing overall country-level progress toward the United Nations Sustainable Development Goals. • gdppc: GDP per capita in constant 2015 US dollars, used as a proxy for income level and broader economic-development capacity. • lngdppc: Natural logarithm of GDP per capita. • trade: Trade as a percentage of GDP, measuring the sum of exports and imports of goods and services relative to national output. • lntrade: Natural logarithm of trade openness. Mapping and visualisation variables: • mean_ai_z: Country-level mean z-score for AI readiness, used for the Datawrapper map. • mean_milex_z: Country-level mean z-score for military expenditure, used for the Datawrapper map. • mean_sdg_z: Country-level mean z-score for SDG performance, used for the Datawrapper map.