TL;DR
This study assesses how measurement errors in remote sensing weather data affect agricultural productivity analysis in Sub-Saharan Africa, highlighting the importance of data choice and methodology for accurate results.
Contribution
It systematically evaluates sources of measurement error in remote sensing weather data and provides best practices for integrating such data with household surveys.
Findings
Obfuscation methods do not significantly affect results.
Simple weather metrics outperform complex ones in most settings.
Different remote sensing products can produce contradictory effects.
Abstract
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. We provide 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, we find no discernible effect of measurement error introduced by different obfuscation methods. Second, we find that simple weather metrics, such as total seasonal rainfall and mean daily temperature,…
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MethodsGreedy Policy Search
