(quadratic, cubic) of T� (recall length). We compare the linear and non-linear models using a variant of the Bayesian Information Criterion (BIC’) as a goodness-of-fit measure to guide which model is best suited.

Recall error may be exacerbated by certain household, individual, or farm characteristics. We explore interaction effects between the recall length and plot size, respondent characteristics (gender and education), and whether the respondent is also the plot manager. The latter is relevant because, to an extent, it is in the survey designers’ purview to enforce that the respondent be the plot manager.

# 4. Results

We find that the recall length has a significant impact on reported outcomes in all areas of interest of this analysis. Farmers report significantly higher quantities of plot-level variables, that is maize harvest, labor and fertilizer input, as the recall length increases. In contrast, farmers report fewer cultivated plots of land in longer recall periods.

## Land

Households list significantly fewer plots (and parcels) as the recall period increases. Results from the baseline linear OLS specification with a full set of controls, shown in Figure 3 and Figure 4 (and Table A.3 in the Appendix), are consistent across the three data sets. The results from a Poisson regression are in line with those from OLS. The magnitude of the effect varies by survey, however. It is small for Tanzania NPS 2012/13 at 0.03 fewer plots per month and Malawi IHS4 (cross section) at 0.05 plot per month, but larger in Malawi IHPS 2016/17 (panel) at 0.1 plot per month. This may be the result of several factors, which we cannot directly assess.

The results from a quadratic specification confirm the overall negative relationship between recall length and plots listed (Table A.4). The BIC’ goodness-of-fit comparison suggests that the linear model is preferred in Tanzania NPS 2012/13 and Malawi IHS4 data and that the quadratic model provides a better fit in Malawi IHPS (Figure 4).

We have no way to observe the ‘true’ number of plots household-farms in our data operate, so we cannot ultimately determine the direction of the reporting bias. However, it seems plausible that farmers forget some of their less important plots as recall decays over time, as was observed previously for example in Gaddis et al. (2019) and Arthi et al. (2018).<sup>7</sup> Turning now to farmer self-reported land area, we assess the effect of recall length on the magnitude of error in self-reported plot area, comparing it to the objective GPS-based measure of plot. We split the sample in plots whose area was over-stated and plots whose areas was under-stated (positive and negative

> 7 The analysis in this paper relies on a different setup than Arthi et al. (2018) and Gaddis et al. (2019), who use a methodological experiment in which farmers are randomly assigned to report on labor inputs either through end-of-season recall modules or in weekly visits during the agricultural season. In contrast, we rely on variation in recall length stemming from variation in interview timing. The findings in Arthi et al. (2018) and Gaddis et al. (2019) are therefore a combination of a recall effect (end-of-season modules imply longer periods of time to recall) and a data collection effect (weekly visits in which respondents are prompted to report in detail on their plots). Our findings isolate and underscore the former.

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