asked at the most disaggregated level and specifically in relation to crop losses in the agricultural season of interest. At the same time, they come with the caveat that they cannot discern idiosyncrasy that is due to differences in famers’ perception from idiosyncrasy in shock reports due to actual differences in highly localized loss occurrence. The latter matters if there are differences in shock exposure between plots, for example because of differences in soil quality (Iizumi and Wagai, 2019) or the adoption of improved varieties of the same crop (Lombardi _et al._ , 2019).

Furthermore, the LSMS-ISA data contains a question whether the household was negatively affected by a number of shocks, including drought, in the last 12 months (Q3). Lastly, the community module of the LSMS-ISA data features a question where a congregation of knowledgeable community members, roughly corresponding to the enumeration area, are asked about the four most significant adverse events in the last 3 years<sup>50</sup> (Q4).<sup>51</sup>

# <u>What do we see in the data?</u>

The most common shocks in this study’s region are rain-related (droughts, irregular/heavy rains, and floods) with some expected variation in incidence between the different proxies ( Table A 8 and Table A 9 ). On an ordinal scale though, they broadly agree and capture similar variation between years and crops. Most notably, proxies consistently identify a large-scale drought for the 2015/16 harvesting season in Ethiopia as well as Malawi. Between the different proxies, the question on reasons for plant productivity reductions seems to pick up shocks most sensitively and leads to the highest incidences.<sup>52</sup> In the following, we study the consistency of shock identification at different levels of aggregation.<sup>53</sup> At the farm level, if drought damage (Q2) is recorded on any plot, this is usually also the case on the remaining plots with the same crop (Table A 10). Particularly in 2015 when drought was most severe in Ethiopia, about eight in ten households with more than one maize plot and nine in ten households with more than one sorghum plot record drought damage on all plots planted with the crop (conditional on at least one plot identifying some drought damage). In

> 50 Ethiopia uses a different calendar that does not align with the Gregorian calendar. “Years” thus roughly correspond to a time period from September to September.

> 51 Household and community reports are what Sohnesen (2020) describes as “socio-economic” drought indicators, i.e. they may relate to a loss of consumption rather than necessarily an agricultural or meteorological drought. Compared to plot-level farmer reports, they have the drawback of not defining shocks in relation to a clear benchmark (e.g. a loss of production) which may cause farmers to define a drought differently and leaves room for endogeneity in responses (e.g. hardship is rationalized ex-post by reporting a drought) (Sohnesen, 2020).

> 52 In Ethiopia, it is also notable that loss incidences are higher for sorghum than maize. Using GIS rainfall data for the past 12 months and comparing this to the 14-year average, it seems like this pattern is explained by sorghum being more frequently planted in regions prone to adverse rainfalls.

> 53 Since only the data in Ethiopia contains all four shock proxy questions (most importantly the damage question which seems most sensitive) it is the focus of our discussion. However, the main conclusions presented are robust across both countries and for the use of different shock proxies than crop damage where applicable. In particular, this should also alleviate concerns that the differences in farmers’ shock identification we observe based on crop damage are because of a different prioritization of loss reasons across farms (farmers may only provide one reason for crop damage but two for area-based losses and the household-level question is asked for all households).

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