DIGNAD

The IMF recently created the Resilience and Sustainability Trust (RST), which helps low-income and vulnerable middle-income countries build resilience to external shocks, such as climate change, and ensure sustainable growth. One of the requirements for the successful deployment of RST funds is the assessment of funding needs to deal with such long-term challenges, including investments in resilience to natural disasters. Motivated by these challenges, the IMF’s Research Department has developed the DIGNAD (Debt-Investment-Growth and Natural Disasters) toolkit. The toolkit builds on the extension of the Debt, Investment and Growth model of Buffie et al. (2012) to natural disasters following Marto, Papageorgiou and Klyuev (2018) , allowing the user to run the model from an Excel interface. The model captures the challenges of closing infrastructure gaps in developing countries that frequently face natural disasters. In addition to permanent damages to public and private capital, natural disasters cause temporary losses of productivity, inefficiencies during the reconstruction process, and damages to the sovereign's creditworthiness. The toolkit enables users to evaluate debt sustainability risks following natural disasters amidst the need to rebuild public infrastructure through the lens of a rich general equilibrium structure. The model can also be used to analyze the effects of ex-ante policies, such as building adaptation infrastructure, increasing fiscal buffers, or improving public investment efficiency. The model can be calibrated using country-specific macroeconomic indicators, and users can additionally calibrate the size and timing of natural disasters, and the various mechanisms through which they affect macroeconomic aggregates. The toolkit will therefore be of use to economists and policymakers looking to develop tailored analysis of the macro-fiscal impacts of natural disasters and investments in resilience. DIGNAD has become a workhorse model in the IMF to study the effects of climate risk due to natural disasters and how investments in adaptation infrastructure can help mitigate these risks. Most recently, DIGNAD-based analyses were published in the Rwanda and Bangladesh staff reports accompanying the request for an arrangement under the RST facility.