Reliability of Recall in Agricultural Data
By Kathleen G. Beegle, C. Carletto, Kristen Himelein. 2011
196 citations recorded by the source corpus.
Despite the importance of agriculture to economic development, and a vast accompanying literature on the subject, little research has been done on the quality of the underlying data. Due to survey logistics, agricultural data are usually collected by asking respondents to recall the details of events occurring during past agricultural seasons that took place a number of months prior to the interview. This gap can lead to recall bias in reported data on agricultural activities. The problem is further complicated when interviews are conducted over the course of several months, thus leading to recall of variable length. To test for such recall bias, the length of time between harvest and interview is examined for three African countries with respect to several common agricultural input and harvest measures. The analysis shows little evidence of recall bias impacting data quality. There is some indication that more salient events are less subject to recall decay. Overall, the results allay some concerns about the quality of some types of agricultural data collected through recall over lengthy periods.
Subfields: Artificial Intelligence, Epidemiology, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/410fae656b23035a661c1622d672df567fc0dbe6
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In Statistics and Probability
Citations in the corpus
- Heterogeneity, Measurement Error, and Misallocation: Evidence from African Agriculture (2019, result): “In this sense, our results are not necessarily inconsistent with those of Adamopoulos and 4 See, e.g., Beegle et al. (2012), Deininger et al. (2012), and De Nicola and Giné (2014), although Beegle, Carletto, and Himelein (2012) offer a more positive view.”
- Land Constraints and Agricultural Intensification in Ethiopia: A Village-Level Analysis of High-Potential Areas (2014, unknown): “Of course, if small farmers over report farm sizes and larger farmers underreport farm sizes, as the research by Beegle, Carletto, and Himelein (2012) suggests, then we have an issue of measurement bias.”
- Not Your Average Job: Measuring Farm Labor in Tanzania (2016, unknown): “…methods hamper comparisons over time and space if survey methods differ, as has been shown in the context of the measurement of welfare, poverty, and hunger (Backiny-Yetna, Steele, and Djima 2014; Beegle et al. 2012, 2016; De Weerdt et al. 2016) and labor measurement (Bardasi et al. 2011).”