Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning

Estimates of poverty are an important input into policy formulation in developing countries. The accurate measurement of poverty rates is therefore a first-order problem for development policy. This paper shows that combining satellite imagery with...

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Main Authors:	Newhouse, David, Merfeld, Joshua, Ramakrishnan, Anusha Pudugramam, Swartz, Tom, Lahiri, Partha
Format:	Working Paper
Language:	English English
Published:	World Bank, Washington, DC 2022
Subjects:	POVERTY SMALL AREA ESTIMATION POVERTY MAPPING SATELLITE DATA MACHINE LEARNING SUSTAINABLE DEVELOPMENT GOALS POVERTY ERADICATION
Online Access:	http://documents.worldbank.org/curated/en/099430309142231728/IDU0660868530404c0414e0bf180797b525682a5 http://hdl.handle.net/10986/38020

id	okr-10986-38020
recordtype	oai_dc
spelling	okr-10986-380202022-09-16T05:10:36Z Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning Newhouse, David Merfeld, Joshua Ramakrishnan, Anusha Pudugramam Swartz, Tom Lahiri, Partha POVERTY SMALL AREA ESTIMATION POVERTY MAPPING SATELLITE DATA MACHINE LEARNING SUSTAINABLE DEVELOPMENT GOALS POVERTY ERADICATION Estimates of poverty are an important input into policy formulation in developing countries. The accurate measurement of poverty rates is therefore a first-order problem for development policy. This paper shows that combining satellite imagery with household surveys can improve the precision and accuracy of estimated poverty rates in Mexican municipalities, a level at which the survey is not considered representative. It also shows that a household-level model outperforms other common small area estimation methods. However, poverty estimates in 2015 derived from geospatial data remain less accurate than 2010 estimates derived from household census data. These results indicate that the incorporation of household survey data and widely available satellite imagery can improve on existing poverty estimates in developing countries when census data are old or when patterns of poverty are changing rapidly, even for small subgroups. 2022-09-15T16:34:57Z 2022-09-15T16:34:57Z 2022-09 Working Paper http://documents.worldbank.org/curated/en/099430309142231728/IDU0660868530404c0414e0bf180797b525682a5 http://hdl.handle.net/10986/38020 English en Policy Research Working Papers;10175 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Policy Research Working Paper Publications & Research Mexico
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
language	English English
topic	POVERTY SMALL AREA ESTIMATION POVERTY MAPPING SATELLITE DATA MACHINE LEARNING SUSTAINABLE DEVELOPMENT GOALS POVERTY ERADICATION
spellingShingle	POVERTY SMALL AREA ESTIMATION POVERTY MAPPING SATELLITE DATA MACHINE LEARNING SUSTAINABLE DEVELOPMENT GOALS POVERTY ERADICATION Newhouse, David Merfeld, Joshua Ramakrishnan, Anusha Pudugramam Swartz, Tom Lahiri, Partha Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning
geographic_facet	Mexico
relation	Policy Research Working Papers;10175
description	Estimates of poverty are an important input into policy formulation in developing countries. The accurate measurement of poverty rates is therefore a first-order problem for development policy. This paper shows that combining satellite imagery with household surveys can improve the precision and accuracy of estimated poverty rates in Mexican municipalities, a level at which the survey is not considered representative. It also shows that a household-level model outperforms other common small area estimation methods. However, poverty estimates in 2015 derived from geospatial data remain less accurate than 2010 estimates derived from household census data. These results indicate that the incorporation of household survey data and widely available satellite imagery can improve on existing poverty estimates in developing countries when census data are old or when patterns of poverty are changing rapidly, even for small subgroups.
format	Working Paper
author	Newhouse, David Merfeld, Joshua Ramakrishnan, Anusha Pudugramam Swartz, Tom Lahiri, Partha
author_facet	Newhouse, David Merfeld, Joshua Ramakrishnan, Anusha Pudugramam Swartz, Tom Lahiri, Partha
author_sort	Newhouse, David
title	Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning
title_short	Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning
title_full	Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning
title_fullStr	Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning
title_full_unstemmed	Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning
title_sort	small area estimation of monetary poverty in mexico using satellite imagery and machine learning
publisher	World Bank, Washington, DC
publishDate	2022
url	http://documents.worldbank.org/curated/en/099430309142231728/IDU0660868530404c0414e0bf180797b525682a5 http://hdl.handle.net/10986/38020
_version_	1764488328334278656

Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning

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