Modeling the role of inventories and heterogeneity in the assessment of the economic costs of natural disasters

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      <subfield code="a">Modeling the role of inventories and heterogeneity in the assessment of the economic costs of natural disasters</subfield>
      <subfield code="c">Stéphane Hallegatte</subfield>
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      <subfield code="a">Estimates of the cost of potential disasters, including indirect economic consequences, are an important input in the design of risk management strategies. The adaptive regional input-output (ARIO) inventory model is a tool to assess indirect disaster losses and to analyze their drivers. It is based on an input-output structure, but it also (i) explicitly represents production bottlenecks and input scarcity and (ii) introduces inventories as an additional flexibility in the production system. This modeling strategy distinguishes between (i) essential supplies that cannot be stocked (e.g., electricity, water) and whose scarcity can paralyze all economic activity; (ii) essential supplies that can be stocked at least temporarily (e.g., steel, chemicals), whose scarcity creates problems only over the medium term; and (iii) supplies that are not essential in the production process, whose scarcity is problematic only over the long run and are therefore easy to replace with imports. The model is applied to the landfall of Hurricane Katrina in Louisiana and identifies two periods in the disaster aftermath: (1) the first year, during which production bottlenecks are responsible for large output losses; (2) the rest of the reconstruction period, during which bottlenecks are inexistent and output losses lower. This analysis also suggests important research questions and policy options to mitigate disaster-related output losses.</subfield>
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      <subfield code="a">Desastres naturales</subfield>
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      <subfield code="a">Gerencia de riesgos</subfield>
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      <subfield code="a">Valoración de daños</subfield>
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      <subfield code="a">Impacto económico</subfield>
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      <subfield code="a">Reconstrucción</subfield>
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      <subfield code="t">Risk analysis : an international journal</subfield>
      <subfield code="d">McLean, Virginia : Society for Risk Analysis, 1987-2015</subfield>
      <subfield code="x">0272-4332</subfield>
      <subfield code="g">13/01/2014 Volumen 34 Número 1 - enero 2014 , p. 152-167</subfield>
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