Our study faces several limitations. The valuation of losses relies on human reporting which is subject to human error, respondents’ incentives, misreporting, and misperceptions. The data allows for a detailed analysis of disaster impacts on crop production, but other aspects of disaster impacts on agriculture are absent. This concerns, for example, damages to agricultural assets, storage losses, or impacts on livestock. Further, the data is representative of the household sector in study countries but misses commercial and larger farms. This implies that we are likely underestimating the full extent of disaster crop losses. As the analysis is focused on agriculture, we do not discuss damages incurred in other sectors.

# Methods

## A. LSMS-ISA microdata

We use plot-level survey data from the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) in Ethiopia, Malawi, Mali, Niger, Nigeria, and Tanzania. The LSMS-ISA comprise a series of harmonized, national, multi-topic household panel surveys with a distinct focus on agriculture. We harmonized data on agricultural outputs, inputs, and plot characteristics for close to 30,000 households over the six countries for a total of 18 survey waves collected between 2008 and 2019. The combined dataset contains over 120,000 plot observations. More specifically, the dataset includes data from the Ethiopian Social Survey (waves 1 to 4), Malawi’s Integrated Household Panel Survey (waves 1 to 4), Mali’s _Enquête Agricole de Conjoncture Intégrée_ (waves 1 and 2), Niger’s _Enquête National sur les Conditions de Vie des Ménages et Agriculture_ (waves 1 and 2), Nigeria’s General Household Survey (wave 4) and Tanzania’s National Panel Survey (waves 1 to 5). Households are selected to be representative of the population at the national and sub-national level using a two-stage stratified sampling design <mark>with census enumeration areas (EAs) as primary sampling units and households as secondary sampling units.</mark> Households are then tracked through time, except for Mali which only tracks enumeration areas (EAs). Each survey wave covers an agricultural production cycle or season.

## B. EM-DAT aggregate data

We compare survey estimates to country-level data from is the Emergency Event Database (EM-DAT). EMDAT is the preeminent public database taking stock of shocks on a global scale and widely used for research and to inform policy<sup>52</sup> . Both natural (e.g., geophysical, meteorological) and technological (e.g., industrial accidents) events are recorded, along with information on disaster damages valued in 2022 USD. The EM-DAT compiles information from a broad range of sources including insurance companies, international organizations, press agencies and governmental agencies. Disasters are recorded if they provoke 10 or more deaths, affect 100 or more people (injured/ homeless/ in need of immediate assistance) or are accompanied by an official declaration of emergency or appeal for international assistance.<sup>29</sup>

## C. Variable construction

## _Valuation of crop production_

Plot-level crop production was calculated using survey variables which were collected after the harvest in each season and country. Farmers report harvest quantities for each seasonal crop grown on each plot.

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