Recall Bias Revisited: Measure Farm Labor Using Mixed-Mode Surveys and Multiple Imputation

Hai-Anh Dang () and Calogero Carletto

No 1020, GLO Discussion Paper Series from Global Labor Organization (GLO)

Abstract: Smallholder farming dominates agriculture in poorer countries. Yet, traditional recall-based surveys on smallholder farming in these countries face challenges with seasonal variations, high survey costs, poor record-keeping, and technical capacity constraints resulting in significant recall bias. We offer the first study that employs a less-costly, imputation-based alternative using mixed modes of data collection to obtain estimates on smallholder farm labor. Using data from Tanzania, we find that parsimonious imputation models based on small samples of a benchmark weekly in-person survey can offer reasonably accurate estimates. Furthermore, we also show how less accurate, but also less resource-intensive, imputation-based measures using a weekly phone survey may provide a viable alternative for the more costly weekly in-person survey. If replicated in other contexts, including for other types of variables that suffer from similar recall bias, these results could open up a new and cost-effective way to collect more accurate data at scale.

Keywords: farm labor; agricultural productivity; multiple imputation; missing data; survey data; Tanzania (search for similar items in EconPapers)
JEL-codes: C8 J2 O12 Q12 (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-agr, nep-dev and nep-lma
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Working Paper: Recall Bias Revisited: Measure Farm Labor Using Mixed-Mode Surveys and Multiple Imputation (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:glodps:1020

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