Abstract
ABSTRACT
Natural hazards can adversely impact society, environment, and human well‐being. The development of mitigation strategies is typically based on estimates of economic losses, but such estimates are difficult when high‐resolution post‐disaster data are scarce. Models such as the input–output model have traditionally served as tools for disaster footprint analysis, although their reliance on equilibrium assumptions and limited treatment of production‐capacity adjustment can constrain their application to short‐run natural‐hazard impacts. This study applies a dynamic sequential interindustry model (DSIM), combining recent developments in dynamic input–output analysis with regression‐based coefficient estimation, to examine conditional temporal output gaps after the 2015 South India Flood. Using monthly industrial production indicators and a two‐region aggregation of the 2015 Indian MRIO table, we investigate aggregate economic propagation between Tamil Nadu and the broader Indian economy. The results should be interpreted as model‐implied, conditional scenario estimates rather than independently validated measurements of the full economic cost of the flood. Under these assumptions, the estimated national output gap is approximately 1.6 times the highest available direct‐damage estimate, suggesting that aggregate interregional spillovers may be policy‐relevant even when only limited post‐disaster data are available.