Regional disaster impact analysis: comparing Input-Output and Computable General Equilibrium models

Abstract

Abstract. A large variety of models has been developed to assess the economic losses of disasters, of which the most common ones are Input-Output (IO) and Computable General Equilibrium (CGE) models. In addition, an increasing numbers of scholars has developed hybrid approaches; one that combines both or either of them in combination with non-economic methods. While both IO and CGE models are widely used, they are mainly compared on theoretical grounds. Few studies have compared disaster impacts of different model types in a systematic way and for the same geographical area, using similar input data. Such a comparison is valuable from both a scientific and policy perspective as the magnitude and the spatial distribution of the estimated losses are likely to vary with the chosen modelling approach (IO, CGE, or hybrid). Hence, regional disaster impact loss estimates resulting from a range of models facilitates better decisions and policy making. Therefore, in this study we analyze one specific case study, using three regional models: two hybrid IO models and a regionally calibrated version of a global CGE model. The case study concerns two flood scenarios in the Po-river basin in Italy. Modelling results indicate that the difference in estimated total (national) economic losses and the regional distribution of those losses may vary by up to a factor of seven between the three models, depending on the type of recovery path. Total economic impact, comprising all Italian regions, is negative in all models though.

Wider economic impacts of heavy flooding in Germany: a non-linear programming approach

“…Our outcomes of adding both assumptions also confirm the outcomes of Koks et al (2015). They compare the regional and national disaster impacts of two flooding scenarios for the Italian Po River delta, as estimated with, respectively, the adaptive regional input-output (ARIO) model developed by Hallegate (2008), a regionalized version of the CGE model developed by Standardi, Bosello, and Eboli (2014), as applied by Carrera, Standardi, Bosello, and Mysiak (2015), and the multiregional impact assessment (MRIA) model of Koks and Thissen (2016).…”

“…Estimates of the direct economic losses are lacking in Koks et al (2015). Hence, we cannot compare our disaster multipliers with theirs.…”

Global economic footprint of the COVID-19 pandemic

“…Constraints on productive capital. Similar to labour constraints, the productive capacity of industrial capital in each region during the aftermath of a disaster ( ) will be constrained by the surviving capacity of the industrial capital 25, [33][34][35][36] . The share of damage to each sector is directly considered as the proportion of the monetized damage to capital assets in relation to the total value of industrial capital for each sector, which is disclosed in the event account vector (EAV) for each region ( ), following 37 .…”

Economic Impacts of Irrigation-Constrained Agriculture in the Lower Po Basin

“…The productivity shock offers aggregated information at a NUTS2 level and can be used as an input to conduct simulations in regionally calibrated macroeconomic models such as the MRIA model (Koks et al 2015). …”

“…IO models, on the other hand, reflect the economic interdependencies between sectors and regions within an economy, through intermediate supply and final demand, based on linear relations (Koks et al 2015). Linear IO models applied to water typically combine input-output matrices and water accounts and apply a structural decomposition analysis that disaggregates the effects of a given shock on economic outputs and water use at different levels, from urban ) to multi-regional (Wan et al 2016).…”