this two-visit setup, the recall length is shorter, especially for planting-related activities. Including this data set thus allows us to assess if recall effects vanish with somewhat shorter recall periods.

All three surveys are part of the Living Standards Measurement Study – Integrated Survey on Agriculture (LSMS-ISA) and, as such, the data sets contain an integrated household and agricultural component. The household survey component collects detailed socioeconomic information, including household-level data on consumption, income, assets and housing, and individual-level data on demographics, education, and health. The agricultural component collects detailed information, among other items, on agricultural inputs used and outputs produced, as well as output disposition, at the plot-level. The integrated survey further allows breaking down which household members are involved in agricultural production, who owns the means of production, and who is responsible for managing the household farm’s plots of land.

As ancillary data to validate our findings on farmer-reported crop production, we use the second round of the Methodological Experiment on Measuring Maize Productivity, Soil Fertility and Variety (MAPS) survey. The MAPS data, which Gourlay et al. (2017) discuss in detail, were collected in two rounds in 2015 and 2016 in Uganda, containing 900 maize-growing households in round 1 and 489 in round 2. The MAPS survey employed objective and subjective survey methods to collect information on maize production (crop cutting), area, soil fertility, and maize variety identification.

# Outcomes of interest

The analysis focuses on some of the agricultural outcomes of major policy interest, namely land input, crop production, labor and input use. The main dependent variable is the approximate length of the recall period between the interview and the activity relating to the outcome of interest (Table 1).

Land input reporting is assessed in two ways. First, as the total number of agricultural plots (or parcels) cultivated by the household. This variable is simply a count of the households’ agricultural plots (and parcels) which respondents list at the beginning of the agricultural questionnaire of the survey. This information sheds light on whether a longer recall period leads households to forget listing some plots (Gaddis et al., 2019). The median (mean) number of plots listed per household is 2 in Tanzania NPS 2012/13 (mean: 2.2 plots) and Malawi IHPS 2016/17 (mean: 2.1 plots) and 1 in Malawi IHS4 (mean: 1.6 plots; Table 2).

Second, as the difference between GPS-measured plot area and farmer self-reported plot area. Given the prevalence of measurement error in land area reporting, LSMS-ISA surveys, including the data used in this analysis, implement an objective, GPS-based area measurement, while retaining farmer self-reported plot area alongside. Comparing these two measures allows assessing whether recall decay exacerbates the error in self-reported land area. This variable is constructed by subtracting the plot area variable based on GPS measurement from the self-reported plot area variable, so that area under-reporting has a negative sign while area over-reporting has a positive sign.

Plot sizes, measured by GPS, differ substantially across the three data sets. In Tanzania NPS 2012/13, the average plot size of 1.23 hectares (median: 0.49 hectares) is much larger than in Malawi at 0.37 hectares in the IHS4 (median: 0.30 hectares) and 0.34 hectares in the IHPS data set (median: 0.26 hectares; Table 2).

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