Generating Gridded Agricultural Gross Domestic Product for Brazil : A Comparison of Methodologies

This paper examines two new methods to generate gridded agricultural Gross Domestic Product (GDP) and compares the results with a traditional method. In the case of Brazil, these two new methods of spatial disaggregation and cross-entropy outperfor...

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Main Authors:	Thomas, Timothy S., You, Liangzhi, Wood-Sichra, Ulrike, Ru, Yating, Blankespoor, Brian, Kalvelagen, Erwin
Format:	Working Paper
Language:	English
Published:	World Bank, Washington, DC 2019
Subjects:	GROSS DOMESTIC PRODUCT AGRICULTURE REGIONAL DEVELOPMENT CROSS-ENTROPY SPATIAL DISAGGREGATION
Online Access:	http://documents.worldbank.org/curated/en/677071566217273585/Generating-Gridded-Agricultural-Gross-Domestic-Product-for-Brazil-A-Comparison-of-Methodologies http://hdl.handle.net/10986/32310

id	okr-10986-32310
recordtype	oai_dc
spelling	okr-10986-323102022-09-19T12:16:45Z Generating Gridded Agricultural Gross Domestic Product for Brazil : A Comparison of Methodologies Thomas, Timothy S. You, Liangzhi Wood-Sichra, Ulrike Ru, Yating Blankespoor, Brian Kalvelagen, Erwin GROSS DOMESTIC PRODUCT AGRICULTURE REGIONAL DEVELOPMENT CROSS-ENTROPY SPATIAL DISAGGREGATION This paper examines two new methods to generate gridded agricultural Gross Domestic Product (GDP) and compares the results with a traditional method. In the case of Brazil, these two new methods of spatial disaggregation and cross-entropy outperform the prediction of agricultural GDP from the traditional method that distributes agricultural GDP using rural population. The paper finds that the best prediction method is spatial disaggregation using a regression approach for all the key crops and contributors to agricultural GDP. However, the issue of degrees of freedom is an important limiting factor, as the approach requires sufficient subnational data. The cross-entropy method with readily available spatially distributed crop, livestock, forest, and fish allocation far outperforms the traditional method, at least in the case of Brazil, and can operate with national- and/or subnational-level data. 2019-08-22T15:49:03Z 2019-08-22T15:49:03Z 2019-08 Working Paper http://documents.worldbank.org/curated/en/677071566217273585/Generating-Gridded-Agricultural-Gross-Domestic-Product-for-Brazil-A-Comparison-of-Methodologies http://hdl.handle.net/10986/32310 English Policy Research Working Paper;No. 8985 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper Latin America & Caribbean Brazil
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
language	English
topic	GROSS DOMESTIC PRODUCT AGRICULTURE REGIONAL DEVELOPMENT CROSS-ENTROPY SPATIAL DISAGGREGATION
spellingShingle	GROSS DOMESTIC PRODUCT AGRICULTURE REGIONAL DEVELOPMENT CROSS-ENTROPY SPATIAL DISAGGREGATION Thomas, Timothy S. You, Liangzhi Wood-Sichra, Ulrike Ru, Yating Blankespoor, Brian Kalvelagen, Erwin Generating Gridded Agricultural Gross Domestic Product for Brazil : A Comparison of Methodologies
geographic_facet	Latin America & Caribbean Brazil
relation	Policy Research Working Paper;No. 8985
description	This paper examines two new methods to generate gridded agricultural Gross Domestic Product (GDP) and compares the results with a traditional method. In the case of Brazil, these two new methods of spatial disaggregation and cross-entropy outperform the prediction of agricultural GDP from the traditional method that distributes agricultural GDP using rural population. The paper finds that the best prediction method is spatial disaggregation using a regression approach for all the key crops and contributors to agricultural GDP. However, the issue of degrees of freedom is an important limiting factor, as the approach requires sufficient subnational data. The cross-entropy method with readily available spatially distributed crop, livestock, forest, and fish allocation far outperforms the traditional method, at least in the case of Brazil, and can operate with national- and/or subnational-level data.
format	Working Paper
author	Thomas, Timothy S. You, Liangzhi Wood-Sichra, Ulrike Ru, Yating Blankespoor, Brian Kalvelagen, Erwin
author_facet	Thomas, Timothy S. You, Liangzhi Wood-Sichra, Ulrike Ru, Yating Blankespoor, Brian Kalvelagen, Erwin
author_sort	Thomas, Timothy S.
title	Generating Gridded Agricultural Gross Domestic Product for Brazil : A Comparison of Methodologies
title_short	Generating Gridded Agricultural Gross Domestic Product for Brazil : A Comparison of Methodologies
title_full	Generating Gridded Agricultural Gross Domestic Product for Brazil : A Comparison of Methodologies
title_fullStr	Generating Gridded Agricultural Gross Domestic Product for Brazil : A Comparison of Methodologies
title_full_unstemmed	Generating Gridded Agricultural Gross Domestic Product for Brazil : A Comparison of Methodologies
title_sort	generating gridded agricultural gross domestic product for brazil : a comparison of methodologies
publisher	World Bank, Washington, DC
publishDate	2019
url	http://documents.worldbank.org/curated/en/677071566217273585/Generating-Gridded-Agricultural-Gross-Domestic-Product-for-Brazil-A-Comparison-of-Methodologies http://hdl.handle.net/10986/32310
_version_	1764476266865491968

Generating Gridded Agricultural Gross Domestic Product for Brazil : A Comparison of Methodologies

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