Akin to the analysis discussed in the previous paragraphs, we use as a proxy for recall length the time in months between the interview day and the first day of fieldwork. This proxy has two advantages in the case at hand. First, productivity measures are composite indicators whose components – output, land area, labor inputs – each have different timing patterns, so that there is no obvious single recall length measure. Second, time between the interview and the beginning of fieldwork has a simple practical interpretation and can be determined by survey designers. We test the effect of the recall length both at the plot level and aggregated at the farm level in the Malawi IHS4 and Tanzania NPS 2012/13 data using an OLS specification as in equation 5.<sup>13</sup> The results are in line with the prediction. Maize yield at the plot-level increases by 3.1 percent with each additional month of fieldwork in Tanzania NPS 2012/13 and by 5.1 percent in Malawi IHS4 (at the household level: 2.2 percent and 5.2 percent, respectively; Figure 16, Table A.19, Table A.20). Output (maize harvest) per day of labor input is also generally positively associated with fieldwork length, with the effect ranging from null to 3.1 percent per month (Tanzania NPS 2012/13: null effect at the plot-level, 1.9 percent at the household-level; Malawi IHS4: 2.7 percent at the plot-level, 3.1 percent at the householdlevel; Table A.19; Table A.20; Figure 16). Finally, output per worker is also positively associated with fieldwork length, with the effect size ranging between 2.4 and 5.1 percent per month (Tanzania NPS 2012/13: 2.4 at the plot-level, 2.5 percent at the household-level; Malawi IHS4: 5.1 percent at the plotlevel, 3.0 percent at the household-level; Table A.19; Table A.20; Figure 16).

# 5. Conclusions, policy implications, and recommendations

In this paper, we set out to evaluate the impact of the recall length and related design choices on the quality of key agricultural input and output data – land, labor, fertilizer, and production. Our results demonstrate, consistently across the three data sets used in the analysis, that the survey-based estimates of these variables depend on the length of the recall period, which indicates the presence of nonrandom measurement error of economically significant size. With longer recall periods, farmers report higher quantities of harvest, labor and fertilizer inputs, all three of which are recorded at the plot-level in our data sets. At the same time, farmers list fewer plots as the recall period increases. We have argued that it is plausible that farmers overestimate plot-level outcomes – harvest, labor and fertilizer inputs – while it is also plausible that they forget some of their more marginal plots, as their memory decays due to longer recall periods. We also show that the recall length has a meaningful impact on agricultural productivity measurement.

<mark>The reliability of agricultural data matters for policy effectiveness</mark> . Policy makers and the international community recognize agricultural input, output, and productivity outcomes as integral to agricultural growth and food security, and a large body of empirical evidence supports this view. These outcomes have therefore been made priorities within several development frameworks. Under the Sustainable Development Goals (SDGs), target 2.3 is to double the agricultural productivity and incomes of small-scale food producers by 2030. The target is monitored through indicator 2.3.1, volume of production per labor unit – that is, one of the productivity measures we found were subject to measurement error related to the

> 13 The Malawi IHPS 2016/17 data are omitted because the two-visit structure of that survey makes time between interview and beginning of fieldwork difficult to interpret.

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