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.
Highlights
In the past two decades, innovations in data systems have led to the production of more real-time, disaggregated, and interoperable data on agriculture than ever before
Emerging literature on a wide array of agricultural measurement issues in land, production, and gender analysis has relied upon innovations in survey design, as fostered in the past decade through data initiatives like the Living Standards Measurement StudyIntegrated Surveys on Agriculture (LSMS-ISA) and the Global Strategy to Improve Agricultural and Rural Statistics (GSARS)
The attention to surveys is warranted by the availability of a fully developed total survey quality framework around which we develop the narrative of the paper
Summary
In the past two decades, innovations in data systems have led to the production of more real-time, disaggregated, and interoperable data on agriculture than ever before. While appreciating the importance of improving agricultural data in all countries along the entire income gradient, this paper intentionally focuses on some of the data challenges and scalable applications and tools most suitable to low- and middle-income countries Because of this geographic focus, we primarily limit our discussion to household and farm surveys, as they are likely to remain the instrument of choice and backbone of agricultural data systems in many countries for years to come. We will argue and provide evidence that renewed attention to data quality issues – in terms of measurement error and data coverage – is critical for advancing the research frontier in agricultural economics and designing better agricultural policy. In the sixth and final section, we conclude with recommendations on priorities for accelerating improvements in the accuracy and coverage of agricultural data, to support higher-quality research for better agricultural policy
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