Estimating the Impact of Weather on Agriculture
This paper quantifies the significance and magnitude of the effect of measurement error in remote sensing weather data in the analysis of smallholder agricultural productivity. The analysis leverages 17 rounds of nationally-representative, panel...
| 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/235241638281693198/Estimating-the-Impact-of-Weather-on-Agriculture http://hdl.handle.net/10986/36643 |
| Summary: | This paper quantifies the
significance and magnitude of the effect of measurement
error in remote sensing weather data in the analysis of
smallholder agricultural productivity. The analysis
leverages 17 rounds of nationally-representative, panel
household survey data from six countries in Sub-Saharan
Africa. These data are spatially linked with a range of
geospatial weather data sources and related metrics. The
paper provides systematic evidence on measurement error
introduced by (1) different methods used to obfuscate the
exact GPS coordinates of households, (2) different metrics
used to quantify precipitation and temperature, and (3)
different remote sensing measurement technologies. First,
the analysis finds no discernible effect of measurement
error introduced by different obfuscation methods. Second,
it finds that simple weather metrics, such as total seasonal
rainfall and mean daily temperature, outperform more complex
metrics, such as deviations in rainfall from the long-run
average or growing degree days, in a broad range of
settings. Finally, the analysis finds substantial amounts of
measurement error based on remote sensing products. In
extreme cases, the data drawn from different remote sensing
products result in opposite signs for coefficients on
weather metrics, meaning that precipitation or temperature
drawn from one product purportedly increases crop output
while the same metrics drawn from a different product
purportedly reduces crop output. The paper concludes with a
set of six best practices for researchers looking to combine
remote sensing weather data with socioeconomic survey data. |
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