- (iii) effortless to integrate with other survey information, e.g. on the behavioral components of crop management in the face of disasters and crop losses.

As such, survey data may facilitate the future development of well-calibrated, accurate, and scalable hybrid models using sensor, remote-sensed, and survey data to measure disaster crop losses in LMICs.

To tap into this potential, however, it is essential to understand the feasibility of measuring crop losses through survey data and develop a robust methodology. This paper pursues the question of operationalizing the recent methodology developed by Conforti, Markova and Tochkov (2020) for micro-level management through existing and future survey data. Specifically, it first analyzes the suitability of existing survey data to ascertain the magnitude of losses and secondly the ability to attribute the observed losses to specific disasters.

The papers’ main findings are threefold. First, directly asking for different types of losses based on farmers’ self-reports provides relatively consistent estimates on an ordinal scale. However, assessing losses is a cognitively complex task and invites satisficing in farmers’ responses. As a result, forming accurate point-estimates of crop losses is impaired by concerns of numeracy and question format. Improvements in the phrasing and approach of asking for crop losses may allow for more precise answers.

Secondly, we find that post-planting harvest expectations often seem fair estimates of the attainable harvest and may thus allow to proxy for losses indirectly, perhaps most so at the extreme when losses are large.

Thirdly, farmers’ self-reported shock identification exhibits considerable variation even within small geographic aggregates and for shocks that should be covariate in nature. To a large extent, the variation in self-reported drought exposure is not explainable by observable factors such as rainfall intensity or sustainable land management (SLM) practices and seems thus driven by differences in farmers’ idiosyncratic perceptions. This is particularly conceivable for slow-onset disasters such as drought that lack a clear-cut benchmark or starting point. Importantly though, the intensity of the shock matters, with severe, salient shocks leading to more consistent shock identification between plots in the same area.

Moving forward, we propose the following.

# **For the quantification of losses**

Changes in the wording of the direct proxy questions for crop losses and perhaps the use of additional aids may reduce the cognitive complexity of the task and elicit answers more accurately. Alternatively, the risk of misreporting may be reduced by combining existing proxies into a single, direct question benchmarked against farmers’ self-reported attainable harvest. A third option could make use of farmers’ post-planting expectation information as a measure of the attainable harvest. While expectations perhaps may not serve as the sole proxy for the attainable harvest, they could be an important feature for econometric models that try to predict (attainable) yields. This approach may be particularly suited to capture losses on fully destroyed plots or otherwise large losses. Furthermore, the endogeneity in expectations as a proxy of the attainable harvest may be reduced 43