The inverse relationship between measured plot size and error in self-reported plot size discussed in the literature is also common to all three data sets used in this analysis (Table A.2).

Crop production is assessed, first, through the quantity of maize harvested per plot by the farm-household during the reference agricultural season. We choose maize since it is the most important staple crop in Malawi and Tanzania, though our findings on maize do not necessarily translate to other crops (see results in Deininger et al., 2012). In addition, we analyze the amount of maize harvest stored and the amount sold at the time of the interview.

Harvest quantities are recorded separately for each crop and plot, and all surveys allowed for reporting in local non-standard units. To construct a standardized maize harvest quantity, non-standard units are converted into kilograms using correspondence tables specific to each survey. Stored and sold harvest is reported at the household level. The crop production variables are summarized for each dataset separately in Table 2. The median maize harvest quantity per plot is 200 kg in Tanzania NPS 2012/13 (mean: 303.7 kg), 213.4 in Malawi IHS4 (mean: 320.5 kg), and 180 kg in Malawi IHPS 2016/17 (mean: 286.2 kg). While harvest per plot is similar, maize yield (harvest per area) is higher in Malawi IHS4 and IHPS 2016/17 than in Tanzania NPS 2012/13, where plots are larger on average (Figure 2, row 1).

The three main surveys used in this analysis rely on farmer self-reporting of the quantity of maize harvested. This means that there is no approximately objective measure of maize harvest to benchmark the results. We therefore make use of ancillary data from the second round of the Methodological Experiment on Measuring Maize Productivity, Soil Fertility and Variety (MAPS). These data contain full crop-cuts (considered benchmark estimates) for maize of 211 entire plots. The data set has two main limitations for our analysis: the data collection was so swift that the recall length is short with little variation between 0 and 4 months, and, at 211 plots, the sample size is small. The full crop cuts are used to quantify the error in farmer-reported maize harvest and relate it to the length of the recall period. On those plots, the mean farmer-reported harvest is 159 kg (median: 70 kg), while the mean crop cut estimate is 113.5 kg (median: 53.45 kg; Table 5).

For farm labor inputs, the headline outcome is total person-days of labor used per plot during the reference agricultural season. The variable is constructed by summing person-days worked by all household members and hired workers in all activities related to crop production, which are recorded separately for each plot. Family labor makes up the lion’s share of total labor input in all three data sets, accounting for between 91 percent in Tanzania NPS 2012/13, 92 percent in Malawi IHPS 2016/17, and 96 percent in IHS4 (cross section). Total labor input is somewhat higher in Tanzania at 70 person-days than in Malawi with 64 and 62 person-days per plot in the cross section and the panel, respectively. However, given larger plot size in Tanzania, labor intensity (labor per area) is lower there than in the two Malawi samples (Figure 2, row 2). Twenty-six percent of plots in Tanzania NPS 2012/13 use hired labor, compared to 13 percent in Malawi IHS4 and 29 percent in IHPS 2016/17 (Table 2). Once again, the data exhibit an inverse relationship between labor intensity and plot size (Figure 2, row 2).

For an analysis of recall length, the total person-days variable has some drawbacks. As it is constructed by summing various work activities (planting, fertilizing, harvest, etc.), which all take place at different times during the agricultural season, there is no one single recall period for total labor person-days. To account for this, we assess whether the effect of recall length differs by type of activity (planting, fertilizing, harvest, 7