Agricultural Data Collection to Minimize Measurement Error and Maximize Coverage
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 integra...
| Main Authors: | , , |
|---|---|
| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2021
|
| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/751081627578468610/Agricultural-Data-Collection-to-Minimize-Measurement-Error-and-Maximize-Coverage http://hdl.handle.net/10986/36056 |
| 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 research. |
|---|