Agricultural Data Collection to Minimize Measurement Error and Maximize Coverage

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Tác giả: Calogero Carletto

Ngôn ngữ: eng

Ký hiệu phân loại: 338.16 Production efficiency

Thông tin xuất bản: World Bank, Washington, DC, 2021

Mô tả vật lý:

Bộ sưu tập: Tài liệu truy cập mở

ID: 309811

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.
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