Estimating the Impact of Weather on Agriculture

This paper quantifies the significance and magnitude of the effect of measurementerror in remote sensing weather data in the analysis of smallholder agricultural productivity. The analysisleverages 17 rounds of nationally-representative, panel household survey data from six countries in Sub-SaharanAfrica. These data are spatially linked with a range of geospatial weather data sources and related metrics. Thepaper provides systematic evidence on measurement error introduced by (1) different methods used to obfuscate theexact 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 measurementerror introduced by different obfuscation methods. Second, it finds that simple weather metrics, such as total seasonalrainfall and mean daily temperature, outperform more complex metrics, such as deviations in rainfall from the long-runaverage or growing degree days, in a broad range of settings. Finally, the analysis finds substantial amounts ofmeasurement error based on remote sensing products. In extreme cases, the data drawn from different remote sensingproducts result in opposite signs for coefficients on weather metrics, meaning that precipitation or temperaturedrawn from one product purportedly increases crop output while the same metrics drawn from a different productpurportedly reduces crop output. The paper concludes with a set of six best practices for researchers looking to combineremote sensing weather data with socioeconomic survey data.