Agricultural data collection to minimize measurement error and maximize coverage

Calogero Carletto; Andrew Dillon; Alberto Zezza

doi:10.1016/bs.hesagr.2021.10.008

Agricultural data collection to minimize measurement error and maximize coverage

Calogero Carletto^*, Andrew Dillon, Alberto Zezza

^*Corresponding author for this work

Kellogg Global Poverty Research Lab

Research output: Chapter in Book/Report/Conference proceeding › Chapter

13 Scopus citations

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 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 language	English (US)
Title of host publication	Handbook of Agricultural Economics
Editors	Christopher B. Barrett
Publisher	Elsevier B.V.
Pages	4407-4480
Number of pages	74
ISBN (Print)	9780323915014
DOIs	https://doi.org/10.1016/bs.hesagr.2021.10.008
State	Published - Jan 2021

Publication series

Name	Handbook of Agricultural Economics
Volume	5
ISSN (Print)	1574-0072

Funding

We are fortunate to have close collaborators who have greatly influenced our thinking on agricultural data collection including past and present colleagues at the World Bank Living Standards Measurement Study team, national statistical office collaborators, Innovations for Poverty Action, and the Global Poverty Research Lab. We are grateful for comments on the draft from two anonymous reviewers, Leah Bevis, Sarah Kopper, Karen Macours, Christopher Udry, and the Handbook's editors Christopher Barrett and David Just. We appreciate research support from Raka Banerjee.

Keywords

Agriculture
Data collection
Measurement error
Sampling error
Survey design

ASJC Scopus subject areas

Animal Science and Zoology
Agronomy and Crop Science
Economics and Econometrics

Access to Document

10.1016/bs.hesagr.2021.10.008

Cite this

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AB - 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.

KW - Agriculture

KW - Data collection

KW - Measurement error

KW - Sampling error

KW - Survey design

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Agricultural data collection to minimize measurement error and maximize coverage

Abstract

Publication series

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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