error). We find no consistent evidence of the self-reporting error being associated with recall length: only in the Malawi IHS4 data set, the overstatement of plot area increases in recall length at a rate significantly different from zero. All other specifications show no significant correlation (Table A.5). Overall, this suggests that respondents’ difficulties in accurately estimating land area are not associated with memory decay over time. It is plausible that farmers at the time of the interview still operate many of the same plots as at the beginning of the season and therefore have a belief or estimate of plot area based on relatively current information.<sup>8</sup>

# Crop production

The maize harvest quantity as reported by farmers increases with the length of the recall period. The results are consistent across all three data sets, with point estimates ranging from 4.9 kg (2 percent) in Tanzania NPS 2012/13, to 11.97 kg (4 percent) in Malawi IHS4, to 15.85 kg (8 percent) for each additional month of recall length in the Malawi IHS4 panel (Figure 5, Table A.6, Table A.7). Based on BIC’, a quadratic specification yields a better fit of the relationship in Malawi IHS4, suggesting that here the recall length effect is strongest in the first 3 to 8 months, leveling off after that (Table A.8, Figure 6).

The results support the hypothesis that there is non-random measurement error in the farmer-reported quantity of maize harvested related to the length of the recall period. However, this result is valid only if no relevant confounding factor is omitted in the regression. There is one potential confounding factor that merits discussion: the weight of maize produced can be reported in different states of the crop. For example, at the time of harvest, maize is still on the cob. Before being sold, stored, or consumed, maize grains may be removed from the maize cob and dried. In this process, the weight of the same harvest may vary depending on the state in which its weight is reported. Moreover, this may be related to the length of the recall period if, for example, farmers more often report the harvest weight of maize on the cob immediately after the harvest and the weight of dried grains more often as more time passes between the harvest and the interview. This, in turn, could mean that the observed recall effect is the result of changes in reported harvest state over time. We assess this possibility using information on the harvest state – shelled (grain) and unshelled (on the cob) – which is available for Malawi IHS4 and IHPS 2016/17 data in three steps. First, we assess whether the state in which harvest weight is reported changes with the length of the recall period. We find that the share of farmers reporting the weight of maize on the cob decreases with recall length at 0.8 percentage point per month in Malawi IHPS 2016/17 and 0.4 percentage point in Malawi IHS4. Next, we include the state in which harvest was reported as a control variable in the main regression. Finally, we convert the quantity of unshelled maize (on the cob) to grain-equivalent shelled weight and then repeat the analysis with this new variable. The unshelled to shelled conversion factor was obtained from the MAPS experiment in which maize was weighed twice, before and after shelling. The main results are robust to these two specifications. Further, we assess whether the correction of the harvest date and hence of the recall length variable (section 3) drives our results. We estimate the main specification including the difference (in months) between the raw and the corrected harvest date variable (results not shown). The inclusion of this correction term is not statistically significant in any of the data sets nor does it meaningfully change the coefficient on the recall length variable.

> 8 This does not mean that the estimate is correct (Carletto et al., 2011; De Groote and Traoré, 2005; Dillon et al., 2019; Goldstein and Udry, 1999; Keita and Carfagna, 2009; Kilic et al., 2017; Schøning, 2005).

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