# <u>What do we see in the data?</u><sup>27</sup>

Comparing the area-based and productivity-based proxy in a histogram suggests that the productivity-based proxy is more sensitive to small losses. That is, farmers report having any plant productivity losses more frequently, but these losses tend to be smaller. Conversely, the plantedarea-based proxy records fewer losses but these tend to be higher. Across years and crops, the incidence of plant productivity losses is much higher with about 1 in 2 plots recording some damage on the crop whereas only every fifth plot records reductions in the area harvested compared to what was planted.<sup>28</sup> Conditional on recording a loss though, planted-area-based losses tend to be larger.

Consistent with what has been found in other cases in the survey methodology literature, respondents tend to round numbers to convenient values divisible by ten or 25. A particularly conspicuous spike can be found at 50 percent likely reflecting the convenience of this value and easy interpretation. This pattern is consistent with the presence of considerable satisficing, the use of heuristics or “mental shortcuts” to reach a (in the respondent’s eyes) satisfactory rather than optimal answer (Krosnick and Presser, 2010).

Three factors mediate the degree of satisficing to be expected (Krosnick, Narayan and Smith, 1996): (i) the difficulty of the task; (ii) the farmer’s ability to complete the task; and (iii) the farmer’s motivation to provide an accurate estimate. Each of these concerns likely applies to our context. First, estimating average losses, particularly when differentiating between average reductions in productivity and the area completely lost, is cognitively challenging. Furthermore, percentages may not be a straightforward concept and lack an intuitive “benchmark” to correctly assess differences in magnitude along a percentage scale.<sup>29</sup> Secondly, the respondents in our sample are overwhelmingly low-educated with over two-thirds not having any school education and only a rough one-fifth having completed primary education in the case of Ethiopia. They also likely have little practice in performing this kind of task as the LSMS-ISA survey only has a twoto three-year frequency. Thirdly, the LSMS-ISA questionnaires are of substantial length, frequently requiring interviewing respondents over several hours and on multiple days. This may increase respondent fatigue and decrease the motivation to provide an accurate estimate.As a result, a percent difference of 10 percent or 20 percent, well within the conceivable margin of error for the amount of heaping we observe, may appear small to the farmer (and hence of satisfactory accuracy) but would lead to substantial measurement error (“weak satisficing”). Alternatively,

> 27 Our exposition will focus on Ethiopia where we can measure both planted area losses and plant productivity reductions. In Malawi, we only have loss reports from one round (IHPS19) and also only for planted area losses which equates to a scenario where di,j,t = 0, that is, where realized yields in harvested areas are equal to attainable yields in harvested areas and the only losses occur due to complete destruction of parts of the plot. When limiting our analysis to this proxy though, results between Ethiopia and Malawi are largely comparable with some notable differences in the incidence of area losses (60 percent of plots in Malawi vs. 20 percent of plots across rounds in Ethiopia). This may reflect the planted area loss question compensating for a lack of a separate question asking for productivity reductions.

> 28 To avoid a hard-to-read histogram due to a dominant spike at 0 percent area-based loss and 0 percent damage, we constrain the histograms depicted to those plots who record any loss (i.e. positive values on the horizontal axis).

> 29 For a similar reason, the use of visual aids is common when eliciting probabilities or other relative numbers from subjects with low numeracy (Delavande, Giné and McKenzie, 2011).

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