Agricultural Data Collection to Minimize Measurement Error and Maximize Coverage / Calogero Carletto
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: Washington, D.C : The World Bank, 2021Description: 1 Online-Ressource (93 pages)Subject(s): Additional physical formats: Erscheint auch als: Agricultural Data Collection to Minimize Measurement Error and Maximize Coverage. Washington, D.C : The World Bank, 2021DOI: DOI: 10.1596/1813-9450-9745Online resources: Summary: Advances in agricultural data production provide ever-increasing opportunities for pushing the research frontier in agricultural economics and designing better agricultural policy. As new technologies present opportunities to create new and integrated data sources, researchers face trade-offs in survey design that may reduce measurement error or increase coverage. This paper first reviews the econometric and survey methodology literatures that focus on the sources of measurement error and coverage bias in agricultural data collection. Second, it provides examples of how agricultural data structure affects testable empirical models. Finally, it reviews the challenges and opportunities offered by technological innovation to meet old and new data demands and address key empirical questions, focusing on the scalable data innovations of greatest potential impact for empirical methods and researchPPN: PPN: 177238304XPackage identifier: Produktsigel: ZDB-1-WBA | ZDB-110-WBL | ZDB-110-WBONo physical items for this record
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