where y<sup>c</sup> has been replaced by a function of the realized yields on other plots that share similar characteristics to the plot in question but did not incur any losses.<sup>32</sup> Multiplying this by the planted area ha<sup>c</sup> thus gives the attainable production. Losses then constitute a deviation of the realized harvest Y<sup>r</sup> from what would have been expected based on past years or the performance of yields on plots in unaffected areas, respectively.

The remainder of this section looks at the data requirements of both approaches as well as their likely accuracy.

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

We start by regarding the suitability of using past yield data to proxy for attainable yields from a data availability perspective. In order to ascertain a robust trend or average in yields, we require high frequency (e.g. annual) panel data reaching back several years. Micro-level panels of this frequency are exceedingly rare and, in our case, the LSMS-ISA panel data only cover recurring survey rounds every two to three years. While such high frequency data is currently unavailable at a sufficiently disaggregated level<sup>33</sup> it may become available in the future.<sup>34</sup> Another challenge in this regard relates to split-off households and those that move. The LSMSISA survey in both Ethiopia and Malawi tracks households that move to a new place whereas the latter also tracks households that split from the originally sampled household. For both groups of households, comparable past information on yields would be missing.<sup>35</sup> Irrespective of this, yields in the context of the LSMS-ISA data are measured at the plot level. Ideally, the panel data used would thus also be identified at the plot-crop level in order to hold differences in soil quality and other plot characteristics constant. However, plot boundaries and crop choice frequently vary across years and the LSMS-ISA data are thus household-level (and in the case of Malawi and Ethiopia parcel-level) panels but not necessarily plot-level panels (Carletto, Gourlay and Winters, 2015). As a result, it would likely be necessary to move to farm-level aggregate yields in order to reduce the idiosyncratic uncertainty associated with varying plot management decisions and growing conditions.

More fundamental than these concerns on data requirements is a conceptual issue with relying on past yield data. By definition, we only observe past yield _realizations_ which will differ from attainable harvest in the presence of crop losses. In Ethiopia and Malawi, however, harvest losses are very common affecting, for instance, between 55 percent and 65 percent of maize plots across years in Malawi and between 41 and 56 percent of maize plots in Ethiopia. As such, any past

> 32 As a reminder, Yr and hac are always observed in the LSMS-ISA data. They are the realized production and GPS measured area, respectively.

> 33 Data sources such as FAOSTAT’s yield database, for example, contain yearly data but cover the country-level which may be too coarse to accurately accommodate sub-national, sub sub-national, or even micro-level variation. 34 Projects such as the 50x2030 initiative are planning on collecting agricultural statistics in recurring patterns including a core of agricultural data at yearly frequency (50x2030 Initiative, 2020).

> 35 Between the first and third round of the Integrated Household Panel Survey (IHPS) in Malawi, for example, almost 40 percent of households moved to a different location.

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