a shock occurred, this variation in farmers’ shock identification, particularly when drought was not severe, suggests that there is either non-negligible idiosyncrasy in shock identification or disaster crop losses itself are highly localized.<sup>60</sup>

# <u>What do we conclude and still need to know?</u>

There are several options to attribute losses to shocks based on self-reports in the LSMS-ISA data, the most disaggregated of which are given by farmers’ reports of loss reasons at the plot-crop level. The most common shocks in the data are rainfall-related and should thus be covariate at small geographic aggregates. In general, the different shock proxies point in similar directions but vary in the shock incidence they suggest and are imperfectly correlated. The most sensitive measure of drought comes from a question on crop damage cause.

Agreement in self-reported shock exposure is limited between plots of the same EA. While in most cases farmers consistently rule out the occurrence of a shock, the primary interest of this section is in the consistency of farmer reports _in the face of a shock_ . We therefore focus on cases where a shock, for example a drought, may have occurred based on farmers reports and find the consistency of shock identification between plots to be generally rather low. However, agreement depends on shock intensity which may alleviate idiosyncrasies due to different _perceptions_ (a source of variation we would like to eliminate) and due to differences in actual loss _occurrence_ (variation we would like to retain).

As this section cannot conclusively discern between the two sources of idiosyncratic variation, we conclude that it is difficult to confidently attribute losses to specific disasters based on self-reports alone. As a next step, it is thus necessary to further disentangle variation in self-reported shock identification due to differences in perception between farmers (a source of measurement error) from the variation that is due to highly localized shock impact at the sub-EA level.

# <u>Question 6: How localized are drought losses and how confidently can we attribute losses to adverse rainfall based on self-reports?</u>

The previous analysis found notable idiosyncrasy in farmers’ self-reported shock identification but was unable to discern what part of this variation comes from differences in perceptions between farmers and what part is due to actual differences in loss occurrence at the micro-level. This section compares self-reported shock exposure data at the plot level to geospatial rainfall data and a set of observable plot characteristics. We hypothesize that any differences in loss occurrence within the same EA do not stem from differences in the occurrence or intensity of rainfall shocks but differences in their actual impact on agricultural production on different plots. For example, a number of sustainable land management (SLM) practices and plot characteristics, on top of irrigation, can reduce the susceptibility of a plot to disaster crop losses (Nkonya _et al._ , 2018; Iizumi and Wagai, 2019; Lombardi _et al._ , 2019; McCarthy _et al._ , 2021).

> 60 We reach a similar conclusion using shock reports based on area-losses or household-level reports and in Malawi.

39