**EV3)** Roe and Just (2009) define the concept of _ecological validity,_ which is useful in this context: “a study has ecological validity to the extent that the context in which subjects cast decisions is similar to the context of interest.” (p1267) In the context of this broader external validity, we may ask “can the consumption model be applied to other countries than Ethiopia?” A slightly narrower and more tractable initial question can be attempted: “is the specified model valid across Ethiopia?” We do conclude however that it is unlikely to be possible to derive a valid model for pastoralists using the crop loss parameters, given their reliance on grazing rather than crop yields. In one of the specifications, the regression model will also explicitly treat agro‐climatic region as an attenuating factor, testing whether the relationship between crop loss and consumption will not hold across regions. This is consistent with the findings of Hsiang and Narita (2012), which suggest that the efficacy of adaptive behaviour to weather shocks varies with average climate conditions.

# **<mark>Box 2.2: Impact Amplification</mark>**

<mark>The limited number of survey years available for the regression poses a number of challenges. One such challenge is the impossibility of capturing impacts arising from the severity of the seasonal drought on a national or regional scale, due to the very small number of data points available to describe this across the two</mark> examined years.

<mark>In the context of catastrophe risk models, these impacts are captured as</mark> **<mark>post‐event loss amplification</mark>** <mark>, or</mark> **<mark>demand surge</mark>**<sup>**15**</sup> <mark>. For the purposes of the exercise presented herein, the equivalent would be impact</mark> amplification.

<mark>Claims data from large catastrophes in developed insurance markets shows that the cost of a given level of damage increases when that damage is situated within a catastrophe of such severity as to have a significant impact at the national or regional scale. In the context of building damage, modelling firm RMS (Souch, C,</mark> 2010) describes the drivers of this loss amplification from the perspective of insurers as being:

- <mark>Economic demand surge leading to inflated costs. This arises from an imbalance in supply and demand for key materials and labour required for recovery and reconstruction due to the extent of the disaster footprint.</mark>

- <mark>Deterioration vulnerability leading to damage escalation. This arises from delays in addressing the impacts from the disaster.</mark>

- <mark>Claims inflation leading to increased payouts from insurers. This arises from a relaxation in insurers’ claims assessment procedures due to the scale of the task.</mark>

- <mark>Coverage expansion leading to increased payouts from insurers. This can arise from political pressure for insurers to honour claims that would not have qualified for payouts under the terms of the insurance</mark> contracts in place at the time of the event.

<mark>These impacts are tied to the overall scale of the event, rather than the local intensity used to calculate damage in the vulnerability methodology laid out in this paper, and as such, require data from a variety of catastrophes of different magnitudes in order to derive factors for amplifying losses/impacts. Although we are not looking through the lens of insurance, a number of aspects of post‐event loss amplification as described</mark> above are relevant to our analysis, and as such are disclosed here as limitations.

<mark>For example, in the aftermath of a large drought, impacts on food prices (and other commodities for which demand has increased), can be expected. This can impact household consumption. Changes in the labour market should also be considered, as a large drought event may push a large number of households to turn to alternative employment as a coping mechanism. This could disrupt the balance of supply and demand for certain types of labour, impacting the effectiveness of this option as a coping mechanism. A large drought may also prompt a political response that influences household consumption in a way not seen in the historical</mark> dataset used to derive damage functions.

<mark>These non‐local factors are complex and would be difficult to capture, even with a more comprehensive dataset. However, the fact that the impact on a household is influenced by the scale of a disaster, as well as by its local intensity, is indisputable, and not accounted for in the methodology</mark> <u><mark>presented in this paper.</mark></u>

> 15 In some models, demand surge is taken as a sub‐component of post‐event loss amplification

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