Latest publication
Preparing African Economies for Transformative AI: Three Essential Policy Reforms
Yolanda Lannquist and Frank Adu
September 10, 2026
How can African policymakers prepare their economies for transformative AI? Many African economies face AI’s disruptions from a position of structural vulnerability: large informal sectors, thin social safety nets, skills and infrastructure gaps, and strained fiscal systems. AI narrows the development pathways many have relied on, as both labor-intensive manufacturing and services-led growth face automation pressure. The authors propose three mutually reinforcing reform areas that help African economies capture AI’s benefits, regardless of how its trajectory unfolds: developing human capital, modernizing social protection, and reforming tax systems. These reforms build resilience, and countries can sequence them differently based on their unique contexts.
Welfare Resilience Assessment Framework
Avinash Kothuri, Raphael Gregorian and Deric Cheng
August 17, 2026
This paper introduces the Welfare Resilience Assessment Framework, a diagnostic framework for welfare policymakers. It serves as a structured method to assess how prepared a country's welfare system is for AI-driven economic disruption.
We set out four dimensions of welfare-relevant risk, the policy levers available to address risks from each dimension, and the enabling conditions that determine whether those levers function in practice.
Welfare in the AI Transition: Brazil Case Study
Taiye Chen, Raphael Gregorian and Deric Cheng
April 24th 2026
This case study examines whether Brazil’s welfare system can protect workers from AI-related disruption.
We find that Brazil can identify and pay households at population scale through CadÚnico, Pix and Bolsa Família, but lacks an already established system linking displaced workers to income support and retraining.
We identify reforms Brazil can make now, alongside the institutional and fiscal changes needed for a more durable response.
Welfare in the AI Transition: Kenya Case Study
Emma Kimani, Raphael Gregorian and Deric Cheng
April 24th 2026
This case study examines whether Kenya’s welfare system can protect households from AI-related disruption in an informal economy where changes in earnings are largely invisible to government systems.
We find strong payment and emergency-response capacity, but weaker systems for identifying need.
We identify the measures Kenya can take now, and the more ambitious forms of income protection currently available.
Mapping Tax Risks From Labour-Displacing AI
Trish Ieong, Akbar Saputra, Anuja Maniar and Deric Cheng
April 24th 2026
This paper addresses an emerging fiscal risk for tax policymakers: the potential for powerful, productivity-enhancing AI systems to erode tax bases through labour displacement.
We identify four channels through which fiscal pressures may arise, simulate scenarios for an ‘average OECD country’ under various assumptions, and examine country-specific factors likely to produce different results across jurisdictions.
Finally, we offer some high-level suggestions for governments to start preparing now.
