needed to better understand the factors that underlie this mental model and, possibly on top of losses, explain the accuracy of expectations and their suitability to proxy for the attainable harvest.

We conclude that inferring losses from the difference between the expected and realized harvest is a coarse measure and may not be preferrable over more fine-grained approaches asking farmers for planted area and plant productivity losses directly. However, expectations may be better suited in the case of full losses (when the methodology from Question 1 is not applicable) or in general when capturing large losses is the primary goal. This is because the expectation gap is likely not sensitive enough to adequately represent small losses but provides a decent approximation of the attainable harvest when small biases matter less, e.g. after severe disasters. In future methodological research, trying to elicit farmers’ expectations even more accurately, for example by priming them to think about the attainable harvest without shocks, may furthermore decrease endogeneity in farmers’ expectations as proxies of the attainable harvest. Lastly, expectations perhaps may not serve as the sole proxy for the attainable harvest but could be an important feature for econometric models that try to predict (attainable) yields.

# **4.2. Attribution: Linking losses to specific disasters**

Measuring crop losses due to disasters for SDG 1.5.2 and Sendai indicator C-2 crucially hinges on the accurate attribution of losses to specific shocks. Most commonly, survey data has relied on self-reports as a fine-grained but subjective measure of shock exposure. At the same time, technological advancements have increased the accuracy of remote-sensed approaches over the last years, potentially alleviating concerns about measurement error at the micro-level and providing an objective measure. Importantly, self-reported data on shocks and index-based data have frequently been found to be at odds with no clear verdict in favor of any one approach (MezeHausken, 2004; Sohnesen, 2020). For instance, survey data may give a better representation of idiosyncratic (highly localized) shocks whereas basing shock occurrence on an index may capture covariate shocks, particularly meteorological disasters, more consistently across farms.

The most common disaster in our study region is drought with 82 percent of its impact in agriculture and accounting for the majority of disaster damages and losses in the sector (FAO, 2018, 2021). This is confirmed by the LSMS-ISA data where adverse rainfalls, either too much or too little rain, constitute the most frequently reported reasons for crop losses. They are therefore the focus of this paper. Our assumption is that these shocks are likely covariate, i.e. they should affect all plots with the same crop within a reasonably small geographic aggregate. Exploring several possible survey-based proxies, the analysis in this section thus aims to ascertain the degree of idiosyncrasy in self-reported data on shock exposure and discern variation due to differences in perception from variation due to actual loss incurrence.<sup>48</sup>

> 48 Farmers’ plot-level shock reports are conditional on having experienced a crop loss due to the shock in question. To reduce the risk that we are measuring differences in disaster risk reduction technology between plots rather than the consistency of shock identification, we focus on rainfed plots that constitute the overwhelming majority of our sample.

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