objective ‘gold standard’ measures of key variables, such as GPS land area measurement or crop cutting, if only for a subsample of farms. That choice, too, has cost implications. To evaluate the trade-off between quality and cost, survey designers require an empirically-informed understanding of the extent to which data quality varies with differences in the recall period, among other data quality considerations. Policy makers and other data users benefit from an appreciation of the limitations and biases in existing data related to recall length.

Previous work by Beegle et al. (2012a) found no consistent evidence of significant recall length effects in agricultural surveys, using data from the early 2000s. In contrast, in this paper, we find a significant effect of the recall length on key agricultural statistics. We take advantage of the more recent availability of highquality and very detailed agricultural survey data to overcome some of the limitations of those data and assess and quantify the impact of the recall length on the quality of agricultural data. Using data from nationally representative surveys conducted by the National Statistical offices of Tanzania and Malawi with support from the World Bank’s Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) program, we find that longer recall periods are associated with overreporting of plot-level outcomes – production quantity, labor input, and fertilizer input – consistently across the three data sets in the analysis. At the same time, respondents appear to forget listing plots as the recall period increases. Our results point to the need to devise and promote the adoption of survey design choices that minimize this source of measurement error.

The remainder of the paper is structured as follows: Section 2 reviews the literature on error in the measurement of agricultural outcomes, especially that related to recall length. Section 3 describes the data used, the variables generated, and the empirical strategy this analysis employs. Section 4 presents the main results. Section 5 discusses conclusions, policy implications, and offers recommendations.

# 2. Background and related literature

This paper contributes to a large body of literature exploring the effect of respondent recall on measurement accuracy in survey data. Systematic reviews of this literature are provided in Biemer et al. (2011), Bound et al. (2001), Groves and Lyberg (2010), and Meyer et al. (2015), among others. The role of recall error in survey data has been explored in a range of topics, including consumption expenditure and food intake (e.g. Beegle et al., 2012b; Troubat and Grünberger, 2017; Backiny-Yetna et al., 2017; Brzozowski et al., 2017; Engle-Stone et al., 2017; D’Alessio, 2017; Schündeln, 2018; Zezza et al., 2017), household enterprises (De Mel et al., 2009; Liedholm, 1991) and income measurement (Moore et al., 2000).

One strand of the literature focuses on the cognitive processes underlying survey response and has built an understanding of how these can lead to measurement error. Sudman and Bradburn (1973) distinguish between _recall decay_ and _telescoping_ . Telescoping refers to inaccurately remembering _timing_ of events, leading to an event of interest incorrectly being moved into the reference period, which, in turn, may lead

> at the end of the main agricultural season, complemented by an Economy module implemented in four visits during the agricultural year. Similarly, the Uganda Annual Agricultural Survey 2019 (AAS), is planned with a post-planting and a post-harvest visit in both agricultural seasons, for a total of four visits.  In contrast, many LSMS-ISA surveys are now implementing two visits, one at the end of the main planting season and one at the end of the main harvest. Finally, some specialized farm surveys visit households/farms multiple times over the course of the agricultural year.

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