Disasters impose large costs on developing economies. Following the initial shock of the disaster event, the immediate priority is to save lives and meet the basic needs of survivors in terms of food, clothing and shelter. In addition, infrastructure must be rebuilt and livelihoods re-established. These tasks must be completed as sustainably, equitably, and resiliently as possible.
The BinD model aids in the evaluation of pre-and post-disaster policy options by simulating dynamically alternative macroeconomic recovery pathways. Building on a structuralist macroeconomic tradition, the BinD model represents the importance of public expenditure, foreign exchange, and domestic savings in enabling sustained economic growth. Being demand-driven in normal times, it allows for the notion of a regime shift, that is, the possibility for an economy to operate within supply constraints in the event of a disaster.
Key features of the BinD model include:
The distinction of a supply vs. demand driven ‘regime’ is an important one, as our understanding of situation-dependent post-disaster economic operation allows for a more nuanced design of policy prescriptions.
Depending on the magnitude of a disaster shock and an economy’s capacity to absorb it, it may be more effective to adapt demand-side policies (e.g., government investment spending and household income transfers) or to follow a supply-side strategy (e.g., increasing foreign and domestic savings) in supporting disaster recovery.
Policy relevant insights from BinD
The BinD model may give insights on policy questions such as:
The BinD model is currently being used to analyze cyclone risk in Madagascar under the MACRO project.
Last edited: 21 February 2019
Mochizuki J & Naqvi A (2019). Reflecting Disaster Risk in Development Indicators. Sustainability 11 (4): e996. DOI:10.3390/su11040996.
Mochizuki J , Schinko T , & Hochrainer-Stigler S (2018). Mainstreaming of climate extreme risk into fiscal and budgetary planning: application of stochastic debt and disaster fund analysis in Austria. Regional Environmental Change 18 (7): 2161-2172. DOI:10.1007/s10113-018-1300-3.
Naqvi A (2017). Deep Impact: Geo-Simulations as a Policy Toolkit for Natural Disasters. World Development 99: 395-418. DOI:10.1016/j.worlddev.2017.05.015.
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