highlighted in the data by comparing yields across different plots of the same farm. Comparing plots of the same farm should hold many of the aforementioned factors constant. However, as Figure 5 emphasizes, there is considerable heterogeneity in yields even within the same farm and even when only regarding pure-stand plots without losses or comparing crop cut yields on plots without losses.

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

These considerations highlight the difficulties in finding a valid counterfactual based solely on realized yields, either exploiting the time or cross-sectional dimension of survey data. Using past harvest observations as a proxy for current season attainable harvest requires panel data of higher frequency than is common in current agricultural surveys and would not capture any households that have changed location or entered the panel. Fundamentally though, they would provide an estimate of attainable harvest that is downward biased to the degree that shocks have also occurred in previous survey rounds, a likely scenario based on our sample, and be prone to bias from intertemporal differences in plot management decisions. Furthermore, the large inter-farm variability in yields due to factors other than shocks that is reported in the literature and heterogeneity in yields across plots of the same farm in our data cast doubt on the suitability of using realized yields from other farms to proxy for the attainable harvest.

However, using rich survey data as is provided by the LSMS-ISA, it may be possible to account for possible confounding factors and predict (attainable) yields using econometric or machine learning models based on past yield data, data from several similar but unaffected plots, and/or planting season crop management decisions. An exploration of this possibility is beyond the scope of this paper but may be a relevant avenue for future research.

# ’ - <u>Question 5: Can farmers post planting harvest expectations proxy for attainable harvest?</u>

As previously discussed, the LSMS-ISA data contains detailed information on plot, input, and management characteristics as well as agricultural production down to the plot-crop level. This is an important feature as accounting for these differences acknowledges the fact that yields can vary greatly between different plots. For example, different field characteristics and management practices can create variation not only in the realized yield but also the attainable yield. Furthermore, taking Ethiopia as an example, between 35 percent and 44 percent of plots (depending on survey round and crop) are intercropped. This means that we may not have accurate information on the actual share of the plot that was planted with the crop and thus the attainable production (in absolute terms).<sup>37</sup> To potentially address these pitfalls, we can rely on farmers’ ability to somewhat accurately estimate the attainable harvest for each crop on each plot at the post-planting visit. At this point,

> 37 The LSMS-ISA data does contain a question that asks for the share of the plot that was planted with the crop for mixed stand plots. However, the estimate is rather coarse (less than a quarter, 50 percent, 75 percent, more than 75 percent).

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