Mullainathan, 2016; Waldman _et al._ , 2020). Examples include availability bias<sup>7</sup> (Tversky and Kahneman, 1973; Karlan _et al._ , 2014; Brown _et al._ , 2018), scarcity<sup>8</sup> (Shah, Mullainathan and Shafir, 2012; Mani _et al._ , 2013; Lichand and Mani, 2020), satisficing<sup>9</sup> (Krosnick and Presser, 2010), and a large body of methodological research on the accuracy of farmers’ self-reports (Carletto, Savastano and Zezza, 2013; Carletto, Gourlay and Winters, 2015; Arthi _et al._ , 2018; Gourlay, Kilic and Lobell, 2019; Wollburg, Tiberti and Zezza, 2021).

# **3. Empirical Strategy and Data**

In exploring the suitability of survey data for disaster crop loss measurement, this paper will be guided by the methodology for damages and losses assessment in agriculture that was recently developed in Conforti, Markova and Tochkov (2020) for the FAO (henceforth also “the FAO methodology”). Complementing previously developed disaster impact assessment approaches by the United Nations Economic Commission for Latin America and the Caribbean (UN ECLAC, 2003) and the multi-agency Post Disaster Needs Assessment methodology (European Union, UN Development Group and World Bank, 2013), they provide a standardized methodology for measuring damages and losses due to disasters across the agricultural sub-sectors of livestock, forestry, aquaculture, fishery, and (this paper’s focus) crops. To measure the impact in the crop sector, the FAO methodology distinguishes between annual and perennial crops as well as damage (e.g. the destruction of stored inputs or stored crops) and loss (e.g. reductions in crop yields).

DL (Crops) = Annual crop prod. damage + Perennial crop prod. damage + Annual crop prod. loss + Perennial crop prod. loss + Crop assets damage where _DL(Crops)_ is the total monetary value of damages and losses in the crop sub-sector. This paper’s focus will be crop production losses of annual crops.<sup>10</sup> In keeping with Conforti, Markova and Tochkov (2020), we can distinguish between two types of crop losses. The first case describes losses due to reductions in harvested area. It occurs when parts of the planted area are fully destroyed and thus all harvest was lost in these areas.<sup>11</sup> The second type of crop loss arises due to reductions in productivity of the plants harvested. This case occurs

> 7 For example, Karlan _et al._ (2014) find that farmers overweight recent adverse events in their investment decisions. 8 Lichand and Mani (2020), for instance, find that the mere prospect of adverse rainfall and thus income reductions reduced farmers cognitive abilities in a range of survey experiments.

> 9 For example, providing an estimate of disaster crop losses, in theory, involves estimating the counterfactual harvest in the absence of shocks which is complex and may lead to the use of heuristics and inaccuracy in self-reports. Such complex questions have been found to be error prone without careful survey design and the provision of appropriate aids that simplify the task (Krosnick, Narayan and Smith, 1996; Krosnick and Presser, 2010; Delavande, Giné and McKenzie, 2011).

> 10 We expect our findings to similarly apply to perennial crops. However, they bring added complexity due to the possible cumulation of shocks across seasons.

> 11 For example, part of a plot may have been flooded completely wiping out the harvest in the flooded area.

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