Agricultural data collection to minimize measurement error and maximize coverage

Calogero Carletto*, Andrew Dillon, Alberto Zezza

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations


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 tradeoffs in survey design that may reduce measurement error or increase coverage. In this chapter, we first review the econometric and survey methodology literatures that focus on the sources of measurement error and coverage bias in agricultural data collection. Second, we provide examples of how agricultural data structure affects testable empirical models. Finally, we review 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.

Original languageEnglish (US)
Title of host publicationHandbook of Agricultural Economics
EditorsChristopher B. Barrett
PublisherElsevier B.V.
Number of pages74
ISBN (Print)9780323915014
StatePublished - Jan 2021

Publication series

NameHandbook of Agricultural Economics
ISSN (Print)1574-0072


  • Agriculture
  • Data collection
  • Measurement error
  • Sampling error
  • Survey design

ASJC Scopus subject areas

  • Animal Science and Zoology
  • Agronomy and Crop Science
  • Economics and Econometrics


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