AI Readiness, Industrialisation and Sustainable Development in Africa: The Moderating Role of Government Effectiveness

Description

This dataset is a balanced cross-country panel covering annual observations from 2020 to 2024. It links digital transformation and economic development by focusing on AI readiness and its association with industrial performance, institutional quality, and sustainable development outcomes. The data helps compare how countries prepare for AI adoption while tracking conventional macroeconomic and social indicators during the same period. Country and time identifiers • cid: Unique numeric country identifier for sorting and processing. • countryname: Official country name. • regionname: Geographic region classification (e.g., Sub-Saharan Africa, Middle East & North Africa). • incomelevelname: World Bank-style income group (e.g., Low Income, Lower Middle Income, Upper Middle Income). • year: Observation year, from 2020 to 2024. AI readiness indicators (independent variables) • ai: Overall AI Readiness Score capturing national preparedness to adopt and deploy AI (strategy, ecosystem maturity, data readiness). • govai: Government pillar, measuring public-sector commitment to AI (strategy, regulation, governance, ethics frameworks). • techai: Technology pillar, capturing technology-sector maturity (innovation capacity, skills/human capital, startup and firm ecosystem). • dataai: Data & infrastructure pillar, capturing foundations for AI (connectivity, telecom quality, digital infrastructure, data representativeness). Institutional and economic metrics • goveff: Government effectiveness indicator reflecting public service quality, civil service capacity, and policy design/implementation. • ind: Industrial value added, measuring industrial sector output (manufacturing, mining, construction, utilities), typically as a share of GDP. • sdg: SDG Index Score (0–100) capturing overall progress toward UN Sustainable Development Goals. • gdppc: GDP per capita, proxy for average living standards and economic well-being. • trade: Trade openness, exports plus imports of goods and services as a share of GDP, indicating global market integration.

Steps to reproduce

Step 1: Indicator sources used in the merged dataset • ai, govai, techai, dataai: Oxford Insights, Government AI Readiness Index (overall score and pillar scores). • goveff: World Bank, Worldwide Governance Indicators (Government Effectiveness). • ind: World Bank, World Development Indicators (industry value added series used in the file). • sdg: Sustainable Development Report (SDG Index score). • gdppc: World Bank, World Development Indicators (GDP per capita series used in the file). • trade: World Bank, World Development Indicators, Trade (% of GDP). Step 2: Check missingness • Import the CSV and declare the panel: xtset cid year (or tsset cid year). • Confirm missing cells: 34 missing cells out of 4,620 total cells = 0.74% overall missingness. • For modelling variables, missingness was concentrated in trade only: 14 missing trade values out of 220 country-year observations = 6.36% (<7%). • Do not impute identifiers/classifiers (adminregion/adminregionname remained missing where originally absent). Step 3: Targeted, income-matched imputation (requested approach) • Burundi (Low income): for each year (2020-2024), compute the Low income mean of trade excluding Burundi; replace trade only where Burundi is missing. • Nigeria (Lower middle income): for each year (2020-2024), compute the Lower middle income mean of trade excluding Nigeria; replace trade only where Nigeria is missing. • Rationale: preserves income-comparable trade levels and avoids borrowing from higher-income distributions. Step 4: Linear regression-based interpolation/extrapolation for the remaining gaps • For any remaining country with missing trade, fit within-country OLS: trade = a + b*year using that country’s non-missing years only. • Predict trade_hat for the missing years and replace trade only where missing (this is linear interpolation/extrapolation in time). • This step applied to the few remaining gaps only: Lesotho (2024), Malawi (2024), Togo (2023-2024). Step 5: Post-imputation validation • Assert trade has no missing values after imputation; confirm all other variables were unchanged. • Re-check trade summary by year and by income group to ensure replacements sit within reasonable group-year ranges. • Save as a new file to keep the raw dataset intact.

Institutions

• Asia Pacific University of Technology & Innovation

  Kuala Lumpur, Kuala Lumpur
• Universiti Putra Malaysia

  Selangor, Seri Kembangan

Categories

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