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Mr. Marcos d Chamon, Erik Klok, Mr. Vimal V Thakoor, and Mr. Jeromin Zettelmeyer
This paper compares debt-for-climate swaps—partial debt relief operations conditional on debtor commitments to undertake climate-related investments—to alternative fiscal support instruments. Because some of the benefits of debt-climate swaps accrue to non-participating creditors, they are generally less efficient forms of support than conditional grants and/or broad debt restructuring (which could be linked to climate adaptation when the latter significantly reduces credit risk). This said, debt-climate swaps could be superior to conditional grants when they can be structured in a way that makes the climate commitment de facto senior to debt service; and they could be superior to comprehensive debt restructuring in narrow settings, when the latter is expected to produce large economic dislocations and the debt-climate swap is expected to materially reduce debt risks (and achieve debt sustainability). Furthermore, debt-climate swaps could be useful to expand fiscal space for climate investment when grants or more comprehensive debt relief are just not on the table. The paper explores policy actions that would benefit both debt-climate swaps and other forms of climate finance, including developing markets for debt instruments linked to climate performance.
International Monetary Fund. African Dept.
Seychelles’ economic recovery in 2021 vastly outperformed projections, fueled by a faster-than-expected rebound of the tourism sector. The recovery is expected to continue in 2022 with projected real GDP growth of 7.1 percent as the tourism sector shows resilience to COVID-19 waves and geopolitical tensions. The recovery has been accompanied by a significant fiscal overperformance.
Mr. Sakai Ando and Mr. Taehoon Kim
Forecasting a macroframework, which consists of many macroeconomic variables and accounting identities, is widely conducted in the policy arena to present an economic narrative and check its consistency. Such forecasting, however, is challenging because forecasters should extend limited information to the entire macroframework in an internally consistent manner. This paper proposes a method to systematically forecast macroframework by integrating (1) conditional forecasting with machine-learning techniques and (2) forecast reconciliation of hierarchical time series. We apply our method to an advanced economy and a tourism-dependent economy using France and Seychelles and show that it can improve the WEO forecast.