etc.) using appropriately defined recall length variables for each activity (Table 1, Table 3). Of the activities, most person-days are used in planting and maintenance activities, with fewer person-days used in harvest (Table 2). We also assess whether the recall effect differs by type of worker (household, hired). Labor inputs are summed to the plot-level, rather than assessed separately for each worker on each plot, because one respondent reports all labor inputs for a given plot, so that this measure likely best captures the recall effect.

Finally, input use is assessed through incidence of organic fertilizer application per plot and incidence and quantity of inorganic fertilizer application per plot. This analysis is possible because the three surveys record fertilizer use at the plot-level. Fertilizer use is considerably more common in Malawi than in Tanzania, for which Malawi’s inorganic fertilizer subsidy program is likely responsible. Organic fertilizer is applied on 19 and 20 percent of plots in Malawi IHS4 and panel, respectively, and on 10 percent of plots in Tanzania NPS 2012/13. The difference is larger for inorganic fertilizer, which 53 and 61 percent of plots receive in Malawi IHS4 and IHPS 2016/17, respectively, compared to 9 percent in Tanzania. Among those plots to which inorganic fertilizer is applied, the applied quantities are similar across all three data sets at 66 kg per plot in Tanzania NPS 2012/13, 62 kg per plot in Malawi IHS4 cross-section, and 58 kg per plot in IHPS 2016/17 (Table 2). Application intensity (inorganic fertilizer applied per hectare) is again lower in Tanzania, owing to larger plot sizes, while fertilizer intensity is generally decreasing in plot size across all three data sets (Figure 2, row 3).

We apply the double Median Absolute Deviation method for outlier detection and correction to all constructed continuous variables in this analysis, that is, harvest quantity, storage and sales quantity, total, family, and hired labor person-days, organic and inorganic fertilizer quantity applied (Leys et al., 2013).

# Determining recall length

The length of the recall period cannot be determined exactly in the survey data for two reasons. On the hand, we do not observe directly when farmers engage in the activities of interest (planting, fertilizing, working, harvesting, etc.). Instead, some information about the timing of the activities can be retrieved from farmer-reported dates and the cropping calendar. On the other hand, farmers engage in these activities over extended time periods, so that strictly no single recall length exists. We therefore proxy the length of the recall period for each outcome of interest.

During the interview, farmers are prompted to list all plots used for crop cultivation since the beginning of the agricultural season, so that this proxy for recall length seems an appropriate choice. The beginning of the planting season is determined by each country’s cropping calendar and is identical for all households in each data set.

First, for harvest (and storage and sales) quantity, we use as recall length the distance in months between the interview date and the end of the harvest period. In all three surveys, farmers are asked to report harvest dates and this information is used to construct this second recall length variable. Harvest dates are likely recorded with error, being subject to memory decay over time in the same way as the outcome variables. To address this concern, we use the mode of harvest end dates at the cluster (enumeration area) level, rather than each household’s individual maize harvest date, provided that there are at least 10 observations per cluster. Where there are fewer than 10 observations per cluster, the variable is instead based on the next 8