proposed options include the use of average yield data from comparable districts unaffected by any shocks or yield forecasts based on meteorological and remote-sensed data (FAO, 2020).

As the accuracy of different approaches for micro-level measurement of the attainable yield through surveys likely varies, their empirical assessment constitutes the first goal of this paper. We call this the “quantification of losses”. Concretely, the paper explores a number of surveybased ways to measure or proxy for the attainable harvest using plot-level data<sup>17</sup> including

- (i) explicitly measuring planted area losses and plant productivity losses based on farmers’ self-reports;

- (ii) expert assessments of the share of the attainable harvest that was lost;

- (iii) obtaining attainable yields from past data or comparable but disaster-unaffected plots;

- (iv) using farmers’ post-planting harvest expectations to proxy for the attainable yield.

Importantly, these approaches need not be mutually exclusive, and the paper will aim to highlight the respective strengths and weaknesses of each approach in an aim to combine them for best results.

In addition to the _quantification_ of losses, the second crucial focus of this paper is the _attribution_ of losses to specific disasters. One possible approach relies on self-reported shock identification, either from the farmer, a community congregation, or an ‘expert’. Here, the paper analyzes the consistency of shock identification on plots of the same crop in close proximity and thus assesses the suitability of self-reports to confidently attribute losses to disasters. Alternatively, the paper compares self-reported drought exposure to geospatial rainfall data to obtain a similar “objective measure” as in the agricultural index insurance literature. Through this, it aims to disentangle variation in self-reports due to different shock exposure from variation due to differences in farmers’ perceptions. Notably again, the paper also explores the scope for integrated approaches that combine self-reported and sensor data to harness the strength of each.

For our analysis, we draw on a diverse set of data sources. Our main data source is rich plot-level household survey panel data<sup>18</sup> for two Sub-Saharan African countries, Ethiopia and Malawi, supported by the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). The LSMS-ISA data have a distinct focus on agriculture and contain, among others, detailed data on harvest, input use, crop management, as well as a large set of community, household, and individual characteristics. The agricultural modules of the surveys feature a twovisit structure in which each household was visited after planting and again after the harvest had been completed. At the first visit, among others, self-reported information on input use, plot characteristics, and expected production are elicited while the second visit asks farmers about their

> 17 This is in contrast to other approaches including the “default” angle of the FAO methodology which aim to measure crop losses at the farm-level or higher.

> 18 The LSMS-ISA data we use are _farm-level_ panels but not necessarily _plot-level_ panels due to constant changes in plot dimensions and the crops cultivated on them (Carletto, Gourlay and Winters, 2015). In this version of the paper, we therefore do not exploit the panel dimension of our data and treat each wave as a separate cross section. However, this does not mean that the fact that we are dealing with panel data does not carry advantages for our analysis. For example, it should attenuate concerns about changes in the sample when comparing descriptives across rounds. An exception is ESS18 in Ethiopia which received a new baseline sample in this round and is thus not strictly comparable (from a panel perspective) to the sample from previous rounds.

9