Malawi, where only information on planted area losses was elicited, drought identification is relatively homogenous within farms. Sample sizes are rather small though.<sup>54</sup> Table 12 confirms the importance of drought intensity for the uniformity of shock reports at the EA level through bivariate regressions. While there is a significant association between plot-level (Q1 or Q2) and community shock reports (Q4) across rounds, the explanatory power of this simple model is very low except, again, for ESS15 where drought was most intense.<sup>55</sup> Conversely, in Malawi, the association between community and plot-level reports is significant in all rounds but IHPS16 but the R<sup>2</sup> is low throughout.

_Table 12_ : Regressions of community-reported drought exposure on plot-level drought reports.

||(1)|(2)<br>Ethiopia|(3)|(4)|(5)<br>Ma|(6)<br>lawi|(7)|
|---|---|---|---|---|---|---|---|
|VARIABLES|ESS18|ESS15|ESS13|IHPS19|IHPS16|IHPS13|IHPS10|
|Drought damage|0.118**|0.572***|0.237***|||||
||(0.0668)|(0.0532)|(0.0848)|||||
|Drought area loss (maize)|0.209|0.0646|0.108|0.381***|0.0364|0.185*|0.405***|
||(0.129)|(0.0675)|(0.113)|(0.144)|(0.106)|(0.108)|(0.0901)|
|Constant|0.0734***|0.202***|0.115***|0.209***|0.524***|0.382***|0.231***|
||(0.0251)|(0.0353)|(0.0316)|(0.0605)|(0.0872)|(0.0728)|(0.0559)|
|Observations|3,090|5,558|5,623|1,575|1,062|975|991|
|R-squared|0.044|0.349|0.069|0.039|0.001|0.009|0.160|

Note: OLS regressions of community-reported drought exposure (last 2 calendar years) on plot-level crop loss reports due to drought. Ethiopia: Sorghum and maize plots. Malawi: Maize plots. Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

A drought is a slow-onset disaster with no objective benchmark (or “start point”) for the community congregation. Furthermore, high drought salience (e.g. as observed on surrounding vegetation, based on conversations in the community, or the news) may prime farmers to attribute crop losses to drought. Similarly, the intensity of the shock may matter for the idiosyncrasy in loss occurrence across plots (e.g. soil with superior water-retention capacity may be able to buffer for little rain on some plots _to some extent_ ) and the clear attribution of crop losses to drought as opposed to other reasons. Therefore, idiosyncratic variation in self-reported drought exposure is

> 54 As noted previously, shock identification of the plot-level proxies is conditional on there being some crop production loss. More precisely, we are thus comparing the consistency of loss occurrence due to drought between plots. This leaves the chance that disagreement between plots is because some plots may not record a loss in the first place since they are less exposed to drought (e.g. due to irrigation or superior soil quality). We assume that this distinction is less relevant after excluding irrigated plots from the analysis and particularly in years with severe drought but investigate this possibility in more detail in the following section.

> 55 Importantly, community reports use yearly (in Ethiopia corresponding to a period between mid-September and mid-September) reporting which means time periods are imperfectly aligned with seasonal patterns in agriculture. We therefore combine community shock reports for the current and previous year. Our main conclusions are robust to the use of only current year or previous year shock reports and indeed correlations are even lower in these cases.

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