Climate Change

Despite external and domestic shocks, Zambia has corrected macroeconomic imbalances, reached agreement on most of the external debt under the perimeter of the debt restructuring, and undertaken sustained fiscal consolidation while safeguarding social spending. Economic activity has expanded in 2025 but at a slower pace than initially expected. Despite trending down amid a sharp kwacha appreciation, inflation remains elevated. The strong reform drive under the ECF arrangement has improved international investors’ confidence including through recent upgrades of the sovereign credit rating. Poverty and inequality remain high, and the economic outlook is vulnerable to external shocks, climate events, and domestic political pressures ahead of the 2026 elections.

This CD Guidance Note provides a one-stop source of information and reference materials on Fund CD-related policies, practices, and procedures. In line with the Management Implementation Plan (MIP) on the Independent Evaluation Office’s (IEO) 2022 Evaluation of CD, this Guidance Note supersedes the 2019 IMF Policies and Practices on Capacity Development and serves to operationalize the recommendations of the 2024 CDSR. It also integrates relevant earlier guidance to staff related to CD delivery and management.

The Philippines is highly exposed to natural hazards, which are increasingly intensified by climate change, yet quantitative studies on the macroeconomic effects of climate shocks in the country remains limited. This paper finds that category-5 typhoons exert inflationary pressures on regional headline and food CPI, by around 0.4 percent and 0.7 percent respectively, with the peak impact occurring approximately one quarter after the typhoon hits. Additionally, category-5 typhoons lower regional GDP by approximately 0.4 percent on impact (or by 0.2-0.3 percent of aggregate GDP), while agricultural labor productivity declines by 2.5 percent. These estimates show that typhoons act as adverse supply shocks, particularly in the agriculture sector, likely raising inflation while dampening economic activity and further posing a dilemma for monetary policy. We then use the IMF’s Global Dynamic Network (GDN) model to construct counterfactual scenarios to assess the importance of sectoral heterogeneity in shaping output and inflation outcomes and examine alternative monetary policy response functions, helping inform the central bank on tradeoffs between supporting output and containing inflationary pressures. Finally, we use the DIGNAD model to simulate the impacts of natural disasters and analyze the policy trade-offs involved in enhancing resilience to natural disasters and the macro-fiscal implications of various policy options for the Philippines.

Recovery is underway with improving fiscal balance, declining debt, and well-contained inflation, though FY2024 real GDP remained below pre-pandemic levels and the debt level is still high. The 2023 Compact Review Agreement (CRA-23) and a successful tax reform have provided Palau with additional fiscal space to navigate long-term challenges, including adverse demographic shifts and climate vulnerability.

The Direction of Planning (DNP) of the Ministry of Finance has sought Technical Assistance from ICD to help develop capacity for public debt projections and analysis. Pre-scoping and pre-mission meetings revealed that there was no single unit responsible for producing public debt projections. It was agreed that capacity across the MOF would be developed using the Debt Dynamics Tool (DDT), involving extensive virtual engagements, asynchronous pre-mission work, and a single in-person mission from October 7 to 11. This note summarizes the main findings and recommendations.

The absence of reliable data on fundamental economic indicators (e.g. real GDP), combined with structural shifts in the economy, can severely constrain the ability to conduct accurate macroeconomic analysis and forecasting. This paper explores alternatives to address data limitations by integrating machine learning and satellite data to estimate real GDP. Specifically, it finds that incorporating satellite-based nightlight data into a random forest model significantly improves the accuracy of quarterly GDP growth estimates compared with models relying solely on traditional indicators. This empirical application contributes to the emerging nowcasting field to enhance economic forecasting in economies with significant data gaps.