forcing them to reduce consumption (or non‐reconstruction‐related investments). The reduction in consumption can then have second‐order consequences on other businesses that will see reduced demand and may respond by cutting on production and employment, magnifying the negative impact of the shock further (and affecting firms and households that have not been directly affected).

A last benefit of insurance is linked to the predictability of post‐disaster support. Government support is often necessary to some extent (for instance, after Katrina, the U.S. Federal government partly replaced insurance for households that had no flood insurance). But when it is ad hoc, government support creates inefficient uncertainty for economic actors (e.g., because, after an event, they cannot known the exact scope of the government support they will receive) and can lead to moral hazard (e.g. if households know that they will be compensated by government, they will have little incentive to reduce their own risks or to pay for insurance). International support (grants or goods provided to affected people) can help reconstruction, but (in addition to the moral hazard issue) is very volatile and unpredictable.

# **Box 3 – The current and potential effect of insurance in Mumbai**

We can use the ARIO model to investigate the benefits of insurance by exploring the sensitivity of indirect losses to the insurance penetration rate (γ) assumed in the model. In ARIO, the insurance penetration affects the response to the shock through two mechanisms: first, if the insurance penetration of households is low, they have to pay for their reconstruction (either directly, or by getting into debt and then paying off later), and reduce their consumption to so. Second, if the insurance penetration of firms is low, firms have to pay for reconstruction, reducing their profits. As a portion of these profits normally goes to local households, this affects the household budget, also reducing their consumption.

The box figure illustrates the effect of insurance penetration<sup>13</sup> on the household’s budget, for a July 2005 like flood estimated using the ARIO model. Three scenarios are included: (i) γ=0, equivalent to the absence of insurance system, but with an access to credit; (ii) the current value of flood insurance penetration estimated by RMS (γ=0.08 for households, γ=0.15 for firms); and (iii) γ=1, representing the situation where all the reconstruction is paid for by insurance. Where the budget is positive in the figure, it can be interpreted as the additional savings of households, and where negative it represents debt. When insurance penetration is large, households make savings after the disaster. This occurs because in this case, households are rationed and consume less than what they would like to and as a result, they involuntarily save money. Variations in household budget affect the local demand, as households in debt reduce their consumption. This decrease in demand affects local production. Total output losses is reduced from $455 million in the absence of insurance to $425 million with the current insurance penetration to $265 million if all losses are covered by insurance.

> 13 It is important to note that our “insurance penetration rate” does not represent the fraction of the total amount of houses that is insured (as is often done), but the fraction of the total value of goods that is insured (never equal to one in the real world, because of ceilings or deductibles).

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