Estimating the Impact of Weather on Agriculture

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, 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, we find substantial amounts of measurement error based on remote sensing product. In extreme cases, data drawn from different remote sensing products result in opposite signs for coefficients on weather metrics, meaning that precipitation or temperature draw from one product purportedly increases crop output while the same metrics drawn from a different product purportedly reduces crop output. We conclude with a set of six best practices for researchers looking to combine remote sensing weather data with socioeconomic survey data.