area vis-à-vis what was planted (“planted area losses”).<sup>22</sup> However, losses may also occur if harvest could occur on part of the plot but plants in the harvested areas have some sort of damage that makes them less productive (e.g. pests destroying some cobs or some plants not developing properly due to poor weather conditions). The latter is what this paper calls “plant productivity losses”.<sup>23</sup> Consequently, it is necessary to find survey-based measures for reductions in the area harvested<sup>24</sup> _and_ reductions in productivity in harvested areas.<sup>25</sup> In relation to the general form expressed in Equation (1), we observe GPS measured plot area, h<sup>c</sup> , and realized production Y<sup>r</sup> . The two key unknowns to measure in the survey data are the reduction in the planted area and the reduction in productivity. We use a question on the percent share of the planted area that was harvested, si,j,t, to obtain the harvested area, h<sup>r</sup> , and the realized yield in harvested areas, y<sup>r</sup> . We then obtain the attainable yield, y<sup>c</sup> , from y<sup>r</sup> by accounting for plant productivity losses through a question on the percentage of damage on the crop, di,j,t. Therefore, in Ethiopia, the loss estimate built in this section will be a compound one consisting of a) planted area losses (the first term), and b) productivity (damage) losses (the second term).

We next take this approach to the data.<sup>26</sup>

> 22 One example may be the partial flooding of a plot which destroys the harvest completely in one part but due to plot slopes etc. leaves the rest of the plot unaffected.

> 23 For example, pests or a crop disease may completely wipe out the harvest in parts of the plot but only lead to partial losses in other parts of the plot that could still be harvested.

> 24 In the LSMS-ISA data, farmers are asked for each crop on each plot at the post-harvest visit whether the planted area was greater than the area harvested and (if applicable) what the percent share of the planted area that was harvested was. A value of 0 percent indicates a full loss and values between 1 percent and 99 percent denote partial losses of the planted area. Farmers are also asked to choose up to two reasons for their loss from a list of shocks or specify another reason.

25 This information is only available in the Ethiopia LSMS-ISA data. Farmers are asked for each crop on each plot whether there was any damage on the crop and what the percentage share of and reason for this damage was. In fact, this set of questions is asked _twice_ , once at the post-planting visit and again at the post-harvest visit. Intuitively, one would assume for damage on the crop to only ever increase. However, we find that, depending on the survey wave, in up to 25 percent of cases damage reports at the post-planting visit exceed those at the post-harvest visit. Furthermore, this is not due to trivial differences that could originate from rounding error or the like. We take note of this and trust post-harvest reports for the purpose of loss calculation as these should constitute the most updated farmer estimates of damage.

26 Unless specified otherwise, the analysis excludes plots where the harvest was completely lost (the equivalent of an area loss of 100 percent on the plot). The main reasons for this is to avoid bias in our estimates stemming from a (by construction) perfect fit between the area loss measure and the realized harvest quantity. Furthermore, the distinction between area and plant productivity losses is not meaningful in this case and there is no way to infer the attainable yield according to Equation (2) in the lack of any realized harvest. To estimate total loss on fully destroyed plots, the attainable yield can, for instance, be inferred from the median attainable yield on similar or near plots with at most partial losses.

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