Knowledge Center Catalog

The benefits and pitfalls of using satellite data for causal inference (Record no. 62584)

MARC details
000 -LEADER
fixed length control field 00595nab|a22002177a|4500
001 - CONTROL NUMBER
control field 62584
003 - CONTROL NUMBER IDENTIFIER
control field MX-TxCIM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200921230240.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200910s2020||||xxu|||p|op||||00||0|eng|d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 1750-6816
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 1750-6824 (Online)
024 8# - OTHER STANDARD IDENTIFIER
Standard number or code https://doi.org/10.1093/reep/rez023
040 ## - CATALOGING SOURCE
Original cataloging agency MX-TxCIM
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Meha Jain
9 (RLIN) 3814
245 14 - TITLE STATEMENT
Title The benefits and pitfalls of using satellite data for causal inference
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc. USA :
Name of publisher, distributor, etc. Oxford University Press,
Date of publication, distribution, etc. 2020.
500 ## - GENERAL NOTE
General note Peer review
520 ## - SUMMARY, ETC.
Summary, etc. There has been growing interest in using satellite data in environmental economics research. This is because satellite data are available for any region across the globe, provide frequent data over time, are becoming available at lower cost, and are becoming easier to process. While satellite data have the potential to be a powerful resource, these data have their own sources of biases and error, which could lead to biased inference, even if analyses are otherwise well-identified. This article discusses the potential benefits and pitfalls of using satellite data for causal inference, focusing on the more technical aspects of using satellite data. In particular, I discuss why it is critical for researchers to understand the error distribution of a given satellite data product and how these errors may result in biased inference. I provide examples of some common types of error, including nonrandom misclassification, saturation effects, atmospheric effects, and cloud cover. If researchers recognize and account for these potential errors and biases, satellite data can be a powerful resource, allowing for large-scale analyses that would otherwise not be possible.
546 ## - LANGUAGE NOTE
Language note Text in English
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
9 (RLIN) 12655
Topical term or geographic name as entry element Satellite observation
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
9 (RLIN) 9002
Topical term or geographic name as entry element Data
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
9 (RLIN) 9142
Topical term or geographic name as entry element Research
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
9 (RLIN) 8619
Topical term or geographic name as entry element Environmental Economics
773 0# - HOST ITEM ENTRY
Title Review of Environmental Economics and Policy
Related parts v. 14, no. 1, p. 157-169
Place, publisher, and date of publication USA : Oxford University Press, 2020.
International Standard Serial Number 1750-6816
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Article
Suppress in OPAC No
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Date last seen Total Checkouts Price effective from Koha item type Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Withdrawn status Home library Current library Date acquired
09/21/2020   09/21/2020 Article Not Lost Dewey Decimal Classification     Reprints Collection   CIMMYT Knowledge Center: John Woolston Library CIMMYT Knowledge Center: John Woolston Library 09/21/2020

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