Agricultural Data Collection to Minimize Measurement Error and Maximize Coverage

Calogero Carletto, Andrew Dillon and Alberto Zezza

No 9745, Policy Research Working Paper Series from The World Bank

Abstract: 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 research.

Keywords: Food Security; International Trade and Trade Rules; Labor&Employment Law; Gender and Development; Climate Change and Agriculture; Crops and Crop Management Systems (search for similar items in EconPapers)
Date: 2021-07-29
New Economics Papers: this item is included in nep-agr
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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