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.
Details
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Author
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Document Date
2021/07/29
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Document Type
Policy Research Working Paper
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Report Number
WPS9745
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Volume No
1
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Total Volume(s)
1
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Country
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Region
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Disclosure Date
2021/07/29
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Disclosure Status
Disclosed
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Doc Name
Agricultural Data Collection to Minimize Measurement Error and Maximize Coverage
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Keywords
measurement error; agricultural data; data quality; Computer-Assisted Personal Interviewing; presence of measurement error; degree of measurement error; household survey data collection; sources of errors
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Citation
Carletto,Calogero Dillon,Andrew S. Zezza,Alberto
Agricultural Data Collection to Minimize Measurement Error and Maximize Coverage (English). Policy Research working paper,no. WPS 9745,LSMS Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/751081627578468610/Agricultural-Data-Collection-to-Minimize-Measurement-Error-and-Maximize-Coverage