Anna Josephson, Jeffrey D. Michler, Talip Kilic, Siobhan Murray
{"title":"The Mismeasure of Weather: Using Remotely Sensed Earth Observation Data in Economic Context","authors":"Anna Josephson, Jeffrey D. Michler, Talip Kilic, Siobhan Murray","doi":"arxiv-2409.07506","DOIUrl":null,"url":null,"abstract":"The availability of weather data from remotely sensed Earth observation (EO)\ndata has reduced the cost of including weather variables in econometric models.\nWeather variables are common instrumental variables used to predict economic\noutcomes and serve as an input into modelling crop yields for rainfed\nagriculture. The use of EO data in econometric applications has only recently\nbeen met with a critical assessment of the suitability and quality of this data\nin economics. We quantify the significance and magnitude of the effect of\nmeasurement error in EO data in the context of smallholder agricultural\nproductivity. We find that different measurement methods from different EO\nsources: findings are not robust to the choice of EO dataset and outcomes are\nnot simply affine transformations of one another. This begs caution on the part\nof researchers using these data and suggests that robustness checks should\ninclude testing alternative sources of EO data.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"75 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - ECON - General Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.07506","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The availability of weather data from remotely sensed Earth observation (EO)
data has reduced the cost of including weather variables in econometric models.
Weather variables are common instrumental variables used to predict economic
outcomes and serve as an input into modelling crop yields for rainfed
agriculture. The use of EO data in econometric applications has only recently
been met with a critical assessment of the suitability and quality of this data
in economics. We quantify the significance and magnitude of the effect of
measurement error in EO data in the context of smallholder agricultural
productivity. We find that different measurement methods from different EO
sources: findings are not robust to the choice of EO dataset and outcomes are
not simply affine transformations of one another. This begs caution on the part
of researchers using these data and suggests that robustness checks should
include testing alternative sources of EO data.