The concern with total labor days is that it is made up from different work activities with different timings. We therefore assess the recall effect separately for each type of activity: Land preparation and planting; weeding, ridging, and fertilizing; and harvest and post-harvest activities. Generally, the positive association between recall length and labor input holds across activities in all three data sets, though not all point estimates are significant (Figure 10). Recall length has the clearly largest and most significant level effect on reporting of labor dedicated to ‘weeding, ridging, and fertilizing’ (Figure 10, Table A.12, Table A.13, Table A.14). This category is arguably the least salient and captures labor applied on the plot between planting and harvest, a period of several months, which make these activities particularly hard to recall correctly.

We also test family and hired labor input per plot separately. The total labor input results are driven by family labor. On the one hand, the number of family labor days make up a large share of total labor days. On the other hand, recall length has a significant effect on family labor days in all three data sets, but not on hired labor (Figure 8, Table A.9, Table A.10). Further, there is no significant effect of recall length on the number of family members reported to have worked per plot and per household (results not shown).

The findings are consistent with plot-level over-reporting of labor inputs in long recall modules discussed in Arthi et al. (2018) and Gaddis et al. (2019).<sup>10</sup> While we cannot benchmark our results against a more objective measure of farm labor, it seems plausible that respondents here too are over-reporting as the recall period increases.

# Inputs

There are two main results regarding the effect of recall length on agricultural inputs (we analyze organic and inorganic fertilizer use in this study): first, respondents tend to report higher fertilizer quantities as the recall length increases. Second, respondents are slightly less likely to report incidence of fertilizer use, that is whether or not any fertilizer was applied to a given plot.

We use a linear OLS specification with a full set of controls to estimate the effect of recall length on quantity of inorganic fertilizer applied per plot, conditional on fertilizer use. The effect size is large in Malawi IHPS 2016/17 at 4.3 kg per plot per month (8.8 percent), and smaller in Malawi IHS4 at 0.9 kg (1.9 percent). In Tanzania NPS 2012/13, the coefficient has the same sign but is not statistically significant (Figure 12, Table A.15, Table A.16, Table A.17). Low fertilizer application rates lead to a small sample of households using inorganic fertilizer on their land at 675 of 7,322 plots, which may explain the recall effect being insignificant. The linear model is the preferred specification based on BIC’ analysis.

Based on a Probit model, we find a small negative recall effect on the binary variable whether any inorganic fertilizer was applied in the case of Malawi IHS4 and IHPS 2016, and whether any organic fertilizer was

> 10 Arthi et al. (2018) and Gaddis et al. (2019) find two countervailing types of reporting bias. First, end-of-season recall modules lead to an over-estimation of reported labor inputs per plot (‘recall bias’). Second, end-of-season recall modules lead to an undercounting of plots listed and household members reported to have worked on them (‘listing bias’). Our results echo those findings: we find longer recall periods are associated with fewer plots listed and higher labor inputs reported. A key difference is that we find no evidence of farmers reporting fewer members having worked on each plot or on the entire farm. Moreover, Arthi et al. (2018) and Gaddis et al. (2019) find a significant educational gradient in recall bias, while we find evidence of education interacting with the recall effect.

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