Survey Measurement Errors and the Assessment of the Relationship between Yields and Inputs in Smallholder Farming Systems : Evidence from Mali
An accurate understanding of how input use affects agricultural productivity in smallholder farming systems is key to designing policies that can improve productivity, food security, and living standards in rural areas. Studies examining the relati...
| Main Authors: | , |
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2021
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/undefined/711441636127459189/Survey-Measurement-Errors-and-the-Assessment-of-the-Relationship-between-Yields-and-Inputs-in-Smallholder-Farming-Systems-Evidence-from-Mali http://hdl.handle.net/10986/36553 |
| Summary: | An accurate understanding of how
input use affects agricultural productivity in smallholder
farming systems is key to designing policies that can
improve productivity, food security, and living standards in
rural areas. Studies examining the relationships between
agricultural productivity and inputs typically rely on land
productivity measures, such as crop yields, that are
informed by self-reported survey data on crop production.
This paper leverages unique survey data from Mali to
demonstrate that self-reported crop yields, vis-à-vis
(objective) crop cut yields, are subject to non-classical
measurement error that in turn biases the estimates of
returns to inputs, including land, labor, fertilizer, and
seeds. The analysis validates an alternative approach to
estimate the relationship between crop yields and
agricultural inputs using large-scale surveys, namely a
within-survey imputation exercise that derives predicted,
otherwise unobserved, objective crop yields that stem from a
machine learning model that is estimated with a random
subsample of plots for which crop cutting and self-reported
yields are both available. Using data from a methodological
survey experiment and a nationally representative survey
conducted in Mali, the analysis demonstrates that it is
possible to obtain predicted objective sorghum yields with
attenuated non-classical measurement error, resulting in a
less biased assessment of the relationship between yields
and agricultural inputs. The discussion expands on the
implications of the findings for (i) future research on
agricultural intensification, and (ii) the design of future
surveys in which objective data collection could be limited
to a subsample to save costs, with the intention to apply
the suggested machine learning approach. |
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