compare end-of-season recall with weekly work diaries, which are considered more reliable, among farmers in Tanzania and Ghana. They find that end-of-season recall leads to over-reporting of labor use at the individual-by-plot level (‘recall bias’) relative to diary-keeping, but at the same time to undercounting of plots cultivated and of individuals who worked on them (‘listing bias’). In contrast, Gollin (2019) argues that diary methods may lead to under-counting of farm labor given the eclectic set of tasks that ‘farm labor’ comprises. Seymour et al. (2017) find significant differences in the recording of time spent working between recall and diary methods in Uganda and Bangladesh.

Finally, empirical evidence on recall error (and measurement error in general) in **agricultural inputs** data (fertilizer, agro-chemicals, seeds, and others) is scant. Beegle et al. (2012a) find little systematic evidence for recall bias in farmer self-reported fertilizer usage in Malawi and Kenya, though results vary somewhat depending on respondent characteristics. Gollin (2019) argues that farmers likely recall quantities (and prices) of purchased inputs mostly accurately but also points to evidence (e.g. in Ashour et al., 2017; Bold et al., 2017) suggesting that fertilizer and agro-chemical counterfeiting and adulteration may leave farmers unsure about the quality of product they apply to their land.

# 3. Data and empirical strategy

## Data

The study uses three data sets from nationally representative household surveys in Malawi and Tanzania: the Tanzania National Panel Survey (TNPS) 2012/13, the Fourth Malawi Integrated Household Survey 2016/17 (IHS4) and the Malawi Integrated Household Panel Survey 2016/17 (IHPS).

Tanzania NPS 2012/13 is the third wave of the Tanzania National Panel Survey and was collected between October 2012 and November 2013. The NPS sample used a multi-stage clustered sampling design covering a total is 5,015 urban and rural households. With households selected to be interviewed over the course of 14 months to account for seasonality in consumption, the data have variation in the recall period, which can be exploited to assess the impact of recall decay on data quality. The reference agricultural season is the long rainy season of 2012, which all farms in the sample reported on.

The IHS4 2016/17 includes both a cross-sectional and a panel component. The cross-sectional sample includes 12,480 households surveyed in 780 enumeration areas. Households were visited once throughout the 12 months of fieldwork between April 2016 and April 2017. The IHS4 data were collected in the same way as the NPS 2012/13 and used for analysis following the same rationale. For our analysis, we use the 2015/16 rainy season as the reference agricultural season. Data collection began in April 2016, at which point not all households had finished the 2015/16 rainy season harvest. These households instead reported on the harvest of the previous, 2014/15 rainy season. For comparability, we dropped these households from the analysis. In addition, some households stated having harvested in the 2015/16 season, but harvest dates revealed respondents were referring to the previous season, and vice versa. We dropped these households as well.

The IHPS 2016/17 sample includes 1,989 households which were interviewed twice between April 2016 and April 2017, in one post-planting and one post-harvest visit relative to the 2015/16 rainy season. Given 5